Application of effective discharge analysis to environmental flow decision-making

USGS Publications Warehouse

McKay, S. Kyle; Freeman, Mary C.; Covich, A.P.

2016-01-01

Well-informed river management decisions rely on an explicit statement of objectives, repeatable analyses, and a transparent system for assessing trade-offs. These components may then be applied to compare alternative operational regimes for water resource infrastructure (e.g., diversions, locks, and dams). Intra- and inter-annual hydrologic variability further complicates these already complex environmental flow decisions. Effective discharge analysis (developed in studies of geomorphology) is a powerful tool for integrating temporal variability of flow magnitude and associated ecological consequences. Here, we adapt the effectiveness framework to include multiple elements of the natural flow regime (i.e., timing, duration, and rate-of-change) as well as two flow variables. We demonstrate this analytical approach using a case study of environmental flow management based on long-term (60 years) daily discharge records in the Middle Oconee River near Athens, GA, USA. Specifically, we apply an existing model for estimating young-of-year fish recruitment based on flow-dependent metrics to an effective discharge analysis that incorporates hydrologic variability and multiple focal taxa. We then compare three alternative methods of environmental flow provision. Percentage-based withdrawal schemes outcompete other environmental flow methods across all levels of water withdrawal and ecological outcomes.

Application of Effective Discharge Analysis to Environmental Flow Decision-Making.

PubMed

McKay, S Kyle; Freeman, Mary C; Covich, Alan P

2016-06-01

Well-informed river management decisions rely on an explicit statement of objectives, repeatable analyses, and a transparent system for assessing trade-offs. These components may then be applied to compare alternative operational regimes for water resource infrastructure (e.g., diversions, locks, and dams). Intra- and inter-annual hydrologic variability further complicates these already complex environmental flow decisions. Effective discharge analysis (developed in studies of geomorphology) is a powerful tool for integrating temporal variability of flow magnitude and associated ecological consequences. Here, we adapt the effectiveness framework to include multiple elements of the natural flow regime (i.e., timing, duration, and rate-of-change) as well as two flow variables. We demonstrate this analytical approach using a case study of environmental flow management based on long-term (60 years) daily discharge records in the Middle Oconee River near Athens, GA, USA. Specifically, we apply an existing model for estimating young-of-year fish recruitment based on flow-dependent metrics to an effective discharge analysis that incorporates hydrologic variability and multiple focal taxa. We then compare three alternative methods of environmental flow provision. Percentage-based withdrawal schemes outcompete other environmental flow methods across all levels of water withdrawal and ecological outcomes.

Use of the partial farm budget technique to predict the economic impact of the flock management decision to use B-mode ultrasonographic pregnancy diagnosis.

PubMed

Sprecher, D J; Ley, W B; Whittier, W D; Bowen, J M; Thatcher, C D; Pelzer, K D; Moore, J M

1989-07-15

A computer spreadsheet was developed to predict the economic impact of a management decision to use B-mode ultrasonographic ovine pregnancy diagnosis. The spreadsheet design and spreadsheet cell formulas are provided. The program used the partial farm budget technique to calculate net return (NR) or cash flow changes that resulted from the decision to use ultrasonography. Using the program, either simple pregnancy diagnosis or pregnancy diagnosis with the ability to determine singleton or multiple pregnancies may be compared with no flock ultrasonographic pregnancy diagnosis. A wide range of user-selected regional variables are used to calculate the cash flow changes associated with the ultrasonography decisions. A variable may be altered through a range of values to conduct a sensitivity analysis of predicted NR. Example sensitivity analyses are included for flock conception rate, veterinary ultrasound fee, and the price of corn. Variables that influence the number of cull animals and the cost of ultrasonography have the greatest impact on predicted NR. Because the determination of singleton or multiple pregnancies is more time consuming, its economic practicality in comparison with simple pregnancy diagnosis is questionable. The value of feed saved by identifying and separately feeding ewes with singleton pregnancies is not offset by the increased ultrasonography cost.

Emotion and decision making: multiple modulatory neural circuits.

PubMed

Phelps, Elizabeth A; Lempert, Karolina M; Sokol-Hessner, Peter

2014-01-01

Although the prevalent view of emotion and decision making is derived from the notion that there are dual systems of emotion and reason, a modulatory relationship more accurately reflects the current research in affective neuroscience and neuroeconomics. Studies show two potential mechanisms for affect's modulation of the computation of subjective value and decisions. Incidental affective states may carry over to the assessment of subjective value and the decision, and emotional reactions to the choice may be incorporated into the value calculation. In addition, this modulatory relationship is reciprocal: Changing emotion can change choices. This research suggests that the neural mechanisms mediating the relation between affect and choice vary depending on which affective component is engaged and which decision variables are assessed. We suggest that a detailed and nuanced understanding of emotion and decision making requires characterizing the multiple modulatory neural circuits underlying the different means by which emotion and affect can influence choices.

Finding structure in data using multivariate tree boosting

PubMed Central

Miller, Patrick J.; Lubke, Gitta H.; McArtor, Daniel B.; Bergeman, C. S.

2016-01-01

Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles such as random forests (Strobl, Malley, & Tutz, 2009) are a useful tool for finding structure, but are difficult to interpret with multiple outcome variables which are often of interest in psychology. To find and interpret structure in data sets with multiple outcomes and many predictors (possibly exceeding the sample size), we introduce a multivariate extension to a decision tree ensemble method called gradient boosted regression trees (Friedman, 2001). Our extension, multivariate tree boosting, is a method for nonparametric regression that is useful for identifying important predictors, detecting predictors with nonlinear effects and interactions without specification of such effects, and for identifying predictors that cause two or more outcome variables to covary. We provide the R package ‘mvtboost’ to estimate, tune, and interpret the resulting model, which extends the implementation of univariate boosting in the R package ‘gbm’ (Ridgeway et al., 2015) to continuous, multivariate outcomes. To illustrate the approach, we analyze predictors of psychological well-being (Ryff & Keyes, 1995). Simulations verify that our approach identifies predictors with nonlinear effects and achieves high prediction accuracy, exceeding or matching the performance of (penalized) multivariate multiple regression and multivariate decision trees over a wide range of conditions. PMID:27918183

A decision tool for selecting trench cap designs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paige, G.B.; Stone, J.J.; Lane, L.J.

1995-12-31

A computer based prototype decision support system (PDSS) is being developed to assist the risk manager in selecting an appropriate trench cap design for waste disposal sites. The selection of the {open_quote}best{close_quote} design among feasible alternatives requires consideration of multiple and often conflicting objectives. The methodology used in the selection process consists of: selecting and parameterizing decision variables using data, simulation models, or expert opinion; selecting feasible trench cap design alternatives; ordering the decision variables and ranking the design alternatives. The decision model is based on multi-objective decision theory and uses a unique approach to order the decision variables andmore » rank the design alternatives. Trench cap designs are evaluated based on federal regulations, hydrologic performance, cover stability and cost. Four trench cap designs, which were monitored for a four year period at Hill Air Force Base in Utah, are used to demonstrate the application of the PDSS and evaluate the results of the decision model. The results of the PDSS, using both data and simulations, illustrate the relative advantages of each of the cap designs and which cap is the {open_quotes}best{close_quotes} alternative for a given set of criteria and a particular importance order of those decision criteria.« less

Computer optimization of cutting yield from multiple ripped boards

Treesearch

A.R. Stern; K.A. McDonald

1978-01-01

RIPYLD is a computer program that optimizes the cutting yield from multiple-ripped boards. Decisions are based on automatically collected defect information, cutting bill requirements, and sawing variables. The yield of clear cuttings from a board is calculated for every possible permutation of specified rip widths and both the maximum and minimum percent yield...

Military Enlistments: What Can We Learn from Geographic Variation? Technical Report 620.

ERIC Educational Resources Information Center

Brown, Charles

Some economic variables were examined that affect enlistment decisions and therefore affect the continued success of the All-Volunteer Force. The study used a multiple regression, pooled cross-section/time-series model over the 1975-1982 period, including pay, unemployment, educational benefits, and recruiting resources as independent variables.…

[Adoption of new technologies by health services: the challenge of analyzing relevant factors].

PubMed

Trindade, Evelinda

2008-05-01

The exponential increase in the incorporation of health technologies has been considered a key factor in increased expenditures by the health sector. Such decisions involve multiple levels and stakeholders. Decentralization has multiplied the decision-making levels, with numerous difficult choices and limited resources. The interrelationship between stakeholders is complex, in creative systems with multiple determinants and confounders. The current review discusses the interaction between the factors influencing the decisions to incorporate technologies by health services, and proposes a structure for their analysis. The application and intensity of these factors in decision-making and the incorporation of products and programs by health services shapes the installed capacity of local and regional networks and modifies the health system. Empirical observation of decision-making and technology incorporation in Brazilian health services poses an important challenge. The structured recognition and measurement of these variables can assist proactive planning of health services.

An integrated decision making approach for assessing healthcare waste treatment technologies from a multiple stakeholder.

PubMed

Shi, Hua; Liu, Hu-Chen; Li, Ping; Xu, Xue-Guo

2017-01-01

With increased worldwide awareness of environmental issues, healthcare waste (HCW) management has received much attention from both researchers and practitioners over the past decade. The task of selecting the optimum treatment technology for HCWs is a challenging decision making problem involving conflicting evaluation criteria and multiple stakeholders. In this paper, we develop an integrated decision making framework based on cloud model and MABAC method for evaluating and selecting the best HCW treatment technology from a multiple stakeholder perspective. The introduced framework deals with uncertain linguistic assessments of alternatives by using interval 2-tuple linguistic variables, determines decision makers' relative weights based on the uncertainty and divergence degrees of every decision maker, and obtains the ranking of all HCW disposal alternatives with the aid of an extended MABAC method. Finally, an empirical example from Shanghai, China, is provided to illustrate the feasibility and effectiveness of the proposed approach. Results indicate that the methodology being proposed is more suitable and effective to handle the HCW treatment technology selection problem under vague and uncertain information environment. Copyright © 2016 Elsevier Ltd. All rights reserved.

Variability of blood alcohol content (BAC) determinations: the role of measurement uncertainty, significant figures, and decision rules for compliance assessment in the frame of a multiple BAC threshold law.

PubMed

Zamengo, Luca; Frison, Giampietro; Tedeschi, Gianpaola; Frasson, Samuela; Zancanaro, Flavio; Sciarrone, Rocco

2014-10-01

The measurement of blood-alcohol content (BAC) is a crucial analytical determination required to assess if an offence (e.g. driving under the influence of alcohol) has been committed. For various reasons, results of forensic alcohol analysis are often challenged by the defence. As a consequence, measurement uncertainty becomes a critical topic when assessing compliance with specification limits for forensic purposes. The aims of this study were: (1) to investigate major sources of variability for BAC determinations; (2) to estimate measurement uncertainty for routine BAC determinations; (3) to discuss the role of measurement uncertainty in compliance assessment; (4) to set decision rules for a multiple BAC threshold law, as provided in the Italian Highway Code; (5) to address the topic of the zero-alcohol limit from the forensic toxicology point of view; and (6) to discuss the role of significant figures and rounding errors on measurement uncertainty and compliance assessment. Measurement variability was investigated by the analysis of data collected from real cases and internal quality control. The contribution of both pre-analytical and analytical processes to measurement variability was considered. The resulting expanded measurement uncertainty was 8.0%. Decision rules for the multiple BAC threshold Italian law were set by adopting a guard-banding approach. 0.1 g/L was chosen as cut-off level to assess compliance with the zero-alcohol limit. The role of significant figures and rounding errors in compliance assessment was discussed by providing examples which stressed the importance of these topics for forensic purposes. Copyright © 2014 John Wiley & Sons, Ltd.

Representing Heterogeneity in Structural Relationships Among Multiple Choice Variables Using a Latent Segmentation Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garikapati, Venu; Astroza, Sebastian; Pendyala, Ram M.

Travel model systems often adopt a single decision structure that links several activity-travel choices together. The single decision structure is then used to predict activity-travel choices, with those downstream in the decision-making chain influenced by those upstream in the sequence. The adoption of a singular sequential causal structure to depict relationships among activity-travel choices in travel demand model systems ignores the possibility that some choices are made jointly as a bundle as well as the possible presence of structural heterogeneity in the population with respect to decision-making processes. As different segments in the population may adopt and follow different causalmore » decision-making mechanisms when making selected choices jointly, it would be of value to develop simultaneous equations model systems relating multiple endogenous choice variables that are able to identify population subgroups following alternative causal decision structures. Because the segments are not known a priori, they are considered latent and determined endogenously within a joint modeling framework proposed in this paper. The methodology is applied to a national mobility survey data set to identify population segments that follow different causal structures relating residential location choice, vehicle ownership, and car-share and mobility service usage. It is found that the model revealing three distinct latent segments best describes the data, confirming the efficacy of the modeling approach and the existence of structural heterogeneity in decision-making in the population. Future versions of activity-travel model systems should strive to incorporate such structural heterogeneity to better reflect varying decision processes across population subgroups.« less

An investigation of the role of some person and situation variables in multiple cue probability learning.

PubMed

Bayindir, Mustafa; Bolger, Fergus; Say, Bilge

2016-07-19

Making decisions using judgements of multiple non-deterministic indicators is an important task, both in everyday and professional life. Learning of such decision making has often been studied as the mapping of stimuli (cues) to an environmental variable (criterion); however, little attention has been paid to the effects of situation-by-person interactions on this learning. Accordingly, we manipulated cue and feedback presentation mode (graphic or numeric) and task difficulty, and measured individual differences in working memory capacity (WMC). We predicted that graphic presentation, fewer cues, and elevated WMC would facilitate learning, and that person and task characteristics would interact such that presentation mode compatible with the decision maker's cognitive capability (enhanced visual or verbal WMC) would assist learning, particularly for more difficult tasks. We found our predicted main effects, but no significant interactions, except that those with greater WMC benefited to a larger extent with graphic than with numeric presentation, regardless of which type of working memory was enhanced or number of cues. Our findings suggest that the conclusions of past research based predominantly on tasks using numeric presentation need to be reevaluated and cast light on how working memory helps us learn multiple cue-criterion relationships, with implications for dual-process theories of cognition.

Ocean Variability Effects on Underwater Acoustic Communications

DTIC Science & Technology

2011-09-01

schemes for accessing wide frequency bands. Compared with OFDM schemes, the multiband MIMO transmission combined with time reversal processing...systems, or multiple- input/multiple-output ( MIMO ) systems, decision feedback equalization and interference cancellation schemes have been integrated...unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 2 MIMO receiver also iterates channel estimation and symbol demodulation with

Shared Decision Making and Other Variables as Correlates of Satisfaction with Health Care Decisions in a United States National Survey

PubMed Central

Wills, Celia E.; Holloman, Christopher; Olson, Jacklyn; Hechmer, Catherine; Miller, Carla K.; Duchemin, Anne-Marie

2012-01-01

Objective The purpose of this study was to examine the relationship between shared decision-making (SDM) and satisfaction with decision (SWD) within a larger survey of patient decision-making in health care consultations. Methods A randomly selected age-proportionate national sample of adults (aged 21–70 years) stratified on race, ethnicity, and gender (N = 488) was recruited from a health research volunteer registry and completed an online survey with reference to a recent health consultation. Measures included the Shared Decision Making-9 questionnaire (SDM-Q-9), Satisfaction With Decision (SWD) scale, sociodemographic, health, and other standardized decision-making measures. Forward selection weighted multiple regression analysis was used to model correlates of SWD. Results After controlling for sociodemographic variables, SDM-Q-9 total score was associated with SWD, adjusted R2 = .368, p < .001. Three of nine SDM-Q-9 items accounted for significant proportions of variance in SWD. Conclusion SDM was positively associated with SWD and was strongest for three areas of SDM: patients being helped in a health care consultation with understanding information, with treatment preference elicitation, and with weighing options thoroughly. Practice Implications By identifying variables such as SDM that are associated with SWD, health care interventions can better target modifiable factors to enhance satisfaction and other outcomes. PMID:22410642

Shared decision making and other variables as correlates of satisfaction with health care decisions in a United States national survey.

PubMed

Glass, Katherine Elizabeth; Wills, Celia E; Holloman, Christopher; Olson, Jacklyn; Hechmer, Catherine; Miller, Carla K; Duchemin, Anne-Marie

2012-07-01

The purpose of this study was to examine the relationship between shared decision-making (SDM) and satisfaction with decision (SWD) within a larger survey of patient decision-making in health care consultations. A randomly selected age-proportionate national sample of adults (aged 21-70 years) stratified on race, ethnicity, and gender (N=488) was recruited from a health research volunteer registry and completed an online survey with reference to a recent health consultation. Measures included the shared decision making-9 questionnaire (SDM-Q-9), Satisfaction With Decision (SWD) scale, sociodemographic, health, and other standardized decision-making measures. Forward selection weighted multiple regression analysis was used to model correlates of SWD. After controlling for sociodemographic variables, SDM-Q-9 total score was associated with SWD, adjusted R(2)=.368, p<.001. Three of nine SDM-Q-9 items accounted for significant proportions of variance in SWD. SDM was positively associated with SWD and was strongest for three areas of SDM: patients being helped in a health care consultation with understanding information, with treatment preference elicitation, and with weighing options thoroughly. By identifying variables such as SDM that are associated with SWD, health care interventions can better target modifiable factors to enhance satisfaction and other outcomes. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Spatial and Temporal Flood Risk Assessment for Decision Making Approach

NASA Astrophysics Data System (ADS)

Azizat, Nazirah; Omar, Wan-Mohd-Sabki Wan

2018-03-01

Heavy rainfall, adversely impacting inundation areas, depends on the magnitude of the flood. Significantly, location of settlements, infrastructure and facilities in floodplains result in many regions facing flooding risks. A problem faced by the decision maker in an assessment of flood vulnerability and evaluation of adaptation measures is recurrent flooding in the same areas. Identification of recurrent flooding areas and frequency of floods should be priorities for flood risk management. However, spatial and temporal variability become major factors of uncertainty in flood risk management. Therefore, dynamic and spatial characteristics of these changes in flood impact assessment are important in making decisions about the future of infrastructure development and community life. System dynamics (SD) simulation and hydrodynamic modelling are presented as tools for modelling the dynamic characteristics of flood risk and spatial variability. This paper discusses the integration between spatial and temporal information that is required by the decision maker for the identification of multi-criteria decision problems involving multiple stakeholders.

Use of personalized Dynamic Treatment Regimes (DTRs) and Sequential Multiple Assignment Randomized Trials (SMARTs) in mental health studies

PubMed Central

Liu, Ying; ZENG, Donglin; WANG, Yuanjia

2014-01-01

Summary Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each point where a clinical decision is made based on each patient’s time-varying characteristics and intermediate outcomes observed at earlier points in time. The complexity, patient heterogeneity, and chronicity of mental disorders call for learning optimal DTRs to dynamically adapt treatment to an individual’s response over time. The Sequential Multiple Assignment Randomized Trial (SMARTs) design allows for estimating causal effects of DTRs. Modern statistical tools have been developed to optimize DTRs based on personalized variables and intermediate outcomes using rich data collected from SMARTs; these statistical methods can also be used to recommend tailoring variables for designing future SMART studies. This paper introduces DTRs and SMARTs using two examples in mental health studies, discusses two machine learning methods for estimating optimal DTR from SMARTs data, and demonstrates the performance of the statistical methods using simulated data. PMID:25642116

To analyse a trace or not? Evaluating the decision-making process in the criminal investigation.

PubMed

Bitzer, Sonja; Ribaux, Olivier; Albertini, Nicola; Delémont, Olivier

2016-05-01

In order to broaden our knowledge and understanding of the decision steps in the criminal investigation process, we started by evaluating the decision to analyse a trace and the factors involved in this decision step. This decision step is embedded in the complete criminal investigation process, involving multiple decision and triaging steps. Considering robbery cases occurring in a geographic region during a 2-year-period, we have studied the factors influencing the decision to submit biological traces, directly sampled on the scene of the robbery or on collected objects, for analysis. The factors were categorised into five knowledge dimensions: strategic, immediate, physical, criminal and utility and decision tree analysis was carried out. Factors in each category played a role in the decision to analyse a biological trace. Interestingly, factors involving information available prior to the analysis are of importance, such as the fact that a positive result (a profile suitable for comparison) is already available in the case, or that a suspect has been identified through traditional police work before analysis. One factor that was taken into account, but was not significant, is the matrix of the trace. Hence, the decision to analyse a trace is not influenced by this variable. The decision to analyse a trace first is very complex and many of the tested variables were taken into account. The decisions are often made on a case-by-case basis. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

An Evaluation of Curriculum Materials Based Upon the Socio-Scientific Reasoning Model.

ERIC Educational Resources Information Center

Henkin, Gayle; And Others

To address the need to develop a scientifically literate citizenry, the socio-scientific reasoning model was created to guide curriculum development. Goals of this developmental approach include increasing: (1) students' skills in dealing with problems containing multiple interacting variables; (2) students' decision-making skills incorporating a…

Distributed Space Mission Design for Earth Observation Using Model-Based Performance Evaluation

NASA Technical Reports Server (NTRS)

Nag, Sreeja; LeMoigne-Stewart, Jacqueline; Cervantes, Ben; DeWeck, Oliver

2015-01-01

Distributed Space Missions (DSMs) are gaining momentum in their application to earth observation missions owing to their unique ability to increase observation sampling in multiple dimensions. DSM design is a complex problem with many design variables, multiple objectives determining performance and cost and emergent, often unexpected, behaviors. There are very few open-access tools available to explore the tradespace of variables, minimize cost and maximize performance for pre-defined science goals, and therefore select the most optimal design. This paper presents a software tool that can multiple DSM architectures based on pre-defined design variable ranges and size those architectures in terms of predefined science and cost metrics. The tool will help a user select Pareto optimal DSM designs based on design of experiments techniques. The tool will be applied to some earth observation examples to demonstrate its applicability in making some key decisions between different performance metrics and cost metrics early in the design lifecycle.

The development of a disease oriented eFolder for multiple sclerosis decision support

NASA Astrophysics Data System (ADS)

Ma, Kevin; Jacobs, Colin; Fernandez, James; Amezcua, Lilyana; Liu, Brent

2010-03-01

Multiple sclerosis (MS) is a demyelinating disease of the central nervous system. The chronic nature of MS necessitates multiple MRI studies to track disease progression. Currently, MRI assessment of multiple sclerosis requires manual lesion measurement and yields an estimate of lesion volume and change that is highly variable and user-dependent. In the setting of a longitudinal study, disease trends and changes become difficult to extrapolate from the lesions. In addition, it is difficult to establish a correlation between these imaged lesions and clinical factors such as treatment course. To address these clinical needs, an MS specific e-Folder for decision support in the evaluation and assessment of MS has been developed. An e-Folder is a disease-centric electronic medical record in contrast to a patient-centric electronic health record. Along with an MS lesion computer aided detection (CAD) package for lesion load, location, and volume, clinical parameters such as patient demographics, disease history, clinical course, and treatment history are incorporated to make the e-Folder comprehensive. With the integration of MRI studies together with related clinical data and informatics tools designed for monitoring multiple sclerosis, it provides a platform to improve the detection of treatment response in patients with MS. The design and deployment of MS e-Folder aims to standardize MS lesion data and disease progression to aid in decision making and MS-related research.

Building Connections between a Cultural Practice and Modeling in Science Education

ERIC Educational Resources Information Center

Schademan, Alfred R.

2015-01-01

The purpose of this study is to examine the kinds of reasoning that African American young men learn and develop when playing Spades, a common cultural practice in African American communities. The qualitative study found that the Spades players routinely consider multiple variables and their mathematical relationships when making decisions. The…

Changes of crop rotation in Iowa determined from the USDA-NASS cropland data layer product

USDA-ARS?s Scientific Manuscript database

Crop rotation is one of the important decisions made independently by numerous farm managers, and is a critical variable in models of crop growth and soil carbon. By combining multiple years (2001-2009) of the USDA National Agricultural Statistics Service (NASS) cropland data layer (CDL), it is pos...

Design of Experiments with Multiple Independent Variables: A Resource Management Perspective on Complete and Reduced Factorial Designs

PubMed Central

Collins, Linda M.; Dziak, John J.; Li, Runze

2009-01-01

An investigator who plans to conduct experiments with multiple independent variables must decide whether to use a complete or reduced factorial design. This article advocates a resource management perspective on making this decision, in which the investigator seeks a strategic balance between service to scientific objectives and economy. Considerations in making design decisions include whether research questions are framed as main effects or simple effects; whether and which effects are aliased (confounded) in a particular design; the number of experimental conditions that must be implemented in a particular design and the number of experimental subjects the design requires to maintain the desired level of statistical power; and the costs associated with implementing experimental conditions and obtaining experimental subjects. In this article four design options are compared: complete factorial, individual experiments, single factor, and fractional factorial designs. Complete and fractional factorial designs and single factor designs are generally more economical than conducting individual experiments on each factor. Although relatively unfamiliar to behavioral scientists, fractional factorial designs merit serious consideration because of their economy and versatility. PMID:19719358

Multiple sclerosis: individualized disease susceptibility and therapy response.

PubMed

Pravica, Vera; Markovic, Milos; Cupic, Maja; Savic, Emina; Popadic, Dusan; Drulovic, Jelena; Mostarica-Stojkovic, Marija

2013-02-01

Multiple sclerosis (MS) is a heterogeneous disease in which diverse genetic, pathological and clinical backgrounds lead to variable therapy response. Accordingly, MS care should be tailored to address disease traits unique to each person. At the core of personalized management is the emergence of new knowledge, enabling optimized treatment and disease-modifying therapies. This overview analyzes the promise of genetic and nongenetic biomarkers in advancing decision-making algorithms to assist diagnosis or in predicting the disease course and therapy response in any given MS patient.

Are multiple-trial experiments appropriate for eyewitness identification studies? Accuracy, choosing, and confidence across trials.

PubMed

Mansour, J K; Beaudry, J L; Lindsay, R C L

2017-12-01

Eyewitness identification experiments typically involve a single trial: A participant views an event and subsequently makes a lineup decision. As compared to this single-trial paradigm, multiple-trial designs are more efficient, but significantly reduce ecological validity and may affect the strategies that participants use to make lineup decisions. We examined the effects of a number of forensically relevant variables (i.e., memory strength, type of disguise, degree of disguise, and lineup type) on eyewitness accuracy, choosing, and confidence across 12 target-present and 12 target-absent lineup trials (N = 349; 8,376 lineup decisions). The rates of correct rejections and choosing (across both target-present and target-absent lineups) did not vary across the 24 trials, as reflected by main effects or interactions with trial number. Trial number had a significant but trivial quadratic effect on correct identifications (OR = 0.99) and interacted significantly, but again trivially, with disguise type (OR = 1.00). Trial number did not significantly influence participants' confidence in correct identifications, confidence in correct rejections, or confidence in target-absent selections. Thus, multiple-trial designs appear to have minimal effects on eyewitness accuracy, choosing, and confidence. Researchers should thus consider using multiple-trial designs for conducting eyewitness identification experiments.

Should learners reason one step at a time? A randomised trial of two diagnostic scheme designs.

PubMed

Blissett, Sarah; Morrison, Deric; McCarty, David; Sibbald, Matthew

2017-04-01

Making a diagnosis can be difficult for learners as they must integrate multiple clinical variables. Diagnostic schemes can help learners with this complex task. A diagnostic scheme is an algorithm that organises possible diagnoses by assigning signs or symptoms (e.g. systolic murmur) to groups of similar diagnoses (e.g. aortic stenosis and aortic sclerosis) and provides distinguishing features to help discriminate between similar diagnoses (e.g. carotid pulse). The current literature does not identify whether scheme layouts should guide learners to reason one step at a time in a terminally branching scheme or weigh multiple variables simultaneously in a hybrid scheme. We compared diagnostic accuracy, perceptual errors and cognitive load using two scheme layouts for cardiac auscultation. Focused on the task of identifying murmurs on Harvey, a cardiopulmonary simulator, 86 internal medicine residents used two scheme layouts. The terminally branching scheme organised the information into single variable decisions. The hybrid scheme combined single variable decisions with a chart integrating multiple distinguishing features. Using a crossover design, participants completed one set of murmurs (diastolic or systolic) with either the terminally branching or the hybrid scheme. The second set of murmurs was completed with the other scheme. A repeated measures manova was performed to compare diagnostic accuracy, perceptual errors and cognitive load between the scheme layouts. There was a main effect of the scheme layout (Wilks' λ = 0.841, F 3,80 = 5.1, p = 0.003). Use of a terminally branching scheme was associated with increased diagnostic accuracy (65 versus 53%, p = 0.02), fewer perceptual errors (0.61 versus 0.98 errors, p = 0.001) and lower cognitive load (3.1 versus 3.5/7, p = 0.023). The terminally branching scheme was associated with improved diagnostic accuracy, fewer perceptual errors and lower cognitive load, suggesting that terminally branching schemes are effective for improving diagnostic accuracy. These findings can inform the design of schemes and other clinical decision aids. © 2017 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Multiple Use One-Sided Hypotheses Testing in Univariate Linear Calibration

NASA Technical Reports Server (NTRS)

Krishnamoorthy, K.; Kulkarni, Pandurang M.; Mathew, Thomas

1996-01-01

Consider a normally distributed response variable, related to an explanatory variable through the simple linear regression model. Data obtained on the response variable, corresponding to known values of the explanatory variable (i.e., calibration data), are to be used for testing hypotheses concerning unknown values of the explanatory variable. We consider the problem of testing an unlimited sequence of one sided hypotheses concerning the explanatory variable, using the corresponding sequence of values of the response variable and the same set of calibration data. This is the situation of multiple use of the calibration data. The tests derived in this context are characterized by two types of uncertainties: one uncertainty associated with the sequence of values of the response variable, and a second uncertainty associated with the calibration data. We derive tests based on a condition that incorporates both of these uncertainties. The solution has practical applications in the decision limit problem. We illustrate our results using an example dealing with the estimation of blood alcohol concentration based on breath estimates of the alcohol concentration. In the example, the problem is to test if the unknown blood alcohol concentration of an individual exceeds a threshold that is safe for driving.

The influence of physical and cognitive factors on reactive agility performance in men basketball players.

PubMed

Scanlan, Aaron; Humphries, Brendan; Tucker, Patrick S; Dalbo, Vincent

2014-01-01

This study explored the influence of physical and cognitive measures on reactive agility performance in basketball players. Twelve men basketball players performed multiple sprint, Change of Direction Speed Test, and Reactive Agility Test trials. Pearson's correlation analyses were used to determine relationships between the predictor variables (stature, mass, body composition, 5-m, 10-m and 20-m sprint times, peak speed, closed-skill agility time, response time and decision-making time) and reactive agility time (response variable). Simple and stepwise regression analyses determined the individual influence of each predictor variable and the best predictor model for reactive agility time. Morphological (r = -0.45 to 0.19), sprint (r = -0.40 to 0.41) and change-of-direction speed measures (r = 0.43) had small to moderate correlations with reactive agility time. Response time (r = 0.76, P = 0.004) and decision-making time (r = 0.58, P = 0.049) had large to very large relationships with reactive agility time. Response time was identified as the sole predictor variable for reactive agility time in the stepwise model (R(2) = 0.58, P = 0.004). In conclusion, cognitive measures had the greatest influence on reactive agility performance in men basketball players. These findings suggest reaction and decision-making drills should be incorporated in basketball training programmes.

Hybrid Discrete-Continuous Markov Decision Processes

NASA Technical Reports Server (NTRS)

Feng, Zhengzhu; Dearden, Richard; Meuleau, Nicholas; Washington, Rich

2003-01-01

This paper proposes a Markov decision process (MDP) model that features both discrete and continuous state variables. We extend previous work by Boyan and Littman on the mono-dimensional time-dependent MDP to multiple dimensions. We present the principle of lazy discretization, and piecewise constant and linear approximations of the model. Having to deal with several continuous dimensions raises several new problems that require new solutions. In the (piecewise) linear case, we use techniques from partially- observable MDPs (POMDPS) to represent value functions as sets of linear functions attached to different partitions of the state space.

Interval-parameter semi-infinite fuzzy-stochastic mixed-integer programming approach for environmental management under multiple uncertainties.

PubMed

Guo, P; Huang, G H

2010-03-01

In this study, an interval-parameter semi-infinite fuzzy-chance-constrained mixed-integer linear programming (ISIFCIP) approach is developed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing interval-parameter semi-infinite programming (ISIP) and fuzzy-chance-constrained programming (FCCP) by incorporating uncertainties expressed as dual uncertainties of functional intervals and multiple uncertainties of distributions with fuzzy-interval admissible probability of violating constraint within a general optimization framework. The binary-variable solutions represent the decisions of waste-management-facility expansion, and the continuous ones are related to decisions of waste-flow allocation. The interval solutions can help decision-makers to obtain multiple decision alternatives, as well as provide bases for further analyses of tradeoffs between waste-management cost and system-failure risk. In the application to the City of Regina, Canada, two scenarios are considered. In Scenario 1, the City's waste-management practices would be based on the existing policy over the next 25 years. The total diversion rate for the residential waste would be approximately 14%. Scenario 2 is associated with a policy for waste minimization and diversion, where 35% diversion of residential waste should be achieved within 15 years, and 50% diversion over 25 years. In this scenario, not only landfill would be expanded, but also CF and MRF would be expanded. Through the scenario analyses, useful decision support for the City's solid-waste managers and decision-makers has been generated. Three special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it is useful for tackling multiple uncertainties expressed as intervals, functional intervals, probability distributions, fuzzy sets, and their combinations; secondly, it has capability in addressing the temporal variations of the functional intervals; thirdly, it can facilitate dynamic analysis for decisions of facility-expansion planning and waste-flow allocation within a multi-facility, multi-period and multi-option context. Copyright 2009 Elsevier Ltd. All rights reserved.

A vertical-energy-thresholding procedure for data reduction with multiple complex curves.

PubMed

Jung, Uk; Jeong, Myong K; Lu, Jye-Chyi

2006-10-01

Due to the development of sensing and computer technology, measurements of many process variables are available in current manufacturing processes. It is very challenging, however, to process a large amount of information in a limited time in order to make decisions about the health of the processes and products. This paper develops a "preprocessing" procedure for multiple sets of complicated functional data in order to reduce the data size for supporting timely decision analyses. The data type studied has been used for fault detection, root-cause analysis, and quality improvement in such engineering applications as automobile and semiconductor manufacturing and nanomachining processes. The proposed vertical-energy-thresholding (VET) procedure balances the reconstruction error against data-reduction efficiency so that it is effective in capturing key patterns in the multiple data signals. The selected wavelet coefficients are treated as the "reduced-size" data in subsequent analyses for decision making. This enhances the ability of the existing statistical and machine-learning procedures to handle high-dimensional functional data. A few real-life examples demonstrate the effectiveness of our proposed procedure compared to several ad hoc techniques extended from single-curve-based data modeling and denoising procedures.

Return to play after infectious mononucleosis.

PubMed

Becker, Jonathan A; Smith, Julie Anne

2014-05-01

Infectious mononucleosis is a disease primarily of adolescence and early adulthood. The risk of splenic injury and chronic fatigue make return-to-play decisions a challenge for the clinician caring for athletes with infectious mononucleosis. Data were obtained from the PubMed and MEDLINE databases through December 2012 by searching for epidemiology, diagnosis, clinical manifestations, management, and the role of the spleen in infectious mononucleosis. Clinical review. Level 4. Infectious mononucleosis is commonly encountered in young athletes. Its disease pattern is variable and can affect multiple organ systems. Supportive care is the cornerstone, with little role for medications such as corticosteroids. Physical examination is unreliable for the spleen, and ultrasound imaging has limitations in its ability to guide return-to-play decisions. Exercise does not appear to place the young athlete at risk for chronic fatigue, but determining who is at risk for persistent symptoms is a challenge. Return-to-play decisions for the athlete with infectious mononucleosis need to be individualized because of the variable disease course and lack of evidence-based guidelines.

The impact of social and organizational factors on workers' coping with musculoskeletal symptoms.

PubMed

Torp, S; Riise, T; Moen, B E

2001-07-01

Workers with musculoskeletal symptoms are often advised to cope with their symptoms by changing their working technique and by using lifting equipment. The main objective of this study was to test the hypothesis that negative social and organizational factors where people are employed may prevent workers from implementing these coping strategies. A total of 1,567 automobile garage workers (72%) returned a questionnaire concerning coping with musculoskeletal symptoms and social and organizational factors. When job demands, decision authority, social support, and management support related to health, environment, and safety (HES) were used as predictor variables in a multiple regression model, coping as the outcome variable was correlated with decision authority, social support, and HES-related management support (standardized beta=.079,.12, and.13, respectively). When an index for health-related support and control was added to the model, it correlated with coping (standardized beta=.36), whereas the other relationships disappeared. Decision authority and social support entail health-related support and control that, in turn, influences coping.

Collective decision dynamics in the presence of external drivers

NASA Astrophysics Data System (ADS)

Bassett, Danielle S.; Alderson, David L.; Carlson, Jean M.

2012-09-01

We develop a sequence of models describing information transmission and decision dynamics for a network of individual agents subject to multiple sources of influence. Our general framework is set in the context of an impending natural disaster, where individuals, represented by nodes on the network, must decide whether or not to evacuate. Sources of influence include a one-to-many externally driven global broadcast as well as pairwise interactions, across links in the network, in which agents transmit either continuous opinions or binary actions. We consider both uniform and variable threshold rules on the individual opinion as baseline models for decision making. Our results indicate that (1) social networks lead to clustering and cohesive action among individuals, (2) binary information introduces high temporal variability and stagnation, and (3) information transmission over the network can either facilitate or hinder action adoption, depending on the influence of the global broadcast relative to the social network. Our framework highlights the essential role of local interactions between agents in predicting collective behavior of the population as a whole.

Revealing, Reducing, and Representing Uncertainties in New Hydrologic Projections for Climate-changed Futures

NASA Astrophysics Data System (ADS)

Arnold, Jeffrey; Clark, Martyn; Gutmann, Ethan; Wood, Andy; Nijssen, Bart; Rasmussen, Roy

2016-04-01

The United States Army Corps of Engineers (USACE) has had primary responsibility for multi-purpose water resource operations on most of the major river systems in the U.S. for more than 200 years. In that time, the USACE projects and programs making up those operations have proved mostly robust against the range of natural climate variability encountered over their operating life spans. However, in some watersheds and for some variables, climate change now is known to be shifting the hydroclimatic baseline around which that natural variability occurs and changing the range of that variability as well. This makes historical stationarity an inappropriate basis for assessing continued project operations under climate-changed futures. That means new hydroclimatic projections are required at multiple scales to inform decisions about specific threats and impacts, and for possible adaptation responses to limit water-resource vulnerabilities and enhance operational resilience. However, projections of possible future hydroclimatologies have myriad complex uncertainties that require explicit guidance for interpreting and using them to inform those decisions about climate vulnerabilities and resilience. Moreover, many of these uncertainties overlap and interact. Recent work, for example, has shown the importance of assessing the uncertainties from multiple sources including: global model structure [Meehl et al., 2005; Knutti and Sedlacek, 2013]; internal climate variability [Deser et al., 2012; Kay et al., 2014]; climate downscaling methods [Gutmann et al., 2012; Mearns et al., 2013]; and hydrologic models [Addor et al., 2014; Vano et al., 2014; Mendoza et al., 2015]. Revealing, reducing, and representing these uncertainties is essential for defining the plausible quantitative climate change narratives required to inform water-resource decision-making. And to be useful, such quantitative narratives, or storylines, of climate change threats and hydrologic impacts must sample from the full range of uncertainties associated with all parts of the simulation chain, from global climate models with simulations of natural climate variability, through regional climate downscaling, and on to modeling of affected hydrologic processes and downstream water resources impacts. This talk will present part of the work underway now both to reveal and reduce some important uncertainties and to develop explicit guidance for future generation of quantitative hydroclimatic storylines. Topics will include: 1- model structural and parameter-set limitations of some methods widely used to quantify climate impacts to hydrologic processes [Gutmann et al., 2014; Newman et al., 2015]; 2- development and evaluation of new, spatially consistent, U.S. national-scale climate downscaling and hydrologic simulation capabilities directly relevant at the multiple scales of water-resource decision-making [Newman et al., 2015; Mizukami et al., 2015; Gutmann et al., 2016]; and 3- development and evaluation of advanced streamflow forecasting methods to reduce and represent integrated uncertainties in a tractable way [Wood et al., 2014; Wood et al., 2015]. A key focus will be areas where climatologic and hydrologic science is currently under-developed to inform decisions - or is perhaps wrongly scaled or misapplied in practice - indicating the need for additional fundamental science and interpretation.

A Decision Support System for Mitigating Stream Temperature Impacts in the Sacramento River

NASA Astrophysics Data System (ADS)

Caldwell, R. J.; Zagona, E. A.; Rajagopalan, B.

2014-12-01

Increasing demands on the limited and variable water supply across the West can result in insufficient streamflow to sustain healthy fish habitat. We develop an integrated decision support system (DSS) for modeling and mitigating stream temperature impacts and demonstrate it on the Sacramento River system in California. Water management in the Sacramento River is a complex task with a diverse set of demands ranging from municipal supply to mitigation of fisheries impacts due to high water temperatures. Current operations utilize the temperature control device (TCD) structure at Shasta Dam to mitigate these high water temperatures downstream at designated compliance points. The TCD structure at Shasta Dam offers a rather unique opportunity to mitigate water temperature violations through adjustments to both release volume and temperature. In this study, we develop and evaluate a model-based DSS with four broad components that are coupled to produce the decision tool for stream temperature mitigation: (i) a suite of statistical models for modeling stream temperature attributes using hydrology and climate variables of critical importance to fish habitat; (ii) a reservoir thermal model for modeling the thermal structure and, consequently, the water release temperature, (iii) a stochastic weather generator to simulate weather sequences consistent with seasonal outlooks; and, (iv) a set of decision rules (i.e., 'rubric') for reservoir water releases in response to outputs from the above components. Multiple options for modifying releases at Shasta Dam were considered in the DSS, including mixing water from multiple elevations through the TCD and using different acceptable levels of risk. The DSS also incorporates forecast uncertainties and reservoir operating options to help mitigate stream temperature impacts for fish habitat, while efficiently using the reservoir water supply and cold pool storage. The use of these coupled tools in simulating impacts of future climate on stream temperature variability is also demonstrated. Results indicate that the DSS could substantially reduce the number of violations of thermal criteria, while ensuring maintenance of the cold pool storage throughout the summer.

Neural mechanisms underlying human consensus decision-making

PubMed Central

Suzuki, Shinsuke; Adachi, Ryo; Dunne, Simon; Bossaerts, Peter; O'Doherty, John P.

2015-01-01

SUMMARY Consensus building in a group is a hallmark of animal societies, yet little is known about its underlying computational and neural mechanisms. Here, we applied a novel computational framework to behavioral and fMRI data from human participants performing a consensus decision-making task with up to five other participants. We found that participants reached consensus decisions through integrating their own preferences with information about the majority of group-members’ prior choices, as well as inferences about how much each option was stuck to by the other people. These distinct decision variables were separately encoded in distinct brain areas: the ventromedial prefrontal cortex, posterior superior temporal sulcus/temporoparietal junction and intraparietal sulcus, and were integrated in the dorsal anterior cingulate cortex. Our findings provide support for a theoretical account in which collective decisions are made through integrating multiple types of inference about oneself, others and environments, processed in distinct brain modules. PMID:25864634

Neural mechanisms underlying human consensus decision-making.

PubMed

Suzuki, Shinsuke; Adachi, Ryo; Dunne, Simon; Bossaerts, Peter; O'Doherty, John P

2015-04-22

Consensus building in a group is a hallmark of animal societies, yet little is known about its underlying computational and neural mechanisms. Here, we applied a computational framework to behavioral and fMRI data from human participants performing a consensus decision-making task with up to five other participants. We found that participants reached consensus decisions through integrating their own preferences with information about the majority group members' prior choices, as well as inferences about how much each option was stuck to by the other people. These distinct decision variables were separately encoded in distinct brain areas-the ventromedial prefrontal cortex, posterior superior temporal sulcus/temporoparietal junction, and intraparietal sulcus-and were integrated in the dorsal anterior cingulate cortex. Our findings provide support for a theoretical account in which collective decisions are made through integrating multiple types of inference about oneself, others, and environments, processed in distinct brain modules. Copyright © 2015 Elsevier Inc. All rights reserved.

Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis.

PubMed

Hostettler, Isabel Charlotte; Muroi, Carl; Richter, Johannes Konstantin; Schmid, Josef; Neidert, Marian Christoph; Seule, Martin; Boss, Oliver; Pangalu, Athina; Germans, Menno Robbert; Keller, Emanuela

2018-01-19

OBJECTIVE The aim of this study was to create prediction models for outcome parameters by decision tree analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH). METHODS The database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7. RESULTS The overall mortality was 18.4%. The accuracy of the decision tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of < 5%. Prediction accuracy for survival on day 1 was 75.2%. The most important differentiating factor was the interleukin-6 (IL-6) level on day 1. Favorable functional outcome, defined as Glasgow Outcome Scale scores of 4 and 5, was observed in 68.6% of patients. Favorable functional outcome at all time points had a prediction accuracy of 71.1% in the training data set, with procalcitonin on day 1 being the most important differentiating factor at all time points. A total of 148 patients (27%) developed VP shunt dependency. The most important differentiating factor was hyperglycemia on admission. CONCLUSIONS The multiple variable analysis capability of decision trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The decision tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.

Decision tree analysis of factors influencing rainfall-related building damage

NASA Astrophysics Data System (ADS)

Spekkers, M. H.; Kok, M.; Clemens, F. H. L. R.; ten Veldhuis, J. A. E.

2014-04-01

Flood damage prediction models are essential building blocks in flood risk assessments. Little research has been dedicated so far to damage of small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities. The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period of 1998-2011. The databases include claims of water-related damage, for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor. Response variables being modelled are average claim size and claim frequency, per district per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households. Analyses were made separately for property and content damage claim data. Results of decision tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), ownership structure (content data only) and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size, which suggest that variability in average claim size is related to explanatory variables that cannot be defined at the district scale. Cross-validation results show that decision trees were able to predict 22-26% of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11-18% of variance explained). Still, a large part of the variance in claim frequency is left unexplained, which is likely to be caused by variations in data at subdistrict scale and missing explanatory variables.

Diffusion of an economic development policy innovation: explaining the international spread of casino gambling.

PubMed

Richard, Brian

2010-06-01

This study uses an event history analysis to examine the factors that lead to the adoption of casino gambling among 13 nations around the world. Specifically, measures of fiscal stress, economic development, tourism, religiosity, and income levels are tested for their relationship to national decisions to legalize casino gambling. This study found that economic development needs, as measured by general unemployment rates, were associated with the casino legalization decisions of national governments. Higher unemployment rates were more likely in the years that nations legalized casino gambling. Religiosity, measured by frequency of church attendance, was also found to be a significant barrier in legalization decisions. Measures of fiscal stress, tourism, and income levels were not found to have significant relationships with the legalization decisions. This is interesting because these factors are often cited in case studies, media reports, and the statements of politicians during legalization processes. This study points to the need for further research in several areas. Further exploration of potential explanatory variables and more appropriate measures of currently theorized factors is warranted. Another area for further research is the seeming contradictory findings of multiple statistical analyses and multiple anecdotal findings of the impacts of fiscal stress on the casino legalization decision.

Bayes multiple decision functions.

PubMed

Wu, Wensong; Peña, Edsel A

2013-01-01

This paper deals with the problem of simultaneously making many ( M ) binary decisions based on one realization of a random data matrix X . M is typically large and X will usually have M rows associated with each of the M decisions to make, but for each row the data may be low dimensional. Such problems arise in many practical areas such as the biological and medical sciences, where the available dataset is from microarrays or other high-throughput technology and with the goal being to decide which among of many genes are relevant with respect to some phenotype of interest; in the engineering and reliability sciences; in astronomy; in education; and in business. A Bayesian decision-theoretic approach to this problem is implemented with the overall loss function being a cost-weighted linear combination of Type I and Type II loss functions. The class of loss functions considered allows for use of the false discovery rate (FDR), false nondiscovery rate (FNR), and missed discovery rate (MDR) in assessing the quality of decision. Through this Bayesian paradigm, the Bayes multiple decision function (BMDF) is derived and an efficient algorithm to obtain the optimal Bayes action is described. In contrast to many works in the literature where the rows of the matrix X are assumed to be stochastically independent, we allow a dependent data structure with the associations obtained through a class of frailty-induced Archimedean copulas. In particular, non-Gaussian dependent data structure, which is typical with failure-time data, can be entertained. The numerical implementation of the determination of the Bayes optimal action is facilitated through sequential Monte Carlo techniques. The theory developed could also be extended to the problem of multiple hypotheses testing, multiple classification and prediction, and high-dimensional variable selection. The proposed procedure is illustrated for the simple versus simple hypotheses setting and for the composite hypotheses setting through simulation studies. The procedure is also applied to a subset of a microarray data set from a colon cancer study.

POWER-ENHANCED MULTIPLE DECISION FUNCTIONS CONTROLLING FAMILY-WISE ERROR AND FALSE DISCOVERY RATES.

PubMed

Peña, Edsel A; Habiger, Joshua D; Wu, Wensong

2011-02-01

Improved procedures, in terms of smaller missed discovery rates (MDR), for performing multiple hypotheses testing with weak and strong control of the family-wise error rate (FWER) or the false discovery rate (FDR) are developed and studied. The improvement over existing procedures such as the Šidák procedure for FWER control and the Benjamini-Hochberg (BH) procedure for FDR control is achieved by exploiting possible differences in the powers of the individual tests. Results signal the need to take into account the powers of the individual tests and to have multiple hypotheses decision functions which are not limited to simply using the individual p -values, as is the case, for example, with the Šidák, Bonferroni, or BH procedures. They also enhance understanding of the role of the powers of individual tests, or more precisely the receiver operating characteristic (ROC) functions of decision processes, in the search for better multiple hypotheses testing procedures. A decision-theoretic framework is utilized, and through auxiliary randomizers the procedures could be used with discrete or mixed-type data or with rank-based nonparametric tests. This is in contrast to existing p -value based procedures whose theoretical validity is contingent on each of these p -value statistics being stochastically equal to or greater than a standard uniform variable under the null hypothesis. Proposed procedures are relevant in the analysis of high-dimensional "large M , small n " data sets arising in the natural, physical, medical, economic and social sciences, whose generation and creation is accelerated by advances in high-throughput technology, notably, but not limited to, microarray technology.

Patient decision-making related to antirheumatic drugs in rheumatoid arthritis: the importance of patient trust of physician.

PubMed

Martin, Richard W; Head, Andrew J; René, Jonathan; Swartz, Timothy J; Fiechtner, Justus J; McIntosh, Barbara A; Holmes-Rovner, Margaret

2008-04-01

To explore how rheumatoid arthritis (RA) antirheumatic drug-specific knowledge and numeric literacy, patient trust in physician, and demographic and disease-related factors relate to the confidence of patient decision-making related to disease modifying antirheumatic drugs (DMARD). Data were analyzed from 628 randomly selected patients with RA receiving care in community rheumatology practices, who responded to a multicenter, cross-sectional mail survey. We used multiple regression models to predict patient confidence in DMARD decision-making related to their most recently initiated DMARD. Significant positive correlation was found between confidence in DMARD decision and trust in physician, DMARD-specific knowledge, and disease duration, but not risk-related numeric literacy, sex, or education. Negative correlations were found with disease severity and current bother with DMARD side effects. A multiple linear regression model of confidence in DMARD decision had an overall R = 0.788, R2 = 0.620 (p < 0.001). The 4 dependent variables contributing significantly to the model were female sex, Medicaid insurance status, satisfaction with RA disease control, and trust in physician, with standardized beta = 0.077, -0.089, 0.147, and 0.687, respectively. In this sample of community patients with RA, the patient trust in physician had substantially greater effect on confidence in DMARD decision than DMARD-specific knowledge, disease-related factors, or demographic characteristics.

Cortical Components of Reaction-Time during Perceptual Decisions in Humans.

PubMed

Dmochowski, Jacek P; Norcia, Anthony M

2015-01-01

The mechanisms of perceptual decision-making are frequently studied through measurements of reaction time (RT). Classical sequential-sampling models (SSMs) of decision-making posit RT as the sum of non-overlapping sensory, evidence accumulation, and motor delays. In contrast, recent empirical evidence hints at a continuous-flow paradigm in which multiple motor plans evolve concurrently with the accumulation of sensory evidence. Here we employ a trial-to-trial reliability-based component analysis of encephalographic data acquired during a random-dot motion task to directly image continuous flow in the human brain. We identify three topographically distinct neural sources whose dynamics exhibit contemporaneous ramping to time-of-response, with the rate and duration of ramping discriminating fast and slow responses. Only one of these sources, a parietal component, exhibits dependence on strength-of-evidence. The remaining two components possess topographies consistent with origins in the motor system, and their covariation with RT overlaps in time with the evidence accumulation process. After fitting the behavioral data to a popular SSM, we find that the model decision variable is more closely matched to the combined activity of the three components than to their individual activity. Our results emphasize the role of motor variability in shaping RT distributions on perceptual decision tasks, suggesting that physiologically plausible computational accounts of perceptual decision-making must model the concurrent nature of evidence accumulation and motor planning.

Using Flow-Ecology Relationships to Evaluate Ecosystem Service Trade-Offs and Complementarities in the Nation's Largest River Swamp.

PubMed

Kozak, Justin P; Bennett, Micah G; Hayden-Lesmeister, Anne; Fritz, Kelley A; Nickolotsky, Aaron

2015-06-01

Large river systems are inextricably linked with social systems; consequently, management decisions must be made within a given ecological, social, and political framework that often defies objective, technical resolution. Understanding flow-ecology relationships in rivers is necessary to assess potential impacts of management decisions, but translating complex flow-ecology relationships into stakeholder-relevant information remains a struggle. The concept of ecosystem services provides a bridge between flow-ecology relationships and stakeholder-relevant data. Flow-ecology relationships were used to explore complementary and trade-off relationships among 12 ecosystem services and related variables in the Atchafalaya River Basin, Louisiana. Results from Indicators of Hydrologic Alteration were reduced to four management-relevant hydrologic variables using principal components analysis. Multiple regression was used to determine flow-ecology relationships and Pearson correlation coefficients, along with regression results, were used to determine complementary and trade-off relationships among ecosystem services and related variables that were induced by flow. Seven ecosystem service variables had significant flow-ecology relationships for at least one hydrologic variable (R (2) = 0.19-0.64). River transportation and blue crab (Callinectes sapidus) landings exhibited a complementary relationship mediated by flow; whereas transportation and crawfish landings, crawfish landings and crappie (Pomoxis spp.) abundance, and blue crab landings and blue catfish (Ictalurus furcatus) abundance exhibited trade-off relationships. Other trade-off and complementary relationships among ecosystem services and related variables, however, were not related to flow. These results give insight into potential conflicts among stakeholders, can reduce the dimensions of management decisions, and provide initial hypotheses for experimental flow modifications.

Using Flow-Ecology Relationships to Evaluate Ecosystem Service Trade-Offs and Complementarities in the Nation's Largest River Swamp

NASA Astrophysics Data System (ADS)

Kozak, Justin P.; Bennett, Micah G.; Hayden-Lesmeister, Anne; Fritz, Kelley A.; Nickolotsky, Aaron

2015-06-01

Large river systems are inextricably linked with social systems; consequently, management decisions must be made within a given ecological, social, and political framework that often defies objective, technical resolution. Understanding flow-ecology relationships in rivers is necessary to assess potential impacts of management decisions, but translating complex flow-ecology relationships into stakeholder-relevant information remains a struggle. The concept of ecosystem services provides a bridge between flow-ecology relationships and stakeholder-relevant data. Flow-ecology relationships were used to explore complementary and trade-off relationships among 12 ecosystem services and related variables in the Atchafalaya River Basin, Louisiana. Results from Indicators of Hydrologic Alteration were reduced to four management-relevant hydrologic variables using principal components analysis. Multiple regression was used to determine flow-ecology relationships and Pearson correlation coefficients, along with regression results, were used to determine complementary and trade-off relationships among ecosystem services and related variables that were induced by flow. Seven ecosystem service variables had significant flow-ecology relationships for at least one hydrologic variable ( R 2 = 0.19-0.64). River transportation and blue crab ( Callinectes sapidus) landings exhibited a complementary relationship mediated by flow; whereas transportation and crawfish landings, crawfish landings and crappie ( Pomoxis spp.) abundance, and blue crab landings and blue catfish ( Ictalurus furcatus) abundance exhibited trade-off relationships. Other trade-off and complementary relationships among ecosystem services and related variables, however, were not related to flow. These results give insight into potential conflicts among stakeholders, can reduce the dimensions of management decisions, and provide initial hypotheses for experimental flow modifications.

PROBING HUMAN AND MONKEY ANTERIOR CINGULATE CORTEX IN VARIABLE ENVIRONMENTS

PubMed Central

Walton, Mark E.; Mars, Rogier B.

2008-01-01

Previous research has identified the anterior cingulate cortex (ACC) as an important node in the neural network underlying decision making in primates. Decision making can, however, be studied under large variety of circumstances, ranging from the standard well-controlled lab situation to more natural, stochastic settings during which multiple agents interact. Here, we illustrate how these different varieties of decision making studied can influence theories of ACC function in monkeys. Converging evidence from unit recordings and lesions studies now suggest that the ACC is important for interpreting outcome information according to the current task context to guide future action selection. We then apply this framework to the study of human ACC function and discuss its potential implications. PMID:18189014

Expert model of decision-making system for efficient orientation of basketball players to positions and roles in the game--empirical verification.

PubMed

Dezman, B; Trninić, S; Dizdar, D

2001-06-01

The purpose of the research was to empirically verify the expert model system designed for more efficient orientation of basketball players to particular positions and /or roles in the game (specialization). Participants were 60 randomly chosen male basketball players (12 players per each position) from the 12 Croatian 1st league teams in season 1998/99. Data were gathered from 10 basketball coaches who estimated overall performance (actual quality) of players on defense (7 variables) and on offense (12 variables). Variables were established by Trninić, Perica and Dizdar. A measure of body height was added to the aforementioned group of variables. The results obtained suggest that the proposed decision-making system can be used as an auxiliary instrument in orienting players to the positions and roles in the game. It has been established that the players have attained the highest grades of overall performance exactly at their primary playing positions in the game. The largest differences were determined between point guards (position 1) and centers (position 5). The greatest difficulties have occurred in determining optimal position for small forwards (position 3), then for shooting guards (position 2) and, last, for power forwards (position 4), because all these basketball players are the most versatile ones. Therefore, reliability of the system is the lowest when it is applied for selecting and orientating players to these positions. Convenient body height significantly contributes to aptitude of these players to play multiple positions and to assume multiple roles in the game. This research has reinforced the thesis that body height is a variable with the greatest influence on orientation of players to particular positions and roles in the game.

Perceptions of Shared Decision Making Among Patients with Spinal Cord Injuries/Disorders.

PubMed

Locatelli, Sara M; Etingen, Bella; Heinemann, Allen; Neumann, Holly DeMark; Miskovic, Ana; Chen, David; LaVela, Sherri L

2016-01-01

Background: Individuals with spinal cord injuries/disorders (SCI/D) are interested in, and benefit from, shared decision making (SDM). Objective: To explore SDM among individuals with SCI/D and how demographics and health and SCI/D characteristics are related to SDM. Method: Individuals with SCI/D who were at least 1 year post injury, resided in the Chicago metropolitan area, and received SCI care at a Veterans Affairs (VA; n = 124) or an SCI Model Systems facility ( n = 326) completed a mailed survey measuring demographics, health and SCI/D characteristics, physical and mental health status, and perceptions of care, including SDM, using the Combined Outcome Measure for Risk Communication and Treatment Decision-Making Effectiveness (COMRADE) that assesses decision-making effectiveness (effectiveness) and risk communication (communication). Bivariate analyses and multiple linear regression were used to identify variables associated with SDM. Results: Participants were mostly male (83%) and White (70%) and were an average age of 54 years ( SD = 14.3). Most had traumatic etiology, 44% paraplegia, and 49% complete injury. Veteran/civilian status and demographics were unrelated to scores. Bivariate analyses showed that individuals with tetraplegia had better effectiveness scores than those with paraplegia. Better effectiveness was correlated with better physical and mental health; better communication was correlated with better mental health. Multiple linear regressions showed that tetraplegia, better physical health, and better mental health were associated with better effectiveness, and better mental health was associated with better communication. Conclusion: SCI/D and health characteristics were the only variables associated with SDM. Interventions to increase engagement in SDM and provider attention to SDM may be beneficial, especially for individuals with paraplegia or in poorer physical and mental health.

Complex neural codes in rat prelimbic cortex are stable across days on a spatial decision task

PubMed Central

Powell, Nathaniel J.; Redish, A. David

2014-01-01

The rodent prelimbic cortex has been shown to play an important role in cognitive processing, and has been implicated in encoding many different parameters relevant to solving decision-making tasks. However, it is not known how the prelimbic cortex represents all these disparate variables, and if they are simultaneously represented when the task requires it. In order to investigate this question, we trained rats to run the Multiple-T Left Right Alternate (MT-LRA) task and recorded multi-unit ensembles from their prelimbic regions. Significant populations of cells in the prelimbic cortex represented the strategy controlling reward receipt on a given lap, whether the animal chose to go right or left on a given lap, and whether the animal made a correct decision or an error on a given lap. These populations overlapped in the cells recorded, with several cells demonstrating differential firing to all three variables. The spatial and strategic firing patterns of individual prelimbic cells were highly conserved across several days of running this task, indicating that each cell encoded the same information across days. PMID:24795579

Return to Play After Infectious Mononucleosis

PubMed Central

Becker, Jonathan A.; Smith, Julie Anne

2014-01-01

Context: Infectious mononucleosis is a disease primarily of adolescence and early adulthood. The risk of splenic injury and chronic fatigue make return-to-play decisions a challenge for the clinician caring for athletes with infectious mononucleosis. Evidence Acquisition: Data were obtained from the PubMed and MEDLINE databases through December 2012 by searching for epidemiology, diagnosis, clinical manifestations, management, and the role of the spleen in infectious mononucleosis. Study Design: Clinical review. Level of Evidence: Level 4. Results: Infectious mononucleosis is commonly encountered in young athletes. Its disease pattern is variable and can affect multiple organ systems. Supportive care is the cornerstone, with little role for medications such as corticosteroids. Physical examination is unreliable for the spleen, and ultrasound imaging has limitations in its ability to guide return-to-play decisions. Exercise does not appear to place the young athlete at risk for chronic fatigue, but determining who is at risk for persistent symptoms is a challenge. Conclusion: Return-to-play decisions for the athlete with infectious mononucleosis need to be individualized because of the variable disease course and lack of evidence-based guidelines. PMID:24790693

Evaluating gambles using dynamics

NASA Astrophysics Data System (ADS)

Peters, O.; Gell-Mann, M.

2016-02-01

Gambles are random variables that model possible changes in wealth. Classic decision theory transforms money into utility through a utility function and defines the value of a gamble as the expectation value of utility changes. Utility functions aim to capture individual psychological characteristics, but their generality limits predictive power. Expectation value maximizers are defined as rational in economics, but expectation values are only meaningful in the presence of ensembles or in systems with ergodic properties, whereas decision-makers have no access to ensembles, and the variables representing wealth in the usual growth models do not have the relevant ergodic properties. Simultaneously addressing the shortcomings of utility and those of expectations, we propose to evaluate gambles by averaging wealth growth over time. No utility function is needed, but a dynamic must be specified to compute time averages. Linear and logarithmic "utility functions" appear as transformations that generate ergodic observables for purely additive and purely multiplicative dynamics, respectively. We highlight inconsistencies throughout the development of decision theory, whose correction clarifies that our perspective is legitimate. These invalidate a commonly cited argument for bounded utility functions.

The precision problem in conservation and restoration

USGS Publications Warehouse

Hiers, J. Kevin; Jackson, Stephen T.; Hobbs, Richard J.; Bernhardt, Emily S.; Valentine, Leonie E.

2016-01-01

Within the varied contexts of environmental policy, conservation of imperilled species populations, and restoration of damaged habitats, an emphasis on idealized optimal conditions has led to increasingly specific targets for management. Overly-precise conservation targets can reduce habitat variability at multiple scales, with unintended consequences for future ecological resilience. We describe this dilemma in the context of endangered species management, stream restoration, and climate-change adaptation. Inappropriate application of conservation targets can be expensive, with marginal conservation benefit. Reduced habitat variability can limit options for managers trying to balance competing objectives with limited resources. Conservation policies should embrace habitat variability, expand decision-space appropriately, and support adaptation to local circumstances to increase ecological resilience in a rapidly changing world.

Emergency physicians' attitudes and preferences regarding computed tomography, radiation exposure, and imaging decision support.

PubMed

Griffey, Richard T; Jeffe, Donna B; Bailey, Thomas

2014-07-01

Although computerized decision support for imaging is often recommended for optimizing computed tomography (CT) use, no studies have evaluated emergency physicians' (EPs') preferences regarding computerized decision support in the emergency department (ED). In this needs assessment, the authors sought to determine if EPs view overutilization as a problem, if they want decision support, and if so, the kinds of support they prefer. A 42-item, Web-based survey of EPs was developed and used to measure EPs' attitudes, preferences, and knowledge. Key contacts at local EDs sent letters describing the study to their physicians. Exploratory principal components analysis (PCA) was used to determine the underlying factor structure of multi-item scales, Cronbach's alpha was used to measure internal consistency of items on a scale, Spearman correlations were used to describe bivariate associations, and multivariable linear regression analysis was used to identify variables independently associated with physician interest in decision support. Of 235 surveys sent, 155 (66%) EPs responded. Five factors emerged from the PCA. EPs felt that: 1) CT overutilization is a problem in the ED (α = 0.75); 2) a patient's cumulative CT study count affects decisions of whether and what type of imaging study to order only some of the time (α = 0.75); 3) knowledge that a patient has had prior CT imaging for the same indication makes EPs less likely to order a CT (α = 0.42); 4) concerns about malpractice, patient satisfaction, or insistence on CTs affect CT ordering decisions (α = 0.62); and 5) EPs want decision support before ordering CTs (α = 0.85). Performance on knowledge questions was poor, with only 18% to 39% correctly responding to each of the three multiple-choice items about effective radiation doses of chest radiograph and single-pass abdominopelvic CT, as well as estimated increased risk of cancer from a 10-mSv exposure. Although EPs wanted information on patients' cumulative exposures, they feel inadequately familiar with this information to make use of it clinically. If provided with patients' cumulative radiation exposures from CT, 87% of EPs said that they would use this information to discuss imaging options with their patients. In the multiple regression model, which included all variables associated with interest in decision support at p < 0.10 in bivariate tests, items independently associated with EPs' greater interest in all types of decision support proposed included lower total knowledge scores, greater frequency that cumulative CT study count affects EP's decision to order CTs, and greater agreement that overutilization of CT is a problem and that awareness of multiple prior CTs for a given indication affects CT ordering decisions. Emergency physicians view overutilization of CT scans as a problem with potential for improvement in the ED and would like to have more information to discuss risks with their patients. EPs are interested in all types of imaging decision support proposed to help optimize imaging ordering in the ED and to reduce radiation to their patients. Findings reveal several opportunities that could potentially affect CT utilization. © 2014 by the Society for Academic Emergency Medicine.

Emergency Physicians’ Attitudes and Preferences Regarding Computed Tomography, Radiation Exposure, and Imaging Decision Support

PubMed Central

Griffey, Richard T.; Jeffe, Donna B.; Bailey, Thomas

2014-01-01

Objectives Although computerized decision support for imaging is often recommended for optimizing computed tomography (CT) use, no studies have evaluated emergency physicians’ (EPs’) preferences regarding computerized decision support in the emergency department (ED). In this needs assessment, the authors sought to determine if EPs view overutilization as a problem, if they want decision support, and if so, the kinds of support they prefer. Methods A 42-item, Web-based survey of EPs was developed and used to measure EPs’ attitudes, preferences, and knowledge. Key contacts at local EDs sent letters describing the study to their physicians. Exploratory principal components analysis (PCA) was used to determine the underlying factor structure of multi-item scales, Cronbach’s alpha was used to measure internal consistency of items on a scale, Spearman correlations were used to describe bivariate associations, and multivariable linear regression analysis was used to identify variables independently associated with physician interest in decision support. Results Of 235 surveys sent, 155 (66%) EPs responded. Five factors emerged from the PCA. EPs felt that: 1) CT overutilization is a problem in the ED (α = 0.75); 2) a patient’s cumulative CT study count affects decisions of whether and what type of imaging study to order only some of the time (α = 0.75); 3) knowledge that a patient has had prior CT imaging for the same indication makes EPs less likely to order a CT (α = 0.42); 4) concerns about malpractice, patient satisfaction, or insistence on CTs affect CT ordering decisions (α = 0.62); and 5) EPs want decision support before ordering CTs (α = 0.85). Performance on knowledge questions was poor, with only 18% to 39% correctly responding to each of the three multiple-choice items about effective radiation doses of chest radiograph and single-pass abdominopelvic CT, as well as estimated increased risk of cancer from a 10-mSv exposure. Although EPs wanted information on patients’ cumulative exposures, they feel inadequately familiar with this information to make use of it clinically. If provided with patients’ cumulative radiation exposures from CT, 87% of EPs said that they would use this information to discuss imaging options with their patients. In the multiple regression model, which included all variables associated with interest in decision support at p < 0.10 in bivariate tests, items independently associated with EPs’ greater interest in all types of decision support proposed included lower total knowledge scores, greater frequency that cumulative CT study count affects EP’s decision to order CTs, and greater agreement that overutilization of CT is a problem and that awareness of multiple prior CTs for a given indication affects CT ordering decisions. Conclusions Emergency physicians view overutilization of CT scans as a problem with potential for improvement in the ED and would like to have more information to discuss risks with their patients. EPs are interested in all types of imaging decision support proposed to help optimize imaging ordering in the ED and to reduce radiation to their patients. Findings reveal several opportunities that could potentially affect CT utilization. PMID:25125272

Mothers of young children cluster into 4 groups based on psychographic food decision influencers.

PubMed

Byrd-Bredbenner, Carol; Abbot, Jaclyn Maurer; Cussler, Ellen

2008-08-01

This study explored how mothers grouped into clusters according to multiple psychographic food decision influencers and how the clusters differed in nutrient intake and nutrient content of their household food supply. Mothers (n = 201) completed a survey assessing basic demographic characteristics, food shopping and meal preparation activities, self and spouse employment, exposure to formal food or nutrition education, education level and occupation, weight status, nutrition and food preparation knowledge and skill, family member health and nutrition status, food decision influencer constructs, and dietary intake. In addition, an in-home inventory of 100 participants' household food supplies was conducted. Four distinct clusters presented when 26 psychographic food choice influencers were evaluated. These clusters appear to be valid and robust classifications of mothers in that they discriminated well on the psychographic variables used to construct the clusters as well as numerous other variables not used in the cluster analysis. In addition, the clusters appear to transcend demographic variables that often segment audiences (eg, race, mother's age, socioeconomic status), thereby adding a new dimension to the way in which this audience can be characterized. Furthermore, psychographically defined clusters predicted dietary quality. This study demonstrates that mothers are not a homogenous group and need to have their unique characteristics taken into consideration when designing strategies to promote health. These results can help health practitioners better understand factors affecting food decisions and tailor interventions to better meet the needs of mothers.

Multiple response optimization for higher dimensions in factors and responses

DOE PAGES

Lu, Lu; Chapman, Jessica L.; Anderson-Cook, Christine M.

2016-07-19

When optimizing a product or process with multiple responses, a two-stage Pareto front approach is a useful strategy to evaluate and balance trade-offs between different estimated responses to seek optimum input locations for achieving the best outcomes. After objectively eliminating non-contenders in the first stage by looking for a Pareto front of superior solutions, graphical tools can be used to identify a final solution in the second subjective stage to compare options and match with user priorities. Until now, there have been limitations on the number of response variables and input factors that could effectively be visualized with existing graphicalmore » summaries. We present novel graphical tools that can be more easily scaled to higher dimensions, in both the input and response spaces, to facilitate informed decision making when simultaneously optimizing multiple responses. A key aspect of these graphics is that the potential solutions can be flexibly sorted to investigate specific queries, and that multiple aspects of the solutions can be simultaneously considered. As a result, recommendations are made about how to evaluate the impact of the uncertainty associated with the estimated response surfaces on decision making with higher dimensions.« less

Neural decoding of collective wisdom with multi-brain computing.

PubMed

Eckstein, Miguel P; Das, Koel; Pham, Binh T; Peterson, Matthew F; Abbey, Craig K; Sy, Jocelyn L; Giesbrecht, Barry

2012-01-02

Group decisions and even aggregation of multiple opinions lead to greater decision accuracy, a phenomenon known as collective wisdom. Little is known about the neural basis of collective wisdom and whether its benefits arise in late decision stages or in early sensory coding. Here, we use electroencephalography and multi-brain computing with twenty humans making perceptual decisions to show that combining neural activity across brains increases decision accuracy paralleling the improvements shown by aggregating the observers' opinions. Although the largest gains result from an optimal linear combination of neural decision variables across brains, a simpler neural majority decision rule, ubiquitous in human behavior, results in substantial benefits. In contrast, an extreme neural response rule, akin to a group following the most extreme opinion, results in the least improvement with group size. Analyses controlling for number of electrodes and time-points while increasing number of brains demonstrate unique benefits arising from integrating neural activity across different brains. The benefits of multi-brain integration are present in neural activity as early as 200 ms after stimulus presentation in lateral occipital sites and no additional benefits arise in decision related neural activity. Sensory-related neural activity can predict collective choices reached by aggregating individual opinions, voting results, and decision confidence as accurately as neural activity related to decision components. Estimation of the potential for the collective to execute fast decisions by combining information across numerous brains, a strategy prevalent in many animals, shows large time-savings. Together, the findings suggest that for perceptual decisions the neural activity supporting collective wisdom and decisions arises in early sensory stages and that many properties of collective cognition are explainable by the neural coding of information across multiple brains. Finally, our methods highlight the potential of multi-brain computing as a technique to rapidly and in parallel gather increased information about the environment as well as to access collective perceptual/cognitive choices and mental states. Copyright © 2011 Elsevier Inc. All rights reserved.

Reward and decision processes in the brains of humans and nonhuman primates.

PubMed

Sirigu, Angela; Duhamel, Jean-René

2016-03-01

Choice behavior requires weighing multiple decision variables, such as utility, uncertainty, delay, or effort, that combine to define a subjective value for each considered option or course of action. This capacity is based on prior learning about potential rewards (and punishments) that result from prior actions. When made in a social context, decisions can involve strategic thinking about the intentions of others and about the impact of others' behavior on one's own outcome. Valuation is also influenced by different emotions that serve to adaptively regulate our choices in order to, for example, stay away from excessively risky gambles, prevent future regrets, or avoid personal rejection or conflicts. Drawing on economic theory and on advances in the study of neuronal mechanisms, we review relevant recent experiments in nonhuman primates and clinical observations made in neurologically impaired patients suffering from impaired decision-making capacities.

Decision tree methods: applications for classification and prediction.

PubMed

Song, Yan-Yan; Lu, Ying

2015-04-25

Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure.

Reward and decision processes in the brains of humans and nonhuman primates

PubMed Central

Sirigu, Angela; Duhamel, Jean-René

2016-01-01

Choice behavior requires weighing multiple decision variables, such as utility, uncertainty, delay, or effort, that combine to define a subjective value for each considered option or course of action. This capacity is based on prior learning about potential rewards (and punishments) that result from prior actions. When made in a social context, decisions can involve strategic thinking about the intentions of others and about the impact of others' behavior on one's own outcome. Valuation is also influenced by different emotions that serve to adaptively regulate our choices in order to, for example, stay away from excessively risky gambles, prevent future regrets, or avoid personal rejection or conflicts. Drawing on economic theory and on advances in the study of neuronal mechanisms, we review relevant recent experiments in nonhuman primates and clinical observations made in neurologically impaired patients suffering from impaired decision-making capacities. PMID:27069379

Identification of the need for home visiting nurse: development of a new assessment tool.

PubMed

Taguchi, Atsuko; Nagata, Satoko; Naruse, Takashi; Kuwahara, Yuki; Yamaguchi, Takuhiro; Murashima, Sachiyo

2014-01-01

To develop a Home Visiting Nursing Service Need Assessment Form (HVNS-NAF) to standardize the decision about the need for home visiting nursing service. The sample consisted of older adults who had received coordinated services by care managers. We defined the need for home visiting nursing service by elderly individuals as the decision of the need by a care manager so that the elderly can continue to live independently. Explanatory variables included demographic factors, medical procedure, severity of illness, and caregiver variables. Multiple logistic regression was carried out after univariate analyses to decide the variables to include and the weight of each variable in the HVNS-NAF. We then calculated the sensitivity and specificity of each cutoff value, and defined the score with the highest sensitivity and specificity as the cutoff value. Nineteen items were included in the final HVNS-NAF. When the cutoff value was 2 points, the sensitivity was 77.0%, specificity 68.5%, and positive predictive value 56.8%. HVNS-NAF is the first validated standard based on characteristics of elderly clients who required home visiting nursing service. Using the HVNS-NAF may result in reducing the unmet need for home visiting nursing service and preventing hospitalization.

A comparative study of job satisfaction among nurses, psychologists/psychotherapists and social workers working in Quebec mental health teams.

PubMed

Fleury, Marie-Josée; Grenier, Guy; Bamvita, Jean-Marie

2017-01-01

This study identified multiple socio-professional and team effectiveness variables, based on the Input-Mediator-Output-Input (IMOI) model, and tested their associations with job satisfaction for three categories of mental health professionals (nurses, psychologists/psychotherapists, and social workers). Job satisfaction was assessed with the Job Satisfaction Survey. Independent variables were classified into four categories: 1) Socio-professional Characteristics; 2) Team Attributes; 3) Team Processes; and 4) Team Emergent States. Variables were entered successively, by category, into a hierarchical regression model. Team Processes contributed the greatest number of variables to job satisfaction among all professional groups, including team support which was the only significant variable common to all three types of professionals. Greater involvement in the decision-making process, and lower levels of team conflict (Team Processes) were associated with job satisfaction among nurses and social workers. Lower seniority on team (Socio-professional Characteristics), and team collaboration (Team Processes) were associated with job satisfaction among nurses, as was belief in the advantages of interdisciplinary collaboration (Team Emergent States) among psychologists. Knowledge sharing (Team Processes) and affective commitment to the team (Team Emergent States) were associated with job satisfaction among social workers. Results suggest the need for mental health decision-makers and team managers to offer adequate support to mental health professionals, to involve nurses and social workers in the decision-making process, and implement procedures and mechanisms favourable to the prevention or resolution of team conflict with a view toward increasing job satisfaction among mental health professionals.

Time to decision: the drivers of innovation adoption decisions

NASA Astrophysics Data System (ADS)

Ciganek, Andrew Paul; (Dave) Haseman, William; Ramamurthy, K.

2014-03-01

Organisations desire timeliness. Timeliness facilitates a better responsiveness to changes in an organisation's external environment to either attain or maintain competitiveness. Despite its importance, decision timeliness has not been explicitly examined. Decision timeliness is measured in this study as the time taken to commit to a decision. The research objective is to identify the drivers of decision timeliness in the context of adopting service-oriented architecture (SOA), an innovation for enterprise computing. A research model rooted in the technology-organisation-environment (TOE) framework is proposed and tested with data collected in a large-scale study. The research variables have been examined before in the context of adoption, but their applicability to the timeliness of innovation decision-making has not received much attention and their salience is unclear. The results support multiple hypothesised relationships, including the finding that a risk-oriented organisational culture as well as normative and coercive pressures accelerates decision timeliness. Top management support as well as the traditional innovation attributes (compatibility, relative advantage and complexity/ease-of-use) were not found to be significant when examining their influence on decision timeliness, which appears inconsistent with generally accepted knowledge and deserves further examination.

Use of structured decision making to identify monitoring variables and management priorities for salt marsh ecosystems

USGS Publications Warehouse

Neckles, Hilary A.; Lyons, James E.; Guntenspergen, Glenn R.; Shriver, W. Gregory; Adamowicz, Susan C.

2015-01-01

Most salt marshes in the USA have been degraded by human activities, and coastal managers are faced with complex choices among possible actions to restore or enhance ecosystem integrity. We applied structured decision making (SDM) to guide selection of monitoring variables and management priorities for salt marshes within the National Wildlife Refuge System in the northeastern USA. In general, SDM is a systematic process for decomposing a decision into its essential elements. We first engaged stakeholders in clarifying regional salt marsh decision problems, defining objectives and attributes to evaluate whether objectives are achieved, and developing a pool of alternative management actions for achieving objectives. Through this process, we identified salt marsh attributes that were applicable to monitoring National Wildlife Refuges on a regional scale and that targeted management needs. We then analyzed management decisions within three salt marsh units at Prime Hook National Wildlife Refuge, coastal Delaware, as a case example of prioritizing management alternatives. Values for salt marsh attributes were estimated from 2 years of baseline monitoring data and expert opinion. We used linear value modeling to aggregate multiple attributes into a single performance score for each alternative, constrained optimization to identify alternatives that maximized total management benefits subject to refuge-wide cost constraints, and used graphical analysis to identify the optimal set of alternatives for the refuge. SDM offers an efficient, transparent approach for integrating monitoring into management practice and improving the quality of management decisions.

Player-Tracking Technology: Half-Full or Half-Empty Glass?

PubMed

Buchheit, Martin; Simpson, Ben Michael

2017-04-01

With the ongoing development of microtechnology, player tracking has become one of the most important components of load monitoring in team sports. The 3 main objectives of player tracking are better understanding of practice (provide an objective, a posteriori evaluation of external load and locomotor demands of any given session or match), optimization of training-load patterns at the team level, and decision making on individual players' training programs to improve performance and prevent injuries (eg, top-up training vs unloading sequences, return to play progression). This paper discusses the basics of a simple tracking approach and the need to integrate multiple systems. The limitations of some of the most used variables in the field (including metabolic-power measures) are debated, and innovative and potentially new powerful variables are presented. The foundations of a successful player-monitoring system are probably laid on the pitch first, in the way practitioners collect their own tracking data, given the limitations of each variable, and how they report and use all this information, rather than in the technology and the variables per se. Overall, the decision to use any tracking technology or new variable should always be considered with a cost/benefit approach (ie, cost, ease of use, portability, manpower/ability to affect the training program).

Interrater Agreement on the Visual Analysis of Individual Tiers and Functional Relations in Multiple Baseline Designs.

PubMed

Wolfe, Katie; Seaman, Michael A; Drasgow, Erik

2016-11-01

Previous research on visual analysis has reported low levels of interrater agreement. However, many of these studies have methodological limitations (e.g., use of AB designs, undefined judgment task) that may have negatively influenced agreement. Our primary purpose was to evaluate whether agreement would be higher than previously reported if we addressed these weaknesses. Our secondary purposes were to investigate agreement at the tier level (i.e., the AB comparison) and at the functional relation level in multiple baseline designs and to examine the relationship between raters' decisions at each of these levels. We asked experts (N = 52) to make judgments about changes in the dependent variable in individual tiers and about the presence of an overall functional relation in 31 multiple baseline graphs. Our results indicate that interrater agreement was just at or just below minimally adequate levels for both types of decisions and that agreement at the individual tier level often resulted in agreement about the overall functional relation. We report additional findings and discuss implications for practice and future research. © The Author(s) 2016.

Assessing the Effects of Financial Literacy on Patient Engagement.

PubMed

Meyer, Melanie A; Hudak, Ronald P

2016-07-01

We investigated the relationship between financial literacy and patient engagement while considering the possible interaction effects due to patient financial responsibility and patient-physician shared decision making, and the impact of personal attributes. Participants consisted of an Internet-based sample of American adults (N = 160). Hierarchical multiple linear regression analysis was conducted to examine the relationship of the study variables on patient engagement. We found that patient financial responsibility (β = -.19, p < .05) and patient-physician shared decision-making (β = .17, p < .05) predicted patient engagement. However, there was no statistically significant relationship between patient financial literacy and patient engagement; moreover, the moderation effects of patient financial responsibility and shared decision making with financial literacy also were not statistically significant. Increasing patient financial responsibility and patient-physician shared decision making can impact patient engagement. Understanding the predictors of patient engagement and the factors that influence financial behaviors may allow for the development of interventions to enable patients to make better healthcare decisions, and ultimately, improve health outcomes.

Striatal activation reflects urgency in perceptual decision making.

PubMed

van Maanen, Leendert; Fontanesi, Laura; Hawkins, Guy E; Forstmann, Birte U

2016-10-01

Deciding between multiple courses of action often entails an increasing need to do something as time passes - a sense of urgency. This notion of urgency is not incorporated in standard theories of speeded decision making that assume information is accumulated until a critical fixed threshold is reached. Yet, it is hypothesized in novel theoretical models of decision making. In two experiments, we investigated the behavioral and neural evidence for an "urgency signal" in human perceptual decision making. Experiment 1 found that as the duration of the decision making process increased, participants made a choice based on less evidence for the selected option. Experiment 2 replicated this finding, and additionally found that variability in this effect across participants covaried with activation in the striatum. We conclude that individual differences in susceptibility to urgency are reflected by striatal activation. By dynamically updating a response threshold, the striatum is involved in signaling urgency in humans. Copyright © 2016 Elsevier Inc. All rights reserved.

Impacts of High Variable Renewable Energy Futures on Wholesale Electricity Prices, and on Electric-Sector Decision Making

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seel, Joachim; Mills, Andrew D.; Wiser, Ryan H.

Increasing penetrations of variable renewable energy (VRE) can affect wholesale electricity price patterns and make them meaningfully different from past, traditional price patterns. Many long-lasting decisions for supply- and demand-side electricity infrastructure and programs are based on historical observations or assume a business-as-usual future with low shares of VRE. Our motivating question is whether certain electric-sector decisions that are made based on assumptions reflecting low VRE levels will still achieve their intended objective in a high VRE future. We qualitatively describe how various decisions may change with higher shares of VRE and outline an analytical framework for quantitatively evaluating themore » impacts of VRE on long-lasting decisions. We then present results from detailed electricity market simulations with capacity expansion and unit commitment models for multiple regions of the U.S. for low and high VRE futures. We find a general decrease in average annual hourly wholesale energy prices with more VRE penetration, increased price volatility and frequency of very low-priced hours, and changing diurnal price patterns. Ancillary service prices rise substantially and peak net-load hours with high capacity value are shifted increasingly into the evening, particularly for high solar futures. While in this report we only highlight qualitatively the possible impact of these altered price patterns on other demand- and supply-side electric sector decisions, the core set of electricity market prices derived here provides a foundation for later planned quantitative evaluations of these decisions in low and high VRE futures.« less

Health Care Decision Making by Mothers for their Adolescent Daughters Regarding the Quadrivalent HPV Vaccine

PubMed Central

Hertweck, S. Paige; LaJoie, A. Scott; Pinto, Melissa D.; Flamini, Laura; Lynch, Tania; Logsdon, M. Cynthia

2013-01-01

Study Objective In this study we sought to understand the predictors of a mother’s decision (behavior) to vaccinate her daughter with the initial dose of the HPV vaccine. Design This prospective, cross sectional study involved a convenience sample of 68 mother-daughter dyads recruited to test the hypothesis that the Theory of Planned Behavior (TPB) variables (attitudes toward vaccine, perception of others’ opinions, and perceived difficulty in obtaining vaccine) would explain a mother’s decision to consent for her daughter to receive the first dose of the HPV vaccine. Main outcome measures Mothers and daughters independently completed survey instruments that measure the variables of the TPB (attitude, subjective norms, and perceived behavioral control). Instruments also included measures of parenting style and conflict. Results The mother’s intention to vaccinate was predicted by her attitude (B=.41, p<.001), subjective norms (B=.33, p=.002) and perceived behavioral control (B=.24, p=.005). The pathway connecting intention to the decision (yes or no) to vaccinate was significant (B=.41, p<.001). Squared multiple correlations for intention and decision, respectively, were .68 and .12. The mothers who chose to vaccinate their daughter did not differ on any of the demographic variables from those who chose not to vaccinate but had had significantly different scores on attitude, subjective norms, and intention but not perceived behavioral control. Conclusions The TPB model demonstrates potential influences on a mother’s intention to choose to initiate the HPV vaccination series for her daughter. Influences of attitude, subjective norms and perceived control are potential targets for interventions and tailored social marketing to improve vaccine acceptance PMID:23518189

Health care decision making by mothers for their adolescent daughters regarding the quadrivalent HPV vaccine.

PubMed

Hertweck, S Paige; LaJoie, A Scott; Pinto, Melissa D; Flamini, Laura; Lynch, Tania; Logsdon, M Cynthia

2013-04-01

In this study we sought to understand the predictors of a mother's decision (behavior) to vaccinate her daughter with the initial dose of the HPV vaccine. This prospective, cross sectional study involved a convenience sample of 68 mother-daughter dyads recruited to test the hypothesis that the Theory of Planned Behavior (TPB) variables (attitudes toward vaccine, perception of others' opinions, and perceived difficulty in obtaining vaccine) would explain a mother's decision to consent for her daughter to receive the first dose of the HPV vaccine. Mothers and daughters independently completed survey instruments that measure the variables of the TPB (attitude, subjective norms, and perceived behavioral control). Instruments also included measures of parenting style and conflict. The mother's intention to vaccinate was predicted by her attitude (β = .41, P < .001), subjective norms (β = .33, P = .002), and perceived behavioral control (β = .24, P = .005). The pathway connecting intention to the decision (yes or no) to vaccinate was significant (β = .41, P < .001). Squared multiple correlations for intention and decision, respectively, were .68 and .12. The mothers who chose to vaccinate their daughter did not differ on any of the demographic variables from those who chose not to vaccinate but had significantly different scores on attitude, subjective norms, and intention but not perceived behavioral control. The TPB model demonstrates potential influences on a mother's intention to choose to initiate the HPV vaccination series for her daughter. Influences of attitude, subjective norms and perceived control are potential targets for interventions and tailored social marketing to improve vaccine acceptance. Copyright © 2013 North American Society for Pediatric and Adolescent Gynecology. Published by Elsevier Inc. All rights reserved.

Classification images reveal decision variables and strategies in forced choice tasks

PubMed Central

Pritchett, Lisa M.; Murray, Richard F.

2015-01-01

Despite decades of research, there is still uncertainty about how people make simple decisions about perceptual stimuli. Most theories assume that perceptual decisions are based on decision variables, which are internal variables that encode task-relevant information. However, decision variables are usually considered to be theoretical constructs that cannot be measured directly, and this often makes it difficult to test theories of perceptual decision making. Here we show how to measure decision variables on individual trials, and we use these measurements to test theories of perceptual decision making more directly than has previously been possible. We measure classification images, which are estimates of templates that observers use to extract information from stimuli. We then calculate the dot product of these classification images with the stimuli to estimate observers' decision variables. Finally, we reconstruct each observer's “decision space,” a map that shows the probability of the observer’s responses for all values of the decision variables. We use this method to examine decision strategies in two-alternative forced choice (2AFC) tasks, for which there are several competing models. In one experiment, the resulting decision spaces support the difference model, a classic theory of 2AFC decisions. In a second experiment, we find unexpected decision spaces that are not predicted by standard models of 2AFC decisions, and that suggest intrinsic uncertainty or soft thresholding. These experiments give new evidence regarding observers’ strategies in 2AFC tasks, and they show how measuring decision variables can answer long-standing questions about perceptual decision making. PMID:26015584

Sum of the Magnitude for Hard Decision Decoding Algorithm Based on Loop Update Detection.

PubMed

Meng, Jiahui; Zhao, Danfeng; Tian, Hai; Zhang, Liang

2018-01-15

In order to improve the performance of non-binary low-density parity check codes (LDPC) hard decision decoding algorithm and to reduce the complexity of decoding, a sum of the magnitude for hard decision decoding algorithm based on loop update detection is proposed. This will also ensure the reliability, stability and high transmission rate of 5G mobile communication. The algorithm is based on the hard decision decoding algorithm (HDA) and uses the soft information from the channel to calculate the reliability, while the sum of the variable nodes' (VN) magnitude is excluded for computing the reliability of the parity checks. At the same time, the reliability information of the variable node is considered and the loop update detection algorithm is introduced. The bit corresponding to the error code word is flipped multiple times, before this is searched in the order of most likely error probability to finally find the correct code word. Simulation results show that the performance of one of the improved schemes is better than the weighted symbol flipping (WSF) algorithm under different hexadecimal numbers by about 2.2 dB and 2.35 dB at the bit error rate (BER) of 10 -5 over an additive white Gaussian noise (AWGN) channel, respectively. Furthermore, the average number of decoding iterations is significantly reduced.

Multicriteria decision analysis: Overview and implications for environmental decision making

USGS Publications Warehouse

Hermans, Caroline M.; Erickson, Jon D.; Erickson, Jon D.; Messner, Frank; Ring, Irene

2007-01-01

Environmental decision making involving multiple stakeholders can benefit from the use of a formal process to structure stakeholder interactions, leading to more successful outcomes than traditional discursive decision processes. There are many tools available to handle complex decision making. Here we illustrate the use of a multicriteria decision analysis (MCDA) outranking tool (PROMETHEE) to facilitate decision making at the watershed scale, involving multiple stakeholders, multiple criteria, and multiple objectives. We compare various MCDA methods and their theoretical underpinnings, examining methods that most realistically model complex decision problems in ways that are understandable and transparent to stakeholders.

Improved Acquisition for System Sustainment: Multiobjective Tradeoff Analysis for Condition-Based Decision-Making

DTIC Science & Technology

2013-10-21

depend on the quality of allocating resources. This work uses a reliability model of system and environmental covariates incorporating information at...state space. Further, the use of condition variables allows for the direct modeling of maintenance impact with the assumption that a nominal value ... value ), the model in the application of aviation maintenance can provide a useful estimation of reliability at multiple levels. Adjusted survival

The Role of Semantic Diversity in Word Recognition across Aging and Bilingualism

PubMed Central

Johns, Brendan T.; Sheppard, Christine L.; Jones, Michael N.; Taler, Vanessa

2016-01-01

Frequency effects are pervasive in studies of language, with higher frequency words being recognized faster than lower frequency words. However, the exact nature of frequency effects has recently been questioned, with some studies finding that contextual information provides a better fit to lexical decision and naming data than word frequency (Adelman et al., 2006). Recent work has cemented the importance of these results by demonstrating that a measure of the semantic diversity of the contexts that a word occurs in provides a powerful measure to account for variability in word recognition latency (Johns et al., 2012, 2015; Jones et al., 2012). The goal of the current study is to extend this measure to examine bilingualism and aging, where multiple theories use frequency of occurrence of linguistic constructs as central to accounting for empirical results (Gollan et al., 2008; Ramscar et al., 2014). A lexical decision experiment was conducted with four groups of subjects: younger and older monolinguals and bilinguals. Consistent with past results, a semantic diversity variable accounted for the greatest amount of variance in the latency data. In addition, the pattern of fits of semantic diversity across multiple corpora suggests that bilinguals and older adults are more sensitive to semantic diversity information than younger monolinguals. PMID:27458392

The Precision Problem in Conservation and Restoration.

PubMed

Hiers, J Kevin; Jackson, Stephen T; Hobbs, Richard J; Bernhardt, Emily S; Valentine, Leonie E

2016-11-01

Within the varied contexts of environmental policy, conservation of imperilled species populations, and restoration of damaged habitats, an emphasis on idealized optimal conditions has led to increasingly specific targets for management. Overly-precise conservation targets can reduce habitat variability at multiple scales, with unintended consequences for future ecological resilience. We describe this dilemma in the context of endangered species management, stream restoration, and climate-change adaptation. Inappropriate application of conservation targets can be expensive, with marginal conservation benefit. Reduced habitat variability can limit options for managers trying to balance competing objectives with limited resources. Conservation policies should embrace habitat variability, expand decision-space appropriately, and support adaptation to local circumstances to increase ecological resilience in a rapidly changing world. Copyright © 2016 Elsevier Ltd. All rights reserved.

Development of Web tools to predict axillary lymph node metastasis and pathological response to neoadjuvant chemotherapy in breast cancer patients.

PubMed

Sugimoto, Masahiro; Takada, Masahiro; Toi, Masakazu

2014-12-09

Nomograms are a standard computational tool to predict the likelihood of an outcome using multiple available patient features. We have developed a more powerful data mining methodology, to predict axillary lymph node (AxLN) metastasis and response to neoadjuvant chemotherapy (NAC) in primary breast cancer patients. We developed websites to use these tools. The tools calculate the probability of AxLN metastasis (AxLN model) and pathological complete response to NAC (NAC model). As a calculation algorithm, we employed a decision tree-based prediction model known as the alternative decision tree (ADTree), which is an analog development of if-then type decision trees. An ensemble technique was used to combine multiple ADTree predictions, resulting in higher generalization abilities and robustness against missing values. The AxLN model was developed with training datasets (n=148) and test datasets (n=143), and validated using an independent cohort (n=174), yielding an area under the receiver operating characteristic curve (AUC) of 0.768. The NAC model was developed and validated with n=150 and n=173 datasets from a randomized controlled trial, yielding an AUC of 0.787. AxLN and NAC models require users to input up to 17 and 16 variables, respectively. These include pathological features, including human epidermal growth factor receptor 2 (HER2) status and imaging findings. Each input variable has an option of "unknown," to facilitate prediction for cases with missing values. The websites developed facilitate the use of these tools, and serve as a database for accumulating new datasets.

Estimating Classification Accuracy for Complex Decision Rules Based on Multiple Scores

ERIC Educational Resources Information Center

Douglas, Karen M.; Mislevy, Robert J.

2010-01-01

Important decisions about students are made by combining multiple measures using complex decision rules. Although methods for characterizing the accuracy of decisions based on a single measure have been suggested by numerous researchers, such methods are not useful for estimating the accuracy of decisions based on multiple measures. This study…

Development of a computer-based clinical decision support tool for selecting appropriate rehabilitation interventions for injured workers.

PubMed

Gross, Douglas P; Zhang, Jing; Steenstra, Ivan; Barnsley, Susan; Haws, Calvin; Amell, Tyler; McIntosh, Greg; Cooper, Juliette; Zaiane, Osmar

2013-12-01

To develop a classification algorithm and accompanying computer-based clinical decision support tool to help categorize injured workers toward optimal rehabilitation interventions based on unique worker characteristics. Population-based historical cohort design. Data were extracted from a Canadian provincial workers' compensation database on all claimants undergoing work assessment between December 2009 and January 2011. Data were available on: (1) numerous personal, clinical, occupational, and social variables; (2) type of rehabilitation undertaken; and (3) outcomes following rehabilitation (receiving time loss benefits or undergoing repeat programs). Machine learning, concerned with the design of algorithms to discriminate between classes based on empirical data, was the foundation of our approach to build a classification system with multiple independent and dependent variables. The population included 8,611 unique claimants. Subjects were predominantly employed (85 %) males (64 %) with diagnoses of sprain/strain (44 %). Baseline clinician classification accuracy was high (ROC = 0.86) for selecting programs that lead to successful return-to-work. Classification performance for machine learning techniques outperformed the clinician baseline classification (ROC = 0.94). The final classifiers were multifactorial and included the variables: injury duration, occupation, job attachment status, work status, modified work availability, pain intensity rating, self-rated occupational disability, and 9 items from the SF-36 Health Survey. The use of machine learning classification techniques appears to have resulted in classification performance better than clinician decision-making. The final algorithm has been integrated into a computer-based clinical decision support tool that requires additional validation in a clinical sample.

Inventory decision in a closed-loop supply chain with inspection, sorting, and waste disposal

NASA Astrophysics Data System (ADS)

Dwicahyani, A. R.; Jauhari, W. A.; Kurdhi, N. A.

2016-02-01

The study of returned item inventory management in a closed-loop supply chain system has become an important issue in recent years. So far, investigations about inventory decision making in a closed-loop supply chain system have been confined to traditional forward and reverse oriented material flow supply chain. In this study, we propose an integrated inventory model consisting a supplier, a manufacturer, and a retailer where the manufacturer inspects all of the returned items collected from the customers and classifies them as recoverable or waste. Returned items that recovered through the remanufacturing process and the newly manufactured products are then used to meet the demand of the retailer. However, some recovered items which are not comparable to the ones in quality, classified as refurbished items, are sold to a secondary market at a reduced price. This study also suggests that the flow of returned items is controlled by a decision variable, namely an acceptance quality level of recoverable item in the system. We apply multiple remanufacturing cycle and multiple production cycle policy to the proposed model and give the corresponding iterative procedure to determine the optimal solutions. Further, numerical examples are presented for illustrative purpose.

Making sense of information in noisy networks: human communication, gossip, and distortion.

PubMed

Laidre, Mark E; Lamb, Alex; Shultz, Susanne; Olsen, Megan

2013-01-21

Information from others can be unreliable. Humans nevertheless act on such information, including gossip, to make various social calculations, thus raising the question of whether individuals can sort through social information to identify what is, in fact, true. Inspired by empirical literature on people's decision-making when considering gossip, we built an agent-based simulation model to examine how well simple decision rules could make sense of information as it propagated through a network. Our simulations revealed that a minimalistic decision-rule 'Bit-wise mode' - which compared information from multiple sources and then sought a consensus majority for each component bit within the message - was consistently the most successful at converging upon the truth. This decision rule attained high relative fitness even in maximally noisy networks, composed entirely of nodes that distorted the message. The rule was also superior to other decision rules regardless of its frequency in the population. Simulations carried out with variable agent memory constraints, different numbers of observers who initiated information propagation, and a variety of network types suggested that the single most important factor in making sense of information was the number of independent sources that agents could consult. Broadly, our model suggests that despite the distortion information is subject to in the real world, it is nevertheless possible to make sense of it based on simple Darwinian computations that integrate multiple sources. Copyright © 2012 Elsevier Ltd. All rights reserved.

Translating climate data for business decisions

NASA Astrophysics Data System (ADS)

Steinberg, N.

2015-12-01

Businesses are bound to play an integral role in global and local climate change adaptation efforts, and integrating climate science into business decision-making can help protect companies' bottom-line and the communities which they depend upon. Yet many companies do not have good means to measure and manage climate risks. There are inherent limiting factors to incorporating climate data into existing operations and sourcing strategies. Spatial and temporal incongruities between climate and business models can make integration cumbersome. Even when such incongruities are resolved, raw climate data must undergo multiple transformations until the data is deemed actionable or otherwise translatable in dollar terms. However, the predictability of future impacts is advancing along with the use of second-order variables such as Cooling Degree Days and Water-Limited Crop productivity, helping business managers make better decisions about future energy and water demand requirements under the prospect of rising temperatures and more variable rainfall. This presentation will discuss the methods and opportunities for transforming raw climate data into business metrics. Results for the 2015 Corporate Adaptation Survey, led by Four Twenty Seven and in partnership with Notre Dame Global Adaptation Index, will also be presented to illustrate existing gaps between climate science and its application in the business context.

System design and improvement of an emergency department using Simulation-Based Multi-Objective Optimization

NASA Astrophysics Data System (ADS)

Goienetxea Uriarte, A.; Ruiz Zúñiga, E.; Urenda Moris, M.; Ng, A. H. C.

2015-05-01

Discrete Event Simulation (DES) is nowadays widely used to support decision makers in system analysis and improvement. However, the use of simulation for improving stochastic logistic processes is not common among healthcare providers. The process of improving healthcare systems involves the necessity to deal with trade-off optimal solutions that take into consideration a multiple number of variables and objectives. Complementing DES with Multi-Objective Optimization (SMO) creates a superior base for finding these solutions and in consequence, facilitates the decision-making process. This paper presents how SMO has been applied for system improvement analysis in a Swedish Emergency Department (ED). A significant number of input variables, constraints and objectives were considered when defining the optimization problem. As a result of the project, the decision makers were provided with a range of optimal solutions which reduces considerably the length of stay and waiting times for the ED patients. SMO has proved to be an appropriate technique to support healthcare system design and improvement processes. A key factor for the success of this project has been the involvement and engagement of the stakeholders during the whole process.

A prospective multiple case study of the impact of emerging scientific evidence on established colorectal cancer screening programs: a study protocol.

PubMed

Geddie, Hannah; Dobrow, Mark J; Hoch, Jeffrey S; Rabeneck, Linda

2012-06-01

Health-policy decision making is a complex and dynamic process, for which strong evidentiary support is required. This includes scientifically produced research, as well as information that relates to the context in which the decision takes place. Unlike scientific evidence, this "contextual evidence" is highly variable and often includes information that is not scientifically produced, drawn from sources such as political judgement, program management experience and knowledge, or public values. As the policy decision-making process is variable and difficult to evaluate, it is often unclear how this heterogeneous evidence is identified and incorporated into "evidence-based policy" decisions. Population-based colorectal cancer screening poses an ideal context in which to examine these issues. In Canada, colorectal cancer screening programs have been established in several provinces over the past five years, based on the fecal occult blood test (FOBT) or the fecal immunochemical test. However, as these programs develop, new scientific evidence for screening continues to emerge. Recently published randomized controlled trials suggest that the use of flexible sigmoidoscopy for population-based screening may pose a greater reduction in mortality than the FOBT. This raises the important question of how policy makers will address this evidence, given that screening programs are being established or are already in place. This study will examine these issues prospectively and will focus on how policy makers monitor emerging scientific evidence and how both scientific and contextual evidence are identified and applied for decisions about health system improvement. This study will employ a prospective multiple case study design, involving participants from Ontario, Alberta, Manitoba, Nova Scotia, and Quebec. In each province, data will be collected via document analysis and key informant interviews. Documents will include policy briefs, reports, meeting minutes, media releases, and correspondence. Interviews will be conducted in person with senior administrative leaders, government officials, screening experts, and high-level cancer system stakeholders. The proposed study comprises the third and final phase of an Emerging Team grant to address the challenges of health-policy decision making and colorectal cancer screening decisions in Canada. This study will contribute a unique prospective look at how policy makers address new, emerging scientific evidence in several different policy environments and at different stages of program planning and implementation. Findings will provide important insight into the various approaches that are or should be used to monitor emerging evidence, the relative importance of scientific versus contextual evidence for decision making, and the tools and processes that may be important to support challenging health-policy decisions.

Shared decision making and serious mental illness.

PubMed

Mahone, Irma H

2008-12-01

This study examined medication decision making by 84 persons with serious mental illness, specifically examining relationships among perceived coercion, decisional capacity, preferences for involvement and actual participation, and the outcomes of medication adherence and quality of life (QoL). Multiple and logistic regression analysis were used in this cross-sectional, descriptive study, controlling for demographic, socioeconomic, and utilization variables. Appreciation was positively related to medication adherence behaviors for the past 6 months. Women, older individuals, and those living independently were more likely to have taken all their medications over the past 6 months. Neither client participation, preference, nor preference-participation agreement was found to be associated with better medication adherence or QoL.

Assessment of health-care waste disposal methods using a VIKOR-based fuzzy multi-criteria decision making method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Hu-Chen; Department of Industrial Engineering and Management, Tokyo Institute of Technology, Tokyo 152-8552; Wu, Jing

Highlights: • Propose a VIKOR-based fuzzy MCDM technique for evaluating HCW disposal methods. • Linguistic variables are used to assess the ratings and weights for the criteria. • The OWA operator is utilized to aggregate individual opinions of decision makers. • A case study is given to illustrate the procedure of the proposed framework. - Abstract: Nowadays selection of the appropriate treatment method in health-care waste (HCW) management has become a challenge task for the municipal authorities especially in developing countries. Assessment of HCW disposal alternatives can be regarded as a complicated multi-criteria decision making (MCDM) problem which requires considerationmore » of multiple alternative solutions and conflicting tangible and intangible criteria. The objective of this paper is to present a new MCDM technique based on fuzzy set theory and VIKOR method for evaluating HCW disposal methods. Linguistic variables are used by decision makers to assess the ratings and weights for the established criteria. The ordered weighted averaging (OWA) operator is utilized to aggregate individual opinions of decision makers into a group assessment. The computational procedure of the proposed framework is illustrated through a case study in Shanghai, one of the largest cities of China. The HCW treatment alternatives considered in this study include “incineration”, “steam sterilization”, “microwave” and “landfill”. The results obtained using the proposed approach are analyzed in a comparative way.« less

Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees.

PubMed

Amirabadizadeh, Alireza; Nezami, Hossein; Vaughn, Michael G; Nakhaee, Samaneh; Mehrpour, Omid

2018-05-12

Substance abuse exacts considerable social and health care burdens throughout the world. The aim of this study was to create a prediction model to better identify risk factors for drug use. A prospective cross-sectional study was conducted in South Khorasan Province, Iran. Of the total of 678 eligible subjects, 70% (n: 474) were randomly selected to provide a training set for constructing decision tree and multiple logistic regression (MLR) models. The remaining 30% (n: 204) were employed in a holdout sample to test the performance of the decision tree and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the testing set. Independent variables were selected from demographic characteristics and history of drug use. For the decision tree model, the sensitivity and specificity for identifying people at risk for drug abuse were 66% and 75%, respectively, while the MLR model was somewhat less effective at 60% and 73%. Key independent variables in the analyses included first substance experience, age at first drug use, age, place of residence, history of cigarette use, and occupational and marital status. While study findings are exploratory and lack generalizability they do suggest that the decision tree model holds promise as an effective classification approach for identifying risk factors for drug use. Convergent with prior research in Western contexts is that age of drug use initiation was a critical factor predicting a substance use disorder.

Robustness of Multiple Objective Decision Analysis Preference Functions

DTIC Science & Technology

2002-06-01

p p′ : The probability of some event. ,i ip q : The probability of event . i Π : An aggregation of proportional data used in calculating a test ...statistical tests of the significance of the term and also is conducted in a multivariate framework rather than the ROSA univariate approach. A...residual error is ˆ−e = y y (45) The coefficient provides a ready indicator of the contribution for the associated variable and statistical tests

Integrated presentation of ecological risk from multiple stressors

NASA Astrophysics Data System (ADS)

Goussen, Benoit; Price, Oliver R.; Rendal, Cecilie; Ashauer, Roman

2016-10-01

Current environmental risk assessments (ERA) do not account explicitly for ecological factors (e.g. species composition, temperature or food availability) and multiple stressors. Assessing mixtures of chemical and ecological stressors is needed as well as accounting for variability in environmental conditions and uncertainty of data and models. Here we propose a novel probabilistic ERA framework to overcome these limitations, which focusses on visualising assessment outcomes by construct-ing and interpreting prevalence plots as a quantitative prediction of risk. Key components include environmental scenarios that integrate exposure and ecology, and ecological modelling of relevant endpoints to assess the effect of a combination of stressors. Our illustrative results demonstrate the importance of regional differences in environmental conditions and the confounding interactions of stressors. Using this framework and prevalence plots provides a risk-based approach that combines risk assessment and risk management in a meaningful way and presents a truly mechanistic alternative to the threshold approach. Even whilst research continues to improve the underlying models and data, regulators and decision makers can already use the framework and prevalence plots. The integration of multiple stressors, environmental conditions and variability makes ERA more relevant and realistic.

Integrated presentation of ecological risk from multiple stressors.

PubMed

Goussen, Benoit; Price, Oliver R; Rendal, Cecilie; Ashauer, Roman

2016-10-26

Current environmental risk assessments (ERA) do not account explicitly for ecological factors (e.g. species composition, temperature or food availability) and multiple stressors. Assessing mixtures of chemical and ecological stressors is needed as well as accounting for variability in environmental conditions and uncertainty of data and models. Here we propose a novel probabilistic ERA framework to overcome these limitations, which focusses on visualising assessment outcomes by construct-ing and interpreting prevalence plots as a quantitative prediction of risk. Key components include environmental scenarios that integrate exposure and ecology, and ecological modelling of relevant endpoints to assess the effect of a combination of stressors. Our illustrative results demonstrate the importance of regional differences in environmental conditions and the confounding interactions of stressors. Using this framework and prevalence plots provides a risk-based approach that combines risk assessment and risk management in a meaningful way and presents a truly mechanistic alternative to the threshold approach. Even whilst research continues to improve the underlying models and data, regulators and decision makers can already use the framework and prevalence plots. The integration of multiple stressors, environmental conditions and variability makes ERA more relevant and realistic.

Identification of the need for home visiting nurse: development of a new assessment tool

PubMed Central

Taguchi, Atsuko; Nagata, Satoko; Naruse, Takashi; Kuwahara, Yuki; Yamaguchi, Takuhiro; Murashima, Sachiyo

2014-01-01

Objective To develop a Home Visiting Nursing Service Need Assessment Form (HVNS-NAF) to standardize the decision about the need for home visiting nursing service. Methods The sample consisted of older adults who had received coordinated services by care managers. We defined the need for home visiting nursing service by elderly individuals as the decision of the need by a care manager so that the elderly can continue to live independently. Explanatory variables included demographic factors, medical procedure, severity of illness, and caregiver variables. Multiple logistic regression was carried out after univariate analyses to decide the variables to include and the weight of each variable in the HVNS-NAF. We then calculated the sensitivity and specificity of each cutoff value, and defined the score with the highest sensitivity and specificity as the cutoff value. Results Nineteen items were included in the final HVNS-NAF. When the cutoff value was 2 points, the sensitivity was 77.0%, specificity 68.5%, and positive predictive value 56.8%. Conclusions HVNS-NAF is the first validated standard based on characteristics of elderly clients who required home visiting nursing service. Using the HVNS-NAF may result in reducing the unmet need for home visiting nursing service and preventing hospitalization. PMID:24665229

Interactive effects of age-of-acquisition and repetition priming in the lexical decision task. A multiple-loci account.

PubMed

Spataro, Pietro; Longobardi, Emiddia; Saraulli, Daniele; Rossi-Arnaud, Clelia

2013-01-01

The analysis of the interaction between repetition priming and age of acquisition may be used to shed further light on the question of which stages of elaboration are affected by this psycholinguistic variable. In the present study we applied this method in the context of two versions of a lexical decision task that differed in the type of non-words employed at test. When the non-words were illegal and unpronounceable, repetition priming was primarily based on the analysis of orthographic information, while phonological processes were additionally recruited only when using legal pronounceable non-words. The results showed a significant interaction between repetition priming and age of acquisition in both conditions, with priming being greater for late- than for early-acquired words. These findings support a multiple-loci account, indicating that age of acquisition influences implicit memory by facilitating the retrieval of both the orthographic and the phonological representations of studied words.

Perceptual Decision-Making as Probabilistic Inference by Neural Sampling.

PubMed

Haefner, Ralf M; Berkes, Pietro; Fiser, József

2016-05-04

We address two main challenges facing systems neuroscience today: understanding the nature and function of cortical feedback between sensory areas and of correlated variability. Starting from the old idea of perception as probabilistic inference, we show how to use knowledge of the psychophysical task to make testable predictions for the influence of feedback signals on early sensory representations. Applying our framework to a two-alternative forced choice task paradigm, we can explain multiple empirical findings that have been hard to account for by the traditional feedforward model of sensory processing, including the task dependence of neural response correlations and the diverging time courses of choice probabilities and psychophysical kernels. Our model makes new predictions and characterizes a component of correlated variability that represents task-related information rather than performance-degrading noise. It demonstrates a normative way to integrate sensory and cognitive components into physiologically testable models of perceptual decision-making. Copyright © 2016 Elsevier Inc. All rights reserved.

The Influence of Math Anxiety, Math Performance, Worry, and Test Anxiety on the Iowa Gambling Task and Balloon Analogue Risk Task.

PubMed

Buelow, Melissa T; Barnhart, Wesley R

2017-01-01

Multiple studies have shown that performance on behavioral decision-making tasks, such as the Iowa Gambling Task (IGT) and Balloon Analogue Risk Task (BART), is influenced by external factors, such as mood. However, the research regarding the influence of worry is mixed, and no research has examined the effect of math or test anxiety on these tasks. The present study investigated the effects of anxiety (including math anxiety) and math performance on the IGT and BART in a sample of 137 undergraduate students. Math performance and worry were not correlated with performance on the IGT, and no variables were correlated with BART performance. Linear regressions indicated math anxiety, physiological anxiety, social concerns/stress, and test anxiety significantly predicted disadvantageous selections on the IGT during the transition from decision making under ambiguity to decision making under risk. Implications for clinical evaluation of decision making are discussed. © The Author(s) 2015.

Efficient group decision making in workshop settings

Treesearch

Daniel L. Schmoldt; David L. Peterson

2001-01-01

Public land managers must treat multiple values coincidentally in time and space, which requires the participation of multiple resource specialists and consideration of diverse clientele interests in the decision process. This implies decision making that includes multiple participants, both internally and externally. Decades of social science research on decision...

Sum of the Magnitude for Hard Decision Decoding Algorithm Based on Loop Update Detection

PubMed Central

Meng, Jiahui; Zhao, Danfeng; Tian, Hai; Zhang, Liang

2018-01-01

In order to improve the performance of non-binary low-density parity check codes (LDPC) hard decision decoding algorithm and to reduce the complexity of decoding, a sum of the magnitude for hard decision decoding algorithm based on loop update detection is proposed. This will also ensure the reliability, stability and high transmission rate of 5G mobile communication. The algorithm is based on the hard decision decoding algorithm (HDA) and uses the soft information from the channel to calculate the reliability, while the sum of the variable nodes’ (VN) magnitude is excluded for computing the reliability of the parity checks. At the same time, the reliability information of the variable node is considered and the loop update detection algorithm is introduced. The bit corresponding to the error code word is flipped multiple times, before this is searched in the order of most likely error probability to finally find the correct code word. Simulation results show that the performance of one of the improved schemes is better than the weighted symbol flipping (WSF) algorithm under different hexadecimal numbers by about 2.2 dB and 2.35 dB at the bit error rate (BER) of 10−5 over an additive white Gaussian noise (AWGN) channel, respectively. Furthermore, the average number of decoding iterations is significantly reduced. PMID:29342963

Health decision-making preferences among African American men recruited from urban barbershops.

PubMed

Hart, Alton; Smith, Wally R; Tademy, Raymond H; McClish, Donna K; McCreary, Micah

2009-07-01

To examine general health decision-making roles among African American men ages 40 to 70 recruited in barbershops in the Richmond, Virginia, metropolitan area. We adapted the 1-item Control Preference scale to study the associations between health decision-making role preferences and demographic variables. Forty African-American men were recruited from barbershops to complete a self-administered survey. After performing descriptive statistics, we dichotomized our outcome into active vs nonactive (collaborative or passive) decision makers. Data were then analyzed using chi2, Wilcoxon-Mann-Whitney rank sum, and multiple logistic regression. Fifteen subjects responded that they engaged in active decision making, 20 in collaborative, and 5 in passive decision making. Almost all (86.7%) active decision makers were home owners, vs 41.7% of nonactive decision makers. Among active decision makers, 46.7% had incomes of more than $70000, vs 12.5% of nonactive decision makers. The active group reported health status that was good to excellent, while 20.8% of those in the nonactive group reported poor/fair health. African American male barbershop clients preferred an active or collaborative health decision-making role with their physician, rather than a passive role. The relationship among home ownership, income, and decision style may best be understood by considering the historical and cultural influences on gender role socialization among African American males. More comprehensive assessment of decision styles is necessary to better understand health decision making among African American male patients.

A decision aid for intensity-modulated radiation-therapy plan selection in prostate cancer based on a prognostic Bayesian network and a Markov model.

PubMed

Smith, Wade P; Doctor, Jason; Meyer, Jürgen; Kalet, Ira J; Phillips, Mark H

2009-06-01

The prognosis of cancer patients treated with intensity-modulated radiation-therapy (IMRT) is inherently uncertain, depends on many decision variables, and requires that a physician balance competing objectives: maximum tumor control with minimal treatment complications. In order to better deal with the complex and multiple objective nature of the problem we have combined a prognostic probabilistic model with multi-attribute decision theory which incorporates patient preferences for outcomes. The response to IMRT for prostate cancer was modeled. A Bayesian network was used for prognosis for each treatment plan. Prognoses included predicting local tumor control, regional spread, distant metastases, and normal tissue complications resulting from treatment. A Markov model was constructed and used to calculate a quality-adjusted life-expectancy which aids in the multi-attribute decision process. Our method makes explicit the tradeoffs patients face between quality and quantity of life. This approach has advantages over current approaches because with our approach risks of health outcomes and patient preferences determine treatment decisions.

Analytical group decision making in natural resources: methodology and application

Treesearch

Daniel L. Schmoldt; David L. Peterson

2000-01-01

Group decision making is becoming increasingly important in natural resource management and associated scientific applications, because multiple values are treated coincidentally in time and space, multiple resource specialists are needed, and multiple stakeholders must be included in the decision process. Decades of social science research on decision making in groups...

INTEGRATING DATA ANALYTICS AND SIMULATION METHODS TO SUPPORT MANUFACTURING DECISION MAKING

PubMed Central

Kibira, Deogratias; Hatim, Qais; Kumara, Soundar; Shao, Guodong

2017-01-01

Modern manufacturing systems are installed with smart devices such as sensors that monitor system performance and collect data to manage uncertainties in their operations. However, multiple parameters and variables affect system performance, making it impossible for a human to make informed decisions without systematic methodologies and tools. Further, the large volume and variety of streaming data collected is beyond simulation analysis alone. Simulation models are run with well-prepared data. Novel approaches, combining different methods, are needed to use this data for making guided decisions. This paper proposes a methodology whereby parameters that most affect system performance are extracted from the data using data analytics methods. These parameters are used to develop scenarios for simulation inputs; system optimizations are performed on simulation data outputs. A case study of a machine shop demonstrates the proposed methodology. This paper also reviews candidate standards for data collection, simulation, and systems interfaces. PMID:28690363

Family members' satisfaction with care and decision-making in intensive care units and post-stay follow-up needs-a cross-sectional survey study.

PubMed

Frivold, Gro; Slettebø, Åshild; Heyland, Daren K; Dale, Bjørg

2018-01-01

The aim of this study was to explore family members' satisfaction with care and decision-making during the intensive care units stay and their follow-up needs after the patient's discharge or death. A cross-sectional survey study was conducted. Family members of patients recently treated in an ICU were participating. The questionnaire contented of background variables, the instrument Family Satisfaction in ICU (FS-ICU 24) and questions about follow-up needs. Descriptive and non-parametric statistics and a multiple linear regression were used in the analysis. A total of 123 (47%) relatives returned the questionnaire. Satisfaction with care was higher scored than satisfaction with decision-making. Follow- up needs after the ICU stay was reported by 19 (17%) of the participants. Gender and length of the ICU stay were shown as factors identified to predict follow-up needs.

Multiple attribute decision making model and application to food safety risk evaluation.

PubMed

Ma, Lihua; Chen, Hong; Yan, Huizhe; Yang, Lifeng; Wu, Lifeng

2017-01-01

Decision making for supermarket food purchase decisions are characterized by network relationships. This paper analyzed factors that influence supermarket food selection and proposes a supplier evaluation index system based on the whole process of food production. The author established the intuitive interval value fuzzy set evaluation model based on characteristics of the network relationship among decision makers, and validated for a multiple attribute decision making case study. Thus, the proposed model provides a reliable, accurate method for multiple attribute decision making.

Development of a chromatographic method with multi-criteria decision making design for simultaneous determination of nifedipine and atenolol in content uniformity testing.

PubMed

Ahmed, Sameh; Alqurshi, Abdulmalik; Mohamed, Abdel-Maaboud Ismail

2018-07-01

A new robust and reliable high-performance liquid chromatography (HPLC) method with multi-criteria decision making (MCDM) approach was developed to allow simultaneous quantification of atenolol (ATN) and nifedipine (NFD) in content uniformity testing. Felodipine (FLD) was used as an internal standard (I.S.) in this study. A novel marriage between a new interactive response optimizer and a HPLC method was suggested for multiple response optimizations of target responses. An interactive response optimizer was used as a decision and prediction tool for the optimal settings of target responses, according to specified criteria, based on Derringer's desirability. Four independent variables were considered in this study: Acetonitrile%, buffer pH and concentration along with column temperature. Eight responses were optimized: retention times of ATN, NFD, and FLD, resolutions between ATN/NFD and NFD/FLD, and plate numbers for ATN, NFD, and FLD. Multiple regression analysis was applied in order to scan the influences of the most significant variables for the regression models. The experimental design was set to give minimum retention times, maximum resolution and plate numbers. The interactive response optimizer allowed prediction of optimum conditions according to these criteria with a good composite desirability value of 0.98156. The developed method was validated according to the International Conference on Harmonization (ICH) guidelines with the aid of the experimental design. The developed MCDM-HPLC method showed superior robustness and resolution in short analysis time allowing successful simultaneous content uniformity testing of ATN and NFD in marketed capsules. The current work presents an interactive response optimizer as an efficient platform to optimize, predict responses, and validate HPLC methodology with tolerable design space for assay in quality control laboratories. Copyright © 2018 Elsevier B.V. All rights reserved.

Factors affecting Korean nursing student empowerment in clinical practice.

PubMed

Ahn, Yang-Heui; Choi, Jihea

2015-12-01

Understanding the phenomenon of nursing student empowerment in clinical practice is important. Investigating the cognition of empowerment and identifying predictors are necessary to enhance nursing student empowerment in clinical practice. To identify empowerment predictors for Korean nursing students in clinical practice based on studies by Bradbury-Jones et al. and Spreitzer. A cross-sectional design was used for this study. This study was performed in three nursing colleges in Korea, all of which had similar baccalaureate nursing curricula. Three hundred seven junior or senior nursing students completed a survey designed to measure factors that were hypothesized to influence nursing student empowerment in clinical practice. Data were collected from November to December 2011. Study variables included self-esteem, clinical decision making, being valued as a learner, satisfaction regarding practice with a team member, perception on professor/instructor/clinical preceptor attitude, and total number of clinical practice fields. Data were analyzed using stepwise multiple regression analyses. All of the hypothesized study variables were significantly correlated to nursing student empowerment. Stepwise multiple regression analysis revealed that clinical decision making in nursing (t=7.59, p<0.001), being valued as a learner (t=6.24, p<0.001), self-esteem (t=3.62, p<0.001), and total number of clinical practice fields (t=2.06, p=0.040). The explanatory power of these predictors was 35% (F=40.71, p<0.001). Enhancing nursing student empowerment in clinical practice will be possible by using educational strategies to improve nursing student clinical decision making. Simultaneously, attitudes of nurse educators are also important to ensure that nursing students are treated as valued learners and to increase student self-esteem in clinical practice. Finally, diverse clinical practice field environments should be considered to enhance experience. Copyright © 2015 Elsevier Ltd. All rights reserved.

Stratification of the severity of critically ill patients with classification trees

PubMed Central

2009-01-01

Background Development of three classification trees (CT) based on the CART (Classification and Regression Trees), CHAID (Chi-Square Automatic Interaction Detection) and C4.5 methodologies for the calculation of probability of hospital mortality; the comparison of the results with the APACHE II, SAPS II and MPM II-24 scores, and with a model based on multiple logistic regression (LR). Methods Retrospective study of 2864 patients. Random partition (70:30) into a Development Set (DS) n = 1808 and Validation Set (VS) n = 808. Their properties of discrimination are compared with the ROC curve (AUC CI 95%), Percent of correct classification (PCC CI 95%); and the calibration with the Calibration Curve and the Standardized Mortality Ratio (SMR CI 95%). Results CTs are produced with a different selection of variables and decision rules: CART (5 variables and 8 decision rules), CHAID (7 variables and 15 rules) and C4.5 (6 variables and 10 rules). The common variables were: inotropic therapy, Glasgow, age, (A-a)O2 gradient and antecedent of chronic illness. In VS: all the models achieved acceptable discrimination with AUC above 0.7. CT: CART (0.75(0.71-0.81)), CHAID (0.76(0.72-0.79)) and C4.5 (0.76(0.73-0.80)). PCC: CART (72(69-75)), CHAID (72(69-75)) and C4.5 (76(73-79)). Calibration (SMR) better in the CT: CART (1.04(0.95-1.31)), CHAID (1.06(0.97-1.15) and C4.5 (1.08(0.98-1.16)). Conclusion With different methodologies of CTs, trees are generated with different selection of variables and decision rules. The CTs are easy to interpret, and they stratify the risk of hospital mortality. The CTs should be taken into account for the classification of the prognosis of critically ill patients. PMID:20003229

What Makes Hydrologic Models Differ? Using SUMMA to Systematically Explore Model Uncertainty and Error

NASA Astrophysics Data System (ADS)

Bennett, A.; Nijssen, B.; Chegwidden, O.; Wood, A.; Clark, M. P.

2017-12-01

Model intercomparison experiments have been conducted to quantify the variability introduced during the model development process, but have had limited success in identifying the sources of this model variability. The Structure for Unifying Multiple Modeling Alternatives (SUMMA) has been developed as a framework which defines a general set of conservation equations for mass and energy as well as a common core of numerical solvers along with the ability to set options for choosing between different spatial discretizations and flux parameterizations. SUMMA can be thought of as a framework for implementing meta-models which allows for the investigation of the impacts of decisions made during the model development process. Through this flexibility we develop a hierarchy of definitions which allows for models to be compared to one another. This vocabulary allows us to define the notion of weak equivalence between model instantiations. Through this weak equivalence we develop the concept of model mimicry, which can be used to investigate the introduction of uncertainty and error during the modeling process as well as provide a framework for identifying modeling decisions which may complement or negate one another. We instantiate SUMMA instances that mimic the behaviors of the Variable Infiltration Capacity (VIC) model and the Precipitation Runoff Modeling System (PRMS) by choosing modeling decisions which are implemented in each model. We compare runs from these models and their corresponding mimics across the Columbia River Basin located in the Pacific Northwest of the United States and Canada. From these comparisons, we are able to determine the extent to which model implementation has an effect on the results, as well as determine the changes in sensitivity of parameters due to these implementation differences. By examining these changes in results and sensitivities we can attempt to postulate changes in the modeling decisions which may provide better estimation of state variables.

A diffusion decision model analysis of evidence variability in the lexical decision task.

PubMed

Tillman, Gabriel; Osth, Adam F; van Ravenzwaaij, Don; Heathcote, Andrew

2017-12-01

The lexical-decision task is among the most commonly used paradigms in psycholinguistics. In both the signal-detection theory and Diffusion Decision Model (DDM; Ratcliff, Gomez, & McKoon, Psychological Review, 111, 159-182, 2004) frameworks, lexical-decisions are based on a continuous source of word-likeness evidence for both words and non-words. The Retrieving Effectively from Memory model of Lexical-Decision (REM-LD; Wagenmakers et al., Cognitive Psychology, 48(3), 332-367, 2004) provides a comprehensive explanation of lexical-decision data and makes the prediction that word-likeness evidence is more variable for words than non-words and that higher frequency words are more variable than lower frequency words. To test these predictions, we analyzed five lexical-decision data sets with the DDM. For all data sets, drift-rate variability changed across word frequency and non-word conditions. For the most part, REM-LD's predictions about the ordering of evidence variability across stimuli in the lexical-decision task were confirmed.

Which health technologies should be funded? A prioritization framework based explicitly on value for money

PubMed Central

2012-01-01

Background Deciding which health technologies to fund involves confronting some of the most difficult choices in medicine. As for other countries, the Israeli health system is faced each year with having to make these difficult decisions. The Public National Advisory Committee, known as ‘the Basket Committee’, selects new technologies for the basic list of health care that all Israelis are entitled to access, known as the ‘health basket’. We introduce a framework for health technology prioritization based explicitly on value for money that enables the main variables considered by decision-makers to be explicitly included. Although the framework’s exposition is in terms of the Basket Committee selecting new technologies for Israel’s health basket, we believe that the framework would also work well for other countries. Methods Our proposed prioritization framework involves comparing four main variables for each technology: 1. Incremental benefits, including ‘equity benefits’, to Israel’s population; 2. Incremental total cost to Israel’s health system; 3. Quality of evidence; and 4. Any additional ‘X-factors’ not elsewhere included, such as strategic or legal factors, etc. Applying methodology from multi-criteria decision analysis, the multiple dimensions comprising the first variable are aggregated via a points system. Results The four variables are combined for each technology and compared across the technologies in the ‘Value for Money (VfM) Chart’. The VfM Chart can be used to identify technologies that are good value for money, and, given a budget constraint, to select technologies that should be funded. This is demonstrated using 18 illustrative technologies. Conclusions The VfM Chart is an intuitively appealing decision-support tool for helping decision-makers to focus on the inherent tradeoffs involved in health technology prioritization. Such deliberations can be performed in a systematic and transparent fashion that can also be easily communicated to stakeholders, including the general public. Possible future research includes pilot-testing the VfM Chart using real-world data. Ideally, this would involve working with the Basket Committee. Likewise, the framework could be tested and applied by health technology prioritization agencies in other countries. PMID:23181391

Which health technologies should be funded? A prioritization framework based explicitly on value for money.

PubMed

Golan, Ofra; Hansen, Paul

2012-11-26

Deciding which health technologies to fund involves confronting some of the most difficult choices in medicine. As for other countries, the Israeli health system is faced each year with having to make these difficult decisions. The Public National Advisory Committee, known as 'the Basket Committee', selects new technologies for the basic list of health care that all Israelis are entitled to access, known as the 'health basket'. We introduce a framework for health technology prioritization based explicitly on value for money that enables the main variables considered by decision-makers to be explicitly included. Although the framework's exposition is in terms of the Basket Committee selecting new technologies for Israel's health basket, we believe that the framework would also work well for other countries. Our proposed prioritization framework involves comparing four main variables for each technology: 1. Incremental benefits, including 'equity benefits', to Israel's population; 2. Incremental total cost to Israel's health system; 3. Quality of evidence; and 4. Any additional 'X-factors' not elsewhere included, such as strategic or legal factors, etc. Applying methodology from multi-criteria decision analysis, the multiple dimensions comprising the first variable are aggregated via a points system. The four variables are combined for each technology and compared across the technologies in the 'Value for Money (VfM) Chart'. The VfM Chart can be used to identify technologies that are good value for money, and, given a budget constraint, to select technologies that should be funded. This is demonstrated using 18 illustrative technologies. The VfM Chart is an intuitively appealing decision-support tool for helping decision-makers to focus on the inherent tradeoffs involved in health technology prioritization. Such deliberations can be performed in a systematic and transparent fashion that can also be easily communicated to stakeholders, including the general public. Possible future research includes pilot-testing the VfM Chart using real-world data. Ideally, this would involve working with the Basket Committee. Likewise, the framework could be tested and applied by health technology prioritization agencies in other countries.

Complex Decision-Making in Heart Failure: A Systematic Review and Thematic Analysis.

PubMed

Hamel, Aimee V; Gaugler, Joseph E; Porta, Carolyn M; Hadidi, Niloufar Niakosari

Heart failure follows a highly variable and difficult course. Patients face complex decisions, including treatment with implantable cardiac defibrillators, mechanical circulatory support, and heart transplantation. The course of decision-making across multiple treatments is unclear yet integral to providing informed and shared decision-making. Recognizing commonalities across treatment decisions could help nurses and physicians to identify opportunities to introduce discussions and support shared decision-making. The specific aims of this review are to examine complex treatment decision-making, specifically implantable cardiac defibrillators, ventricular assist device, and cardiac transplantation, and to recognize commonalities and key points in the decisional process. MEDLINE, CINAHL, PsycINFO, and Web of Science were searched for English-language studies that included qualitative findings reflecting the complexity of heart failure decision-making. Using a 3-step process, findings were synthesized into themes and subthemes. Twelve articles met criteria for inclusion. Participants included patients, caregivers, and clinicians and included decisions to undergo and decline treatment. Emergent themes were "processing the decision," "timing and prognostication," and "considering the future." Subthemes described how participants received and understood information about the therapy, making and changing a treatment decision, timing their decision and gauging health status outcomes in the context of their decision, the influence of a life or death decision, and the future as a factor in their decisional process. Commonalities were present across therapies, which involved the timing of discussions, the delivery of information, and considerations of the future. Exploring this further could help support patient-centered care and optimize shared decision-making interventions.

Integrative evaluation for sustainable decisions of urban wastewater system management under uncertainty

NASA Astrophysics Data System (ADS)

Hadjimichael, A.; Corominas, L.; Comas, J.

2017-12-01

With sustainable development as their overarching goal, urban wastewater system (UWS) managers need to take into account multiple social, economic, technical and environmental facets related to their decisions. In this complex decision-making environment, uncertainty can be formidable. It is present both in the ways the system is interpreted stochastically, but also in its natural ever-shifting behavior. This inherent uncertainty suggests that wiser decisions would be made under an adaptive and iterative decision-making regime. No decision-support framework has been presented in the literature to effectively addresses all these needs. The objective of this work is to describe such a conceptual framework to evaluate and compare alternative solutions for various UWS challenges within an adaptive management structure. Socio-economic aspects such as externalities are taken into account, along with other traditional criteria as necessary. Robustness, reliability and resilience analyses test the performance of the system against present and future variability. A valuation uncertainty analysis incorporates uncertain valuation assumptions in the decision-making process. The framework is demonstrated with an application to a case study presenting a typical problem often faced by managers: poor river water quality, increasing population, and more stringent water quality legislation. The application of the framework made use of: i) a cost-benefit analysis including monetized environmental benefits and damages; ii) a robustness analysis of system performance against future conditions; iii) reliability and resilience analyses of the system given contextual variability; and iv) a valuation uncertainty analysis of model parameters. The results suggest that the installation of bigger volumes would give rise to increased benefits despite larger capital costs, as well as increased robustness and resilience. Population numbers appear to affect the estimated benefits most, followed by electricity prices and climate change projections. The presented framework is expected to be a valuable tool for the next generation of UWS decision-making and the application demonstrates a novel and valuable integration of metrics and methods for UWS analysis.

Decisions reduce sensitivity to subsequent information.

PubMed

Bronfman, Zohar Z; Brezis, Noam; Moran, Rani; Tsetsos, Konstantinos; Donner, Tobias; Usher, Marius

2015-07-07

Behavioural studies over half a century indicate that making categorical choices alters beliefs about the state of the world. People seem biased to confirm previous choices, and to suppress contradicting information. These choice-dependent biases imply a fundamental bound of human rationality. However, it remains unclear whether these effects extend to lower level decisions, and only little is known about the computational mechanisms underlying them. Building on the framework of sequential-sampling models of decision-making, we developed novel psychophysical protocols that enable us to dissect quantitatively how choices affect the way decision-makers accumulate additional noisy evidence. We find robust choice-induced biases in the accumulation of abstract numerical (experiment 1) and low-level perceptual (experiment 2) evidence. These biases deteriorate estimations of the mean value of the numerical sequence (experiment 1) and reduce the likelihood to revise decisions (experiment 2). Computational modelling reveals that choices trigger a reduction of sensitivity to subsequent evidence via multiplicative gain modulation, rather than shifting the decision variable towards the chosen alternative in an additive fashion. Our results thus show that categorical choices alter the evidence accumulation mechanism itself, rather than just its outcome, rendering the decision-maker less sensitive to new information. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Medical decision-making capacity and its cognitive predictors in progressive MS: Preliminary evidence.

PubMed

Gerstenecker, Adam; Lowry, Kathleen; Myers, Terina; Bashir, Khurram; Triebel, Kristen L; Martin, Roy C; Marson, Daniel C

2017-09-15

Medical decision-making capacity (MDC) refers to the ability to make informed decisions about treatment and declines in cognition are associated with declines in MDC across multiple disease entities. However, although it is well known that cognitive impairment is prevalent in multiple sclerosis (MS), little is known about MDC in the disease. Data from 22 persons with progressive MS and 18 healthy controls were analyzed. All diagnoses were made by a board-certified neurologist with experience in MS. All study participants were administered a vignette-based measure of MDC and also a neuropsychological battery. Performance on three MDC consent standards (i.e., Appreciation, Reasoning, Understanding) was significantly lower for people with progressive MS as compared to healthy controls. In the progressive MS group, verbal fluency was the primary cognitive predictor for both Reasoning and Understanding consent standards. Verbal learning and memory was the primary cognitive predictor for Appreciation. MS severity was not significantly correlated with any MDC variable. MDC is a complex and cognitively mediated functional ability that is impaired in many people with progressive MS. Verbal measures of fluency and memory are strongly associated with MDC performances in the current sample of people with MS and could potentially be utilized to quickly screen for MDC impairment in MS. Copyright © 2017 Elsevier B.V. All rights reserved.

Computational Precision of Mental Inference as Critical Source of Human Choice Suboptimality.

PubMed

Drugowitsch, Jan; Wyart, Valentin; Devauchelle, Anne-Dominique; Koechlin, Etienne

2016-12-21

Making decisions in uncertain environments often requires combining multiple pieces of ambiguous information from external cues. In such conditions, human choices resemble optimal Bayesian inference, but typically show a large suboptimal variability whose origin remains poorly understood. In particular, this choice suboptimality might arise from imperfections in mental inference rather than in peripheral stages, such as sensory processing and response selection. Here, we dissociate these three sources of suboptimality in human choices based on combining multiple ambiguous cues. Using a novel quantitative approach for identifying the origin and structure of choice variability, we show that imperfections in inference alone cause a dominant fraction of suboptimal choices. Furthermore, two-thirds of this suboptimality appear to derive from the limited precision of neural computations implementing inference rather than from systematic deviations from Bayes-optimal inference. These findings set an upper bound on the accuracy and ultimate predictability of human choices in uncertain environments. Copyright © 2016 Elsevier Inc. All rights reserved.

The TRIO Framework: Conceptual insights into family caregiver involvement and influence throughout cancer treatment decision-making.

PubMed

Laidsaar-Powell, Rebekah; Butow, Phyllis; Charles, Cathy; Gafni, Amiram; Entwistle, Vikki; Epstein, Ronald; Juraskova, Ilona

2017-11-01

Family caregivers are regularly involved in cancer consultations and treatment decision-making (DM). Yet there is limited conceptual description of caregiver influence/involvement in DM. To address this, an empirically-grounded conceptual framework of triadic DM (TRIO Framework) and corresponding graphical aid (TRIO Triangle) were developed. Jabareen's model for conceptual framework development informed multiple phases of development/validation, incorporation of empirical research and theory, and iterative revisions by an expert advisory group. Findings coalesced into six empirically-grounded conceptual insights: i) Caregiver influence over a decision is variable amongst different groups; ii) Caregiver influence is variable within the one triad over time; iii) Caregivers are involved in various ways in the wider DM process; iv) DM is not only amongst three, but can occur among wider social networks; v) Many factors may affect the form and extent of caregiver involvement in DM; vi) Caregiver influence over, and involvement in, DM is linked to their everyday involvement in illness care/management. The TRIO Framework/Triangle may serve as a useful guide for future empirical, ethical and/or theoretical work. This Framework can deepen clinicians's and researcher's understanding of the diverse and varying scope of caregiver involvement and influence in DM. Copyright © 2017 Elsevier B.V. All rights reserved.

Characteristics of physicians who frequently see pharmaceutical sales representatives.

PubMed

Alkhateeb, Fadi M; Khanfar, Nile M; Clauson, Kevin A

2009-01-01

Pharmaceutical sales representatives (PSRs) can impact physician prescribing. The objective of this study was to test a model of physician and practice setting characteristics as influences on decisions by physicians to see PSRs. A survey was sent to a random sample of 2000 physicians. Multiple linear regression analyses were used to test models for predicting influences on decisions to see PSRs frequently, defined as at least monthly. Independent variables included: presence of restrictive policy for pharmaceutical detailing, volume of prescriptions, gender, age, type of specialty, academic affiliation, practice setting size, and urban versus rural. The dependent variable was frequency of PSRs visits to physicians. Six hundred seventy-one responses were received yielding a response rate of 34.7%. Four hundred thirty-two physicians (79.5%) reported seeing PSRs at least monthly. The decision influence model was found to be significant. Primary care physicians and high-volume prescribers showed increased likelihood to see PSRs. Physicians practicing in settings that were small, urban, without restrictive policies for pharmaceutical detailing, and not academically affiliated were more likely to see PSRs frequently. This model of physician and practice characteristics is useful in explaining the variations in physicians' characteristics who see PSRs frequently. These characteristics could be used to guide the development of future academic or counter-detailing initiatives to improve evidence-based prescribing.

Self-reported psychological demands, skill discretion and decision authority at work: A twin study.

PubMed

Theorell, Töres; De Manzano, Örjan; Lennartsson, Anna-Karin; Pedersen, Nancy L; Ullén, Fredrik

2016-06-01

To examine the contribution of genetic factors to self-reported psychological demands (PD), skill discretion (SD) and decision authority (DA) and the possible importance of such influence on the association between these work variables and depressive symptoms. 11,543 participants aged 27-54 in the Swedish Twin Registry participated in a web survey. First of all, in multiple regressions, phenotypic associations between each one of the three work environment variables and depressive symptoms were analysed. Secondly, by means of classical twin analysis, the genetic contribution to PD, SD and DA was assessed. After this, cross-twin cross-trait correlations were computed between PD, SD and DA, on the one hand, and depressive symptom score, on the other hand. The genetic contribution to self-reported PD, DS and DA ranged from 18% for decision authority to 30% for skill discretion. Cross-twin cross-trait correlations were very weak (r values < .1) and non-significant for dizygotic twins, and we lacked power to analyse the genetic architecture of the phenotypic associations using bivariate twin modelling. However, substantial genetic contribution to these associations seems unlikely. CONCLUSIONS GENETIC CONTRIBUTIONS TO THE SELF-REPORTED WORK ENVIRONMENT SCORES WERE 18-30%. © 2016 the Nordic Societies of Public Health.

Pregnancy interruption after second trimester diagnosis of fetal structural anomalies: the New Jersey Fetal Abnormalities Registry.

PubMed

Rauch, Eden R; Smulian, John C; DePrince, Kristin; Ananth, Cande V; Marcella, Stephen W

2005-10-01

The purpose of this study was to identify factors that predict a decision to interrupt a pregnancy in which there are fetal anomalies in the second trimester. The New Jersey Fetal Abnormalities Registry prospectively recruits and collects information on pregnancies (> or = 15 weeks of gestation) from New Jersey residents in whom a fetal structural anomaly has been suspected by maternal-fetal medicine specialists. Enrolled pregnancies that have major fetal structural abnormalities identified from 15 to 23 weeks of gestation were included. Outcomes were classified as either elective interruption or a natural pregnancy course, which might include a spontaneous fetal death or live birth. Predictors of elective interruption of pregnancy were examined with univariable and multivariable logistic regression analyses. Of the 97 cases, 33% of the women (n = 32) interrupted the pregnancy. Significant variables in the regression model that were associated with a decision to interrupt a pregnancy were earlier identification of fetal anomalies (19.0 +/- 2 weeks of gestation vs 20.5 +/- 2 weeks of gestation; P = .003), the presence of multiple anomalies (78% [25/32] vs 52% [33/63]; P = .01], and a presumption of lethality (56% [18/32] vs 14% [9/65]; P = .0001). These variables corresponded to an odds ratio for pregnancy interruption of 4.2 (95% CI, 1.0, 17.0) for multiple anomalies, 0.8 (95% CI, 0.7, 1.0) for each week of advancing gestational age, and 36.1 (95% CI, 2.9, 450.7) for presumed lethal abnormalities. Early diagnosis, the identification of multiple abnormalities, and an assessment of likely lethality of fetal anomalies are important factors for the optimization of parental autonomy in deciding pregnancy management.

Optimal information networks: Application for data-driven integrated health in populations

PubMed Central

Servadio, Joseph L.; Convertino, Matteo

2018-01-01

Development of composite indicators for integrated health in populations typically relies on a priori assumptions rather than model-free, data-driven evidence. Traditional variable selection processes tend not to consider relatedness and redundancy among variables, instead considering only individual correlations. In addition, a unified method for assessing integrated health statuses of populations is lacking, making systematic comparison among populations impossible. We propose the use of maximum entropy networks (MENets) that use transfer entropy to assess interrelatedness among selected variables considered for inclusion in a composite indicator. We also define optimal information networks (OINs) that are scale-invariant MENets, which use the information in constructed networks for optimal decision-making. Health outcome data from multiple cities in the United States are applied to this method to create a systemic health indicator, representing integrated health in a city. PMID:29423440

Demands, skill discretion, decision authority and social climate at work as determinants of major depression in a 3-year follow-up study.

PubMed

Fandiño-Losada, Andrés; Forsell, Yvonne; Lundberg, Ingvar

2013-07-01

The psychosocial work environment may be a determinant of the development and course of depressive disorders, but the literature shows inconsistent findings. Thus, the aim of this study is to determine longitudinal effects of the job demands-control-support model (JDCSM) variables on the occurrence of major depression among working men and women from the general population. The sample comprised 4,710 working women and men living in Stockholm, who answered the same questionnaire twice, 3 years apart, who were not depressed during the first wave and had the same job in both waves. The questionnaire included JDCSM variables (demands, skill discretion, decision authority and social climate) and other co-variables (income, education, occupational group, social support, help and small children at home, living with an adult and depressive symptoms at time 1; and negative life events at time 2). Multiple logistic regressions were run to calculate odds ratios of having major depression at time 2, after adjustment for other JDCSM variables and co-variables. Among women, inadequate work social climate was the only significant risk indicator for major depression. Surprisingly, among men, high job demands and low skill discretion appeared as protective factors against major depression. The results showed a strong relationship between inadequate social climate and major depression among women, while there were no certain effects for the remaining exposure variables. Among men, few cases of major depression hampered well-founded conclusions regarding our findings of low job demands and high skill discretion as related to major depression.

Integrated presentation of ecological risk from multiple stressors

PubMed Central

Goussen, Benoit; Price, Oliver R.; Rendal, Cecilie; Ashauer, Roman

2016-01-01

Current environmental risk assessments (ERA) do not account explicitly for ecological factors (e.g. species composition, temperature or food availability) and multiple stressors. Assessing mixtures of chemical and ecological stressors is needed as well as accounting for variability in environmental conditions and uncertainty of data and models. Here we propose a novel probabilistic ERA framework to overcome these limitations, which focusses on visualising assessment outcomes by construct-ing and interpreting prevalence plots as a quantitative prediction of risk. Key components include environmental scenarios that integrate exposure and ecology, and ecological modelling of relevant endpoints to assess the effect of a combination of stressors. Our illustrative results demonstrate the importance of regional differences in environmental conditions and the confounding interactions of stressors. Using this framework and prevalence plots provides a risk-based approach that combines risk assessment and risk management in a meaningful way and presents a truly mechanistic alternative to the threshold approach. Even whilst research continues to improve the underlying models and data, regulators and decision makers can already use the framework and prevalence plots. The integration of multiple stressors, environmental conditions and variability makes ERA more relevant and realistic. PMID:27782171

Factors associated with health-related decision-making in older adults from Southern Brazil.

PubMed

Morsch, Patricia; Mirandola, Andrea Ribeiro; Caberlon, Iride Cristofoli; Bós, Ângelo José Gonçalves

2017-05-01

To analyze older adults' health-related decision-making profile. Secondary analysis of a population-based study with 6945 older-adults (aged ≥60 years) in Southern Brazil. Multiple logistic regressions were calculated to describe the odds of deciding alone or asking for advice, compared with the chance of letting someone else decide about health-related issues. Associated variables were age, sex, marital status, education level, number of chronic morbidities, having children and quality of life. The odds of asking for advice instead of letting others decide were significantly higher in the younger group and those with better levels of quality of life, independent of other variables. The chance of asking for advice was lower for unmarried (62%), widowed (76%) and those with children (50%). The chance of men deciding for themselves about their health instead of letting others decide was 47% higher compared with women (P = 0.0002), but 45% lower in the older group (P < 0.0001). Participants who where unmarried and childless, and individuals with better levels of quality of life were more likely to decide alone instead of letting others decide (P < 0.05). Decision-making is fundamental for older adults' good quality of life. Aging makes older adults more vulnerable to dependence; however, it does not necessarily mean that they lose or decrease their ability to make decisions regarding their own health and desires. Geriatr Gerontol Int 2017; 17: 798-803. © 2016 Japan Geriatrics Society.

Multiple-attribute group decision making with different formats of preference information on attributes.

PubMed

Xu, Zeshui

2007-12-01

Interval utility values, interval fuzzy preference relations, and interval multiplicative preference relations are three common uncertain-preference formats used by decision-makers to provide their preference information in the process of decision making under fuzziness. This paper is devoted in investigating multiple-attribute group-decision-making problems where the attribute values are not precisely known but the value ranges can be obtained, and the decision-makers provide their preference information over attributes by three different uncertain-preference formats i.e., 1) interval utility values; 2) interval fuzzy preference relations; and 3) interval multiplicative preference relations. We first utilize some functions to normalize the uncertain decision matrix and then transform it into an expected decision matrix. We establish a goal-programming model to integrate the expected decision matrix and all three different uncertain-preference formats from which the attribute weights and the overall attribute values of alternatives can be obtained. Then, we use the derived overall attribute values to get the ranking of the given alternatives and to select the best one(s). The model not only can reflect both the subjective considerations of all decision-makers and the objective information but also can avoid losing and distorting the given objective and subjective decision information in the process of information integration. Furthermore, we establish some models to solve the multiple-attribute group-decision-making problems with three different preference formats: 1) utility values; 2) fuzzy preference relations; and 3) multiplicative preference relations. Finally, we illustrate the applicability and effectiveness of the developed models with two practical examples.

A probabilistic approach to aircraft design emphasizing stability and control uncertainties

NASA Astrophysics Data System (ADS)

Delaurentis, Daniel Andrew

In order to address identified deficiencies in current approaches to aerospace systems design, a new method has been developed. This new method for design is based on the premise that design is a decision making activity, and that deterministic analysis and synthesis can lead to poor, or misguided decision making. This is due to a lack of disciplinary knowledge of sufficient fidelity about the product, to the presence of uncertainty at multiple levels of the aircraft design hierarchy, and to a failure to focus on overall affordability metrics as measures of goodness. Design solutions are desired which are robust to uncertainty and are based on the maximum knowledge possible. The new method represents advances in the two following general areas. 1. Design models and uncertainty. The research performed completes a transition from a deterministic design representation to a probabilistic one through a modeling of design uncertainty at multiple levels of the aircraft design hierarchy, including: (1) Consistent, traceable uncertainty classification and representation; (2) Concise mathematical statement of the Probabilistic Robust Design problem; (3) Variants of the Cumulative Distribution Functions (CDFs) as decision functions for Robust Design; (4) Probabilistic Sensitivities which identify the most influential sources of variability. 2. Multidisciplinary analysis and design. Imbedded in the probabilistic methodology is a new approach for multidisciplinary design analysis and optimization (MDA/O), employing disciplinary analysis approximations formed through statistical experimentation and regression. These approximation models are a function of design variables common to the system level as well as other disciplines. For aircraft, it is proposed that synthesis/sizing is the proper avenue for integrating multiple disciplines. Research hypotheses are translated into a structured method, which is subsequently tested for validity. Specifically, the implementation involves the study of the relaxed static stability technology for a supersonic commercial transport aircraft. The probabilistic robust design method is exercised resulting in a series of robust design solutions based on different interpretations of "robustness". Insightful results are obtained and the ability of the method to expose trends in the design space are noted as a key advantage.

Cellular Decision Making by Non-Integrative Processing of TLR Inputs.

PubMed

Kellogg, Ryan A; Tian, Chengzhe; Etzrodt, Martin; Tay, Savaş

2017-04-04

Cells receive a multitude of signals from the environment, but how they process simultaneous signaling inputs is not well understood. Response to infection, for example, involves parallel activation of multiple Toll-like receptors (TLRs) that converge on the nuclear factor κB (NF-κB) pathway. Although we increasingly understand inflammatory responses for isolated signals, it is not clear how cells process multiple signals that co-occur in physiological settings. We therefore examined a bacterial infection scenario involving co-stimulation of TLR4 and TLR2. Independent stimulation of these receptors induced distinct NF-κB dynamic profiles, although surprisingly, under co-stimulation, single cells continued to show ligand-specific dynamic responses characteristic of TLR2 or TLR4 signaling rather than a mixed response, comprising a cellular decision that we term "non-integrative" processing. Iterating modeling and microfluidic experiments revealed that non-integrative processing occurred through interaction of switch-like NF-κB activation, receptor-specific processing timescales, cell-to-cell variability, and TLR cross-tolerance mediated by multilayer negative feedback. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Using complexity science and negotiation theory to resolve boundary-crossing water issues

NASA Astrophysics Data System (ADS)

Islam, Shafiqul; Susskind, Lawrence

2018-07-01

Many water governance and management issues are complex. The complexity of these issues is related to crossing of multiple boundaries: political, social and jurisdictional, as well as physical, ecological and biogeochemical. Resolution of these issues usually requires interactions of many parties with conflicting values and interests operating across multiple boundaries and scales to make decisions. The interdependence and feedback among interacting variables, processes, actors and institutions are hard to model and difficult to forecast. Thus, decision-making related to complex water problems needs be contingent and adaptive. This paper draws on a number of ideas from complexity science and negotiation theory that may make it easier to cope with the complexities and difficulties of managing boundary crossing water disputes. It begins with the Water Diplomacy Framework that was developed and tested over the past several years. Then, it uses three key ideas from complexity science (interdependence and interconnectedness; uncertainty and feedback; emergence and adaptation) and three from negotiation theory (stakeholder identification and engagement; joint fact finding; and value creation through option generation) to show how application of these ideas can help enhance effectiveness of water management.

Assessment of health-care waste disposal methods using a VIKOR-based fuzzy multi-criteria decision making method.

PubMed

Liu, Hu-Chen; Wu, Jing; Li, Ping

2013-12-01

Nowadays selection of the appropriate treatment method in health-care waste (HCW) management has become a challenge task for the municipal authorities especially in developing countries. Assessment of HCW disposal alternatives can be regarded as a complicated multi-criteria decision making (MCDM) problem which requires consideration of multiple alternative solutions and conflicting tangible and intangible criteria. The objective of this paper is to present a new MCDM technique based on fuzzy set theory and VIKOR method for evaluating HCW disposal methods. Linguistic variables are used by decision makers to assess the ratings and weights for the established criteria. The ordered weighted averaging (OWA) operator is utilized to aggregate individual opinions of decision makers into a group assessment. The computational procedure of the proposed framework is illustrated through a case study in Shanghai, one of the largest cities of China. The HCW treatment alternatives considered in this study include "incineration", "steam sterilization", "microwave" and "landfill". The results obtained using the proposed approach are analyzed in a comparative way. Copyright © 2013. Published by Elsevier Ltd.

Alberta's and Ontario's liquor boards: why such divergent outcomes?

PubMed

Bird, Malcolm G

2010-01-01

The provinces of Alberta and Ontario have chosen very different methods to distribute alcoholic beverages: Alberta privatized the Alberta Liquor Control Board (ALCB) in 1993 and established a private market to sell beverage alcohol, while Ontario, in stark contrast, opted to retain and expand the Liquor Control Board of Ontario (LCBO). This article examines the reasons for the divergent policy choices made by Ralph Klein and Mike Harris' Conservative governments in each province. The article draws on John Kingdon's “multiple streams decision-making model,” to examine the mindsets of the key decision-makers, as well as “historical institutionalism,” to organize the pertinent structural, historical and institutional variables that shaped the milieu in which decision-makers acted. Unique, province-specific political cultures, histories, institutional configurations (including the relative influence of a number of powerful actors), as well as the fact that the two liquor control boards were on opposing trajectories towards their ultimate fates, help to explain the different decisions made by each government. Endogenous preference construction in this sector, furthermore, implies that each system is able to satisfy all relevant stakeholders, including consumers.

Preference, resistance to change, and the cumulative decision model.

PubMed

Grace, Randolph C

2018-01-01

According to behavioral momentum theory (Nevin & Grace, 2000a), preference in concurrent chains and resistance to change in multiple schedules are independent measures of a common construct representing reinforcement history. Here I review the original studies on preference and resistance to change in which reinforcement variables were manipulated parametrically, conducted by Nevin, Grace and colleagues between 1997 and 2002, as well as more recent research. The cumulative decision model proposed by Grace and colleagues for concurrent chains is shown to provide a good account of both preference and resistance to change, and is able to predict the increased sensitivity to reinforcer rate and magnitude observed with constant-duration components. Residuals from fits of the cumulative decision model to preference and resistance to change data were positively correlated, supporting the prediction of behavioral momentum theory. Although some questions remain, the learning process assumed by the cumulative decision model, in which outcomes are compared against a criterion that represents the average outcome value in the current context, may provide a plausible model for the acquisition of differential resistance to change. © 2018 Society for the Experimental Analysis of Behavior.

Death Penalty Disposition in China: What Matters?

PubMed

Li, Yudu; Longmire, Dennis; Lu, Hong

2018-01-01

In theory, sentencing decisions should be driven by legal factors, not extra-legal factors. However, some empirical research on the death penalty in the United States shows significant relationships between offender and victim characteristics and death sentence decisions. Despite the fact that China frequently imposes death sentences, few studies have examined these sanctions to see if similar correlations occur in China's capital cases. Using data from published court cases in China involving three violent crimes-homicide, robbery, and intentional assault-this study examines the net impact of offender's gender, race, and victim-offender relationship on death sentence decisions in China. Our overall multiple regression results indicate that, after controlling for other legal and extra-legal variables, an offender's gender, race, and victim-offender relationship did not produce similar results in China when compared with those in the United States. In contrast, it is the legal factors that played the most significant role in influencing the death penalty decisions. The article concludes with explanations and speculations on the unique social, cultural, and legal conditions in China that may have contributed to these correlations.

Maximizing gait and balance: behaviors and decision-making processes of persons with multiple sclerosis and physical therapists.

PubMed

Held Bradford, Elissa; Finlayson, Marcia; White Gorman, Andrea; Wagner, Joanne

2018-05-01

To describe the behavioral decisions used by persons with multiple sclerosis (MS) and physical therapists to maximize gait and balance following outpatient physical therapy. A multi-method case series with seven matched pairs (persons with MS-physical therapists). Quota sampling maximized variability among persons with MS (disease steps score range 3-6). Three of the four physical therapists were MS or neurology certified. Persons with MS completed a phone survey, follow-up interview, and standardized questionnaires. Physical therapists completed an interview. Data were collected 2-8 weeks following discharge. Content and constant comparison analyses were used for thematic development and triangulation. Core themes arose exemplifying the decision-making processes and actions of persons with MS (challenging self by pushing but respecting limits) and physical therapists (finding the right fit). One overarching theme, keeping their lived world large, or participation in valued life roles, emerged integrating both perspectives driving decision-making. Participants have a shared goal of maximizing gait and balance so persons with MS can participate in valued life roles. Understanding the differences in the behavioral decisions and optimizing skill sets in shared decision-making and self-management may enhance the therapeutic partnership and engagement in gait- and balance-enhancing behaviors. Implications for Rehabilitation Persons with MS and physical therapists have a shared goal of maximizing gait and balance so persons with MS can participate in valued activities and life roles, or more poetically, keep their lived world large. Knowledge that persons with MS aim to challenge themselves by pushing but respecting limits can provide physical therapists with greater insight in helping persons with MS resolve uncertainty, set meaningful goals, and build the routines and resilience needed for engagement in gait- and balance-enhancing behaviors. Enriching skill sets in shared decision-making, behavior change and self-management may optimize the physical therapist toolbox.

A mathematical model approach toward combining information from multiple image projections of the same patient

NASA Astrophysics Data System (ADS)

Chawla, Amarpreet S.; Samei, Ehsan; Abbey, Craig

2007-03-01

In this study, we used a mathematical observer model to combine information obtained from multiple angular projections of the same breast to determine the overall detection performance of a multi-projection breast imaging system in detectability of a simulated mass. 82 subjects participated in the study and 25 angular projections of each breast were acquired. Projections from a simulated 3 mm 3-D lesion were added to the projection images. The lesion was assumed to be embedded in the compressed breast at a distance of 3 cm from the detector. Hotelling observer with Laguerre-Gauss channels (LG CHO) was applied to each image. Detectability was analyzed in terms of ROC curves and the area under ROC curves (AUC). The critical question studied is how to best integrate the individual decision variables across multiple (correlated) views. Towards that end, three different methods were investigated. Specifically, 1) ROCs from different projections were simply averaged; 2) the test statistics from different projections were averaged; and 3) a Bayesian decision fusion rule was used. Finally, AUC of the combined ROC was used as a parameter to optimize the acquisition parameters to maximize the performance of the system. It was found that the Bayesian decision fusion technique performs better than the other two techniques and likely offers the best approximation of the diagnostic process. Furthermore, if the total dose level is held constant at 1/25th of dual-view mammographic screening dose, the highest detectability performance is observed when considering only two projections spread along an angular span of 11.4°.

Negative and positive urgency may both be risk factors for compulsive buying.

PubMed

Rose, Paul; Segrist, Daniel J

2014-06-01

Descriptions of compulsive buying often emphasize the roles of negative moods and trait impulsivity in the development of problematic buying habits. Trait impulsivity is sometimes treated as a unidimensional trait in compulsive buying research, but recent factor analyses suggest that impulsivity consists of multiple components that are probably best treated as independent predictors of problem behavior. In order to draw greater attention to the role of positive moods in compulsive buying, in this study we tested whether negative urgency (the tendency to act rashly while in negative moods) and positive urgency (the tendency to act rashly while in positive moods) account for similar amounts of variance in compulsive buying. North American adults (N = 514) completed an online survey containing the Richmond Compulsive Buying Scale (Ridgway, Kukar-Kinney & Monroe, 2008), established measures of positive and negative urgency (Cyders et al., 2007), ad hoc measures of buying-specific positive and negative urgency, measures of extraversion and neuroticism obtained from the International Personality Item Pool (http://ipip.ori.org/), and demographic questions. In several multiple regression analyses, when demographic variables, neuroticism, and extraversion were controlled, positive urgency and negative urgency both emerged as significant predictors of compulsive buying. Whether the two urgency variables were domain-general or buying-specific, they accounted for similar amounts of variance in compulsive buying. Preventing and reducing compulsive buying may require attention not only to the purchasing decisions people make while in negative states, but also to the purchasing decisions they make while in positive states.

Insecticide treated bednet strategy in rural settings: can we exploit women's decision making power?

PubMed

Tilak, Rina; Tilak, V W; Bhalwar, R

2007-01-01

Use of insecticide treated bednets in prevention of malaria is a widely propagated global strategy, however, its use has been reported to be influenced and limited by many variables especially gender bias. A cross sectional field epidemiological study was conducted in a rural setting with two outcome variables, 'Bednet use'(primary outcome variable) and 'Women's Decision Making Power' which were studied in reference to various predictor variables. Analysis reveals a significant effect on the primary outcome variable 'Bednet use' of the predictor variables- age, occupation, bednet purchase decision, women's decision making power, husband's education and knowledge about malaria and its prevention. The study recommends IEC on treated bednets to be disseminated through TV targeting the elderly women who have better decision making power and mobilizing younger women who were found to prefer bednets for prevention of mosquito bites for optimizing the use of treated bednets in similar settings.

Planned versus actual outcomes as a result of animal feeding operation decisions for managing phosphorus.

PubMed

Cabot, Perry E; Nowak, Pete

2005-01-01

The paper explores how decisions made on animal feeding operations (AFOs) influence the management of manure and phosphorus. Variability among these decisions from operation to operation and from field to field can influence the validity of nutrient loss risk assessments. These assessments are based on assumptions that the decision outcomes regarding manure distribution will occur as they are planned. The discrepancy between planned versus actual outcomes in phosphorus management was explored on nine AFOs managing a contiguous set of 210 fields in south-central Wisconsin. A total of 2611 soil samples were collected and multiple interviews conducted to assign phosphorus index (PI) ratings to the fields. Spearman's rank correlation coefficients (r(S)) indicated that PI ratings were less sensitive to soil test phosphorus (STP) levels (r(S) = 0.378), universal soil loss equation (USLE) (r(S) = 0.261), ratings for chemical fertilizer application (r(S) = 0.185), and runoff class (r(S) = -0.089), and more sensitive to ratings for manure application (r(S) = 0.854). One-way ANOVA indicated that mean field STP levels were more homogenous than field PI ratings between AFOs. Kolmogorov-Smirnov (K-S) tests displayed several nonsignificant comparisons for cumulative distribution functions, S(x), of mean STP levels on AFO fields. On the other hand, the K-S tests of S(x) for PI ratings indicated that the majority of these S(x) functions were significantly different between AFOs at or greater than the 0.05 significance level. Interviews suggested multiple reasons for divergence between planned and actual outcomes in managing phosphorus, and that this divergence arises at the strategic, tactical, and operational levels of decision-making.

Multiple imputation in the presence of non-normal data.

PubMed

Lee, Katherine J; Carlin, John B

2017-02-20

Multiple imputation (MI) is becoming increasingly popular for handling missing data. Standard approaches for MI assume normality for continuous variables (conditionally on the other variables in the imputation model). However, it is unclear how to impute non-normally distributed continuous variables. Using simulation and a case study, we compared various transformations applied prior to imputation, including a novel non-parametric transformation, to imputation on the raw scale and using predictive mean matching (PMM) when imputing non-normal data. We generated data from a range of non-normal distributions, and set 50% to missing completely at random or missing at random. We then imputed missing values on the raw scale, following a zero-skewness log, Box-Cox or non-parametric transformation and using PMM with both type 1 and 2 matching. We compared inferences regarding the marginal mean of the incomplete variable and the association with a fully observed outcome. We also compared results from these approaches in the analysis of depression and anxiety symptoms in parents of very preterm compared with term-born infants. The results provide novel empirical evidence that the decision regarding how to impute a non-normal variable should be based on the nature of the relationship between the variables of interest. If the relationship is linear in the untransformed scale, transformation can introduce bias irrespective of the transformation used. However, if the relationship is non-linear, it may be important to transform the variable to accurately capture this relationship. A useful alternative is to impute the variable using PMM with type 1 matching. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Multiple Criteria Decision Analysis for Health Care Decision Making--An Introduction: Report 1 of the ISPOR MCDA Emerging Good Practices Task Force.

PubMed

Thokala, Praveen; Devlin, Nancy; Marsh, Kevin; Baltussen, Rob; Boysen, Meindert; Kalo, Zoltan; Longrenn, Thomas; Mussen, Filip; Peacock, Stuart; Watkins, John; Ijzerman, Maarten

2016-01-01

Health care decisions are complex and involve confronting trade-offs between multiple, often conflicting, objectives. Using structured, explicit approaches to decisions involving multiple criteria can improve the quality of decision making and a set of techniques, known under the collective heading multiple criteria decision analysis (MCDA), are useful for this purpose. MCDA methods are widely used in other sectors, and recently there has been an increase in health care applications. In 2014, ISPOR established an MCDA Emerging Good Practices Task Force. It was charged with establishing a common definition for MCDA in health care decision making and developing good practice guidelines for conducting MCDA to aid health care decision making. This initial ISPOR MCDA task force report provides an introduction to MCDA - it defines MCDA; provides examples of its use in different kinds of decision making in health care (including benefit risk analysis, health technology assessment, resource allocation, portfolio decision analysis, shared patient clinician decision making and prioritizing patients' access to services); provides an overview of the principal methods of MCDA; and describes the key steps involved. Upon reviewing this report, readers should have a solid overview of MCDA methods and their potential for supporting health care decision making. Copyright © 2016. Published by Elsevier Inc.

Space Transportation Operations: Assessment of Methodologies and Models

NASA Technical Reports Server (NTRS)

Joglekar, Prafulla

2001-01-01

The systems design process for future space transportation involves understanding multiple variables and their effect on lifecycle metrics. Variables such as technology readiness or potential environmental impact are qualitative, while variables such as reliability, operations costs or flight rates are quantitative. In deciding what new design concepts to fund, NASA needs a methodology that would assess the sum total of all relevant qualitative and quantitative lifecycle metrics resulting from each proposed concept. The objective of this research was to review the state of operations assessment methodologies and models used to evaluate proposed space transportation systems and to develop recommendations for improving them. It was found that, compared to the models available from other sources, the operations assessment methodology recently developed at Kennedy Space Center has the potential to produce a decision support tool that will serve as the industry standard. Towards that goal, a number of areas of improvement in the Kennedy Space Center's methodology are identified.

Space Transportation Operations: Assessment of Methodologies and Models

NASA Technical Reports Server (NTRS)

Joglekar, Prafulla

2002-01-01

The systems design process for future space transportation involves understanding multiple variables and their effect on lifecycle metrics. Variables such as technology readiness or potential environmental impact are qualitative, while variables such as reliability, operations costs or flight rates are quantitative. In deciding what new design concepts to fund, NASA needs a methodology that would assess the sum total of all relevant qualitative and quantitative lifecycle metrics resulting from each proposed concept. The objective of this research was to review the state of operations assessment methodologies and models used to evaluate proposed space transportation systems and to develop recommendations for improving them. It was found that, compared to the models available from other sources, the operations assessment methodology recently developed at Kennedy Space Center has the potential to produce a decision support tool that will serve as the industry standard. Towards that goal, a number of areas of improvement in the Kennedy Space Center's methodology are identified.

How to Assess the Value of Medicines?

PubMed Central

Simoens, Steven

2010-01-01

This study aims to discuss approaches to assessing the value of medicines. Economic evaluation assesses value by means of the incremental cost-effectiveness ratio (ICER). Health is maximized by selecting medicines with increasing ICERs until the budget is exhausted. The budget size determines the value of the threshold ICER and vice versa. Alternatively, the threshold value can be inferred from pricing/reimbursement decisions, although such values vary between countries. Threshold values derived from the value-of-life literature depend on the technique used. The World Health Organization has proposed a threshold value tied to the national GDP. As decision makers may wish to consider multiple criteria, variable threshold values and weighted ICERs have been suggested. Other approaches (i.e., replacement approach, program budgeting and marginal analysis) have focused on improving resource allocation, rather than maximizing health subject to a budget constraint. Alternatively, the generalized optimization framework and multi-criteria decision analysis make it possible to consider other criteria in addition to value. PMID:21607066

How to assess the value of medicines?

PubMed

Simoens, Steven

2010-01-01

This study aims to discuss approaches to assessing the value of medicines. Economic evaluation assesses value by means of the incremental cost-effectiveness ratio (ICER). Health is maximized by selecting medicines with increasing ICERs until the budget is exhausted. The budget size determines the value of the threshold ICER and vice versa. Alternatively, the threshold value can be inferred from pricing/reimbursement decisions, although such values vary between countries. Threshold values derived from the value-of-life literature depend on the technique used. The World Health Organization has proposed a threshold value tied to the national GDP. As decision makers may wish to consider multiple criteria, variable threshold values and weighted ICERs have been suggested. Other approaches (i.e., replacement approach, program budgeting and marginal analysis) have focused on improving resource allocation, rather than maximizing health subject to a budget constraint. Alternatively, the generalized optimization framework and multi-criteria decision analysis make it possible to consider other criteria in addition to value.

Parietal neurons encode expected gains in instrumental information

PubMed Central

Foley, Nicholas C.; Kelly, Simon P.; Mhatre, Himanshu; Gottlieb, Jacqueline

2017-01-01

In natural behavior, animals have access to multiple sources of information, but only a few of these sources are relevant for learning and actions. Beyond choosing an appropriate action, making good decisions entails the ability to choose the relevant information, but fundamental questions remain about the brain’s information sampling policies. Recent studies described the neural correlates of seeking information about a reward, but it remains unknown whether, and how, neurons encode choices of instrumental information, in contexts in which the information guides subsequent actions. Here we show that parietal cortical neurons involved in oculomotor decisions encode, before an information sampling saccade, the reduction in uncertainty that the saccade is expected to bring for a subsequent action. These responses were distinct from the neurons’ visual and saccadic modulations and from signals of expected reward or reward prediction errors. Therefore, even in an instrumental context when information and reward gains are closely correlated, individual cells encode decision variables that are based on informational factors and can guide the active sampling of action-relevant cues. PMID:28373569

Capturing the temporal evolution of choice across prefrontal cortex

PubMed Central

Hunt, Laurence T; Behrens, Timothy EJ; Hosokawa, Takayuki; Wallis, Jonathan D; Kennerley, Steven W

2015-01-01

Activity in prefrontal cortex (PFC) has been richly described using economic models of choice. Yet such descriptions fail to capture the dynamics of decision formation. Describing dynamic neural processes has proven challenging due to the problem of indexing the internal state of PFC and its trial-by-trial variation. Using primate neurophysiology and human magnetoencephalography, we here recover a single-trial index of PFC internal states from multiple simultaneously recorded PFC subregions. This index can explain the origins of neural representations of economic variables in PFC. It describes the relationship between neural dynamics and behaviour in both human and monkey PFC, directly bridging between human neuroimaging data and underlying neuronal activity. Moreover, it reveals a functionally dissociable interaction between orbitofrontal cortex, anterior cingulate cortex and dorsolateral PFC in guiding cost-benefit decisions. We cast our observations in terms of a recurrent neural network model of choice, providing formal links to mechanistic dynamical accounts of decision-making. DOI: http://dx.doi.org/10.7554/eLife.11945.001 PMID:26653139

Success in School for Justice-Involved Girls: Do Specific Aspects of Developmental Immaturity Matter?

PubMed Central

Haney-Caron, Emily; Goldstein, Naomi E. S.; Giallella, Christy L.; Kemp, Kathleen; Romaine, Christina Riggs

2016-01-01

Developmental immaturity (DI) may help explain some of the variability in aspects of academic achievement among girls in the juvenile justice system, a population with high rates of truancy, dropout, and school failure. This study examined the relationships among the decision making and independent functioning components of DI, verbal intelligence, and academic achievement within this population. Using data from 60 girls in residential juvenile justice facilities, multiple regression analyses indicated that verbal IQ moderated the relationship between the DI construct of decision making and academic achievement. Self-reported school attendance and number of previous arrests did not significantly mediate the relationship between DI and academic achievement. These results may indicate that the decision-making factor of DI may be particularly important, and, if results are replicated, future intervention efforts could focus more on improving this skill within this juvenile justice population. Additionally, the overall importance of the full DI construct is an important area of future study. PMID:28082833

Optimal Solutions of Multiproduct Batch Chemical Process Using Multiobjective Genetic Algorithm with Expert Decision System

PubMed Central

Mokeddem, Diab; Khellaf, Abdelhafid

2009-01-01

Optimal design problem are widely known by their multiple performance measures that are often competing with each other. In this paper, an optimal multiproduct batch chemical plant design is presented. The design is firstly formulated as a multiobjective optimization problem, to be solved using the well suited non dominating sorting genetic algorithm (NSGA-II). The NSGA-II have capability to achieve fine tuning of variables in determining a set of non dominating solutions distributed along the Pareto front in a single run of the algorithm. The NSGA-II ability to identify a set of optimal solutions provides the decision-maker DM with a complete picture of the optimal solution space to gain better and appropriate choices. Then an outranking with PROMETHEE II helps the decision-maker to finalize the selection of a best compromise. The effectiveness of NSGA-II method with multiojective optimization problem is illustrated through two carefully referenced examples. PMID:19543537

The Moderating Role of Power Distance on the Relationship between Employee Participation and Outcome Variables.

PubMed

Rafiei, Sima; Pourreza, Abolghasem

2013-06-01

Many organisations have realised the importance of human resource for their competitive advantage. Empowering employees is therefore essential for organisational effectiveness. This study aimed to investigate the relationship between employee participation with outcome variables such as organisational commitment, job satisfaction, perception of justice in an organisation and readiness to accept job responsibilities. It further examined the impact of power distance on the relationship between participation and four outcome variables. This was a cross sectional study with a descriptive research design conducted among employees and managers of hospitals affiliated with Tehran University of Medical Sciences, Tehran, Iran. A questionnaire as a main procedure to gather data was developed, distributed and collected. Descriptive statistics, Pearson correlation coefficient and moderated multiple regression were used to analyse the study data. Findings of the study showed that the level of power distance perceived by employees had a significant relationship with employee participation, organisational commitment, job satisfaction, perception of justice and readiness to accept job responsibilities. There was also a significant relationship between employee participation and four outcome variables. The moderated multiple regression results supported the hypothesis that power distance had a significant effect on the relationship between employee participation and four outcome variables. Organisations in which employee empowerment is practiced through diverse means such as participating them in decision making related to their field of work, appear to have more committed and satisfied employees with positive perception toward justice in the organisational interactions and readiness to accept job responsibilities.

Experiencing new forms of genetic choice: findings from an ethnographic study of preimplantation genetic diagnosis.

PubMed

Roberts, Celia; Franklin, Sarah

2004-12-01

Contemporary scientific and clinical knowledges and practices continue to make available new forms of genetic information, and to create new forms of reproductive choice. For example, couples at high risk of passing on a serious genetic condition to their offspring in Britain today have the opportunity to use Preimplantation Genetic Diagnosis (PGD) to select embryos that are unaffected by serious genetic disease. This information assists these couples in making reproductive choices. This article presents an analysis of patients' experiences of making the decision to undertake PGD treatment and of making reproductive choices based on genetic information. We present qualitative interview data from an ethnographic study of PGD based in two British clinics which indicate how these new forms of genetic choice are experienced by patients. Our data suggest that PGD patients make decisions about treatment in a complex way, taking multiple variables into account, and maintaining ongoing assessments of the multiple costs of engaging with PGD. Patients are aware of broader implications of their decisions, at personal, familial, and societal levels, as well as clinical ones. Based on these findings we argue that the ethical and social aspects of PGD are often as innovative as the scientific and medical aspects of this technique, and that in this sense, science cannot be described as "racing ahead" of society.

Multiple stakeholders in multi-criteria decision-making in the context of Municipal Solid Waste Management: A review.

PubMed

Soltani, Atousa; Hewage, Kasun; Reza, Bahareh; Sadiq, Rehan

2015-01-01

Municipal Solid Waste Management (MSWM) is a complicated process that involves multiple environmental and socio-economic criteria. Decision-makers look for decision support frameworks that can guide in defining alternatives, relevant criteria and their weights, and finding a suitable solution. In addition, decision-making in MSWM problems such as finding proper waste treatment locations or strategies often requires multiple stakeholders such as government, municipalities, industries, experts, and/or general public to get involved. Multi-criteria Decision Analysis (MCDA) is the most popular framework employed in previous studies on MSWM; MCDA methods help multiple stakeholders evaluate the often conflicting criteria, communicate their different preferences, and rank or prioritize MSWM strategies to finally agree on some elements of these strategies and make an applicable decision. This paper reviews and brings together research on the application of MCDA for solving MSWM problems with more focus on the studies that have considered multiple stakeholders and offers solutions for such problems. Results of this study show that AHP is the most common approach in consideration of multiple stakeholders and experts and governments/municipalities are the most common participants in these studies. Copyright © 2014 Elsevier Ltd. All rights reserved.

Distinct Roles of Dopamine and Subthalamic Nucleus in Learning and Probabilistic Decision Making

ERIC Educational Resources Information Center

Coulthard, Elizabeth J.; Bogacz, Rafal; Javed, Shazia; Mooney, Lucy K.; Murphy, Gillian; Keeley, Sophie; Whone, Alan L.

2012-01-01

Even simple behaviour requires us to make decisions based on combining multiple pieces of learned and new information. Making such decisions requires both learning the optimal response to each given stimulus as well as combining probabilistic information from multiple stimuli before selecting a response. Computational theories of decision making…

Exploration of how women make treatment decisions after a breast cancer diagnosis.

PubMed

Spittler, Cheryl A; Pallikathayil, Leonie; Bott, Marjorie

2012-09-01

To examine the information needs of women after receiving a diagnosis of breast cancer, investigate how decisions about treatment options are made, and assess personal responses to the decisions made. Mixed-methods approach using quantitative and qualitative data. The University of Kansas Medical Center and Quinn Plastic Surgery Center, both in the midwestern United States. 102 breast cancer survivors who had completed all forms of treatment for at least three months and less than five years. Phase I participants completed five questionnaires about informational needs, confidence and satisfaction with the decision, decisional regret, and conflict. In phase II, 15 participants were purposively sampled from the 102 survivors to participate in a focus group session. Data analysis included frequencies and multiple regression for phase I and qualitative content analysis for phase II. Informational needs, confidence and satisfaction with the decision, and decisional regret and conflict. The variables (widowed, confidence and satisfaction with decision, and decisional conflict and regret) significantly (p = 0.01) accounted for 14% of the variance in informational needs. Two themes emerged from the study: (a) feelings, thoughts, and essential factors that impact treatment considerations, and (b) tips for enhancing treatment consideration options. The study's results show that women viewed informational needs as very important in making treatment decisions after being diagnosed with breast cancer. The treatment team should provide the information, with consideration of the patient's personal preferences, that will assist women to make informed, confident, and satisfied decisions about treatment choices.

Nurse-led immunotreatment DEcision Coaching In people with Multiple Sclerosis (DECIMS) - Feasibility testing, pilot randomised controlled trial and mixed methods process evaluation.

PubMed

Rahn, A C; Köpke, S; Backhus, I; Kasper, J; Anger, K; Untiedt, B; Alegiani, A; Kleiter, I; Mühlhauser, I; Heesen, C

2018-02-01

Treatment decision-making is complex for people with multiple sclerosis. Profound information on available options is virtually not possible in regular neurologist encounters. The "nurse decision coach model" was developed to redistribute health professionals' tasks in supporting immunotreatment decision-making following the principles of informed shared decision-making. To test the feasibility of a decision coaching programme and recruitment strategies to inform the main trial. Feasibility testing and parallel pilot randomised controlled trial, accompanied by a mixed methods process evaluation. Two German multiple sclerosis university centres. People with suspected or relapsing-remitting multiple sclerosis facing immunotreatment decisions on first line drugs were recruited. Randomisation to the intervention (n = 38) or control group (n = 35) was performed on a daily basis. Quantitative and qualitative process data were collected from people with multiple sclerosis, nurses and physicians. We report on the development and piloting of the decision coaching programme. It comprises a training course for multiple sclerosis nurses and the coaching intervention. The intervention consists of up to three structured nurse-led decision coaching sessions, access to an evidence-based online information platform (DECIMS-Wiki) and a final physician consultation. After feasibility testing, a pilot randomised controlled trial was performed. People with multiple sclerosis were randomised to the intervention or control group. The latter had also access to the DECIMS-Wiki, but received otherwise care as usual. Nurses were not blinded to group assignment, while people with multiple sclerosis and physicians were. The primary outcome was 'informed choice' after six months including the sub-dimensions' risk knowledge (after 14 days), attitude concerning immunotreatment (after physician consultation), and treatment uptake (after six months). Quantitative process evaluation data were collected via questionnaires. Qualitative interviews were performed with all nurses and a convenience sample of nine people with multiple sclerosis. 116 people with multiple sclerosis fulfilled the inclusion criteria and 73 (63%) were included. Groups were comparable at baseline. Data of 51 people with multiple sclerosis (70%) were available for the primary endpoint. In the intervention group 15 of 31 (48%) people with multiple sclerosis achieved an informed choice after six months and 6 of 20 (30%) in the control group. Process evaluation data illustrated a positive response towards the coaching programme as well as good acceptance. The pilot-phase showed promising results concerning acceptability and feasibility of the intervention, which was well perceived by people with multiple sclerosis, most nurses and physicians. Delegating parts of the immunotreatment decision-making process to trained nurses has the potential to increase informed choice and participation as well as effectiveness of patient-physician consultations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Configural frequency analysis as a method of determining patients' preferred decision-making roles in dialysis.

PubMed

Loeffert, Sabine; Ommen, Oliver; Kuch, Christine; Scheibler, Fueloep; Woehrmann, Andrej; Baldamus, Conrad; Pfaff, Holger

2010-09-11

Numerous studies examined factors in promoting a patient preference for active participation in treatment decision making with only modest success. The purpose of this study was to identify types of patients wishing to participate in treatment decisions as well as those wishing to play a completely active or passive role based on a Germany-wide survey of dialysis patients; using a prediction typal analysis method that defines types as configurations of categories belonging to different attributes and takes particularly higher order interactions between variables into account. After randomly splitting the original patient sample into two halves, an exploratory prediction configural frequency analysis (CFA) was performed on one-half of the sample (n = 1969) and the identified types were considered as hypotheses for an inferential prediction CFA for the second half (n = 1914). 144 possible prediction types were tested by using five predictor variables and control preferences as criterion. An α-adjustment (0.05) for multiple testing was performed by the Holm procedure. 21 possible prediction types were identified as hypotheses in the exploratory prediction CFA; four patient types were confirmed in the confirmatory prediction CFA: patients preferring a passive role show low information seeking preference, above average trust in their physician, perceive their physician's participatory decision-making (PDM)-style positive, have a lower educational level, and are 56-75 years old (Type 1; p < 0.001) or > 76 years old (Type 2; p < 0.001). Patients preferring an active role show high information seeking preference, a higher educational level, and are < 55 years old. They have either below average trust, perceive the PDM-style negative (Type 3; p < 0.001) or above average trust and perceive the PDM-style positive (Type 4; p < 0.001). The method prediction configural frequency analysis was newly introduced to the research field of patient participation and could demonstrate how a particular control preference role is determined by an association of five variables.

Determinants of residential mobility decisions: the role of government services in relation to other factors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Varady, D.P.

This article is one of the first to test for the relative importance of concerns about public services in affecting residential mobility decisions over and beyond normal mobility factors. A secondary aim is to test for the validity of a residential mobility model formulated by Speare and associates. Multiple regression analysis was employed using 1974 to 1977 data from the longitudinal version of the Annual Housing Survey. Concerns about public services did not play a meaningful role in the analysis. This implies that efforts to hold middle-income residents in declining neighborhoods, through improved services, will not succeed. The results supportedmore » the Speare mobility model; housing satisfaction acted as an intermediary variable between background characteristics and mobility behavior. 30 references, 4 figures, 5 tables.« less

Aircrew coordination and decisionmaking: Peer ratings of video tapes made during a full mission simulation

NASA Technical Reports Server (NTRS)

Murphy, M. R.; Awe, C. A.

1986-01-01

Six professionally active, retired captains rated the coordination and decisionmaking performances of sixteen aircrews while viewing videotapes of a simulated commercial air transport operation. The scenario featured a required diversion and a probable minimum fuel situation. Seven point Likert-type scales were used in rating variables on the basis of a model of crew coordination and decisionmaking. The variables were based on concepts of, for example, decision difficulty, efficiency, and outcome quality; and leader-subordin ate concepts such as person and task-oriented leader behavior, and competency motivation of subordinate crewmembers. Five-front-end variables of the model were in turn dependent variables for a hierarchical regression procedure. The variance in safety performance was explained 46%, by decision efficiency, command reversal, and decision quality. The variance of decision quality, an alternative substantive dependent variable to safety performance, was explained 60% by decision efficiency and the captain's quality of within-crew communications. The variance of decision efficiency, crew coordination, and command reversal were in turn explained 78%, 80%, and 60% by small numbers of preceding independent variables. A principle component, varimax factor analysis supported the model structure suggested by regression analyses.

Multi-criteria clinical decision support: A primer on the use of multiple criteria decision making methods to promote evidence-based, patient-centered healthcare.

PubMed

Dolan, James G

2010-01-01

Current models of healthcare quality recommend that patient management decisions be evidence-based and patient-centered. Evidence-based decisions require a thorough understanding of current information regarding the natural history of disease and the anticipated outcomes of different management options. Patient-centered decisions incorporate patient preferences, values, and unique personal circumstances into the decision making process and actively involve both patients along with health care providers as much as possible. Fundamentally, therefore, evidence-based, patient-centered decisions are multi-dimensional and typically involve multiple decision makers.Advances in the decision sciences have led to the development of a number of multiple criteria decision making methods. These multi-criteria methods are designed to help people make better choices when faced with complex decisions involving several dimensions. They are especially helpful when there is a need to combine "hard data" with subjective preferences, to make trade-offs between desired outcomes, and to involve multiple decision makers. Evidence-based, patient-centered clinical decision making has all of these characteristics. This close match suggests that clinical decision support systems based on multi-criteria decision making techniques have the potential to enable patients and providers to carry out the tasks required to implement evidence-based, patient-centered care effectively and efficiently in clinical settings.The goal of this paper is to give readers a general introduction to the range of multi-criteria methods available and show how they could be used to support clinical decision-making. Methods discussed include the balance sheet, the even swap method, ordinal ranking methods, direct weighting methods, multi-attribute decision analysis, and the analytic hierarchy process (AHP).

Multi-criteria clinical decision support: A primer on the use of multiple criteria decision making methods to promote evidence-based, patient-centered healthcare

PubMed Central

Dolan, James G.

2010-01-01

Current models of healthcare quality recommend that patient management decisions be evidence-based and patient-centered. Evidence-based decisions require a thorough understanding of current information regarding the natural history of disease and the anticipated outcomes of different management options. Patient-centered decisions incorporate patient preferences, values, and unique personal circumstances into the decision making process and actively involve both patients along with health care providers as much as possible. Fundamentally, therefore, evidence-based, patient-centered decisions are multi-dimensional and typically involve multiple decision makers. Advances in the decision sciences have led to the development of a number of multiple criteria decision making methods. These multi-criteria methods are designed to help people make better choices when faced with complex decisions involving several dimensions. They are especially helpful when there is a need to combine “hard data” with subjective preferences, to make trade-offs between desired outcomes, and to involve multiple decision makers. Evidence-based, patient-centered clinical decision making has all of these characteristics. This close match suggests that clinical decision support systems based on multi-criteria decision making techniques have the potential to enable patients and providers to carry out the tasks required to implement evidence-based, patient-centered care effectively and efficiently in clinical settings. The goal of this paper is to give readers a general introduction to the range of multi-criteria methods available and show how they could be used to support clinical decision-making. Methods discussed include the balance sheet, the even swap method, ordinal ranking methods, direct weighting methods, multi-attribute decision analysis, and the analytic hierarchy process (AHP) PMID:21394218

Development and Validation of a Questionnaire to Detect Behavior Change in Multiple Advance Care Planning Behaviors

PubMed Central

Sudore, Rebecca L.; Stewart, Anita L.; Knight, Sara J.; McMahan, Ryan D.; Feuz, Mariko; Miao, Yinghui; Barnes, Deborah E.

2013-01-01

Introduction Advance directives have traditionally been considered the gold standard for advance care planning. However, recent evidence suggests that advance care planning involves a series of multiple discrete behaviors for which people are in varying stages of behavior change. The goal of our study was to develop and validate a survey to measure the full advance care planning process. Methods The Advance Care Planning Engagement Survey assesses “Process Measures” of factors known from Behavior Change Theory to affect behavior (knowledge, contemplation, self-efficacy, and readiness, using 5-point Likert scales) and “Action Measures” (yes/no) of multiple behaviors related to surrogate decision makers, values and quality of life, flexibility for surrogate decision making, and informed decision making. We administered surveys at baseline and 1 week later to 50 diverse, older adults from San Francisco hospitals. Internal consistency reliability of Process Measures was assessed using Cronbach's alpha (only continuous variables) and test-retest reliability of Process and Action Measures was examined using intraclass correlations. For discriminant validity, we compared Process and Action Measure scores between this cohort and 20 healthy college students (mean age 23.2 years, SD 2.7). Results Mean age was 69.3 (SD 10.5) and 42% were non-White. The survey took a mean of 21.4 minutes (±6.2) to administer. The survey had good internal consistency (Process Measures Cronbach's alpha, 0.94) and test-retest reliability (Process Measures intraclass correlation, 0.70; Action Measures, 0.87). Both Process and Action Measure scores were higher in the older than younger group, p<.001. Conclusion A new Advance Care Planning Engagement Survey that measures behavior change (knowledge, contemplation, self-efficacy, and readiness) and multiple advance care planning actions demonstrates good reliability and validity. Further research is needed to assess whether survey scores improve in response to advance care planning interventions and whether scores are associated with receipt of care consistent with one's wishes. PMID:24039772

An enhanced export coefficient based optimization model for supporting agricultural nonpoint source pollution mitigation under uncertainty.

PubMed

Rong, Qiangqiang; Cai, Yanpeng; Chen, Bing; Yue, Wencong; Yin, Xin'an; Tan, Qian

2017-02-15

In this research, an export coefficient based dual inexact two-stage stochastic credibility constrained programming (ECDITSCCP) model was developed through integrating an improved export coefficient model (ECM), interval linear programming (ILP), fuzzy credibility constrained programming (FCCP) and a fuzzy expected value equation within a general two stage programming (TSP) framework. The proposed ECDITSCCP model can effectively address multiple uncertainties expressed as random variables, fuzzy numbers, pure and dual intervals. Also, the model can provide a direct linkage between pre-regulated management policies and the associated economic implications. Moreover, the solutions under multiple credibility levels can be obtained for providing potential decision alternatives for decision makers. The proposed model was then applied to identify optimal land use structures for agricultural NPS pollution mitigation in a representative upstream subcatchment of the Miyun Reservoir watershed in north China. Optimal solutions of the model were successfully obtained, indicating desired land use patterns and nutrient discharge schemes to get a maximum agricultural system benefits under a limited discharge permit. Also, numerous results under multiple credibility levels could provide policy makers with several options, which could help get an appropriate balance between system benefits and pollution mitigation. The developed ECDITSCCP model can be effectively applied to addressing the uncertain information in agricultural systems and shows great applicability to the land use adjustment for agricultural NPS pollution mitigation. Copyright © 2016 Elsevier B.V. All rights reserved.

Interpretable Decision Sets: A Joint Framework for Description and Prediction

PubMed Central

Lakkaraju, Himabindu; Bach, Stephen H.; Jure, Leskovec

2016-01-01

One of the most important obstacles to deploying predictive models is the fact that humans do not understand and trust them. Knowing which variables are important in a model’s prediction and how they are combined can be very powerful in helping people understand and trust automatic decision making systems. Here we propose interpretable decision sets, a framework for building predictive models that are highly accurate, yet also highly interpretable. Decision sets are sets of independent if-then rules. Because each rule can be applied independently, decision sets are simple, concise, and easily interpretable. We formalize decision set learning through an objective function that simultaneously optimizes accuracy and interpretability of the rules. In particular, our approach learns short, accurate, and non-overlapping rules that cover the whole feature space and pay attention to small but important classes. Moreover, we prove that our objective is a non-monotone submodular function, which we efficiently optimize to find a near-optimal set of rules. Experiments show that interpretable decision sets are as accurate at classification as state-of-the-art machine learning techniques. They are also three times smaller on average than rule-based models learned by other methods. Finally, results of a user study show that people are able to answer multiple-choice questions about the decision boundaries of interpretable decision sets and write descriptions of classes based on them faster and more accurately than with other rule-based models that were designed for interpretability. Overall, our framework provides a new approach to interpretable machine learning that balances accuracy, interpretability, and computational efficiency. PMID:27853627

Association of a traditional vs shared meal decision-making and preparation style with eating behavior of Hispanic women in San Diego County.

PubMed

Arredondo, Elva M; Elder, John P; Ayala, Guadalupe X; Slymen, Donald; Campbell, Nadia R

2006-01-01

To examine the influence of meal decision-making and preparation on Hispanic women's dietary practices. One-on-one structured interviews were conducted, assessing meal decision-making and preparation practices, barriers, and behavioral strategies to eating low-fat and high-fiber diets, fat and fiber intake, demographic, and other psychosocial factors. The study population included 357 Hispanic women living in the southern or central regions of San Diego County. Participants were recruited via random-digit dialing to a tailored nutrition communication intervention. Household decision-making style (alone vs with family) by household activity (decides meals, prepares meals, and decides snacks). Multiple logistic regressions were used to evaluate associations between the predictors and dependent variable. All models included adjustments for potential confounders, such as marital status, education, employment, age, and acculturation. A positive statistical association between Hispanic women's acculturation level and shared decision-making style was found. Also, Hispanic women in shared decision-making households faced greater psychosocial barriers to healthful eating and reported less healthful eating compared with Hispanic women in traditional households. Women in shared decision-making households were more likely to eat at fast-food restaurants, less likely to engage in behavioral strategies promoting fiber consumption, eat more saturated fat, and encounter more barriers to reduce dietary fat as compared with Hispanic women in traditional households. Acculturation did not attenuate differences in psychosocial and dietary practices between shared decision-making and traditional households. Study findings suggest intervention efforts should focus on different aspects of healthful eating among Hispanic women in shared-decision, compared with traditional, households.

Relationship of Solar Energy Installation Permits to Renewable Portfolio Standards and Insolation

NASA Astrophysics Data System (ADS)

Butler, Kirt Gordon

Legislated renewable portfolio standards (RPSs) may not be the key to ensure forecast energy demands are met. States without a legislated RPS and with efficient permitting procedures were found to have approved and issued 28.57% more permits on average than those with a legislated RPS. Assessment models to make informed decisions about the need and effect of legislated RPSs do not exist. Decision makers and policy creators need to use empirical data and a viable model to resolve the debate over a nationally legislated RPS. The purpose of this cross-sectional study was to determine if relationships between the independent variables of RPS and insolation levels and the dependent variable of the percentage of permits approved would prove to be a viable model. The research population was 68 cities in the United States, of which 55 were used in this study. The return on investment economic decision model provided the theoretical framework for this study and the model generated. The output of multiple regression analysis indicated a weak to medium positive relationship among the variables. None of these relationships were statistically significant at the 0.05 level. A model using site specific data might yield significant results and be useful for determining which solar energy projects to pursue and where to implement them without Federal or State mandated RPSs. A viable model would bring about efficiency gains in the permitting process and effectiveness gains in promoting installations of solar energy-based systems. Research leading to the development of a viable model would benefit society by encouraging the development of sustainable energy sources and helping to meet forecast energy demands.

Multiple stakeholders in multi-criteria decision-making in the context of Municipal Solid Waste Management: A review

DOE Office of Scientific and Technical Information (OSTI.GOV)

Soltani, Atousa; Hewage, Kasun; Reza, Bahareh

2015-01-15

Highlights: • We review Municipal Solid Waste Management studies with focus on multiple stakeholders. • We focus on studies with multi-criteria decision analysis methods and discover their trends. • Most studies do not offer solutions for situations where stakeholders compete for more benefits or have unequal voting powers. • Governments and experts are the most participated stakeholders and AHP is the most dominant method. - Abstract: Municipal Solid Waste Management (MSWM) is a complicated process that involves multiple environmental and socio-economic criteria. Decision-makers look for decision support frameworks that can guide in defining alternatives, relevant criteria and their weights, andmore » finding a suitable solution. In addition, decision-making in MSWM problems such as finding proper waste treatment locations or strategies often requires multiple stakeholders such as government, municipalities, industries, experts, and/or general public to get involved. Multi-criteria Decision Analysis (MCDA) is the most popular framework employed in previous studies on MSWM; MCDA methods help multiple stakeholders evaluate the often conflicting criteria, communicate their different preferences, and rank or prioritize MSWM strategies to finally agree on some elements of these strategies and make an applicable decision. This paper reviews and brings together research on the application of MCDA for solving MSWM problems with more focus on the studies that have considered multiple stakeholders and offers solutions for such problems. Results of this study show that AHP is the most common approach in consideration of multiple stakeholders and experts and governments/municipalities are the most common participants in these studies.« less

A priori discretization quality metrics for distributed hydrologic modeling applications

NASA Astrophysics Data System (ADS)

Liu, Hongli; Tolson, Bryan; Craig, James; Shafii, Mahyar; Basu, Nandita

2016-04-01

In distributed hydrologic modelling, a watershed is treated as a set of small homogeneous units that address the spatial heterogeneity of the watershed being simulated. The ability of models to reproduce observed spatial patterns firstly depends on the spatial discretization, which is the process of defining homogeneous units in the form of grid cells, subwatersheds, or hydrologic response units etc. It is common for hydrologic modelling studies to simply adopt a nominal or default discretization strategy without formally assessing alternative discretization levels. This approach lacks formal justifications and is thus problematic. More formalized discretization strategies are either a priori or a posteriori with respect to building and running a hydrologic simulation model. A posteriori approaches tend to be ad-hoc and compare model calibration and/or validation performance under various watershed discretizations. The construction and calibration of multiple versions of a distributed model can become a seriously limiting computational burden. Current a priori approaches are more formalized and compare overall heterogeneity statistics of dominant variables between candidate discretization schemes and input data or reference zones. While a priori approaches are efficient and do not require running a hydrologic model, they do not fully investigate the internal spatial pattern changes of variables of interest. Furthermore, the existing a priori approaches focus on landscape and soil data and do not assess impacts of discretization on stream channel definition even though its significance has been noted by numerous studies. The primary goals of this study are to (1) introduce new a priori discretization quality metrics considering the spatial pattern changes of model input data; (2) introduce a two-step discretization decision-making approach to compress extreme errors and meet user-specified discretization expectations through non-uniform discretization threshold modification. The metrics for the first time provides quantification of the routing relevant information loss due to discretization according to the relationship between in-channel routing length and flow velocity. Moreover, it identifies and counts the spatial pattern changes of dominant hydrological variables by overlaying candidate discretization schemes upon input data and accumulating variable changes in area-weighted way. The metrics are straightforward and applicable to any semi-distributed or fully distributed hydrological model with grid scales are greater than input data resolutions. The discretization metrics and decision-making approach are applied to the Grand River watershed located in southwestern Ontario, Canada where discretization decisions are required for a semi-distributed modelling application. Results show that discretization induced information loss monotonically increases as discretization gets rougher. With regards to routing information loss in subbasin discretization, multiple interesting points rather than just the watershed outlet should be considered. Moreover, subbasin and HRU discretization decisions should not be considered independently since subbasin input significantly influences the complexity of HRU discretization result. Finally, results show that the common and convenient approach of making uniform discretization decisions across the watershed domain performs worse compared to a metric informed non-uniform discretization approach as the later since is able to conserve more watershed heterogeneity under the same model complexity (number of computational units).

Response threshold variance as a basis of collective rationality

PubMed Central

Yamamoto, Tatsuhiro

2017-01-01

Determining the optimal choice among multiple options is necessary in various situations, and the collective rationality of groups has recently become a major topic of interest. Social insects are thought to make such optimal choices by collecting individuals' responses relating to an option's value (=a quality-graded response). However, this behaviour cannot explain the collective rationality of brains because neurons can make only ‘yes/no’ responses on the basis of the response threshold. Here, we elucidate the basic mechanism underlying the collective rationality of such simple units and show that an ant species uses this mechanism. A larger number of units respond ‘yes’ to the best option available to a collective decision-maker using only the yes/no mechanism; thus, the best option is always selected by majority decision. Colonies of the ant Myrmica kotokui preferred the better option in a binary choice experiment. The preference of a colony was demonstrated by the workers, which exhibited variable thresholds between two options' qualities. Our results demonstrate how a collective decision-maker comprising simple yes/no judgement units achieves collective rationality without using quality-graded responses. This mechanism has broad applicability to collective decision-making in brain neurons, swarm robotics and human societies. PMID:28484636

Response threshold variance as a basis of collective rationality.

PubMed

Yamamoto, Tatsuhiro; Hasegawa, Eisuke

2017-04-01

Determining the optimal choice among multiple options is necessary in various situations, and the collective rationality of groups has recently become a major topic of interest. Social insects are thought to make such optimal choices by collecting individuals' responses relating to an option's value (=a quality-graded response). However, this behaviour cannot explain the collective rationality of brains because neurons can make only 'yes/no' responses on the basis of the response threshold. Here, we elucidate the basic mechanism underlying the collective rationality of such simple units and show that an ant species uses this mechanism. A larger number of units respond 'yes' to the best option available to a collective decision-maker using only the yes/no mechanism; thus, the best option is always selected by majority decision. Colonies of the ant Myrmica kotokui preferred the better option in a binary choice experiment. The preference of a colony was demonstrated by the workers, which exhibited variable thresholds between two options' qualities. Our results demonstrate how a collective decision-maker comprising simple yes/no judgement units achieves collective rationality without using quality-graded responses. This mechanism has broad applicability to collective decision-making in brain neurons, swarm robotics and human societies.

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Data to decisions.

PubMed

White, B J; Amrine, D E; Larson, R L

2018-04-14

Big data are frequently used in many facets of business and agronomy to enhance knowledge needed to improve operational decisions. Livestock operations collect data of sufficient quantity to perform predictive analytics. Predictive analytics can be defined as a methodology and suite of data evaluation techniques to generate a prediction for specific target outcomes. The objective of this manuscript is to describe the process of using big data and the predictive analytic framework to create tools to drive decisions in livestock production, health, and welfare. The predictive analytic process involves selecting a target variable, managing the data, partitioning the data, then creating algorithms, refining algorithms, and finally comparing accuracy of the created classifiers. The partitioning of the datasets allows model building and refining to occur prior to testing the predictive accuracy of the model with naive data to evaluate overall accuracy. Many different classification algorithms are available for predictive use and testing multiple algorithms can lead to optimal results. Application of a systematic process for predictive analytics using data that is currently collected or that could be collected on livestock operations will facilitate precision animal management through enhanced livestock operational decisions.

Negative and positive urgency may both be risk factors for compulsive buying

PubMed Central

ROSE, PAUL; SEGRIST, DANIEL J.

2014-01-01

Background and aims: Descriptions of compulsive buying often emphasize the roles of negative moods and trait impulsivity in the development of problematic buying habits. Trait impulsivity is sometimes treated as a unidimensional trait in compulsive buying research, but recent factor analyses suggest that impulsivity consists of multiple components that are probably best treated as independent predictors of problem behavior. In order to draw greater attention to the role of positive moods in compulsive buying, in this study we tested whether negative urgency (the tendency to act rashly while in negative moods) and positive urgency (the tendency to act rashly while in positive moods) account for similar amounts of variance in compulsive buying. Methods: North American adults (N = 514) completed an online survey containing the Richmond Compulsive Buying Scale (Ridgway, Kukar-Kinney & Monroe, 2008), established measures of positive and negative urgency (Cyders et al., 2007), ad hoc measures of buying-specific positive and negative urgency, measures of extraversion and neuroticism obtained from the International Personality Item Pool (http://ipip.ori.org/), and demographic questions. Results: In several multiple regression analyses, when demographic variables, neuroticism, and extraversion were controlled, positive urgency and negative urgency both emerged as significant predictors of compulsive buying. Whether the two urgency variables were domain-general or buying-specific, they accounted for similar amounts of variance in compulsive buying. Conclusions: Preventing and reducing compulsive buying may require attention not only to the purchasing decisions people make while in negative states, but also to the purchasing decisions they make while in positive states. PMID:25215224

[Analyzing the impact of decisions in the scope of long term care by fuzzy cognitive maps, Spain].

PubMed

Gutiérrez Moya, Ester; González Camacho, M Carmen; Salmerón Silvera, Jose Luis

2012-12-01

System for Autonomy and Care for Dependency (Spanish acronym SAAD) was created to provide a framework for the protection of dependent people. The priority established by law on benefits in kind over cash benefits, together with the efficient management of public resources provided economic returns for the SAAD, such as employment generation. The variables that influence the implementation of the SAAD are extremely complex and dynamic, and there are multiple relationships between them. The aim of this paper is to analyze the problem of satisfying a growing demand for protection, at minimum cost, and reaps the economic returns using fuzzy logic (fuzzy cognitive map). This technique is designed as a tool for decision-making in this area, to analyze the evolution of causal variables to a state of equilibrium. To do this, we have developed 4 scenarios (E1: Ageing, E2: Ageing and benefits in kind, E3: Ageing and cash benefits, E4: Ageing and cash benefit for care in the family), to analyze the evolution of variables, especially public expenditure and employment. Among the main results are: ageing is critical for the increased spending in all scenarios, but only in E1 and E2 is generated employment, residential care is not altered, even in E2; Telecare increases in all scenarios, and the cash benefit for personal attendant increases in E1 and E2.

Complex Dynamics in a Triopoly Game with Multiple Delays in the Competition of Green Product Level

NASA Astrophysics Data System (ADS)

Si, Fengshan; Ma, Junhai

Research on the output game behavior of oligopoly has greatly advanced in recent years. But many unknowns remain, particularly the influence of consumers’ willingness to buy green products on the oligopoly output game. This paper constructs a triopoly output game model with multiple delays in the competition of green products. The influence of the parameters on the stability and complexity of the system is studied by analyzing the existence and local asymptotic stability of the equilibrium point. It is found that the system loses stability and increases complexity if delay parameters exceed a certain range. In the unstable or chaotic game market, the decisions of oligopoly will be counterproductive. It is also observed that the influence of weight and output adjustment speed on the firm itself is obviously stronger than the influence of other firms. In addition, it is important that weight and output adjustment speed cannot increase indefinitely, otherwise it will bring unnecessary losses to the firm. Finally, chaos control is realized by using the variable feedback control method. The research results of this paper can provide a reference for decision-making for the output of the game of oligopoly.

Reactivation of Reward-Related Patterns from Single Past Episodes Supports Memory-Based Decision Making.

PubMed

Wimmer, G Elliott; Büchel, Christian

2016-03-09

Rewarding experiences exert a strong influence on later decision making. While decades of neuroscience research have shown how reinforcement gradually shapes preferences, decisions are often influenced by single past experiences. Surprisingly, relatively little is known about the influence of single learning episodes. Although recent work has proposed a role for episodes in decision making, it is largely unknown whether and how episodic experiences contribute to value-based decision making and how the values of single episodes are represented in the brain. In multiple behavioral experiments and an fMRI experiment, we tested whether and how rewarding episodes could support later decision making. Participants experienced episodes of high reward or low reward in conjunction with incidental, trial-unique neutral pictures. In a surprise test phase, we found that participants could indeed remember the associated level of reward, as evidenced by accurate source memory for value and preferences to re-engage with rewarded objects. Further, in a separate experiment, we found that high-reward objects shown as primes before a gambling task increased financial risk taking. Neurally, re-exposure to objects in the test phase led to significant reactivation of reward-related patterns. Importantly, individual variability in the strength of reactivation predicted value memory performance. Our results provide a novel demonstration that affect-related neural patterns are reactivated during later experience. Reactivation of value information represents a mechanism by which memory can guide decision making. Copyright © 2016 the authors 0270-6474/16/362868-13$15.00/0.

Optimal crop selection and water allocation under limited water supply in irrigation

NASA Astrophysics Data System (ADS)

Stange, Peter; Grießbach, Ulrike; Schütze, Niels

2015-04-01

Due to climate change, extreme weather conditions such as droughts may have an increasing impact on irrigated agriculture. To cope with limited water resources in irrigation systems, a new decision support framework is developed which focuses on an integrated management of both irrigation water supply and demand at the same time. For modeling the regional water demand, local (and site-specific) water demand functions are used which are derived from optimized agronomic response on farms scale. To account for climate variability the agronomic response is represented by stochastic crop water production functions (SCWPF). These functions take into account different soil types, crops and stochastically generated climate scenarios. The SCWPF's are used to compute the water demand considering different conditions, e.g., variable and fixed costs. This generic approach enables the consideration of both multiple crops at farm scale as well as of the aggregated response to water pricing at a regional scale for full and deficit irrigation systems. Within the SAPHIR (SAxonian Platform for High Performance IRrigation) project a prototype of a decision support system is developed which helps to evaluate combined water supply and demand management policies.

Risk management for sulfur dioxide abatement under multiple uncertainties

NASA Astrophysics Data System (ADS)

Dai, C.; Sun, W.; Tan, Q.; Liu, Y.; Lu, W. T.; Guo, H. C.

2016-03-01

In this study, interval-parameter programming, two-stage stochastic programming (TSP), and conditional value-at-risk (CVaR) were incorporated into a general optimization framework, leading to an interval-parameter CVaR-based two-stage programming (ICTP) method. The ICTP method had several advantages: (i) its objective function simultaneously took expected cost and risk cost into consideration, and also used discrete random variables and discrete intervals to reflect uncertain properties; (ii) it quantitatively evaluated the right tail of distributions of random variables which could better calculate the risk of violated environmental standards; (iii) it was useful for helping decision makers to analyze the trade-offs between cost and risk; and (iv) it was effective to penalize the second-stage costs, as well as to capture the notion of risk in stochastic programming. The developed model was applied to sulfur dioxide abatement in an air quality management system. The results indicated that the ICTP method could be used for generating a series of air quality management schemes under different risk-aversion levels, for identifying desired air quality management strategies for decision makers, and for considering a proper balance between system economy and environmental quality.

Pattern extraction for high-risk accidents in the construction industry: a data-mining approach.

PubMed

Amiri, Mehran; Ardeshir, Abdollah; Fazel Zarandi, Mohammad Hossein; Soltanaghaei, Elahe

2016-09-01

Accidents involving falls and falling objects (group I) are highly frequent accidents in the construction industry. While being hit by a vehicle, electric shock, collapse in the excavation and fire or explosion accidents (group II) are much less frequent, they make up a considerable proportion of severe accidents. In this study, multiple-correspondence analysis, decision tree, ensembles of decision tree and association rules methods are employed to analyse a database of construction accidents throughout Iran between 2007 and 2011. The findings indicate that in group I, there is a significant correspondence among these variables: time of accident, place of accident, body part affected, final consequence of accident and lost workdays. Moreover, the frequency of accidents in the night shift is less than others, and the frequency of injury to the head, back, spine and limbs are more. In group II, the variables time of accident and body part affected are mostly related and the frequency of accidents among married and older workers is more than single and young workers. There was a higher frequency in the evening, night shifts and weekends. The results of this study are totally in line with the previous research.

Evaluating the Use of Existing Data Sources, Probabilistic Linkage, and Multiple Imputation to Build Population-based Injury Databases Across Phases of Trauma Care

PubMed Central

Newgard, Craig; Malveau, Susan; Staudenmayer, Kristan; Wang, N. Ewen; Hsia, Renee Y.; Mann, N. Clay; Holmes, James F.; Kuppermann, Nathan; Haukoos, Jason S.; Bulger, Eileen M.; Dai, Mengtao; Cook, Lawrence J.

2012-01-01

Objectives The objective was to evaluate the process of using existing data sources, probabilistic linkage, and multiple imputation to create large population-based injury databases matched to outcomes. Methods This was a retrospective cohort study of injured children and adults transported by 94 emergency medical systems (EMS) agencies to 122 hospitals in seven regions of the western United States over a 36-month period (2006 to 2008). All injured patients evaluated by EMS personnel within specific geographic catchment areas were included, regardless of field disposition or outcome. The authors performed probabilistic linkage of EMS records to four hospital and postdischarge data sources (emergency department [ED] data, patient discharge data, trauma registries, and vital statistics files) and then handled missing values using multiple imputation. The authors compare and evaluate matched records, match rates (proportion of matches among eligible patients), and injury outcomes within and across sites. Results There were 381,719 injured patients evaluated by EMS personnel in the seven regions. Among transported patients, match rates ranged from 14.9% to 87.5% and were directly affected by the availability of hospital data sources and proportion of missing values for key linkage variables. For vital statistics records (1-year mortality), estimated match rates ranged from 88.0% to 98.7%. Use of multiple imputation (compared to complete case analysis) reduced bias for injury outcomes, although sample size, percentage missing, type of variable, and combined-site versus single-site imputation models all affected the resulting estimates and variance. Conclusions This project demonstrates the feasibility and describes the process of constructing population-based injury databases across multiple phases of care using existing data sources and commonly available analytic methods. Attention to key linkage variables and decisions for handling missing values can be used to increase match rates between data sources, minimize bias, and preserve sampling design. PMID:22506952

Reliability of Classifying Multiple Sclerosis Disease Activity Using Magnetic Resonance Imaging in a Multiple Sclerosis Clinic

PubMed Central

Altay, Ebru Erbayat; Fisher, Elizabeth; Jones, Stephen E.; Hara-Cleaver, Claire; Lee, Jar-Chi; Rudick, Richard A.

2013-01-01

Objective To assess the reliability of new magnetic resonance imaging (MRI) lesion counts by clinicians in a multiple sclerosis specialty clinic. Design An observational study. Setting A multiple sclerosis specialty clinic. Patients Eighty-five patients with multiple sclerosis participating in a National Institutes of Health–supported longitudinal study were included. Intervention Each patient had a brain MRI scan at entry and 6 months later using a standardized protocol. Main Outcome Measures The number of new T2 lesions, newly enlarging T2 lesions, and gadolinium-enhancing lesions were measured on the 6-month MRI using a computer-based image analysis program for the original study. For this study, images were reanalyzed by an expert neuroradiologist and 3 clinician raters. The neuroradiologist evaluated the original image pairs; the clinicians evaluated image pairs that were modified to simulate clinical practice. New lesion counts were compared across raters, as was classification of patients as MRI active or inactive. Results Agreement on lesion counts was highest for gadolinium-enhancing lesions, intermediate for new T2 lesions, and poor for enlarging T2 lesions. In 18% to 25% of the cases, MRI activity was classified differently by the clinician raters compared with the neuroradiologist or computer program. Variability among the clinical raters for estimates of new T2 lesions was affected most strongly by the image modifications that simulated low image quality and different head position. Conclusions Between-rater variability in new T2 lesion counts may be reduced by improved standardization of image acquisitions, but this approach may not be practical in most clinical environments. Ultimately, more reliable, robust, and accessible image analysis methods are needed for accurate multiple sclerosis disease-modifying drug monitoring and decision making in the routine clinic setting. PMID:23599930

Randomness in the network inhibits cooperation based on the bounded rational collective altruistic decision

NASA Astrophysics Data System (ADS)

Ohdaira, Tetsushi

2014-07-01

Previous studies discussing cooperation employ the best decision that every player knows all information regarding the payoff matrix and selects the strategy of the highest payoff. Therefore, they do not discuss cooperation based on the altruistic decision with limited information (bounded rational altruistic decision). In addition, they do not cover the case where every player can submit his/her strategy several times in a match of the game. This paper is based on Ohdaira's reconsideration of the bounded rational altruistic decision, and also employs the framework of the prisoner's dilemma game (PDG) with sequential strategy. The distinction between this study and the Ohdaira's reconsideration is that the former covers the model of multiple groups, but the latter deals with the model of only two groups. Ohdaira's reconsideration shows that the bounded rational altruistic decision facilitates much more cooperation in the PDG with sequential strategy than Ohdaira and Terano's bounded rational second-best decision does. However, the detail of cooperation of multiple groups based on the bounded rational altruistic decision has not been resolved yet. This study, therefore, shows how randomness in the network composed of multiple groups affects the increase of the average frequency of mutual cooperation (cooperation between groups) based on the bounded rational altruistic decision of multiple groups. We also discuss the results of the model in comparison with related studies which employ the best decision.

Administrative Decision Making and Quasi Decision Making: An Empirical Study Using the Protocol Method.

ERIC Educational Resources Information Center

Fraser, Hugh W.; Anderson, Mary E.

1982-01-01

This exploratory study attempted to identify variables in need of further investigation. Those to emerge included heuristics or rules of thumb used by administrators in decision making, personality variables, and methods for evaluating alternatives. (Author/JM)

The Moderating Role of Power Distance on the Relationship between Employee Participation and Outcome Variables

PubMed Central

Rafiei, Sima; Pourreza, Abolghasem

2013-01-01

Background: Many organisations have realised the importance of human resource for their competitive advantage. Empowering employees is therefore essential for organisational effectiveness. This study aimed to investigate the relationship between employee participation with outcome variables such as organisational commitment, job satisfaction, perception of justice in an organisation and readiness to accept job responsibilities. It further examined the impact of power distance on the relationship between participation and four outcome variables. Methods: This was a cross sectional study with a descriptive research design conducted among employees and managers of hospitals affiliated with Tehran University of Medical Sciences, Tehran, Iran. A questionnaire as a main procedure to gather data was developed, distributed and collected. Descriptive statistics, Pearson correlation coefficient and moderated multiple regression were used to analyse the study data. Results: Findings of the study showed that the level of power distance perceived by employees had a significant relationship with employee participation, organisational commitment, job satisfaction, perception of justice and readiness to accept job responsibilities. There was also a significant relationship between employee participation and four outcome variables. The moderated multiple regression results supported the hypothesis that power distance had a significant effect on the relationship between employee participation and four outcome variables. Conclusion: Organisations in which employee empowerment is practiced through diverse means such as participating them in decision making related to their field of work, appear to have more committed and satisfied employees with positive perception toward justice in the organisational interactions and readiness to accept job responsibilities. PMID:24596840

Modeling pedestrian gap crossing index under mixed traffic condition.

PubMed

Naser, Mohamed M; Zulkiple, Adnan; Al Bargi, Walid A; Khalifa, Nasradeen A; Daniel, Basil David

2017-12-01

There are a variety of challenges faced by pedestrians when they walk along and attempt to cross a road, as the most recorded accidents occur during this time. Pedestrians of all types, including both sexes with numerous aging groups, are always subjected to risk and are characterized as the most exposed road users. The increased demand for better traffic management strategies to reduce the risks at intersections, improve quality traffic management, traffic volume, and longer cycle time has further increased concerns over the past decade. This paper aims to develop a sustainable pedestrian gap crossing index model based on traffic flow density. It focusses on the gaps accepted by pedestrians and their decision for street crossing, where (Log-Gap) logarithm of accepted gaps was used to optimize the result of a model for gap crossing behavior. Through a review of extant literature, 15 influential variables were extracted for further empirical analysis. Subsequently, data from the observation at an uncontrolled mid-block in Jalan Ampang in Kuala Lumpur, Malaysia was gathered and Multiple Linear Regression (MLR) and Binary Logit Model (BLM) techniques were employed to analyze the results. From the results, different pedestrian behavioral characteristics were considered for a minimum gap size model, out of which only a few (four) variables could explain the pedestrian road crossing behavior while the remaining variables have an insignificant effect. Among the different variables, age, rolling gap, vehicle type, and crossing were the most influential variables. The study concludes that pedestrians' decision to cross the street depends on the pedestrian age, rolling gap, vehicle type, and size of traffic gap before crossing. The inferences from these models will be useful to increase pedestrian safety and performance evaluation of uncontrolled midblock road crossings in developing countries. Copyright © 2017 National Safety Council and Elsevier Ltd. All rights reserved.

Bayesian LASSO, scale space and decision making in association genetics.

PubMed

Pasanen, Leena; Holmström, Lasse; Sillanpää, Mikko J

2015-01-01

LASSO is a penalized regression method that facilitates model fitting in situations where there are as many, or even more explanatory variables than observations, and only a few variables are relevant in explaining the data. We focus on the Bayesian version of LASSO and consider four problems that need special attention: (i) controlling false positives, (ii) multiple comparisons, (iii) collinearity among explanatory variables, and (iv) the choice of the tuning parameter that controls the amount of shrinkage and the sparsity of the estimates. The particular application considered is association genetics, where LASSO regression can be used to find links between chromosome locations and phenotypic traits in a biological organism. However, the proposed techniques are relevant also in other contexts where LASSO is used for variable selection. We separate the true associations from false positives using the posterior distribution of the effects (regression coefficients) provided by Bayesian LASSO. We propose to solve the multiple comparisons problem by using simultaneous inference based on the joint posterior distribution of the effects. Bayesian LASSO also tends to distribute an effect among collinear variables, making detection of an association difficult. We propose to solve this problem by considering not only individual effects but also their functionals (i.e. sums and differences). Finally, whereas in Bayesian LASSO the tuning parameter is often regarded as a random variable, we adopt a scale space view and consider a whole range of fixed tuning parameters, instead. The effect estimates and the associated inference are considered for all tuning parameters in the selected range and the results are visualized with color maps that provide useful insights into data and the association problem considered. The methods are illustrated using two sets of artificial data and one real data set, all representing typical settings in association genetics.

Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems.

PubMed

Ortiz-Bayliss, José Carlos; Amaya, Ivan; Conant-Pablos, Santiago Enrique; Terashima-Marín, Hugo

2018-01-01

When solving constraint satisfaction problems (CSPs), it is a common practice to rely on heuristics to decide which variable should be instantiated at each stage of the search. But, this ordering influences the search cost. Even so, and to the best of our knowledge, no earlier work has dealt with how first variable orderings affect the overall cost. In this paper, we explore the cost of finding high-quality orderings of variables within constraint satisfaction problems. We also study differences among the orderings produced by some commonly used heuristics and the way bad first decisions affect the search cost. One of the most important findings of this work confirms the paramount importance of first decisions. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. We propose a simple method to improve early decisions of heuristics. By using it, performance of heuristics increases.

Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems

PubMed Central

Amaya, Ivan

2018-01-01

When solving constraint satisfaction problems (CSPs), it is a common practice to rely on heuristics to decide which variable should be instantiated at each stage of the search. But, this ordering influences the search cost. Even so, and to the best of our knowledge, no earlier work has dealt with how first variable orderings affect the overall cost. In this paper, we explore the cost of finding high-quality orderings of variables within constraint satisfaction problems. We also study differences among the orderings produced by some commonly used heuristics and the way bad first decisions affect the search cost. One of the most important findings of this work confirms the paramount importance of first decisions. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. We propose a simple method to improve early decisions of heuristics. By using it, performance of heuristics increases. PMID:29681923

Interactive and Participatory Decision Support: Linking Cyberinfrastructure, Multi-Touch Interfaces, and Substantive Dialogue for Geothermal Systems

NASA Astrophysics Data System (ADS)

Malin, R.; Pierce, S. A.; Bass, B. J.

2012-12-01

Socio-technical approaches to complex, ill-structured decision problems are needed to identify adaptive responses for earth resource management. This research presents a hybrid approach to create decision tools and engender dialogue among stakeholders for geothermal development in Idaho, United States and El Tatio, Chile. Based on the scarcity of data, limited information availability, and tensions across stakeholder interests we designed and constructed a decision support model that allows stakeholders to rapidly collect, input, and visualize geoscientific data to assess geothermal system impacts and possible development strategies. We have integrated this decision support model into multi-touch interfaces that can be easily used by scientists and stakeholders alike. This toolkit is part of a larger cyberinfrastructure project designed to collect and present geoscientific information to support decision making processes. Consultation with stakeholders at the El Tatio geothermal complex of northern Chile—indigenous communities, local and national government agencies, developers, and geoscientists - informed the implementation of a sustained dialogue process. The El Tatio field case juxtaposes basic parameters such as pH, spring temperature, geochemical content, and FLIR imagery with stakeholder perceptions of risks due to mineral extraction and energy exploration efforts. The results of interviews and a participatory workshop are driving the creation of three initiatives within an indigenous community group; 1) microentrepreneurial efforts for science-based tourism, 2) design of a citizen-led environmental monitoring network in the Altiplano, and 3) business planning for an indigenous renewable energy cooperative. This toolkit is also being applied in the Snake River Plain of Idaho has as part of the DOE sponsored National Student Geothermal Competition. The Idaho case extends results from the Chilean case to implement a more streamlined system to analyze geothermal resource potential as well as integrate the decision support system with multi-touch interfaces which allow multiple stakeholders to view and interact with data. Beyond visual and tactile appeal, these interfaces also allow participants to dynamically update decision variables and decision preferences to create multiple scenarios and evaluate potential outcomes. Through this interactive scenario building, potential development sites can be targeted and stakeholders can interact with data to engage in substantive dialogue for related long-term planning or crisis response.

Climate variability and demand growth as drivers of water scarcity in the Turkwel river basin: a bottom-up risk assessment of a data-sparse basin in Kenya

NASA Astrophysics Data System (ADS)

Hirpa, F. A.; Dyer, E.; Hope, R.; Dadson, S. J.

2017-12-01

Sustainable water management and allocation are essential for maintaining human well-being, sustaining healthy ecosystems, and supporting steady economic growth. The Turkwel river basin, located in north-western Kenya, experiences a high level of water scarcity due to its arid climate, high rainfall variability, and rapidly growing water demand. However, due to sparse hydro-climatic data and limited literature, the water resources system of the basin has been poorly understood. Here we apply a bottom-up climate risk assessment method to estimate the resilience of the basin's water resources system to growing demand and climate stressors. First, using a water resource system model and historical climate data, we construct a climate risk map that depicts the way in which the system responds to climate change and variability. Then we develop a set of water demand scenarios to identify the conditions that potentially lead to the risk of unmet water demand and groundwater depletion. Finally, we investigate the impact of climate change and variability by stress testing these development scenarios against historically strong El Niño/Southern Oscillation (ENSO) years and future climate projections from multiple Global Circulation Models (GCMs). The results reveal that climate variability and increased water demand are the main drivers of water scarcity in the basin. Our findings show that increases in water demand due to expanded irrigation and population growth exert the strongest influence on the ability of the system to meet water resource supply requirements, and in all cases considered increase the impacts of droughts caused by future climate variability. Our analysis illustrates the importance of combining analysis of future climate risks with other development decisions that affect water resources planning. Policy and investment decisions which maximise water use efficiency in the present day are likely to impart resilience to climate change and variability under a wide range of future scenarios and therefore constitute low regret measures for climate adaptation.

Altered functional MR imaging language activation in elderly individuals with cerebral leukoaraiosis.

PubMed

Welker, Kirk M; De Jesus, Reordan O; Watson, Robert E; Machulda, Mary M; Jack, Clifford R

2012-10-01

To test the hypothesis that leukoaraiosis alters functional activation during a semantic decision language task. With institutional review board approval and written informed consent, 18 right-handed, cognitively healthy elderly participants with an aggregate leukoaraiosis lesion volume of more than 25 cm(3) and 18 age-matched control participants with less than 5 cm(3) of leukoaraiosis underwent functional MR imaging to allow comparison of activation during semantic decisions with that during visual perceptual decisions. Brain statistical maps were derived from the general linear model. Spatially normalized group t maps were created from individual contrast images. A cluster extent threshold of 215 voxels was used to correct for multiple comparisons. Intergroup random effects analysis was performed. Language laterality indexes were calculated for each participant. In control participants, semantic decisions activated the bilateral visual cortex, left posteroinferior temporal lobe, left posterior cingulate gyrus, left frontal lobe expressive language regions, and left basal ganglia. Visual perceptual decisions activated the right parietal and posterior temporal lobes. Participants with leukoaraiosis showed reduced activation in all regions associated with semantic decisions; however, activation associated with visual perceptual decisions increased in extent. Intergroup analysis showed significant activation decreases in the left anterior occipital lobe (P=.016), right posterior temporal lobe (P=.048), and right basal ganglia (P=.009) in particpants with leukoariosis. Individual participant laterality indexes showed a strong trend (P=.059) toward greater left lateralization in the leukoaraiosis group. Moderate leukoaraiosis is associated with atypical functional activation during semantic decision tasks. Consequently, leukoaraiosis is an important confounding variable in functional MR imaging studies of elderly individuals. © RSNA, 2012.

A Biologically Plausible Computational Theory for Value Integration and Action Selection in Decisions with Competing Alternatives

PubMed Central

Christopoulos, Vassilios; Bonaiuto, James; Andersen, Richard A.

2015-01-01

Decision making is a vital component of human and animal behavior that involves selecting between alternative options and generating actions to implement the choices. Although decisions can be as simple as choosing a goal and then pursuing it, humans and animals usually have to make decisions in dynamic environments where the value and the availability of an option change unpredictably with time and previous actions. A predator chasing multiple prey exemplifies how goals can dynamically change and compete during ongoing actions. Classical psychological theories posit that decision making takes place within frontal areas and is a separate process from perception and action. However, recent findings argue for additional mechanisms and suggest the decisions between actions often emerge through a continuous competition within the same brain regions that plan and guide action execution. According to these findings, the sensorimotor system generates concurrent action-plans for competing goals and uses online information to bias the competition until a single goal is pursued. This information is diverse, relating to both the dynamic value of the goal and the cost of acting, creating a challenging problem in integrating information across these diverse variables in real time. We introduce a computational framework for dynamically integrating value information from disparate sources in decision tasks with competing actions. We evaluated the framework in a series of oculomotor and reaching decision tasks and found that it captures many features of choice/motor behavior, as well as its neural underpinnings that previously have eluded a common explanation. PMID:25803729

The Attitudes of First Year Senior Secondary School Students toward Their Science Classes in the Sudan

NASA Astrophysics Data System (ADS)

Lado, Longun Moses

This study examined the influence of a set of relevant independent variables on students' decision to major in math or science disciplines, on the one hand, or arts or humanities disciplines, on the other. The independent variables of interest in the study were students' attitudes toward science, their gender, their socioeconomic status, their age, and the strength and direction of parents' and peers' influences on their academic decisions. The study answered five research questions that concerned students' intention in math or science, the association between students' attitudes and their choice to major in math or science, the extent to which parents' and peers' perspectives influence students' choice of major, and the influence of a combination of relevant variables on students' choice of major. The scholarly context for the study was literature relating to students' attitudes toward science and math, their likelihood of taking courses or majoring in science or math and various conditions influencing their attitudes and actions with respect to enrollment in science or math disciplines. This literature suggested that students' experiences, their gender, parents' and peers' influence, their socio-economic status, teachers' treatment of them, school curricula, school culture, and other variables may influence students' attitudes toward science and math and their decision regarding the study of these subjects. The study used a questionnaire comprised of 28 items to elicit information from students. Based upon cluster sampling of secondary schools, the researcher surveyed 1000 students from 10 secondary schools and received 987 responses. The researcher used SPSS to analyze students' responses. Descriptive statistics, logistic regression, and multiple regression analyses to provide findings that address the study's research questions. The following are the major findings from the study: (1) The instrument used to measure students' attitudes toward science and mathematics was not highly reliable, perhaps contributing to an attenuation of the relationship between attitude toward science and mathematics and choice of a science or mathematics major (rather than an arts or humanities major). (2) Far more students than the researcher had anticipated provided responses indicating that they planned to major in a science or mathematics discipline rather than an arts or humanities discipline. (3) Students' attitudes towards math and science were more favorable than the researcher anticipated based on findings from previous related studies. This result suggests the possibility of social desirability bias in students' responses. (4) Three significant predicator variables contributed to a significant logistic regression equation in which choice of science or mathematics major was the dependent variable: gender (negative association), attitude toward science and math (positive association), and peer influence 1 (positive association). Gender was the strongest predictor. (5) Five significant predictor variables contributed to a significant multiple linear regression equation in which attitude toward science and mathematics was the dependent variable: peer influence 1 (positive association), parent influence 1 (positive association), parent influence 2 (positive association), books in home (positive association), and peer influence 2 (positive association). The results reveal that among the targeted variables (gender, attitude, peer influence 1, peer influence 2, parent influence 1, parent influence 2, books in home, and age) only gender, peer influence 1, and attitude were significant predictors of students' major in math or science.

Two approaches to incorporate clinical data uncertainty into multiple criteria decision analysis for benefit-risk assessment of medicinal products.

PubMed

Wen, Shihua; Zhang, Lanju; Yang, Bo

2014-07-01

The Problem formulation, Objectives, Alternatives, Consequences, Trade-offs, Uncertainties, Risk attitude, and Linked decisions (PrOACT-URL) framework and multiple criteria decision analysis (MCDA) have been recommended by the European Medicines Agency for structured benefit-risk assessment of medicinal products undergoing regulatory review. The objective of this article was to provide solutions to incorporate the uncertainty from clinical data into the MCDA model when evaluating the overall benefit-risk profiles among different treatment options. Two statistical approaches, the δ-method approach and the Monte-Carlo approach, were proposed to construct the confidence interval of the overall benefit-risk score from the MCDA model as well as other probabilistic measures for comparing the benefit-risk profiles between treatment options. Both approaches can incorporate the correlation structure between clinical parameters (criteria) in the MCDA model and are straightforward to implement. The two proposed approaches were applied to a case study to evaluate the benefit-risk profile of an add-on therapy for rheumatoid arthritis (drug X) relative to placebo. It demonstrated a straightforward way to quantify the impact of the uncertainty from clinical data to the benefit-risk assessment and enabled statistical inference on evaluating the overall benefit-risk profiles among different treatment options. The δ-method approach provides a closed form to quantify the variability of the overall benefit-risk score in the MCDA model, whereas the Monte-Carlo approach is more computationally intensive but can yield its true sampling distribution for statistical inference. The obtained confidence intervals and other probabilistic measures from the two approaches enhance the benefit-risk decision making of medicinal products. Copyright © 2014 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

A Search for the tt¯H (H → bb) Large Hadron Collider with the atlas detector using a matrix element method

NASA Astrophysics Data System (ADS)

Basye, Austin T.

A matrix element method analysis of the Standard Model Higgs boson, produced in association with two top quarks decaying to the lepton-plus-jets channel is presented. Based on 20.3 fb--1 of s=8 TeV data, produced at the Large Hadron Collider and collected by the ATLAS detector, this analysis utilizes multiple advanced techniques to search for ttH signatures with a 125 GeV Higgs boson decaying to two b -quarks. After categorizing selected events based on their jet and b-tag multiplicities, signal rich regions are analyzed using the matrix element method. Resulting variables are then propagated to two parallel multivariate analyses utilizing Neural Networks and Boosted Decision Trees respectively. As no significant excess is found, an observed (expected) limit of 3.4 (2.2) times the Standard Model cross-section is determined at 95% confidence, using the CLs method, for the Neural Network analysis. For the Boosted Decision Tree analysis, an observed (expected) limit of 5.2 (2.7) times the Standard Model cross-section is determined at 95% confidence, using the CLs method. Corresponding unconstrained fits of the Higgs boson signal strength to the observed data result in the measured signal cross-section to Standard Model cross-section prediction of mu = 1.2 +/- 1.3(total) +/- 0.7(stat.) for the Neural Network analysis, and mu = 2.9 +/- 1.4(total) +/- 0.8(stat.) for the Boosted Decision Tree analysis.

Rigor of cell fate decision by variable p53 pulses and roles of cooperative gene expression by p53

PubMed Central

Murakami, Yohei; Takada, Shoji

2012-01-01

Upon DNA damage, the cell fate decision between survival and apoptosis is largely regulated by p53-related networks. Recent experiments found a series of discrete p53 pulses in individual cells, which led to the hypothesis that the cell fate decision upon DNA damage is controlled by counting the number of p53 pulses. Under this hypothesis, Sun et al. (2009) modeled the Bax activation switch in the apoptosis signal transduction pathway that can rigorously “count” the number of uniform p53 pulses. Based on experimental evidence, here we use variable p53 pulses with Sun et al.’s model to investigate how the variability in p53 pulses affects the rigor of the cell fate decision by the pulse number. Our calculations showed that the experimentally anticipated variability in the pulse sizes reduces the rigor of the cell fate decision. In addition, we tested the roles of the cooperativity in PUMA expression by p53, finding that lower cooperativity is plausible for more rigorous cell fate decision. This is because the variability in the p53 pulse height is more amplified in PUMA expressions with more cooperative cases. PMID:27857606

Robust optimization modelling with applications to industry and environmental problems

NASA Astrophysics Data System (ADS)

Chaerani, Diah; Dewanto, Stanley P.; Lesmana, Eman

2017-10-01

Robust Optimization (RO) modeling is one of the existing methodology for handling data uncertainty in optimization problem. The main challenge in this RO methodology is how and when we can reformulate the robust counterpart of uncertain problems as a computationally tractable optimization problem or at least approximate the robust counterpart by a tractable problem. Due to its definition the robust counterpart highly depends on how we choose the uncertainty set. As a consequence we can meet this challenge only if this set is chosen in a suitable way. The development on RO grows fast, since 2004, a new approach of RO called Adjustable Robust Optimization (ARO) is introduced to handle uncertain problems when the decision variables must be decided as a ”wait and see” decision variables. Different than the classic Robust Optimization (RO) that models decision variables as ”here and now”. In ARO, the uncertain problems can be considered as a multistage decision problem, thus decision variables involved are now become the wait and see decision variables. In this paper we present the applications of both RO and ARO. We present briefly all results to strengthen the importance of RO and ARO in many real life problems.

Standardized data collection to build prediction models in oncology: a prototype for rectal cancer.

PubMed

Meldolesi, Elisa; van Soest, Johan; Damiani, Andrea; Dekker, Andre; Alitto, Anna Rita; Campitelli, Maura; Dinapoli, Nicola; Gatta, Roberto; Gambacorta, Maria Antonietta; Lanzotti, Vito; Lambin, Philippe; Valentini, Vincenzo

2016-01-01

The advances in diagnostic and treatment technology are responsible for a remarkable transformation in the internal medicine concept with the establishment of a new idea of personalized medicine. Inter- and intra-patient tumor heterogeneity and the clinical outcome and/or treatment's toxicity's complexity, justify the effort to develop predictive models from decision support systems. However, the number of evaluated variables coming from multiple disciplines: oncology, computer science, bioinformatics, statistics, genomics, imaging, among others could be very large thus making traditional statistical analysis difficult to exploit. Automated data-mining processes and machine learning approaches can be a solution to organize the massive amount of data, trying to unravel important interaction. The purpose of this paper is to describe the strategy to collect and analyze data properly for decision support and introduce the concept of an 'umbrella protocol' within the framework of 'rapid learning healthcare'.

Fuzzy set methods for object recognition in space applications

NASA Technical Reports Server (NTRS)

Keller, James M.

1991-01-01

During the reporting period, the development of the theory and application of methodologies for decision making under uncertainty was addressed. Two subreports are included; the first on properties of general hybrid operators, while the second considers some new research on generalized threshold logic units. In the first part, the properties of the additive gamma-model, where the intersection part is first considered to be the product of the input values and the union part is obtained by an extension of De Morgan's law to fuzzy sets, is explored. Then the Yager's class of union and intersection is used in the additive gamma-model. The inputs are weighted to some power that represents their importance and thus their contribution to the compensation process. In the second part, the extension of binary logic synthesis methods to multiple valued logic synthesis methods to enable the synthesis of decision networks when the input/output variables are not binary is discussed.

[Not Available].

PubMed

Mokeddem, Diab; Khellaf, Abdelhafid

2009-01-01

Optimal design problem are widely known by their multiple performance measures that are often competing with each other. In this paper, an optimal multiproduct batch chemical plant design is presented. The design is firstly formulated as a multiobjective optimization problem, to be solved using the well suited non dominating sorting genetic algorithm (NSGA-II). The NSGA-II have capability to achieve fine tuning of variables in determining a set of non dominating solutions distributed along the Pareto front in a single run of the algorithm. The NSGA-II ability to identify a set of optimal solutions provides the decision-maker DM with a complete picture of the optimal solution space to gain better and appropriate choices. Then an outranking with PROMETHEE II helps the decision-maker to finalize the selection of a best compromise. The effectiveness of NSGA-II method with multiojective optimization problem is illustrated through two carefully referenced examples.

Identifying psychophysiological indices of expert vs. novice performance in deadly force judgment and decision making

PubMed Central

Johnson, Robin R.; Stone, Bradly T.; Miranda, Carrie M.; Vila, Bryan; James, Lois; James, Stephen M.; Rubio, Roberto F.; Berka, Chris

2014-01-01

Objective: To demonstrate that psychophysiology may have applications for objective assessment of expertise development in deadly force judgment and decision making (DFJDM). Background: Modern training techniques focus on improving decision-making skills with participative assessment between trainees and subject matter experts primarily through subjective observation. Objective metrics need to be developed. The current proof of concept study explored the potential for psychophysiological metrics in deadly force judgment contexts. Method: Twenty-four participants (novice, expert) were recruited. All wore a wireless Electroencephalography (EEG) device to collect psychophysiological data during high-fidelity simulated deadly force judgment and decision-making simulations using a modified Glock firearm. Participants were exposed to 27 video scenarios, one-third of which would have justified use of deadly force. Pass/fail was determined by whether the participant used deadly force appropriately. Results: Experts had a significantly higher pass rate compared to novices (p < 0.05). Multiple metrics were shown to distinguish novices from experts. Hierarchical regression analyses indicate that psychophysiological variables are able to explain 72% of the variability in expert performance, but only 37% in novices. Discriminant function analysis (DFA) using psychophysiological metrics was able to discern between experts and novices with 72.6% accuracy. Conclusion: While limited due to small sample size, the results suggest that psychophysiology may be developed for use as an objective measure of expertise in DFDJM. Specifically, discriminant function measures may have the potential to objectively identify expert skill acquisition. Application: Psychophysiological metrics may create a performance model with the potential to optimize simulator-based DFJDM training. These performance models could be used for trainee feedback, and/or by the instructor to assess performance objectively. PMID:25100966

Demand driven decision support for efficient water resources allocation in irrigated agriculture

NASA Astrophysics Data System (ADS)

Schuetze, Niels; Grießbach, Ulrike Ulrike; Röhm, Patric; Stange, Peter; Wagner, Michael; Seidel, Sabine; Werisch, Stefan; Barfus, Klemens

2014-05-01

Due to climate change, extreme weather conditions, such as longer dry spells in the summer months, may have an increasing impact on the agriculture in Saxony (Eastern Germany). For this reason, and, additionally, declining amounts of rainfall during the growing season the use of irrigation will be more important in future in Eastern Germany. To cope with this higher demand of water, a new decision support framework is developed which focuses on an integrated management of both irrigation water supply and demand. For modeling the regional water demand, local (and site-specific) water demand functions are used which are derived from the optimized agronomic response at farms scale. To account for climate variability the agronomic response is represented by stochastic crop water production functions (SCWPF) which provide the estimated yield subject to the minimum amount of irrigation water. These functions take into account the different soil types, crops and stochastically generated climate scenarios. By applying mathematical interpolation and optimization techniques, the SCWPF's are used to compute the water demand considering different constraints, for instance variable and fix costs or the producer price. This generic approach enables the computation for both multiple crops at farm scale as well as of the aggregated response to water pricing at a regional scale for full and deficit irrigation systems. Within the SAPHIR (SAxonian Platform for High Performance Irrigation) project a prototype of a decision support system is developed which helps to evaluate combined water supply and demand management policies for an effective and efficient utilization of water in order to meet future demands. The prototype is implemented as a web-based decision support system and it is based on a service-oriented geo-database architecture.

An evaluation of consensus techniques for diagnostic interpretation

NASA Astrophysics Data System (ADS)

Sauter, Jake N.; LaBarre, Victoria M.; Furst, Jacob D.; Raicu, Daniela S.

2018-02-01

Learning diagnostic labels from image content has been the standard in computer-aided diagnosis. Most computer-aided diagnosis systems use low-level image features extracted directly from image content to train and test machine learning classifiers for diagnostic label prediction. When the ground truth for the diagnostic labels is not available, reference truth is generated from the experts diagnostic interpretations of the image/region of interest. More specifically, when the label is uncertain, e.g. when multiple experts label an image and their interpretations are different, techniques to handle the label variability are necessary. In this paper, we compare three consensus techniques that are typically used to encode the variability in the experts labeling of the medical data: mean, median and mode, and their effects on simple classifiers that can handle deterministic labels (decision trees) and probabilistic vectors of labels (belief decision trees). Given that the NIH/NCI Lung Image Database Consortium (LIDC) data provides interpretations for lung nodules by up to four radiologists, we leverage the LIDC data to evaluate and compare these consensus approaches when creating computer-aided diagnosis systems for lung nodules. First, low-level image features of nodules are extracted and paired with their radiologists semantic ratings (1= most likely benign, , 5 = most likely malignant); second, machine learning multi-class classifiers that handle deterministic labels (decision trees) and probabilistic vectors of labels (belief decision trees) are built to predict the lung nodules semantic ratings. We show that the mean-based consensus generates the most robust classi- fier overall when compared to the median- and mode-based consensus. Lastly, the results of this study show that, when building CAD systems with uncertain diagnostic interpretation, it is important to evaluate different strategies for encoding and predicting the diagnostic label.

Multiple Criteria Decision Analysis for Health Care Decision Making--Emerging Good Practices: Report 2 of the ISPOR MCDA Emerging Good Practices Task Force.

PubMed

Marsh, Kevin; IJzerman, Maarten; Thokala, Praveen; Baltussen, Rob; Boysen, Meindert; Kaló, Zoltán; Lönngren, Thomas; Mussen, Filip; Peacock, Stuart; Watkins, John; Devlin, Nancy

2016-01-01

Health care decisions are complex and involve confronting trade-offs between multiple, often conflicting objectives. Using structured, explicit approaches to decisions involving multiple criteria can improve the quality of decision making. A set of techniques, known under the collective heading, multiple criteria decision analysis (MCDA), are useful for this purpose. In 2014, ISPOR established an Emerging Good Practices Task Force. The task force's first report defined MCDA, provided examples of its use in health care, described the key steps, and provided an overview of the principal methods of MCDA. This second task force report provides emerging good-practice guidance on the implementation of MCDA to support health care decisions. The report includes: a checklist to support the design, implementation and review of an MCDA; guidance to support the implementation of the checklist; the order in which the steps should be implemented; illustrates how to incorporate budget constraints into an MCDA; provides an overview of the skills and resources, including available software, required to implement MCDA; and future research directions. Copyright © 2016 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

The role of physician characteristics in clinical trial acceptance: testing pathways of influence.

PubMed

Curbow, Barbara; Fogarty, Linda A; McDonnell, Karen A; Chill, Julia; Scott, Lisa Benz

2006-03-01

Eight videotaped vignettes were developed that assessed the effects of three physician-related experimental variables (in a 2 x 2 x 2 factorial design) on clinical trial (CT) knowledge, video knowledge, information processing, CT beliefs, affective evaluations (attitudes), and CT acceptance. It was hypothesized that the physician variables (community versus academic-based affiliation, enthusiastic versus neutral presentation of the trial, and new versus previous relationship with the patient) would serve as communication cues that would interrupt message processing, leading to lower knowledge gain but more positive beliefs, attitudes, and CT acceptance. A total of 262 women (161 survivors and 101 controls) participated in the study. The manipulated variables primarily influenced the intermediary variables of post-test CT beliefs and satisfaction with information rather than knowledge or information processing. Multiple regression results indicated that CT acceptance was associated with positive post-CT beliefs, a lower level of information processing, satisfaction with information, and control status. Based on these results, CT acceptance does not appear to be based on a rational decision-making model; this has implications for both the ethics of informed consent and research conceptual models.

Assessing the chances of success: naïve statistics versus kind experience.

PubMed

Hogarth, Robin M; Mukherjee, Kanchan; Soyer, Emre

2013-01-01

Additive integration of information is ubiquitous in judgment and has been shown to be effective even when multiplicative rules of probability theory are prescribed. We explore the generality of these findings in the context of estimating probabilities of success in contests. We first define a normative model of these probabilities that takes account of relative skill levels in contests where only a limited number of entrants can win. We then report 4 experiments using a scenario about a competition. Experiments 1 and 2 both elicited judgments of probabilities, and, although participants' responses demonstrated considerable variability, their mean judgments provide a good fit to a simple linear model. Experiment 3 explored choices. Most participants entered most contests and showed little awareness of appropriate probabilities. Experiment 4 investigated effects of providing aids to calculate probabilities, specifically, access to expert advice and 2 simulation tools. With these aids, estimates were accurate and decisions varied appropriately with economic consequences. We discuss implications by considering when additive decision rules are dysfunctional, the interpretation of overconfidence based on contest-entry behavior, and the use of aids to help people make better decisions.

Influence of Science, Technology, and Engineering Curriculum on Rural Midwestern High School Student Career Decisions

NASA Astrophysics Data System (ADS)

Killingsworth, John

Low degree completion in technical and engineering degrees is a growing concern for policymakers and educators in the United States. This study was an examination of the behaviors of adolescents specific to career decisions related to technology and engineering. The central research question for this study was: do rural, Midwestern high school technical and engineering curricula serve to engage students sufficiently to encourage them to persist through high school while sustaining their interests in technology and engineering careers? Engaging students in technology and engineering fields is the challenge for educators throughout the country and the Midwest. Rural schools have the additional challenge of meeting those issues because of resource limitations. Students in three Midwestern schools were surveyed to determine the level of interest in technology and engineering. The generalized likelihood ratio test was used to overcome concerns for small sample sizes. Accounting for dependent variables, multiple independent variables are examined using descriptive statistics to determine which have greater influence on career decisions, specifically those related to technology and engineering. A typical science curriculum is defined for rural Midwestern high schools. This study concludes that such curriculum achieves the goal of maintaining or increasing student interest and engagement in STEM careers. Furthermore, those schools that incorporate contextual and experiential learning activities into the curriculum demonstrate increased results in influencing student career choices toward technology and engineering careers. Implications for parents, educators, and industry professionals are discussed.

Local search heuristic for the discrete leader-follower problem with multiple follower objectives

NASA Astrophysics Data System (ADS)

Kochetov, Yury; Alekseeva, Ekaterina; Mezmaz, Mohand

2016-10-01

We study a discrete bilevel problem, called as well as leader-follower problem, with multiple objectives at the lower level. It is assumed that constraints at the upper level can include variables of both levels. For such ill-posed problem we define feasible and optimal solutions for pessimistic case. A central point of this work is a two stage method to get a feasible solution under the pessimistic case, given a leader decision. The target of the first stage is a follower solution that violates the leader constraints. The target of the second stage is a pessimistic feasible solution. Each stage calls a heuristic and a solver for a series of particular mixed integer programs. The method is integrated inside a local search based heuristic that is designed to find near-optimal leader solutions.

Analysis of strength-of-preference measures in dichotomous choice models

Treesearch

Donald F. Dennis; Peter Newman; Robert Manning

2008-01-01

Choice models are becoming increasingly useful for soliciting and analyzing multiple objective decisions faced by recreation managers and others interested in decisions involving natural resources. Choice models are used to estimate relative values for multiple aspects of natural resource management, not individually but within the context of other relevant decision...

Large-Scale Transport Model Uncertainty and Sensitivity Analysis: Distributed Sources in Complex Hydrogeologic Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sig Drellack, Lance Prothro

2007-12-01

The Underground Test Area (UGTA) Project of the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office is in the process of assessing and developing regulatory decision options based on modeling predictions of contaminant transport from underground testing of nuclear weapons at the Nevada Test Site (NTS). The UGTA Project is attempting to develop an effective modeling strategy that addresses and quantifies multiple components of uncertainty including natural variability, parameter uncertainty, conceptual/model uncertainty, and decision uncertainty in translating model results into regulatory requirements. The modeling task presents multiple unique challenges to the hydrological sciences as a result ofmore » the complex fractured and faulted hydrostratigraphy, the distributed locations of sources, the suite of reactive and non-reactive radionuclides, and uncertainty in conceptual models. Characterization of the hydrogeologic system is difficult and expensive because of deep groundwater in the arid desert setting and the large spatial setting of the NTS. Therefore, conceptual model uncertainty is partially addressed through the development of multiple alternative conceptual models of the hydrostratigraphic framework and multiple alternative models of recharge and discharge. Uncertainty in boundary conditions is assessed through development of alternative groundwater fluxes through multiple simulations using the regional groundwater flow model. Calibration of alternative models to heads and measured or inferred fluxes has not proven to provide clear measures of model quality. Therefore, model screening by comparison to independently-derived natural geochemical mixing targets through cluster analysis has also been invoked to evaluate differences between alternative conceptual models. Advancing multiple alternative flow models, sensitivity of transport predictions to parameter uncertainty is assessed through Monte Carlo simulations. The simulations are challenged by the distributed sources in each of the Corrective Action Units, by complex mass transfer processes, and by the size and complexity of the field-scale flow models. An efficient methodology utilizing particle tracking results and convolution integrals provides in situ concentrations appropriate for Monte Carlo analysis. Uncertainty in source releases and transport parameters including effective porosity, fracture apertures and spacing, matrix diffusion coefficients, sorption coefficients, and colloid load and mobility are considered. With the distributions of input uncertainties and output plume volumes, global analysis methods including stepwise regression, contingency table analysis, and classification tree analysis are used to develop sensitivity rankings of parameter uncertainties for each model considered, thus assisting a variety of decisions.« less

Factors affecting self-regulatory driving practices among older adults.

PubMed

Molnar, Lisa J; Charlton, Judith L; Eby, David W; Langford, Jim; Koppel, Sjaan; Kolenic, Giselle E; Marshall, Shawn

2014-01-01

The primary objective of this study was to better understand how self-regulatory driving practices at multiple levels of driver decision making are influenced by various factors. Specifically, the study investigated patterns of tactical and strategic self-regulation among a sample of 246 Australian older drivers. Of special interest was the relative influence of several variables on the adoption of self-regulation, including self-perceptions of health, functioning, and abilities for safe driving and driving confidence and comfort. The research was carried out at the Monash University Accident Research Centre, as part of its Ozcandrive study, a partnership with the Canadian Driving Research Initiative for Vehicular Safety in the Elderly (Candrive), and in conjunction with the University of Michigan Transportation Research Institute (UMTRI). Candrive/Ozcandrive represents the first study to follow a large group of older drivers over several years and collect comprehensive self-reported and objectively derived data on health, functioning, and driving. This study used a subset of data from the Candrive/Ozcandrive study. Upon enrolling in the study, participants underwent a comprehensive clinical assessment during which data on visual, cognitive, and psychomotor functioning were collected. Approximately 4 months after study enrollment, participants completed the Advanced Driving Decisions and Patterns of Travel (ADDAPT) questionnaire, a computer-based self-regulation instrument developed and pilot-tested at UMTRI. Self-regulation among older adults was found to be a multidimensional concept. Rates of self-regulation were tied closely to specific driving situations, as well as level of decision making. In addition, self-regulatory practices at the strategic and tactical levels of decision making were influenced by different sets of factors. Continuing efforts to better understand the self-regulatory practices of older drivers at multiple levels of driver performance and decision making should provide important insights into how the transition from driving to nondriving can be better managed to balance the interdependent needs of public safety and personal mobility.

Informed decision-making in elective major vascular surgery: analysis of 145 surgeon-patient consultations.

PubMed

Etchells, Edward; Ferrari, Michel; Kiss, Alex; Martyn, Nikki; Zinman, Deborah; Levinson, Wendy

2011-06-01

Prior studies show significant gaps in the informed decision-making process, a central goal of surgical care. These studies have been limited by their focus on low-risk decisions, single visits rather than entire consultations, or both. Our objectives were, first, to rate informed decision-making for major elective vascular surgery based on audiotapes of actual physician-patient conversations and, second, to compare ratings of informed decision-making for first visits to ratings for multiple visits by the same patient over time. We prospectively enrolled patients for whom vascular surgical treatment was a potential option at a tertiary care outpatient vascular surgery clinic. We audio-taped all surgeon-patient conversations, including multiple visits when necessary, until a decision was made. Using an existing method, we evaluated the transcripts for elements of decision-making, including basic elements (e.g., an explanation of the clinical condition), intermediate elements (e.g., risks and benefits) and complex elements (e.g., uncertainty around the decision). We analyzed 145 surgeon-patient consultations. Overall, 45% of consultations contained complex elements, whereas 23% did not contain the basic elements of decision-making. For the 67 consultations that involved multiple visits, ratings were significantly higher when evaluating all visits (50% complex elements) compared with evaluating only the first visit (33% complex elements, p < 0.001.) We found that 45% of consultations contained complex elements, which is higher than prior studies with similar methods. Analyzing decision-making over multiple visits yielded different results than analyzing decision-making for single visits.

The Development of a Strategic Prioritisation Method for Green Supply Chain Initiatives.

PubMed

Masoumik, S Maryam; Abdul-Rashid, Salwa Hanim; Olugu, Ezutah Udoncy

2015-01-01

To maintain a competitive position, companies are increasingly required to integrate their proactive environmental strategies into their business strategies. The shift from reactive and compliance-based to proactive and strategic environmental management has driven companies to consider the strategic factors while identifying the areas in which they should focus their green initiatives. In previous studies little attention was given to providing the managers with a basis from which they could strategically prioritise these green initiatives across their companies' supply chains. Considering this lacuna in the literature, we present a decision-making method for prioritising green supply chain initiatives aligned with the preferred green strategies alternatives for the manufacturing companies. To develop this method, the study considered a position between determinism and the voluntarism orientation of environmental management involving both external pressures and internal competitive drivers and key resources as decision factors. This decision-making method was developed using the analytic network process (ANP) technique. The elements of the decision model were derived from the literature. The causal relationships among the multiple decision variables were validated based on the results of structural equation modelling (SEM) using a dataset collected from a survey of the ISO 14001-certified manufacturers in Malaysia. A portion of the relative weights required for computation in ANP was also calculated using the SEM results. A case study is presented to demonstrate the applicability of the method.

The Development of a Strategic Prioritisation Method for Green Supply Chain Initiatives

PubMed Central

Masoumik, S. Maryam; Abdul-Rashid, Salwa Hanim; Olugu, Ezutah Udoncy

2015-01-01

To maintain a competitive position, companies are increasingly required to integrate their proactive environmental strategies into their business strategies. The shift from reactive and compliance-based to proactive and strategic environmental management has driven companies to consider the strategic factors while identifying the areas in which they should focus their green initiatives. In previous studies little attention was given to providing the managers with a basis from which they could strategically prioritise these green initiatives across their companies’ supply chains. Considering this lacuna in the literature, we present a decision-making method for prioritising green supply chain initiatives aligned with the preferred green strategies alternatives for the manufacturing companies. To develop this method, the study considered a position between determinism and the voluntarism orientation of environmental management involving both external pressures and internal competitive drivers and key resources as decision factors. This decision-making method was developed using the analytic network process (ANP) technique. The elements of the decision model were derived from the literature. The causal relationships among the multiple decision variables were validated based on the results of structural equation modelling (SEM) using a dataset collected from a survey of the ISO 14001-certified manufacturers in Malaysia. A portion of the relative weights required for computation in ANP was also calculated using the SEM results. A case study is presented to demonstrate the applicability of the method. PMID:26618353

Understanding the stakeholders' intention to use economic decision-support tools: A cross-sectional study with the tobacco return on investment tool.

PubMed

Cheung, Kei Long; Evers, Silvia M A A; Hiligsmann, Mickaël; Vokó, Zoltán; Pokhrel, Subhash; Jones, Teresa; Muñoz, Celia; Wolfenstetter, Silke B; Józwiak-Hagymásy, Judit; de Vries, Hein

2016-01-01

Despite an increased number of economic evaluations of tobacco control interventions, the uptake by stakeholders continues to be limited. Understanding the underlying mechanism in adopting such economic decision-support tools by stakeholders is therefore important. By applying the I-Change Model, this study aims to identify which factors determine potential uptake of an economic decision-support tool, i.e., the Return on Investment tool. Stakeholders (decision-makers, purchasers of services/pharma products, professionals/service providers, evidence generators and advocates of health promotion) were interviewed in five countries, using an I-Change based questionnaire. MANOVA's were conducted to assess differences between intenders and non-intenders regarding beliefs. A multiple regression analysis was conducted to identify the main explanatory variables of intention to use an economic decision-support tool. Ninety-three stakeholders participated. Significant differences in beliefs were found between non-intenders and intenders: risk perception, attitude, social support, and self-efficacy towards using the tool. Regression showed that demographics, pre-motivational, and motivational factors explained 69% of the variation in intention. This study is the first to provide a theoretical framework to understand differences in beliefs between stakeholders who do or do not intend to use economic decision-support tools, and empirically corroborating the framework. This contributes to our understanding of the facilitators and barriers to the uptake of these studies. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

The Effects of Evidence Bounds on Decision-Making: Theoretical and Empirical Developments

PubMed Central

Zhang, Jiaxiang

2012-01-01

Converging findings from behavioral, neurophysiological, and neuroimaging studies suggest an integration-to-boundary mechanism governing decision formation and choice selection. This mechanism is supported by sequential sampling models of choice decisions, which can implement statistically optimal decision strategies for selecting between multiple alternative options on the basis of sensory evidence. This review focuses on recent developments in understanding the evidence boundary, an important component of decision-making raised by experimental findings and models. The article starts by reviewing the neurobiology of perceptual decisions and several influential sequential sampling models, in particular the drift-diffusion model, the Ornstein–Uhlenbeck model and the leaky-competing-accumulator model. In the second part, the article examines how the boundary may affect a model’s dynamics and performance and to what extent it may improve a model’s fits to experimental data. In the third part, the article examines recent findings that support the presence and site of boundaries in the brain. The article considers two questions: (1) whether the boundary is a spontaneous property of neural integrators, or is controlled by dedicated neural circuits; (2) if the boundary is variable, what could be the driving factors behind boundary changes? The review brings together studies using different experimental methods in seeking answers to these questions, highlights psychological and physiological factors that may be associated with the boundary and its changes, and further considers the evidence boundary as a generic mechanism to guide complex behavior. PMID:22870070

PeerShield: determining control and resilience criticality of collaborative cyber assets in networks

NASA Astrophysics Data System (ADS)

Cam, Hasan

2012-06-01

As attackers get more coordinated and advanced in cyber attacks, cyber assets are required to have much more resilience, control effectiveness, and collaboration in networks. Such a requirement makes it essential to take a comprehensive and objective approach for measuring the individual and relative performances of cyber security assets in network nodes. To this end, this paper presents four techniques as to how the relative importance of cyber assets can be measured more comprehensively and objectively by considering together the main variables of risk assessment (e.g., threats, vulnerabilities), multiple attributes (e.g., resilience, control, and influence), network connectivity and controllability among collaborative cyber assets in networks. In the first technique, a Bayesian network is used to include the random variables for control, recovery, and resilience attributes of nodes, in addition to the random variables of threats, vulnerabilities, and risk. The second technique shows how graph matching and coloring can be utilized to form collaborative pairs of nodes to shield together against threats and vulnerabilities. The third technique ranks the security assets of nodes by incorporating multiple weights and thresholds of attributes into a decision-making algorithm. In the fourth technique, the hierarchically well-separated tree is enhanced to first identify critical nodes of a network with respect to their attributes and network connectivity, and then selecting some nodes as driver nodes for network controllability.

Tailoring Software for Multiple Processor Systems

DTIC Science & Technology

1982-10-01

resource management decisions . Despite the lack of programming support, the use of multiple processor systems has grown sub- -stantially. Software has...making resource management decisions . Specifically, program- 1 mers need not allocate specific hardware resources to individual program components...Instead, such allocation decisions are automatically made based on high-level resource directives stated by ap- plication programmers, where each directive

A Decision Support System for Solving Multiple Criteria Optimization Problems

ERIC Educational Resources Information Center

Filatovas, Ernestas; Kurasova, Olga

2011-01-01

In this paper, multiple criteria optimization has been investigated. A new decision support system (DSS) has been developed for interactive solving of multiple criteria optimization problems (MOPs). The weighted-sum (WS) approach is implemented to solve the MOPs. The MOPs are solved by selecting different weight coefficient values for the criteria…

The multiple resource inventory decision-making process

Treesearch

Victor A. Rudis

1993-01-01

A model of the multiple resource inventory decision-making process is presented that identifies steps in conducting inventories, describes the infrastructure, and points out knowledge gaps that are common to many interdisciplinary studies.Successful efforts to date suggest the need to bridge the gaps by sharing elements, maintain dialogue among stakeholders in multiple...

Female Adolescent Contraceptive Decision Making and Risk Taking.

ERIC Educational Resources Information Center

Johnson, Sharon A.; Green, Vicki

1993-01-01

Findings from 60 sexually active, unmarried females, ages 14 through 18, revealed that cognitive capacity and cognitive egocentrism variables as well as age, grade, and ethnic status significantly predicted 6 of 7 decision-making variables in contraceptive use model. One cognitive capacity variable and one sexual contraceptive behavior variable…

Modelling the participation decision and duration of sporting activity in Scotland

PubMed Central

Eberth, Barbara; Smith, Murray D.

2010-01-01

Motivating individuals to actively engage in physical activity due to its beneficial health effects has been an integral part of Scotland's health policy agenda. The current Scottish guidelines recommend individuals participate in physical activity of moderate vigour for 30 min at least five times per week. For an individual contemplating the recommendation, decisions have to be made in regard of participation, intensity, duration and multiplicity. For the policy maker, understanding the determinants of each decision will assist in designing an intervention to effect the recommended policy. With secondary data sourced from the 2003 Scottish Health Survey (SHeS) we statistically model the combined decisions process, employing a copula approach to model specification. In taking this approach the model flexibly accounts for any statistical associations that may exist between the component decisions. Thus, we model the endogenous relationship between the decision of individuals to participate in sporting activities and, amongst those who participate, the duration of time spent undertaking their chosen activities. The main focus is to establish whether dependence exists between the two random variables assuming the vigour with which sporting activity is performed to be independent of the participation and duration decision. We allow for a variety of controls including demographic factors such as age and gender, economic factors such as income and educational attainment, lifestyle factors such as smoking, alcohol consumption, healthy eating and medical history. We use the model to compare the effect of interventions designed to increase the vigour with which individuals undertake their sport, relating it to obesity as a health outcome. PMID:20640033

Insights from triangulation of two purchase choice elicitation methods to predict social decision making in healthcare.

PubMed

Whitty, Jennifer A; Rundle-Thiele, Sharyn R; Scuffham, Paul A

2012-03-01

Discrete choice experiments (DCEs) and the Juster scale are accepted methods for the prediction of individual purchase probabilities. Nevertheless, these methods have seldom been applied to a social decision-making context. To gain an overview of social decisions for a decision-making population through data triangulation, these two methods were used to understand purchase probability in a social decision-making context. We report an exploratory social decision-making study of pharmaceutical subsidy in Australia. A DCE and selected Juster scale profiles were presented to current and past members of the Australian Pharmaceutical Benefits Advisory Committee and its Economic Subcommittee. Across 66 observations derived from 11 respondents for 6 different pharmaceutical profiles, there was a small overall median difference of 0.024 in the predicted probability of public subsidy (p = 0.003), with the Juster scale predicting the higher likelihood. While consistency was observed at the extremes of the probability scale, the funding probability differed over the mid-range of profiles. There was larger variability in the DCE than Juster predictions within each individual respondent, suggesting the DCE is better able to discriminate between profiles. However, large variation was observed between individuals in the Juster scale but not DCE predictions. It is important to use multiple methods to obtain a complete picture of the probability of purchase or public subsidy in a social decision-making context until further research can elaborate on our findings. This exploratory analysis supports the suggestion that the mixed logit model, which was used for the DCE analysis, may fail to adequately account for preference heterogeneity in some contexts.

Collaborative Response and Recovery from a Foot-and-Mouth Disease Animal Health Emergency: Supporting Decision Making in a Complex Environment with Multiple Stakeholders

DTIC Science & Technology

2013-12-01

RESPONSE AND RECOVERY FROM A FOOT-AND- MOUTH DISEASE ANIMAL HEALTH EMERGENCY: SUPPORTING DECISION MAKING IN A COMPLEX ENVIRONMENT WITH MULTIPLE...Thesis 4. TITLE AND SUBTITLE COLLABORATIVE RESPONSE AND RECOVERY FROM A FOOT-AND- MOUTH DISEASE ANIMAL HEALTH EMERGENCY: SUPPORTING DECISION MAKING...200 words ) This thesis recommends ways to support decision makers who must operate within the multi-stakeholder complex situation of response and

Development of a support tool for complex decision-making in the provision of rural maternity care.

PubMed

Hearns, Glen; Klein, Michael C; Trousdale, William; Ulrich, Catherine; Butcher, David; Miewald, Christiana; Lindstrom, Ronald; Eftekhary, Sahba; Rosinski, Jessica; Gómez-Ramírez, Oralia; Procyk, Andrea

2010-02-01

Decisions in the organization of safe and effective rural maternity care are complex, difficult, value laden and fraught with uncertainty, and must often be based on imperfect information. Decision analysis offers tools for addressing these complexities in order to help decision-makers determine the best use of resources and to appreciate the downstream effects of their decisions. To develop a maternity care decision-making tool for the British Columbia Northern Health Authority (NH) for use in low birth volume settings. Based on interviews with community members, providers, recipients and decision-makers, and employing a formal decision analysis approach, we sought to clarify the influences affecting rural maternity care and develop a process to generate a set of value-focused objectives for use in designing and evaluating rural maternity care alternatives. Four low-volume communities with variable resources (with and without on-site births, with or without caesarean section capability) were chosen. Physicians (20), nurses (18), midwives and maternity support service providers (4), local business leaders, economic development officials and elected officials (12), First Nations (women [pregnant and non-pregnant], chiefs and band members) (40), social workers (3), pregnant women (2) and NH decision-makers/administrators (17). We developed a Decision Support Manual to assist with assessing community needs and values, context for decision-making, capacity of the health authority or healthcare providers, identification of key objectives for decision-making, developing alternatives for care, and a process for making trade-offs and balancing multiple objectives. The manual was deemed an effective tool for the purpose by the client, NH. Beyond assisting the decision-making process itself, the methodology provides a transparent communication tool to assist in making difficult decisions. While the manual was specifically intended to deal with rural maternity issues, the NH decision-makers feel the method can be easily adapted to assist decision-making in other contexts in medicine where there are conflicting objectives, values and opinions. Decisions on the location of new facilities or infrastructure, or enhancing or altering services such as surgical or palliative care, would be examples of complex decisions that might benefit from this methodology.

Development of a Support Tool for Complex Decision-Making in the Provision of Rural Maternity Care

PubMed Central

Hearns, Glen; Klein, Michael C.; Trousdale, William; Ulrich, Catherine; Butcher, David; Miewald, Christiana; Lindstrom, Ronald; Eftekhary, Sahba; Rosinski, Jessica; Gómez-Ramírez, Oralia; Procyk, Andrea

2010-01-01

Context: Decisions in the organization of safe and effective rural maternity care are complex, difficult, value laden and fraught with uncertainty, and must often be based on imperfect information. Decision analysis offers tools for addressing these complexities in order to help decision-makers determine the best use of resources and to appreciate the downstream effects of their decisions. Objective: To develop a maternity care decision-making tool for the British Columbia Northern Health Authority (NH) for use in low birth volume settings. Design: Based on interviews with community members, providers, recipients and decision-makers, and employing a formal decision analysis approach, we sought to clarify the influences affecting rural maternity care and develop a process to generate a set of value-focused objectives for use in designing and evaluating rural maternity care alternatives. Setting: Four low-volume communities with variable resources (with and without on-site births, with or without caesarean section capability) were chosen. Participants: Physicians (20), nurses (18), midwives and maternity support service providers (4), local business leaders, economic development officials and elected officials (12), First Nations (women [pregnant and non-pregnant], chiefs and band members) (40), social workers (3), pregnant women (2) and NH decision-makers/administrators (17). Results: We developed a Decision Support Manual to assist with assessing community needs and values, context for decision-making, capacity of the health authority or healthcare providers, identification of key objectives for decision-making, developing alternatives for care, and a process for making trade-offs and balancing multiple objectives. The manual was deemed an effective tool for the purpose by the client, NH. Conclusions: Beyond assisting the decision-making process itself, the methodology provides a transparent communication tool to assist in making difficult decisions. While the manual was specifically intended to deal with rural maternity issues, the NH decision-makers feel the method can be easily adapted to assist decision-making in other contexts in medicine where there are conflicting objectives, values and opinions. Decisions on the location of new facilities or infrastructure, or enhancing or altering services such as surgical or palliative care, would be examples of complex decisions that might benefit from this methodology. PMID:21286270

Empowerment of women and its association with the health of the community.

PubMed

Varkey, Prathibha; Mbbs; Kureshi, Sarah; Lesnick, Timothy

2010-01-01

Empowerment and opportunities to experience power and control in one's life contribute to health and wellness. Although studies have assessed specific factors related to women's empowerment and their influence on health outcomes, there is a dearth of published literature assessing the relationship of the empowerment of women with the overall health of a community. By means of this article, we aim to assess the relationship of women's empowerment with health in 75 countries. We used the gender empowerment measure (GEM), a composite index measuring gender inequality in economic participation and decision making, political participation and decision making, and power over economic resources. All 75 countries with GEM values in the 2006 Human Development Report (HDR) were included in the study. Association between the GEM values and seven health indicators was evaluated using descriptive statistics, scatter plots, and simple and multiple linear regression models. We also controlled for gross domestic product (GDP) as a possible confounding factor and included this variable in the multiple regression models. When GDP was not considered, GEM had a statistically significant association with all health indicator variables except for proportion of 1-year-olds immunized against measles (correlation coefficient 0.063, p = 0.597). After adjusting for GDP, GEM was significantly associated with low birth weight, fertility rate, infant mortality, and age

Cephalometric landmark detection in dental x-ray images using convolutional neural networks

NASA Astrophysics Data System (ADS)

Lee, Hansang; Park, Minseok; Kim, Junmo

2017-03-01

In dental X-ray images, an accurate detection of cephalometric landmarks plays an important role in clinical diagnosis, treatment and surgical decisions for dental problems. In this work, we propose an end-to-end deep learning system for cephalometric landmark detection in dental X-ray images, using convolutional neural networks (CNN). For detecting 19 cephalometric landmarks in dental X-ray images, we develop a detection system using CNN-based coordinate-wise regression systems. By viewing x- and y-coordinates of all landmarks as 38 independent variables, multiple CNN-based regression systems are constructed to predict the coordinate variables from input X-ray images. First, each coordinate variable is normalized by the length of either height or width of an image. For each normalized coordinate variable, a CNN-based regression system is trained on training images and corresponding coordinate variable, which is a variable to be regressed. We train 38 regression systems with the same CNN structure on coordinate variables, respectively. Finally, we compute 38 coordinate variables with these trained systems from unseen images and extract 19 landmarks by pairing the regressed coordinates. In experiments, the public database from the Grand Challenges in Dental X-ray Image Analysis in ISBI 2015 was used and the proposed system showed promising performance by successfully locating the cephalometric landmarks within considerable margins from the ground truths.

Quality of life in elders living alone in Taiwan.

PubMed

Lin, Pao-Chen; Yen, Miaofen; Fetzer, Susan Jane

2008-06-01

The aim of this study was to identify and describe predictors of QOL of elders who live alone in Taiwan. Despite a growing population of elders who live alone, research on their quality of life, important for policy decisions and health care provider interventions is virtually absent. A descriptive correlational design surveyed 192 Taiwanese elders living alone, selected at random from urban and rural areas. During home visits elders completed the WHO-QOL-BREF, Social Support Scale and Center for Epidemiological Studies Depression Scale (CES-D) in addition to providing demographic data. Multiple linear regressions showed that six variables predicted physical health and the psychological wellbeing QOL domains, accounting for 74.5 and 60.1% of the variance, respectively. Four variables predicted 46.7 and 34.3% of the environmental and the social relationship QOL domains, respectively. Elders who live alone in rural areas and suffer from depression are at high risk for a low quality of life. However, elders living alone reported a better quality of life than their institutionalized counterparts. Interventional research and policy decisions focused on treatment for depression and providing social support networks, as these elders age, will be particularly important. By understanding variables associated with elders' quality of life, nurses can coordinate interventions to improve their quality of life. Poorly educated rural women who live alone are particularly vulnerable. Nursing assessment of quality of life indicators and implementation of strategies for increased social support are needed for high-risk elders.

MULTIPLE SCALES FOR SUSTAINABLE RESULTS

EPA Science Inventory

This session will highlight recent research that incorporates the use of multiple scales and innovative environmental accounting to better inform decisions that affect sustainability, resilience, and vulnerability at all scales. Effective decision-making involves assessment at mu...

Decisions with Uncertain Consequences—A Total Ordering on Loss-Distributions

PubMed Central

König, Sandra; Schauer, Stefan

2016-01-01

Decisions are often based on imprecise, uncertain or vague information. Likewise, the consequences of an action are often equally unpredictable, thus putting the decision maker into a twofold jeopardy. Assuming that the effects of an action can be modeled by a random variable, then the decision problem boils down to comparing different effects (random variables) by comparing their distribution functions. Although the full space of probability distributions cannot be ordered, a properly restricted subset of distributions can be totally ordered in a practically meaningful way. We call these loss-distributions, since they provide a substitute for the concept of loss-functions in decision theory. This article introduces the theory behind the necessary restrictions and the hereby constructible total ordering on random loss variables, which enables decisions under uncertainty of consequences. Using data obtained from simulations, we demonstrate the practical applicability of our approach. PMID:28030572

Modeling Feedbacks Between Individual Human Decisions and Hydrology Using Interconnected Physical and Social Models

NASA Astrophysics Data System (ADS)

Murphy, J.; Lammers, R. B.; Proussevitch, A. A.; Ozik, J.; Altaweel, M.; Collier, N. T.; Alessa, L.; Kliskey, A. D.

2014-12-01

The global hydrological cycle intersects with human decision making at multiple scales, from dams and irrigation works to the taps in individuals' homes. Residential water consumers are commonly encouraged to conserve; these messages are heard against a background of individual values and conceptions about water quality, uses, and availability. The degree to which these values impact the larger-hydrological dynamics, the way that changes in those values have impacts on the hydrological cycle through time, and the feedbacks by which water availability and quality in turn shape those values, are not well explored. To investigate this domain we employ a global-scale water balance model (WBM) coupled with a social-science-grounded agent-based model (ABM). The integration of a hydrological model with an agent-based model allows us to explore driving factors in the dynamics in coupled human-natural systems. From the perspective of the physical hydrologist, the ABM offers a richer means of incorporating the human decisions that drive the hydrological system; from the view of the social scientist, a physically-based hydrological model allows the decisions of the agents to play out against constraints faithful to the real world. We apply the interconnected models to a study of Tucson, Arizona, USA, and its role in the larger Colorado River system. Our core concept is Technology-Induced Environmental Distancing (TIED), which posits that layers of technology can insulate consumers from direct knowledge of a resource. In Tucson, multiple infrastructure and institutional layers have arguably increased the conceptual distance between individuals and their water supply, offering a test case of the TIED framework. Our coupled simulation allows us to show how the larger system transforms a resource with high temporal and spatial variability into a consumer constant, and the effects of this transformation on the regional system. We use this to explore how pricing, messaging, and social dynamics impact demand, how changes in demand affect the regional water system, and under what system challenges the values of the individuals are likely to change. This study is a preamble to modeling multiple regionally connected cities and larger systems with impacts on hydrology at the continental and global scales.

Enhanced Requirements for Assessment in a Competency-Based, Time-Variable Medical Education System.

PubMed

Gruppen, Larry D; Ten Cate, Olle; Lingard, Lorelei A; Teunissen, Pim W; Kogan, Jennifer R

2018-03-01

Competency-based, time-variable medical education has reshaped the perceptions and practices of teachers, curriculum designers, faculty developers, clinician educators, and program administrators. This increasingly popular approach highlights the fact that learning among different individuals varies in duration, foundation, and goal. Time variability places particular demands on the assessment data that are so necessary for making decisions about learner progress. These decisions may be formative (e.g., feedback for improvement) or summative (e.g., decisions about advancing a student). This article identifies challenges to collecting assessment data and to making assessment decisions in a time-variable system. These challenges include managing assessment data, defining and making valid assessment decisions, innovating in assessment, and modeling the considerable complexity of assessment in real-world settings and richly interconnected social systems. There are hopeful signs of creativity in assessment both from researchers and practitioners, but the transition from a traditional to a competency-based medical education system will likely continue to create much controversy and offer opportunities for originality and innovation in assessment.

Impact of advanced monitoring variables on intraoperative clinical decision-making: an international survey.

PubMed

Joosten, Alexandre; Desebbe, Olivier; Suehiro, Koichi; Essiet, Mfonobong; Alexander, Brenton; Ricks, Cameron; Rinehart, Joseph; Faraoni, David; Cecconi, Maurizio; Van der Linden, Philippe; Cannesson, Maxime

2017-02-01

To assess the relationship between the addition of advanced monitoring variables and changes in clinical decision-making. A 15-questions survey was anonymously emailed to international experts and physician members of five anesthesia societies which focused on assessing treatment decisions of clinicians during three realistic clinical scenarios measured at two distinct time points. The first is when typical case information and basic monitoring (T1) were provided, and then once again after the addition of advanced monitoring variables (T2). We hypothesized that the addition of advanced variables would increase the incidence of an optimal therapeutic decision (a priori defined as the answer with the highest percentage of expert agreement) and decrease the variability among the physician's suggested treatments. The survey was completed by 18 experts and 839 physicians. Overall, adding advanced monitoring did not significantly increase physician response accuracy, with the least substantial changes noted on questions related to volume expansion or vasopressor administration. Moreover, advanced monitoring data did not significantly decrease the high level of initial practice variability in physician suggested treatments (P = 0.13), in contrast to the low variability observed within the expert group (P = 0.039). Additionally, 5-10 years of practice (P < 0.0001) and a cardiovascular subspecialty (P = 0.048) were both physician characteristics associated with a higher rate of optimal therapeutic decisions. The addition of advanced variables was of limited benefit for most physicians, further indicating the need for more in depth education on the clinical value and technical understanding of such variables.

Invited review: Helping dairy farmers to improve economic performance utilizing data-driving decision support tools.

PubMed

Cabrera, V E

2018-01-01

The objective of this review paper is to describe the development and application of a suite of more than 40 computerized dairy farm decision support tools contained at the University of Wisconsin-Madison (UW) Dairy Management website http://DairyMGT.info. These data-driven decision support tools are aimed to help dairy farmers improve their decision-making, environmental stewardship and economic performance. Dairy farm systems are highly dynamic in which changing market conditions and prices, evolving policies and environmental restrictions together with every time more variable climate conditions determine performance. Dairy farm systems are also highly integrated with heavily interrelated components such as the dairy herd, soils, crops, weather and management. Under these premises, it is critical to evaluate a dairy farm following a dynamic integrated system approach. For this approach, it is crucial to use meaningful data records, which are every time more available. These data records should be used within decision support tools for optimal decision-making and economic performance. Decision support tools in the UW-Dairy Management website (http://DairyMGT.info) had been developed using combination and adaptation of multiple methods together with empirical techniques always with the primary goal for these tools to be: (1) highly user-friendly, (2) using the latest software and computer technologies, (3) farm and user specific, (4) grounded on the best scientific information available, (5) remaining relevant throughout time and (6) providing fast, concrete and simple answers to complex farmers' questions. DairyMGT.info is a translational innovative research website in various areas of dairy farm management that include nutrition, reproduction, calf and heifer management, replacement, price risk and environment. This paper discusses the development and application of 20 selected (http://DairyMGT.info) decision support tools.

Multiple Forensic Interviews during Investigations of Child Sexual Abuse: A Cost-Effectiveness Analysis

ERIC Educational Resources Information Center

Block, Stephanie D.; Foster, E. Michael; Pierce, Matthew W.; Berkoff, Molly C.; Runyan, Desmond K.

2013-01-01

In suspected child sexual abuse some professionals recommend multiple child interviews to increase the likelihood of disclosure or more details to improve decision-making and increase convictions. We modeled the yield of a policy of routinely conducting multiple child interviews and increased convictions. Our decision tree reflected the path of a…

Decision Neuroscience: Neuroeconomics

PubMed Central

Smith, David V.; Huettel, Scott A.

2012-01-01

Few aspects of human cognition are more personal than the choices we make. Our decisions – from the mundane to the impossibly complex – continually shape the courses of our lives. In recent years, researchers have applied the tools of neuroscience to understand the mechanisms that underlie decision making, as part of the new discipline of decision neuroscience. A primary goal of this emerging field has been to identify the processes that underlie specific decision variables, including the value of rewards, the uncertainty associated with particular outcomes, and the consequences of social interactions. Recent work suggests potential neural substrates that integrate these variables, potentially reflecting a common neural currency for value, to facilitate value comparisons. Despite the successes of decision neuroscience research for elucidating brain mechanisms, significant challenges remain. These include building new conceptual frameworks for decision making, integrating research findings across disparate techniques and species, and extending results from neuroscience to shape economic theory. To overcome these challenges, future research will likely focus on interpersonal variability in decision making, with the eventual goal of creating biologically plausible models for individual choice. PMID:22754602

Closed loop supply chain network design with fuzzy tactical decisions

NASA Astrophysics Data System (ADS)

Sherafati, Mahtab; Bashiri, Mahdi

2016-09-01

One of the most strategic and the most significant decisions in supply chain management is reconfiguration of the structure and design of the supply chain network. In this paper, a closed loop supply chain network design model is presented to select the best tactical and strategic decision levels simultaneously considering the appropriate transportation mode in activated links. The strategic decisions are made for a long term; thus, it is more satisfactory and more appropriate when the decision variables are considered uncertain and fuzzy, because it is more flexible and near to the real world. This paper is the first research which considers fuzzy decision variables in the supply chain network design model. Moreover, in this study a new fuzzy optimization approach is proposed to solve a supply chain network design problem with fuzzy tactical decision variables. Finally, the proposed approach and model are verified using several numerical examples. The comparison of the results with other existing approaches confirms efficiency of the proposed approach. Moreover the results confirms that by considering the vagueness of tactical decisions some properties of the supply chain network will be improved.

GWM-a ground-water management process for the U.S. Geological Survey modular ground-water model (MODFLOW-2000)

USGS Publications Warehouse

Ahlfeld, David P.; Barlow, Paul M.; Mulligan, Anne E.

2005-01-01

GWM is a Ground?Water Management Process for the U.S. Geological Survey modular three?dimensional ground?water model, MODFLOW?2000. GWM uses a response?matrix approach to solve several types of linear, nonlinear, and mixed?binary linear ground?water management formulations. Each management formulation consists of a set of decision variables, an objective function, and a set of constraints. Three types of decision variables are supported by GWM: flow?rate decision variables, which are withdrawal or injection rates at well sites; external decision variables, which are sources or sinks of water that are external to the flow model and do not directly affect the state variables of the simulated ground?water system (heads, streamflows, and so forth); and binary variables, which have values of 0 or 1 and are used to define the status of flow?rate or external decision variables. Flow?rate decision variables can represent wells that extend over one or more model cells and be active during one or more model stress periods; external variables also can be active during one or more stress periods. A single objective function is supported by GWM, which can be specified to either minimize or maximize the weighted sum of the three types of decision variables. Four types of constraints can be specified in a GWM formulation: upper and lower bounds on the flow?rate and external decision variables; linear summations of the three types of decision variables; hydraulic?head based constraints, including drawdowns, head differences, and head gradients; and streamflow and streamflow?depletion constraints. The Response Matrix Solution (RMS) Package of GWM uses the Ground?Water Flow Process of MODFLOW to calculate the change in head at each constraint location that results from a perturbation of a flow?rate variable; these changes are used to calculate the response coefficients. For linear management formulations, the resulting matrix of response coefficients is then combined with other components of the linear management formulation to form a complete linear formulation; the formulation is then solved by use of the simplex algorithm, which is incorporated into the RMS Package. Nonlinear formulations arise for simulated conditions that include water?table (unconfined) aquifers or head?dependent boundary conditions (such as streams, drains, or evapotranspiration from the water table). Nonlinear formulations are solved by sequential linear programming; that is, repeated linearization of the nonlinear features of the management problem. In this approach, response coefficients are recalculated for each iteration of the solution process. Mixed?binary linear (or mildly nonlinear) formulations are solved by use of the branch and bound algorithm, which is also incorporated into the RMS Package. Three sample problems are provided to demonstrate the use of GWM for typical ground?water flow management problems. These sample problems provide examples of how GWM input files are constructed to specify the decision variables, objective function, constraints, and solution process for a GWM run. The GWM Process runs with the MODFLOW?2000 Global and Ground?Water Flow Processes, but in its current form GWM cannot be used with the Observation, Sensitivity, Parameter?Estimation, or Ground?Water Transport Processes. The GWM Process is written with a modular structure so that new objective functions, constraint types, and solution algorithms can be added.

Integrating Decision Making and Mental Health Interventions Research: Research Directions

PubMed Central

Wills, Celia E.; Holmes-Rovner, Margaret

2006-01-01

The importance of incorporating patient and provider decision-making processes is in the forefront of the National Institute of Mental Health (NIMH) agenda for improving mental health interventions and services. Key concepts in patient decision making are highlighted within a simplified model of patient decision making that links patient-level/“micro” variables to services-level/“macro” variables via the decision-making process that is a target for interventions. The prospective agenda for incorporating decision-making concepts in mental health research includes (a) improved measures for characterizing decision-making processes that are matched to study populations, complexity, and types of decision making; (b) testing decision aids in effectiveness research for diverse populations and clinical settings; and (c) improving the understanding and incorporation of preference concepts in enhanced intervention designs. PMID:16724158

Applying voting theory in natural resource management: a case of multiple-criteria group decision support.

PubMed

Laukkanen, Sanna; Kangas, Annika; Kangas, Jyrki

2002-02-01

Voting theory has a lot in common with utility theory, and especially with group decision-making. An expected-utility-maximising strategy exists in voting situations, as well as in decision-making situations. Therefore, it is natural to utilise the achievements of voting theory also in group decision-making. Most voting systems are based on a single criterion or holistic preference information on decision alternatives. However, a voting scheme called multicriteria approval is specially developed for decision-making situations with multiple criteria. This study considers the voting theory from the group decision support point of view and compares it with some other methods applied to similar purposes in natural resource management. A case study is presented, where the approval voting approach is introduced to natural resources planning and tested in a forestry group decision-making process. Applying multicriteria approval method was found to be a potential approach for handling some challenges typical for forestry group decision support. These challenges include (i) utilising ordinal information in the evaluation of decision alternatives, (ii) being readily understandable for and treating equally all the stakeholders in possession of different levels of knowledge on the subject considered, (iii) fast and cheap acquisition of preference information from several stakeholders, and (iv) dealing with multiple criteria.

Multimorbidity and Decision-Making Preferences Among Older Adults.

PubMed

Chi, Winnie C; Wolff, Jennifer; Greer, Raquel; Dy, Sydney

2017-11-01

Understanding individuals' preferences for participating in health care decisions is foundational to delivering person-centered care. We aimed to (1) explore preferences for health care decision making among older adults, and (2) identify multimorbidity profiles associated with preferring less active, ie, passive, participation among older US adults. Ours was a cross-sectional, nationally representative study of 2,017 National Health and Aging Trends Study respondents. Passive decision-making preference was defined as preferring to leave decisions to physicians. Multimorbidity profiles, based on 13 prevalent chronic conditions, were examined as (1) presence of 2 or more conditions, (2) a simple conditions count, and (3) a condition clusters count. Multiple logistic regression was used with adjustment for age, sex, education, English proficiency, and mobility limitation. Most older adults preferred to participate actively in making health care decisions. Older adults with 4 or more conditions, however, and those with multiple condition clusters are relatively less likely to prefer active decision making. Primary care physicians should initiate a shared decision-making process with older adults with 4 or more conditions or multiple condition clusters. Physicians should anticipate variation in decision-making preferences among older adults and adapt a decision-making process that suits individuals' preferences for participation to ensure person-centered care delivery. © 2017 Annals of Family Medicine, Inc.

The role of family planning communications--an agent of reinforcement or change.

PubMed

Chen, E C

1981-12-01

Results are presented of a multiple classification analysis of responses to a 1972 KAP survey in Taiwan of 2013 married women aged 18-34 designed to determine whether family planning communication is primarily a reinforcement agent or a change agent. 2 types of independent variables, social demographic variables including age, number of children, residence, education, employment status, and duration of marriage; and social climate variables including ever receiving family planning information from mass media and ever discussing family planning with others, were used. KAP levels, the dependent variables, were measured by 2 variables each: awareness of effective methods and awareness of government supply of contraceptives for knowledge, wish for additional children and approve of 2-child family for attitude, and never use contraception and neither want children nor use contraception for practice. Social demographic and attitudinal variables were found to be the critical ones, while social climate and knowledge variables had only negligible effects on various stages of family planning adoption, indicating that family planning communications functioned primarily as a reinforcement agent. The effects of social demographic variables were prominent in all stages of contraceptive adoption. Examination of effects of individual variables on various stages of family planning adoption still supported the argument that family planning communications played a reinforcement role. Family planning communications functioned well in diffusing family planning knowledge and accessibility, but social demographic variables and desire for additional children were the most decisive influences on use of contraception.

Integrated multi-choice goal programming and multi-segment goal programming for supplier selection considering imperfect-quality and price-quantity discounts in a multiple sourcing environment

NASA Astrophysics Data System (ADS)

Chang, Ching-Ter; Chen, Huang-Mu; Zhuang, Zheng-Yun

2014-05-01

Supplier selection (SS) is a multi-criteria and multi-objective problem, in which multi-segment (e.g. imperfect-quality discount (IQD) and price-quantity discount (PQD)) and multi-aspiration level problems may be significantly important; however, little attention had been given to dealing with both of them simultaneously in the past. This study proposes a model for integrating multi-choice goal programming and multi-segment goal programming to solve the above-mentioned problems by providing the following main contributions: (1) it allows decision-makers to set multiple aspiration levels on the right-hand side of each goal to suit real-world situations, (2) the PQD and IQD conditions are considered in the proposed model simultaneously and (3) the proposed model can solve a SS problem with n suppliers where each supplier offers m IQD with r PQD intervals, where only ? extra binary variables are required. The usefulness of the proposed model is explained using a real case. The results indicate that the proposed model not only can deal with a SS problem with multi-segment and multi-aspiration levels, but also can help the decision-maker to find the appropriate order quantities for each supplier by considering cost, quality and delivery.

Parameter selection for and implementation of a web-based decision-support tool to predict extubation outcome in premature infants.

PubMed

Mueller, Martina; Wagner, Carol L; Annibale, David J; Knapp, Rebecca G; Hulsey, Thomas C; Almeida, Jonas S

2006-03-01

Approximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census. A five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN. CEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0-1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data originally used for the development of the prediction tool. State-of-the-art, machine-learning methods can be employed for the development of sophisticated tools to aid clinicians' decisions. We identified numerous variables considered relevant for extubation decisions for mechanically ventilated premature infants with RDS. We then developed a web-based decision-support tool for clinicians which can be made widely available and potentially improve patient care world wide.

An integrated modeling approach to support management decisions of coupled groundwater-agricultural systems under multiple uncertainties

NASA Astrophysics Data System (ADS)

Hagos Subagadis, Yohannes; Schütze, Niels; Grundmann, Jens

2015-04-01

The planning and implementation of effective water resources management strategies need an assessment of multiple (physical, environmental, and socio-economic) issues, and often requires new research in which knowledge of diverse disciplines are combined in a unified methodological and operational frameworks. Such integrative research to link different knowledge domains faces several practical challenges. Such complexities are further compounded by multiple actors frequently with conflicting interests and multiple uncertainties about the consequences of potential management decisions. A fuzzy-stochastic multiple criteria decision analysis tool was developed in this study to systematically quantify both probabilistic and fuzzy uncertainties associated with complex hydrosystems management. It integrated physical process-based models, fuzzy logic, expert involvement and stochastic simulation within a general framework. Subsequently, the proposed new approach is applied to a water-scarce coastal arid region water management problem in northern Oman, where saltwater intrusion into a coastal aquifer due to excessive groundwater extraction for irrigated agriculture has affected the aquifer sustainability, endangering associated socio-economic conditions as well as traditional social structure. Results from the developed method have provided key decision alternatives which can serve as a platform for negotiation and further exploration. In addition, this approach has enabled to systematically quantify both probabilistic and fuzzy uncertainties associated with the decision problem. Sensitivity analysis applied within the developed tool has shown that the decision makers' risk aversion and risk taking attitude may yield in different ranking of decision alternatives. The developed approach can be applied to address the complexities and uncertainties inherent in water resources systems to support management decisions, while serving as a platform for stakeholder participation.

Creating ensembles of decision trees through sampling

DOEpatents

Kamath, Chandrika; Cantu-Paz, Erick

2005-08-30

A system for decision tree ensembles that includes a module to read the data, a module to sort the data, a module to evaluate a potential split of the data according to some criterion using a random sample of the data, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method is based on statistical sampling techniques and includes the steps of reading the data; sorting the data; evaluating a potential split according to some criterion using a random sample of the data, splitting the data, and combining multiple decision trees in ensembles.

Realized niche width of a brackish water submerged aquatic vegetation under current environmental conditions and projected influences of climate change.

PubMed

Kotta, Jonne; Möller, Tiia; Orav-Kotta, Helen; Pärnoja, Merli

2014-12-01

Little is known about how organisms might respond to multiple climate stressors and this lack of knowledge limits our ability to manage coastal ecosystems under contemporary climate change. Ecological models provide managers and decision makers with greater certainty that the systems affected by their decisions are accurately represented. In this study Boosted Regression Trees modelling was used to relate the cover of submerged aquatic vegetation to the abiotic environment in the brackish Baltic Sea. The analyses showed that the majority of the studied submerged aquatic species are most sensitive to changes in water temperature, current velocity and winter ice scour. Surprisingly, water salinity, turbidity and eutrophication have little impact on the distributional pattern of the studied biota. Both small and large scale environmental variability contributes to the variability of submerged aquatic vegetation. When modelling species distribution under the projected influences of climate change, all of the studied submerged aquatic species appear to be very resilient to a broad range of environmental perturbation and biomass gains are expected when seawater temperature increases. This is mainly because vegetation develops faster in spring and has a longer growing season under the projected climate change scenario. Copyright © 2014 Elsevier Ltd. All rights reserved.

Methods for understanding super-efficient data envelopment analysis results with an application to hospital inpatient surgery.

PubMed

O'Neill, Liam; Dexter, Franklin

2005-11-01

We compare two techniques for increasing the transparency and face validity of Data Envelopment Analysis (DEA) results for managers at a single decision-making unit: multifactor efficiency (MFE) and non-radial super-efficiency (NRSE). Both methods incorporate the slack values from the super-efficient DEA model to provide a more robust performance measure than radial super-efficiency scores. MFE and NRSE are equivalent for unique optimal solutions and a single output. MFE incorporates the slack values from multiple output variables, whereas NRSE does not. MFE can be more transparent to managers since it involves no additional optimization steps beyond the DEA, whereas NRSE requires several. We compare results for operating room managers at an Iowa hospital evaluating its growth potential for multiple surgical specialties. In addition, we address the problem of upward bias of the slack values of the super-efficient DEA model.

Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.

2014-04-14

To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation.more » We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.« less

Climate change and health modeling: horses for courses.

PubMed

Ebi, Kristie L; Rocklöv, Joacim

2014-01-01

Mathematical and statistical models are needed to understand the extent to which weather, climate variability, and climate change are affecting current and may affect future health burdens in the context of other risk factors and a range of possible development pathways, and the temporal and spatial patterns of any changes. Such understanding is needed to guide the design and the implementation of adaptation and mitigation measures. Because each model projection captures only a narrow range of possible futures, and because models serve different purposes, multiple models are needed for each health outcome ('horses for courses'). Multiple modeling results can be used to bracket the ranges of when, where, and with what intensity negative health consequences could arise. This commentary explores some climate change and health modeling issues, particularly modeling exposure-response relationships, developing early warning systems, projecting health risks over coming decades, and modeling to inform decision-making. Research needs are also suggested.

MatLab Script and Functional Programming

NASA Technical Reports Server (NTRS)

Shaykhian, Gholam Ali

2007-01-01

MatLab Script and Functional Programming: MatLab is one of the most widely used very high level programming languages for scientific and engineering computations. It is very user-friendly and needs practically no formal programming knowledge. Presented here are MatLab programming aspects and not just the MatLab commands for scientists and engineers who do not have formal programming training and also have no significant time to spare for learning programming to solve their real world problems. Specifically provided are programs for visualization. The MatLab seminar covers the functional and script programming aspect of MatLab language. Specific expectations are: a) Recognize MatLab commands, script and function. b) Create, and run a MatLab function. c) Read, recognize, and describe MatLab syntax. d) Recognize decisions, loops and matrix operators. e) Evaluate scope among multiple files, and multiple functions within a file. f) Declare, define and use scalar variables, vectors and matrices.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Flory, John Andrew; Padilla, Denise D.; Gauthier, John H.

Upcoming weapon programs require an aggressive increase in Application Specific Integrated Circuit (ASIC) production at Sandia National Laboratories (SNL). SNL has developed unique modeling and optimization tools that have been instrumental in improving ASIC production productivity and efficiency, identifying optimal operational and tactical execution plans under resource constraints, and providing confidence in successful mission execution. With ten products and unprecedented levels of demand, a single set of shared resources, highly variable processes, and the need for external supplier task synchronization, scheduling is an integral part of successful manufacturing. The scheduler uses an iterative multi-objective genetic algorithm and a multi-dimensional performancemore » evaluator. Schedule feasibility is assessed using a discrete event simulation (DES) that incorporates operational uncertainty, variability, and resource availability. The tools provide rapid scenario assessments and responses to variances in the operational environment, and have been used to inform major equipment investments and workforce planning decisions in multiple SNL facilities.« less

The rise of global warming skepticism: exploring affective image associations in the United States over time.

PubMed

Smith, Nicholas; Leiserowitz, Anthony

2012-06-01

This article explores how affective image associations to global warming have changed over time. Four nationally representative surveys of the American public were conducted between 2002 and 2010 to assess public global warming risk perceptions, policy preferences, and behavior. Affective images (positive or negative feelings and cognitive representations) were collected and content analyzed. The results demonstrate a large increase in "naysayer" associations, indicating extreme skepticism about the issue of climate change. Multiple regression analyses found that holistic affect and "naysayer" associations were more significant predictors of global warming risk perceptions than cultural worldviews or sociodemographic variables, including political party and ideology. The results demonstrate the important role affective imagery plays in judgment and decision-making processes, how these variables change over time, and how global warming is currently perceived by the American public. © 2012 Society for Risk Analysis.

Visual analytics techniques for large multi-attribute time series data

NASA Astrophysics Data System (ADS)

Hao, Ming C.; Dayal, Umeshwar; Keim, Daniel A.

2008-01-01

Time series data commonly occur when variables are monitored over time. Many real-world applications involve the comparison of long time series across multiple variables (multi-attributes). Often business people want to compare this year's monthly sales with last year's sales to make decisions. Data warehouse administrators (DBAs) want to know their daily data loading job performance. DBAs need to detect the outliers early enough to act upon them. In this paper, two new visual analytic techniques are introduced: The color cell-based Visual Time Series Line Charts and Maps highlight significant changes over time in a long time series data and the new Visual Content Query facilitates finding the contents and histories of interesting patterns and anomalies, which leads to root cause identification. We have applied both methods to two real-world applications to mine enterprise data warehouse and customer credit card fraud data to illustrate the wide applicability and usefulness of these techniques.

Determinants of corporate dividend policy in Indonesia

NASA Astrophysics Data System (ADS)

Lestari, H. S.

2018-01-01

This study aims to investigate the determinants factors that effect the dividend policy. The sample used in this research is manufacture companies listed in Indonesia Stock Exchange (IDX) and the period 2011 - 2015. There are independent variables such as earning, cash flow, free cash flow, debt, growth opportunities, investment opportunities, firm size, largest shareholder, firm risk, lagged dividend and dividend policy used as dependent variable. The study examines a total of 32 manufacture companies. After analyzing the data using the program software Eviews 9.0 by multiples regression analysis reveal that earning, cash flow, free cash flow, firm size, and lagged dividend have significant effect on dividend policy, whereas debt, growth opportunities, investment opportunities, largest shareholder, and firm risk have no significant effect on dividend policy. The results of this study are expected to be implemented by the financial managers in improving corporate profits and basic information as return on investment decisions.

Examining the Relationships Between Education, Social Networks and Democratic Support With ABM

NASA Technical Reports Server (NTRS)

Drucker, Nick; Campbell, Kenyth

2011-01-01

This paper introduces an agent-based model that explores the relationships between education, social networks, and support for democratic ideals. This study examines two factors thai affect democratic support, education, and social networks. Current theory concerning these two variables suggests that positive relationships exist between education and democratic support and between social networks and the spread of ideas. The model contains multiple variables of democratic support, two of which are evaluated through experimentation. The model allows individual entities within the system to make "decisions" about their democratic support independent of one another. The agent based approach also allows entities to utilize their social networks to spread ideas. Current theory supports experimentation results. In addion , these results show the model is capable of reproducing real world outcomes. This paper addresses the model creation process and the experimentation procedure, as well as future research avenues and potential shortcomings of the model

The behavior of multiple independent managers and ecological traits interact to determine prevalence of weeds.

PubMed

Coutts, Shaun R; Yokomizo, Hiroyuki; Buckley, Yvonne M

2013-04-01

Management of damaging invasive plants is often undertaken by multiple decision makers, each managing only a small part of the invader's population. As weeds can move between properties and re-infest eradicated sites from unmanaged sources, the dynamics of multiple decision makers plays a significant role in weed prevalence and invasion risk at the landscape scale. We used a spatially explicit agent-based simulation to determine how individual agent behavior, in concert with weed population ecology, determined weed prevalence. We compared two invasive grass species that differ in ecology, control methods, and costs: Nassella trichotoma (serrated tussock) and Eragrostis curvula (African love grass). The way decision makers reacted to the benefit of management had a large effect on the extent of a weed. If benefits of weed control outweighed the costs, and either net benefit was very large or all agents were very sensitive to net benefits, then agents tended to act synchronously, reducing the pool of infested agents available to spread the weed. As N. trichotoma was more damaging than E. curvula and had more effective control methods, agents chose to manage it more often, which resulted in lower prevalence of N. trichotoma. A relatively low number of agents who were intrinsically less motivated to control weeds led to increased prevalence of both species. This was particularly apparent when long-distance dispersal meant each infested agent increased the invasion risk for a large portion of the landscape. In this case, a small proportion of land mangers reluctant to control, regardless of costs and benefits, could lead to the whole landscape being infested, even when local control stopped new infestations. Social pressure was important, but only if it was independent of weed prevalence, suggesting that early access to information, and incentives to act on that information, may be crucial in stopping a weed from infesting large areas. The response of our model to both behavioral and ecological parameters was highly nonlinear. This implies that the outcomes of weed management programs that deal with multiple land mangers could be highly variable in both space and through time.

Distinct roles of dopamine and subthalamic nucleus in learning and probabilistic decision making.

PubMed

Coulthard, Elizabeth J; Bogacz, Rafal; Javed, Shazia; Mooney, Lucy K; Murphy, Gillian; Keeley, Sophie; Whone, Alan L

2012-12-01

Even simple behaviour requires us to make decisions based on combining multiple pieces of learned and new information. Making such decisions requires both learning the optimal response to each given stimulus as well as combining probabilistic information from multiple stimuli before selecting a response. Computational theories of decision making predict that learning individual stimulus-response associations and rapid combination of information from multiple stimuli are dependent on different components of basal ganglia circuitry. In particular, learning and retention of memory, required for optimal response choice, are significantly reliant on dopamine, whereas integrating information probabilistically is critically dependent upon functioning of the glutamatergic subthalamic nucleus (computing the 'normalization term' in Bayes' theorem). Here, we test these theories by investigating 22 patients with Parkinson's disease either treated with deep brain stimulation to the subthalamic nucleus and dopaminergic therapy or managed with dopaminergic therapy alone. We use computerized tasks that probe three cognitive functions-information acquisition (learning), memory over a delay and information integration when multiple pieces of sequentially presented information have to be combined. Patients performed the tasks ON or OFF deep brain stimulation and/or ON or OFF dopaminergic therapy. Consistent with the computational theories, we show that stopping dopaminergic therapy impairs memory for probabilistic information over a delay, whereas deep brain stimulation to the region of the subthalamic nucleus disrupts decision making when multiple pieces of acquired information must be combined. Furthermore, we found that when participants needed to update their decision on the basis of the last piece of information presented in the decision-making task, patients with deep brain stimulation of the subthalamic nucleus region did not slow down appropriately to revise their plan, a pattern of behaviour that mirrors the impulsivity described clinically in some patients with subthalamic nucleus deep brain stimulation. Thus, we demonstrate distinct mechanisms for two important facets of human decision making: first, a role for dopamine in memory consolidation, and second, the critical importance of the subthalamic nucleus in successful decision making when multiple pieces of information must be combined.

The value of forecasting key-decision variables for rain-fed farming

NASA Astrophysics Data System (ADS)

Winsemius, Hessel; Werner, Micha

2013-04-01

Rain-fed farmers are highly vulnerable to variability in rainfall. Timely knowledge of the onset of the rainy season, the expected amount of rainfall and the occurrence of dry spells can help rain-fed farmers to plan the cropping season. Seasonal probabilistic weather forecasts may provide such information to farmers, but need to provide reliable forecasts of key variables with which farmers can make decisions. In this contribution, we present a new method to evaluate the value of meteorological forecasts in predicting these key variables. The proposed method measures skill by assessing whether a forecast was useful to this decision. This is done by taking into account the required accuracy of timing of the event to make the decision useful. The method progresses the estimate of forecast skill to forecast value by taking into account the required accuracy that is needed to make the decision valuable, based on the cost/loss ratio of possible decisions. The method is applied over the Limpopo region in Southern Africa. We demonstrate the method using the example of temporary water harvesting techniques. Such techniques require time to construct and must be ready long enough before the occurrence of a dry spell to be effective. The value of the forecasts to the decision used as an example is shown to be highly sensitive to the accuracy in the timing of forecasted dry spells, and the tolerance in the decision to timing error. The skill with which dry spells can be predicted is shown to be higher in some parts of the basin, indicating that these forecasts have higher value for the decision in those parts than in others. Through assessing the skill of forecasting key decision variables to the farmers we show that it is easier to understand if the forecasts have value in reducing risk, or if other adaptation strategies should be implemented.

Precision Farming and Precision Pest Management: The Power of New Crop Production Technologies

PubMed Central

Strickland, R. Mack; Ess, Daniel R.; Parsons, Samuel D.

1998-01-01

The use of new technologies including Geographic Information Systems (GIS), the Global Positioning System (GPS), Variable Rate Technology (VRT), and Remote Sensing (RS) is gaining acceptance in the present high-technology, precision agricultural industry. GIS provides the ability to link multiple data values for the same geo-referenced location, and provides the user with a graphical visualization of such data. When GIS is coupled with GPS and RS, management decisions can be applied in a more precise "micro-managed" manner by using VRT techniques. Such technology holds the potential to reduce agricultural crop production costs as well as crop and environmental damage. PMID:19274236

Factors associated with divorce in intrafamily child sexual abuse cases.

PubMed

Sirles, E A; Lofberg, C E

1990-01-01

This study analyzes factors related to the decision to divorce in intrafamilial sexual abuse cases. Data was collected on 128 cases of incest in St. Louis, Missouri, through the Washington University Child Guidance Center. Examination of multiple variables revealed there are differences between child sexual abuse cases that elect to stay together vs. those that divorce subsequent to the discovery of abuse. Families that broke up were more likely to have young child victims and have additional problems with domestic violence. The child was more likely to have revealed the abuse to the mother and have been believed by her in divorcing cases.

Integrated Decision Strategies for Skin Sensitization Hazard

PubMed Central

Strickland, Judy; Zang, Qingda; Kleinstreuer, Nicole; Paris, Michael; Lehmann, David M.; Choksi, Neepa; Matheson, Joanna; Jacobs, Abigail; Lowit, Anna; Allen, David; Casey, Warren

2016-01-01

One of the top priorities of ICCVAM is the identification and evaluation of non-animal alternatives for skin sensitization testing. Although skin sensitization is a complex process, the key biological events of the process have been well characterized in an adverse outcome pathway (AOP) proposed by OECD. Accordingly, ICCVAM is working to develop integrated decision strategies based on the AOP using in vitro, in chemico, and in silico information. Data were compiled for 120 substances tested in the murine local lymph node assay (LLNA), direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT), and KeratinoSens assay. Data for six physicochemical properties that may affect skin penetration were also collected, and skin sensitization read-across predictions were performed using OECD QSAR Toolbox. All data were combined into a variety of potential integrated decision strategies to predict LLNA outcomes using a training set of 94 substances and an external test set of 26 substances. Fifty-four models were built using multiple combinations of machine learning approaches and predictor variables. The seven models with the highest accuracy (89–96% for the test set and 96–99% for the training set) for predicting LLNA outcomes used a support vector machine (SVM) approach with different combinations of predictor variables. The performance statistics of the SVM models were higher than any of the non-animal tests alone and higher than simple test battery approaches using these methods. These data suggest that computational approaches are promising tools to effectively integrate data sources to identify potential skin sensitizers without animal testing. PMID:26851134

Diagnoses as Moderators of the Relationship Between Biographical Variables and Psychiatric Decision in a Combat Zone

ERIC Educational Resources Information Center

Clum, George A.; Hoiberg, Anne

1971-01-01

The decision to return a man to combat duty was found to be related to biographical variables, and the nature of these relationships were found to have significant reliability. Also, evidence suggested that biographical variables were salient depending on whether the diagnostic group was character disorder or neurotic. (Author)

A model of human decision making in multiple process monitoring situations

NASA Technical Reports Server (NTRS)

Greenstein, J. S.; Rouse, W. B.

1982-01-01

Human decision making in multiple process monitoring situations is considered. It is proposed that human decision making in many multiple process monitoring situations can be modeled in terms of the human's detection of process related events and his allocation of attention among processes once he feels event have occurred. A mathematical model of human event detection and attention allocation performance in multiple process monitoring situations is developed. An assumption made in developing the model is that, in attempting to detect events, the human generates estimates of the probabilities that events have occurred. An elementary pattern recognition technique, discriminant analysis, is used to model the human's generation of these probability estimates. The performance of the model is compared to that of four subjects in a multiple process monitoring situation requiring allocation of attention among processes.

Hesitant triangular fuzzy information aggregation operators based on Bonferroni means and their application to multiple attribute decision making.

PubMed

Wang, Chunyong; Li, Qingguo; Zhou, Xiaoqiang; Yang, Tian

2014-01-01

We investigate the multiple attribute decision-making (MADM) problems with hesitant triangular fuzzy information. Firstly, definition and some operational laws of hesitant triangular fuzzy elements are introduced. Then, we develop some hesitant triangular fuzzy aggregation operators based on Bonferroni means and discuss their basic properties. Some existing operators can be viewed as their special cases. Next, we apply the proposed operators to deal with multiple attribute decision-making problems under hesitant triangular fuzzy environment. Finally, an illustrative example is given to show the developed method and demonstrate its practicality and effectiveness.

Hesitant Triangular Fuzzy Information Aggregation Operators Based on Bonferroni Means and Their Application to Multiple Attribute Decision Making

PubMed Central

Zhou, Xiaoqiang; Yang, Tian

2014-01-01

We investigate the multiple attribute decision-making (MADM) problems with hesitant triangular fuzzy information. Firstly, definition and some operational laws of hesitant triangular fuzzy elements are introduced. Then, we develop some hesitant triangular fuzzy aggregation operators based on Bonferroni means and discuss their basic properties. Some existing operators can be viewed as their special cases. Next, we apply the proposed operators to deal with multiple attribute decision-making problems under hesitant triangular fuzzy environment. Finally, an illustrative example is given to show the developed method and demonstrate its practicality and effectiveness. PMID:25140338

Structured decision making as a conceptual framework to identify thresholds for conservation and management

USGS Publications Warehouse

Martin, J.; Runge, M.C.; Nichols, J.D.; Lubow, B.C.; Kendall, W.L.

2009-01-01

Thresholds and their relevance to conservation have become a major topic of discussion in the ecological literature. Unfortunately, in many cases the lack of a clear conceptual framework for thinking about thresholds may have led to confusion in attempts to apply the concept of thresholds to conservation decisions. Here, we advocate a framework for thinking about thresholds in terms of a structured decision making process. The purpose of this framework is to promote a logical and transparent process for making informed decisions for conservation. Specification of such a framework leads naturally to consideration of definitions and roles of different kinds of thresholds in the process. We distinguish among three categories of thresholds. Ecological thresholds are values of system state variables at which small changes bring about substantial changes in system dynamics. Utility thresholds are components of management objectives (determined by human values) and are values of state or performance variables at which small changes yield substantial changes in the value of the management outcome. Decision thresholds are values of system state variables at which small changes prompt changes in management actions in order to reach specified management objectives. The approach that we present focuses directly on the objectives of management, with an aim to providing decisions that are optimal with respect to those objectives. This approach clearly distinguishes the components of the decision process that are inherently subjective (management objectives, potential management actions) from those that are more objective (system models, estimates of system state). Optimization based on these components then leads to decision matrices specifying optimal actions to be taken at various values of system state variables. Values of state variables separating different actions in such matrices are viewed as decision thresholds. Utility thresholds are included in the objectives component, and ecological thresholds may be embedded in models projecting consequences of management actions. Decision thresholds are determined by the above-listed components of a structured decision process. These components may themselves vary over time, inducing variation in the decision thresholds inherited from them. These dynamic decision thresholds can then be determined using adaptive management. We provide numerical examples (that are based on patch occupancy models) of structured decision processes that include all three kinds of thresholds. ?? 2009 by the Ecological Society of America.

Assessing Neurocognition via Gamified Experimental Logic: A Novel Approach to Simultaneous Acquisition of Multiple ERPs.

PubMed

Nair, Ajay K; Sasidharan, Arun; John, John P; Mehrotra, Seema; Kutty, Bindu M

2016-01-01

The present study describes the development of a neurocognitive paradigm: "Assessing Neurocognition via Gamified Experimental Logic" (ANGEL), for performing the parametric evaluation of multiple neurocognitive functions simultaneously. ANGEL employs an audiovisual sensory motor design for the acquisition of multiple event related potentials (ERPs)-the C1, P50, MMN, N1, N170, P2, N2pc, LRP, P300, and ERN. The ANGEL paradigm allows assessment of 10 neurocognitive variables over the course of three "game" levels of increasing complexity ranging from simple passive observation to complex discrimination and response in the presence of multiple distractors. The paradigm allows assessment of several levels of rapid decision making: speeded up response vs. response-inhibition; responses to easy vs. difficult tasks; responses based on gestalt perception of clear vs. ambiguous stimuli; and finally, responses with set shifting during challenging tasks. The paradigm has been tested using 18 healthy participants from both sexes and the possibilities of varied data analyses have been presented in this paper. The ANGEL approach provides an ecologically valid assessment (as compared to existing tools) that quickly yields a very rich dataset and helps to assess multiple ERPs that can be studied extensively to assess cognitive functions in health and disease conditions.

Assessing Neurocognition via Gamified Experimental Logic: A Novel Approach to Simultaneous Acquisition of Multiple ERPs

PubMed Central

Nair, Ajay K.; Sasidharan, Arun; John, John P.; Mehrotra, Seema; Kutty, Bindu M.

2016-01-01

The present study describes the development of a neurocognitive paradigm: “Assessing Neurocognition via Gamified Experimental Logic” (ANGEL), for performing the parametric evaluation of multiple neurocognitive functions simultaneously. ANGEL employs an audiovisual sensory motor design for the acquisition of multiple event related potentials (ERPs)—the C1, P50, MMN, N1, N170, P2, N2pc, LRP, P300, and ERN. The ANGEL paradigm allows assessment of 10 neurocognitive variables over the course of three “game” levels of increasing complexity ranging from simple passive observation to complex discrimination and response in the presence of multiple distractors. The paradigm allows assessment of several levels of rapid decision making: speeded up response vs. response-inhibition; responses to easy vs. difficult tasks; responses based on gestalt perception of clear vs. ambiguous stimuli; and finally, responses with set shifting during challenging tasks. The paradigm has been tested using 18 healthy participants from both sexes and the possibilities of varied data analyses have been presented in this paper. The ANGEL approach provides an ecologically valid assessment (as compared to existing tools) that quickly yields a very rich dataset and helps to assess multiple ERPs that can be studied extensively to assess cognitive functions in health and disease conditions. PMID:26858586

Adaptation to Interannual and Interdecadal Climate Variability in Agricultural Production Systems of the Argentine Pampas

NASA Astrophysics Data System (ADS)

Podestá, G. P.; Bert, F.; Weber, E.; Laciana, C.; Rajagopalan, B.; Letson, D.

2007-05-01

Agricultural ecosystems play a central role in world food production and food security, and involve one of the most climate-sensitive sectors of society-agriculture. We focus on crop production in the Argentine Pampas, one of the world's major agricultural regions. Climate of the Pampas shows marked variability at both interannual and decadal time scales. We explored the scope for adaptive management in response to climate information on interannual scales. We show that different assumptions about what decision makers are trying to achieve (i.e., their objective functions) may change what actions are considered as "optimal" for a given climate context. Optimal actions also were used to estimate the economic value of forecasts of an ENSO phase. Decision constraints (e.g., crop rotations) have critical influence on value of the forecasting system. Gaps in knowledge or misconceptions about climate variability were identified in open-ended "mental model" interviews. Results were used to design educational interventions. A marked increase in precipitation since the 1970s, together with new production technologies, led to major changes in land use patterns in the Pampas. Continuous cropping has widely replaced agriculture-pasture rotations. Nevertheless, production systems that evolved partly in response to increased rainfall may not be viable if climate reverts to a drier epoch. We use historical data to define a range of plausible climate trajectories 20-30 years hence. Regional scenarios are downscaled using semi-parametric weather generators to produce multiple realizations of daily weather consistent with decadal scenarios. Finally, we use the synthetic climate, crop growth models, and realistic models of decision-making under risk to compute risk metrics (e.g., probability of yields or profits being below a threshold). Climatically optimal and marginal locations show differential responses: probabilities of negative economic results are much higher in currently marginal areas if precipitations decrease.

Combining disparate data for decision making

NASA Astrophysics Data System (ADS)

Gettings, M. E.

2010-12-01

Combining information of disparate types from multiple data or model sources is a fundamental task in decision making theory. Procedures for combining and utilizing quantitative data with uncertainties are well-developed in several approaches, but methods for including qualitative and semi-quantitative data are much less so. Possibility theory offers an approach to treating all three data types in an objective and repeatable way. In decision making, biases are frequently present in several forms, including those arising from data quality, data spatial and temporal distribution, and the analyst's knowledge and beliefs as to which data or models are most important. The latter bias is particularly evident in the case of qualitative data and there are numerous examples of analysts feeling that a qualitative dataset is more relevant than a quantified one. Possibility theory and fuzzy logic now provide fairly general rules for quantifying qualitative and semi-quantitative data in ways that are repeatable and minimally biased. Once a set of quantified data and/or model layers is obtained, there are several methods of combining them to obtain insight useful in decision making. These include: various combinations of layers using formal fuzzy logic (for example, layer A and (layer B or layer C) but not layer D); connecting the layers with varying influence links in a Fuzzy Cognitive Map; and using the set of layers for the universe of discourse for agent based model simulations. One example of logical combinations that have proven useful is the definition of possible habitat for valley fever fungus (Coccidioides sp.) using variables such as soil type, altitude, aspect, moisture and temperature. A second example is the delineation of the lithology and possible mineralization of several areas beneath basin fill in southern Arizona. A Fuzzy Cognitive Map example is the impacts of development and operation of a hypothetical mine in an area adjacent to a city. In this model variables such as water use, environmental quality measures (visual and geochemical), deposit quality, rate of development, and commodity price combine in complex ways to yield frequently counter-intuitive results. By varying the interaction strengths linking the variables, insight into the complex interactions of the system can be gained. An example using agent-based modeling is a model designed to test the hypothesis that new valley fever fungus sites could be established from existing sites by wind transport of fungal spores. The variables include layers simulating precipitation, temperature, soil moisture, and soil chemistry based on historical climate records and studies of known valley fever habitat. Numerous agent-based model runs show that the system is self organizing to the extent that there will be new sites established by wind transport over decadal scales. Possibility theory provides a framework for gaining insight into the interaction of known or suspected variables in a complex system. Once the data layers are quantified into possibility functions, varying hypotheses of the relative importance of variables and processes can be obtained by repeated combinations with varying weights. This permits an evaluation of the effects of various data layers, their uncertainties, and biases from the layers, all of which improve the objectivity of decision making.

A Representation for Gaining Insight into Clinical Decision Models

PubMed Central

Jimison, Holly B.

1988-01-01

For many medical domains uncertainty and patient preferences are important components of decision making. Decision theory is useful as a representation for such medical models in computer decision aids, but the methodology has typically had poor performance in the areas of explanation and user interface. The additional representation of probabilities and utilities as random variables serves to provide a framework for graphical and text insight into complicated decision models. The approach allows for efficient customization of a generic model that describes the general patient population of interest to a patient- specific model. Monte Carlo simulation is used to calculate the expected value of information and sensitivity for each model variable, thus providing a metric for deciding what to emphasize in the graphics and text summary. The computer-generated explanation includes variables that are sensitive with respect to the decision or that deviate significantly from what is typically observed. These techniques serve to keep the assessment and explanation of the patient's decision model concise, allowing the user to focus on the most important aspects for that patient.

Forms of work organization and associations with shoulder disorders: Results from a French working population.

PubMed

Bodin, Julie; Garlantézec, Ronan; Costet, Nathalie; Descatha, Alexis; Fouquet, Natacha; Caroly, Sandrine; Roquelaure, Yves

2017-03-01

The aim of this study was to identify forms of work organization in a French region and to study associations with the occurrence of symptomatic and clinically diagnosed shoulder disorders in workers. Workers were randomly included in this cross-sectional study from 2002 to 2005. Sixteen organizational variables were assessed by a self-administered questionnaire: i.e. shift work, job rotation, repetitiveness of tasks, paced work/automatic rate, work pace dependent on quantified targets, permanent controls or surveillance, colleagues' work and customer demand, and eight variables measuring decision latitude. Five forms of work organization were identified using hierarchical cluster analysis (HCA) of variables and HCA of workers: low decision latitude with pace constraints, medium decision latitude with pace constraints, low decision latitude with low pace constraints, high decision latitude with pace constraints and high decision latitude with low pace constraints. There were significant associations between forms of work organization and symptomatic and clinically-diagnosed shoulder disorders. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dynamic modulation of decision biases by brainstem arousal systems.

PubMed

de Gee, Jan Willem; Colizoli, Olympia; Kloosterman, Niels A; Knapen, Tomas; Nieuwenhuis, Sander; Donner, Tobias H

2017-04-11

Decision-makers often arrive at different choices when faced with repeated presentations of the same evidence. Variability of behavior is commonly attributed to noise in the brain's decision-making machinery. We hypothesized that phasic responses of brainstem arousal systems are a significant source of this variability. We tracked pupil responses (a proxy of phasic arousal) during sensory-motor decisions in humans, across different sensory modalities and task protocols. Large pupil responses generally predicted a reduction in decision bias. Using fMRI, we showed that the pupil-linked bias reduction was (i) accompanied by a modulation of choice-encoding pattern signals in parietal and prefrontal cortex and (ii) predicted by phasic, pupil-linked responses of a number of neuromodulatory brainstem centers involved in the control of cortical arousal state, including the noradrenergic locus coeruleus. We conclude that phasic arousal suppresses decision bias on a trial-by-trial basis, thus accounting for a significant component of the variability of choice behavior.

Dynamic modulation of decision biases by brainstem arousal systems

PubMed Central

de Gee, Jan Willem; Colizoli, Olympia; Kloosterman, Niels A; Knapen, Tomas; Nieuwenhuis, Sander; Donner, Tobias H

2017-01-01

Decision-makers often arrive at different choices when faced with repeated presentations of the same evidence. Variability of behavior is commonly attributed to noise in the brain’s decision-making machinery. We hypothesized that phasic responses of brainstem arousal systems are a significant source of this variability. We tracked pupil responses (a proxy of phasic arousal) during sensory-motor decisions in humans, across different sensory modalities and task protocols. Large pupil responses generally predicted a reduction in decision bias. Using fMRI, we showed that the pupil-linked bias reduction was (i) accompanied by a modulation of choice-encoding pattern signals in parietal and prefrontal cortex and (ii) predicted by phasic, pupil-linked responses of a number of neuromodulatory brainstem centers involved in the control of cortical arousal state, including the noradrenergic locus coeruleus. We conclude that phasic arousal suppresses decision bias on a trial-by-trial basis, thus accounting for a significant component of the variability of choice behavior. DOI: http://dx.doi.org/10.7554/eLife.23232.001 PMID:28383284

Sampling the stream landscape: Improving the applicability of an ecoregion-level capture probability model for stream fishes

USGS Publications Warehouse

Mollenhauer, Robert; Mouser, Joshua B.; Brewer, Shannon K.

2018-01-01

Temporal and spatial variability in streams result in heterogeneous gear capture probability (i.e., the proportion of available individuals identified) that confounds interpretation of data used to monitor fish abundance. We modeled tow-barge electrofishing capture probability at multiple spatial scales for nine Ozark Highland stream fishes. In addition to fish size, we identified seven reach-scale environmental characteristics associated with variable capture probability: stream discharge, water depth, conductivity, water clarity, emergent vegetation, wetted width–depth ratio, and proportion of riffle habitat. The magnitude of the relationship between capture probability and both discharge and depth varied among stream fishes. We also identified lithological characteristics among stream segments as a coarse-scale source of variable capture probability. The resulting capture probability model can be used to adjust catch data and derive reach-scale absolute abundance estimates across a wide range of sampling conditions with similar effort as used in more traditional fisheries surveys (i.e., catch per unit effort). Adjusting catch data based on variable capture probability improves the comparability of data sets, thus promoting both well-informed conservation and management decisions and advances in stream-fish ecology.

Multiple benefits of alloparental care in a fluctuating environment.

PubMed

Guindre-Parker, Sarah; Rubenstein, Dustin R

2018-02-01

Although cooperatively breeding vertebrates occur disproportionately in unpredictable environments, the underlying mechanism shaping this biogeographic pattern remains unclear. Cooperative breeding may buffer against harsh conditions (hard life hypothesis), or additionally allow for sustained breeding under benign conditions (temporal variability hypothesis). To distinguish between the hard life and temporal variability hypotheses, we investigated whether the number of alloparents at a nest increased reproductive success or load-lightening in superb starlings ( Lamprotornis superbus ), and whether these two types of benefits varied in harsh and benign years. We found that mothers experienced both types of benefits consistent with the temporal variability hypothesis, as larger contingents of alloparents increased the number of young fledged while simultaneously allowing mothers to reduce their provisioning rates under both harsh and benign rainfall conditions. By contrast, fathers experienced load-lightening only under benign rainfall conditions, suggesting that cooperative breeding may serve to take advantage of unpredictable benign breeding seasons when they do occur. Cooperative breeding in unpredictable environments may thus promote flexibility in offspring care behaviour, which could mitigate variability in the cost of raising young. Our results highlight the importance of considering how offspring care decisions vary among breeding roles and across fluctuating environmental conditions.

Multiple benefits of alloparental care in a fluctuating environment

PubMed Central

2018-01-01

Although cooperatively breeding vertebrates occur disproportionately in unpredictable environments, the underlying mechanism shaping this biogeographic pattern remains unclear. Cooperative breeding may buffer against harsh conditions (hard life hypothesis), or additionally allow for sustained breeding under benign conditions (temporal variability hypothesis). To distinguish between the hard life and temporal variability hypotheses, we investigated whether the number of alloparents at a nest increased reproductive success or load-lightening in superb starlings (Lamprotornis superbus), and whether these two types of benefits varied in harsh and benign years. We found that mothers experienced both types of benefits consistent with the temporal variability hypothesis, as larger contingents of alloparents increased the number of young fledged while simultaneously allowing mothers to reduce their provisioning rates under both harsh and benign rainfall conditions. By contrast, fathers experienced load-lightening only under benign rainfall conditions, suggesting that cooperative breeding may serve to take advantage of unpredictable benign breeding seasons when they do occur. Cooperative breeding in unpredictable environments may thus promote flexibility in offspring care behaviour, which could mitigate variability in the cost of raising young. Our results highlight the importance of considering how offspring care decisions vary among breeding roles and across fluctuating environmental conditions. PMID:29515910

A simple solution for model comparison in bold imaging: the special case of reward prediction error and reward outcomes.

PubMed

Erdeniz, Burak; Rohe, Tim; Done, John; Seidler, Rachael D

2013-01-01

Conventional neuroimaging techniques provide information about condition-related changes of the BOLD (blood-oxygen-level dependent) signal, indicating only where and when the underlying cognitive processes occur. Recently, with the help of a new approach called "model-based" functional neuroimaging (fMRI), researchers are able to visualize changes in the internal variables of a time varying learning process, such as the reward prediction error or the predicted reward value of a conditional stimulus. However, despite being extremely beneficial to the imaging community in understanding the neural correlates of decision variables, a model-based approach to brain imaging data is also methodologically challenging due to the multicollinearity problem in statistical analysis. There are multiple sources of multicollinearity in functional neuroimaging including investigations of closely related variables and/or experimental designs that do not account for this. The source of multicollinearity discussed in this paper occurs due to correlation between different subjective variables that are calculated very close in time. Here, we review methodological approaches to analyzing such data by discussing the special case of separating the reward prediction error signal from reward outcomes.

Cloud E-Learning Service Strategies for Improving E-Learning Innovation Performance in a Fuzzy Environment by Using a New Hybrid Fuzzy Multiple Attribute Decision-Making Model

ERIC Educational Resources Information Center

Su, Chiu Hung; Tzeng, Gwo-Hshiung; Hu, Shu-Kung

2016-01-01

The purpose of this study was to address this problem by applying a new hybrid fuzzy multiple criteria decision-making model including (a) using the fuzzy decision-making trial and evaluation laboratory (DEMATEL) technique to construct the fuzzy scope influential network relationship map (FSINRM) and determine the fuzzy influential weights of the…

Real-Time Optimal Flood Control Decision Making and Risk Propagation Under Multiple Uncertainties

NASA Astrophysics Data System (ADS)

Zhu, Feilin; Zhong, Ping-An; Sun, Yimeng; Yeh, William W.-G.

2017-12-01

Multiple uncertainties exist in the optimal flood control decision-making process, presenting risks involving flood control decisions. This paper defines the main steps in optimal flood control decision making that constitute the Forecast-Optimization-Decision Making (FODM) chain. We propose a framework for supporting optimal flood control decision making under multiple uncertainties and evaluate risk propagation along the FODM chain from a holistic perspective. To deal with uncertainties, we employ stochastic models at each link of the FODM chain. We generate synthetic ensemble flood forecasts via the martingale model of forecast evolution. We then establish a multiobjective stochastic programming with recourse model for optimal flood control operation. The Pareto front under uncertainty is derived via the constraint method coupled with a two-step process. We propose a novel SMAA-TOPSIS model for stochastic multicriteria decision making. Then we propose the risk assessment model, the risk of decision-making errors and rank uncertainty degree to quantify the risk propagation process along the FODM chain. We conduct numerical experiments to investigate the effects of flood forecast uncertainty on optimal flood control decision making and risk propagation. We apply the proposed methodology to a flood control system in the Daduhe River basin in China. The results indicate that the proposed method can provide valuable risk information in each link of the FODM chain and enable risk-informed decisions with higher reliability.

Ensemble modelling and structured decision-making to support Emergency Disease Management.

PubMed

Webb, Colleen T; Ferrari, Matthew; Lindström, Tom; Carpenter, Tim; Dürr, Salome; Garner, Graeme; Jewell, Chris; Stevenson, Mark; Ward, Michael P; Werkman, Marleen; Backer, Jantien; Tildesley, Michael

2017-03-01

Epidemiological models in animal health are commonly used as decision-support tools to understand the impact of various control actions on infection spread in susceptible populations. Different models contain different assumptions and parameterizations, and policy decisions might be improved by considering outputs from multiple models. However, a transparent decision-support framework to integrate outputs from multiple models is nascent in epidemiology. Ensemble modelling and structured decision-making integrate the outputs of multiple models, compare policy actions and support policy decision-making. We briefly review the epidemiological application of ensemble modelling and structured decision-making and illustrate the potential of these methods using foot and mouth disease (FMD) models. In case study one, we apply structured decision-making to compare five possible control actions across three FMD models and show which control actions and outbreak costs are robustly supported and which are impacted by model uncertainty. In case study two, we develop a methodology for weighting the outputs of different models and show how different weighting schemes may impact the choice of control action. Using these case studies, we broadly illustrate the potential of ensemble modelling and structured decision-making in epidemiology to provide better information for decision-making and outline necessary development of these methods for their further application. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

A framework for multi-stakeholder decision-making and conflict resolution

EPA Science Inventory

We propose a decision-making framework to compute compromise solutions that balance conflicting priorities of multiple stakeholders on multiple objectives. In our setting, we shape the stakeholder dis-satisfaction distribution by solving a conditional-value-at-risk (CVaR) minimiz...

Presenting evidence and summary measures to best inform societal decisions when comparing multiple strategies.

PubMed

Eckermann, Simon; Willan, Andrew R

2011-07-01

Multiple strategy comparisons in health technology assessment (HTA) are becoming increasingly important, with multiple alternative therapeutic actions, combinations of therapies and diagnostic and genetic testing alternatives. Comparison under uncertainty of incremental cost, effects and cost effectiveness across more than two strategies is conceptually and practically very different from that for two strategies, where all evidence can be summarized in a single bivariate distribution on the incremental cost-effectiveness plane. Alternative methods for comparing multiple strategies in HTA have been developed in (i) presenting cost and effects on the cost-disutility plane and (ii) summarizing evidence with multiple strategy cost-effectiveness acceptability (CEA) and expected net loss (ENL) curves and frontiers. However, critical questions remain for the analyst and decision maker of how these techniques can be best employed across multiple strategies to (i) inform clinical and cost inference in presenting evidence, and (ii) summarize evidence of cost effectiveness to inform societal reimbursement decisions where preferences may be risk neutral or somewhat risk averse under the Arrow-Lind theorem. We critically consider how evidence across multiple strategies can be best presented and summarized to inform inference and societal reimbursement decisions, given currently available methods. In the process, we make a number of important original findings. First, in presenting evidence for multiple strategies, the joint distribution of costs and effects on the cost-disutility plane with associated flexible comparators varying across replicates for cost and effect axes ensure full cost and effect inference. Such inference is usually confounded on the cost-effectiveness plane with comparison relative to a fixed origin and axes. Second, in summarizing evidence for risk-neutral societal decision making, ENL curves and frontiers are shown to have advantages over the CEA frontier in directly presenting differences in expected net benefit (ENB). The CEA frontier, while identifying strategies that maximize ENB, only presents their probability of maximizing net benefit (NB) and, hence, fails to explain why strategies maximize ENB at any given threshold value. Third, in summarizing evidence for somewhat risk-averse societal decision making, trade-offs between the strategy maximizing ENB and other potentially optimal strategies with higher probability of maximizing NB should be presented over discrete threshold values where they arise. However, the probabilities informing these trade-offs and associated discrete threshold value regions should be derived from bilateral CEA curves to prevent confounding by other strategies inherent in multiple strategy CEA curves. Based on these findings, a series of recommendations are made for best presenting and summarizing cost-effectiveness evidence for reimbursement decisions when comparing multiple strategies, which are contrasted with advice for comparing two strategies. Implications for joint research and reimbursement decisions are also discussed.

The Role of Dysfunctional Myths in a Decision-Making Process under Bounded Rationality: A Complex Dynamical Systems Perspective.

PubMed

Stamovlasis, Dimitrios; Vaiopoulou, Julie

2017-07-01

The present study examines the factors influencing a decision-making process, with specific focus on the role of dysfunctional myths (DM). DM are thoughts or beliefs that are rather irrational, however influential to people's decisions. In this paper a decision-making process regarding the career choice of university students majoring in natural sciences and education (N=496) is examined by analyzing survey data taken via Career Decision Making Difficulties Questionnaire (CDDQ). The difficulty of making the choice and the certainty about one's decision were the state variables, while the independent variables were factors related to the lack of information or knowledge needed, which actually reflect a bounded rationality. Cusp catastrophe analysis, based on both least squares and maximum likelihood procedures, showed that the nonlinear models predicting the two state variables were superior to linear alternatives. Factors related to lack of knowledge about the steps involved in the process of career decision-making, lack of information about the various occupations, lack of information about self and lack of motivation acted as asymmetry, while dysfunctional myths acted as bifurcation factor for both state variables. The catastrophe model, grounded in empirical data, revealed a unique role for DM and a better interpretation within the context of complexity and the notion of bounded rationality. The analysis opens the nonlinear dynamical systems (NDS) perspective in studying decision-making processes. Theoretical and practical implications are discussed.

Sex-role stereotype, self-concept, education and experience: do they influence decision-making?

PubMed

Joseph, D H

1985-01-01

The purpose of this study was to investigate the effects of self-concept, sex-role stereotype, educational preparation and years of experience upon the nurse's attitudes regarding decision-making. The Joseph Decision-making Tool (JDMT) was designed by the investigator to measure nurses' attitudes regarding decision-making. The tool consists of 20 scenarios in which the subject is asked to make a decision regarding a patient problem. Having an alpha reliability of 0.79, the JDMT was found to be exceedingly useful and easy to administer. Self-concept was measured by the BEM Scale. A heterogeneous population of female staff nurses working in medical-surgical units of two large metropolitan hospitals was used. A stepwise multiple regression technique was used to measure the potency of the particular variables in question. In a selected sample of 85 nurses, it was found that nurses with masculine sex-type scores and diploma education (P less than 0.05) felt that nurses should assume responsibility for decision-making. Experience was found to have an inverse relationship (P less than 0.01) with the JDMT. The more experience the nurse has, the less willing she is to make decisions. The majority of nurses (62%) who participated in the study were found to have androgynous rather than feminine sex-role stereotype scores. These two findings indicate changing trends in the traditional view of staff nurses. These new findings will assist nurses in changing the current image of a typically feminine nurse with a low self-concept. This study found strengths in nurses that are often overlooked by the feminists when they study nurses.

Vulnerable patients' perceptions of health care quality and quality data.

PubMed

Raven, Maria Catherine; Gillespie, Colleen C; DiBennardo, Rebecca; Van Busum, Kristin; Elbel, Brian

2012-01-01

Little is known about how patients served by safety-net hospitals utilize and respond to hospital quality data. To understand how vulnerable, lower income patients make health care decisions and define quality of care and whether hospital quality data factor into such decisions and definitions. Mixed quantitative and qualitative methods were used to gather primary data from patients at an urban, tertiary-care safety-net hospital. The study hospital is a member of the first public hospital system to voluntarily post hospital quality data online for public access. Patients were recruited from outpatient and inpatient clinics. Surveys were used to collect data on participants' sociodemographic characteristics, health literacy, health care experiences, and satisfaction variables. Focus groups were used to explore a representative sample of 24 patients' health care decision making and views of quality. Data from focus group transcripts were iteratively coded and analyzed by the authors. Focus group participants were similar to the broader diverse, low-income clinic. Participants reported exercising choice in making decisions about where to seek health care. Multiple sources influenced decision-making processes including participants' own beliefs and values, social influences, and prior experiences. Hospital quality data were notably absent as a source of influence in health care decision making for this population largely because participants were unaware of its existence. Participants' views of hospital quality were influenced by the quality and efficiency of services provided (with an emphasis on the doctor-patient relationship) and patient centeredness. When presented with it, patients appreciated the hospital quality data and, with guidance, were interested in incorporating it into health care decision making. Results suggest directions for optimizing the presentation, content, and availability of hospital quality data. Future research will explore how similar populations form and make choices based on presentation of hospital quality data.

Bridging groundwater models and decision support with a Bayesian network

USGS Publications Warehouse

Fienen, Michael N.; Masterson, John P.; Plant, Nathaniel G.; Gutierrez, Benjamin T.; Thieler, E. Robert

2013-01-01

Resource managers need to make decisions to plan for future environmental conditions, particularly sea level rise, in the face of substantial uncertainty. Many interacting processes factor in to the decisions they face. Advances in process models and the quantification of uncertainty have made models a valuable tool for this purpose. Long-simulation runtimes and, often, numerical instability make linking process models impractical in many cases. A method for emulating the important connections between model input and forecasts, while propagating uncertainty, has the potential to provide a bridge between complicated numerical process models and the efficiency and stability needed for decision making. We explore this using a Bayesian network (BN) to emulate a groundwater flow model. We expand on previous approaches to validating a BN by calculating forecasting skill using cross validation of a groundwater model of Assateague Island in Virginia and Maryland, USA. This BN emulation was shown to capture the important groundwater-flow characteristics and uncertainty of the groundwater system because of its connection to island morphology and sea level. Forecast power metrics associated with the validation of multiple alternative BN designs guided the selection of an optimal level of BN complexity. Assateague island is an ideal test case for exploring a forecasting tool based on current conditions because the unique hydrogeomorphological variability of the island includes a range of settings indicative of past, current, and future conditions. The resulting BN is a valuable tool for exploring the response of groundwater conditions to sea level rise in decision support.

Price Strategies between a Dominant Retailer and Manufacturers

NASA Astrophysics Data System (ADS)

Cho, Hsun Jung; Mak, Hou Kit

2009-08-01

Supply chain-related game theoretical applications have been discussed for decades. This research accounts for the emergence of a dominant retailer, and the retailer Stackelberg pricing models of distribution channels. Research in the channel pricing game may use different definitions of pricing decision variables. In this research, we pay attentions to the retailer Stackelberg pricing game, and discuss the effects when choosing different decision variables. According the literature it was shown that the strategies between channel members depend critically on the form of the demand function. Two different demand forms—linear and non-linear—will be considered in our numerical example respectively. Our major finding is the outcomes are not relative to manufacturers' pricing decisions but to the retailer's pricing decision and choosing percentage margin as retailer's decision variable is the best strategy for the retailer but worst for manufacturers. The numerical results show that it is consistence between linear and non-linear demand form.

The development of an MRI lesion quantifying system for multiple sclerosis patients undergoing treatment

NASA Astrophysics Data System (ADS)

Moin, Paymann; Ma, Kevin; Amezcua, Lilyana; Gertych, Arkadiusz; Liu, Brent

2009-02-01

Multiple sclerosis (MS) is a demyelinating disease of the central nervous system that affects approximately 2.5 million people worldwide. Magnetic resonance imaging (MRI) is an established tool for the assessment of disease activity, progression and response to treatment. The progression of the disease is variable and requires routine follow-up imaging studies. Currently, MRI quantification of multiple sclerosis requires a manual approach to lesion measurement and yields an estimate of lesion volume and interval change. In the setting of several prior studies and a long treatment history, trends related to treatment change quickly become difficult to extrapolate. Our efforts seek to develop an imaging informatics based MS lesion computer aided detection (CAD) package to quantify and track MS lesions including lesion load, volume, and location. Together, with select clinical parameters, this data will be incorporated into an MS specific e- Folder to provide decision support to evaluate and assess treatment options for MS in a manner tailored specifically to an individual based on trends in MS presentation and progression.

Assessing Portuguese Guadiana Basin water management impacts under climate change and paleoclimate variability

NASA Astrophysics Data System (ADS)

Maia, Rodrigo; Oliveira, Bruno; Ramos, Vanessa; Brekke, Levi

2014-05-01

The water balance in each reservoir and the subsequent, related, water resource management decisions are, presently, highly information dependent and are therefore often limited to a reactive response (even if aimed towards preventing future issues regarding the water system). Taking advantage of the availability of scenarios for climate projections, it is now possible to estimate the likely future evolution of climate which represents an important stepping stone towards proactive, adaptative, water resource management. The purpose of the present study was to assess the potential effects of climate change in terms of temperature, precipitation, runoff and water availability/scarcity for application in water resource management decisions. The analysis here presented was applied to the Portuguese portion of the Guadiana River Basin, using a combination of observed climate and runoff data and the results of the Global Climate Models. The Guadiana River Basin was represented by its reservoirs on the Portuguese portion of the basin and, for the future period, an estimated value of the inflows originating in the Spanish part of the Basin. The change in climate was determined in terms of relative and absolute variations of climate (precipitation and temperature) and hydrology (runoff and water balance related information). Apart from the previously referred data, an hydrological model and a water management model were applied so as to obtain an extended range of data regarding runoff generation (calibrated to observed data) and water balance in the reservoirs (considering the climate change impacts in the inflows, outflows and water consumption). The water management model was defined in order to represent the reservoirs interaction including upstream to downstream discharges and water transfers. Under the present climate change context, decision-makers and stakeholders are ever more vulnerable to the uncertainties of climate. Projected climate in the Guadiana basin indicates an increase in temperatures and a reduction of the precipitation values which go well beyond the observed values and, therefore, must be forcefully included in any realistic proactive water resource management decision. Using the results of this study it is possible to estimate future water availability and consumption satisfaction allowing for the elaboration of informed management decisions. In this study, the CMIP 3 Global Climate Models were considered for the definition of the effects of climate change, using the median and extreme tendencies based on the range of variation of the multiple climate projection scenarios. The observed climate variability, along with these model-derived tendencies, were used to inform the hydrology and water management models for the historical and future periods, respectively. Additionally, for a more comprehensive analysis on climate variability, a stochastic model was implemented based on the paleoclimate variability obtained from tree-ring records.

Functional Freedom: A Psychological Model of Freedom in Decision-Making.

PubMed

Lau, Stephan; Hiemisch, Anette

2017-07-05

The freedom of a decision is not yet sufficiently described as a psychological variable. We present a model of functional decision freedom that aims to fill that role. The model conceptualizes functional freedom as a capacity of people that varies depending on certain conditions of a decision episode. It denotes an inner capability to consciously shape complex decisions according to one's own values and needs. Functional freedom depends on three compensatory dimensions: it is greatest when the decision-maker is highly rational, when the structure of the decision is highly underdetermined, and when the decision process is strongly based on conscious thought and reflection. We outline possible research questions, argue for psychological benefits of functional decision freedom, and explicate the model's implications on current knowledge and research. In conclusion, we show that functional freedom is a scientific variable, permitting an additional psychological foothold in research on freedom, and that is compatible with a deterministic worldview.

Climate change/variability science and adaptive strategies for state and regional transportation decision making.

DOT National Transportation Integrated Search

2010-04-01

The objective of this study was to generate a baseline understanding of current policy responses to climate : change/variability at the state and regional transportation-planning and -decision levels. Specifically, : researchers were interested in th...

Decision theory for computing variable and value ordering decisions for scheduling problems

NASA Technical Reports Server (NTRS)

Linden, Theodore A.

1993-01-01

Heuristics that guide search are critical when solving large planning and scheduling problems, but most variable and value ordering heuristics are sensitive to only one feature of the search state. One wants to combine evidence from all features of the search state into a subjective probability that a value choice is best, but there has been no solid semantics for merging evidence when it is conceived in these terms. Instead, variable and value ordering decisions should be viewed as problems in decision theory. This led to two key insights: (1) The fundamental concept that allows heuristic evidence to be merged is the net incremental utility that will be achieved by assigning a value to a variable. Probability distributions about net incremental utility can merge evidence from the utility function, binary constraints, resource constraints, and other problem features. The subjective probability that a value is the best choice is then derived from probability distributions about net incremental utility. (2) The methods used for rumor control in Bayesian Networks are the primary way to prevent cycling in the computation of probable net incremental utility. These insights lead to semantically justifiable ways to compute heuristic variable and value ordering decisions that merge evidence from all available features of the search state.

Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models.

PubMed

Mehra, Lucky K; Cowger, Christina; Gross, Kevin; Ojiambo, Peter S

2016-01-01

Pre-planting factors have been associated with the late-season severity of Stagonospora nodorum blotch (SNB), caused by the fungal pathogen Parastagonospora nodorum, in winter wheat (Triticum aestivum). The relative importance of these factors in the risk of SNB has not been determined and this knowledge can facilitate disease management decisions prior to planting of the wheat crop. In this study, we examined the performance of multiple regression (MR) and three machine learning algorithms namely artificial neural networks, categorical and regression trees, and random forests (RF), in predicting the pre-planting risk of SNB in wheat. Pre-planting factors tested as potential predictor variables were cultivar resistance, latitude, longitude, previous crop, seeding rate, seed treatment, tillage type, and wheat residue. Disease severity assessed at the end of the growing season was used as the response variable. The models were developed using 431 disease cases (unique combinations of predictors) collected from 2012 to 2014 and these cases were randomly divided into training, validation, and test datasets. Models were evaluated based on the regression of observed against predicted severity values of SNB, sensitivity-specificity ROC analysis, and the Kappa statistic. A strong relationship was observed between late-season severity of SNB and specific pre-planting factors in which latitude, longitude, wheat residue, and cultivar resistance were the most important predictors. The MR model explained 33% of variability in the data, while machine learning models explained 47 to 79% of the total variability. Similarly, the MR model correctly classified 74% of the disease cases, while machine learning models correctly classified 81 to 83% of these cases. Results show that the RF algorithm, which explained 79% of the variability within the data, was the most accurate in predicting the risk of SNB, with an accuracy rate of 93%. The RF algorithm could allow early assessment of the risk of SNB, facilitating sound disease management decisions prior to planting of wheat.

Patterns and Correlates of Serostatus Disclosure to Sexual Partners by Perinatally-Infected Adolescents and Young Adults.

PubMed

Weintraub, Amy; Mellins, Claude A; Warne, Patricia; Dolezal, Curtis; Elkington, Katherine; Bucek, Amelia; Leu, Cheng-Shiun; Bamji, Mahrukh; Wiznia, Andrew; Abrams, Elaine J

2017-01-01

Similar to same-age peers, perinatally HIV-infected (PHIV+) youth in the US are engaging in sex, including condomless sex. Understanding decisions about serostatus disclosure to sexual partners is important to domestic and global HIV prevention efforts, since large numbers of PHIV+ children are entering adolescence and becoming sexually active. Using Social Action Theory (SAT) to inform variable selection, we examined correlates of disclosure among 98 PHIV+ adolescents/young adults in New York City. Over half of these youth reported not disclosing to any casual partners (59 %) or to any partners when using condoms (55 %). In bivariate analyses, increased disclosure was associated with older age; being female; earlier age of learning one's serostatus; and increased STD knowledge, disclosure intentions, and parent-child communication. Multiple regression analyses revealed a strong fit with the SAT model. As with adults, disclosure to sexual partners is difficult for PHIV+ youth and challenges prevention efforts. Effective interventions that help youth with disclosure decisions are needed to curb the epidemic.

Manipulating the Alpha Level Cannot Cure Significance Testing.

PubMed

Trafimow, David; Amrhein, Valentin; Areshenkoff, Corson N; Barrera-Causil, Carlos J; Beh, Eric J; Bilgiç, Yusuf K; Bono, Roser; Bradley, Michael T; Briggs, William M; Cepeda-Freyre, Héctor A; Chaigneau, Sergio E; Ciocca, Daniel R; Correa, Juan C; Cousineau, Denis; de Boer, Michiel R; Dhar, Subhra S; Dolgov, Igor; Gómez-Benito, Juana; Grendar, Marian; Grice, James W; Guerrero-Gimenez, Martin E; Gutiérrez, Andrés; Huedo-Medina, Tania B; Jaffe, Klaus; Janyan, Armina; Karimnezhad, Ali; Korner-Nievergelt, Fränzi; Kosugi, Koji; Lachmair, Martin; Ledesma, Rubén D; Limongi, Roberto; Liuzza, Marco T; Lombardo, Rosaria; Marks, Michael J; Meinlschmidt, Gunther; Nalborczyk, Ladislas; Nguyen, Hung T; Ospina, Raydonal; Perezgonzalez, Jose D; Pfister, Roland; Rahona, Juan J; Rodríguez-Medina, David A; Romão, Xavier; Ruiz-Fernández, Susana; Suarez, Isabel; Tegethoff, Marion; Tejo, Mauricio; van de Schoot, Rens; Vankov, Ivan I; Velasco-Forero, Santiago; Wang, Tonghui; Yamada, Yuki; Zoppino, Felipe C M; Marmolejo-Ramos, Fernando

2018-01-01

We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p -value threshold of 0.05, 0.01, 0.005, or anything else, is not acceptable.

The impact of nonclinical factors on repeat cesarean section.

PubMed

Stafford, R S

1991-01-02

Nonclinical factors, including the setting in which health care takes place, influence clinical decisions. This research measures the independent effects of organizational and socioeconomic factors on repeat cesarean section use in California. Of 45,425 births to women with previous cesarean sections in 1986, vaginal birth after cesarean section occurred in 10.9%. Sizable nonclinical variations were noted. By hospital ownership, rates ranged from 4.9% (for-profit hospitals) to 29.2% (University of California). Variations also existed by hospital teaching level (nonteaching hospitals, 7.0%, vs formalized teaching hospitals, 23.3%); payment source (private insurance, 8.1%, vs indigent services, 25.2%); and obstetric volume (low-volume hospitals, 5.4%, vs high-volume hospitals, 16.6%). Multiple logistic regression demonstrated that these variables had independent effects after accounting for their overlapping influences and the effects of patient characteristics. The observed variations demonstrate the prominence of nonclinical factors in decision making and question the clinical appropriateness of current practice patterns.

Demands on attention and the role of response priming in visual discrimination of feature conjunctions.

PubMed

Fournier, Lisa R; Herbert, Rhonda J; Farris, Carrie

2004-10-01

This study examined how response mapping of features within single- and multiple-feature targets affects decision-based processing and attentional capacity demands. Observers judged the presence or absence of 1 or 2 target features within an object either presented alone or with distractors. Judging the presence of 2 features relative to the less discriminable of these features alone was faster (conjunction benefits) when the task-relevant features differed in discriminability and were consistently mapped to responses. Conjunction benefits were attributed to asynchronous decision priming across attended, task-relevant dimensions. A failure to find conjunction benefits for disjunctive conjunctions was attributed to increased memory demands and variable feature-response mapping for 2- versus single-feature targets. Further, attentional demands were similar between single- and 2-feature targets when response mapping, memory demands, and discriminability of the task-relevant features were equated between targets. Implications of the findings for recent attention models are discussed. (c) 2004 APA, all rights reserved

Building a computer program to support children, parents, and distraction during healthcare procedures.

PubMed

Hanrahan, Kirsten; McCarthy, Ann Marie; Kleiber, Charmaine; Ataman, Kaan; Street, W Nick; Zimmerman, M Bridget; Ersig, Anne L

2012-10-01

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children's responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, titled Children, Parents and Distraction, is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure.

Representing causal knowledge in environmental policy interventions: Advantages and opportunities for qualitative influence diagram applications.

PubMed

Carriger, John F; Dyson, Brian E; Benson, William H

2018-01-15

This article develops and explores a methodology for using qualitative influence diagrams in environmental policy and management to support decision making efforts that minimize risk and increase resiliency. Influence diagrams are representations of the conditional aspects of a problem domain. Their graphical properties are useful for structuring causal knowledge relevant to policy interventions and can be used to enhance inference and inclusivity of multiple viewpoints. Qualitative components of influence diagrams are beneficial tools for identifying and examining the interactions among the critical variables in complex policy development and implementation. Policy interventions on social-environmental systems can be intuitively diagrammed for representing knowledge of critical relationships among economic, environmental, and social attributes. Examples relevant to coastal resiliency issues in the U.S. Gulf Coast region are developed to illustrate model structures for developing qualitative influence diagrams useful for clarifying important policy intervention issues and enhancing transparency in decision making. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Eye-gaze control of the computer interface: Discrimination of zoom intent

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goldberg, J.H.; Schryver, J.C.

1993-10-01

An analysis methodology and associated experiment were developed to assess whether definable and repeatable signatures of eye-gaze characteristics are evident, preceding a decision to zoom-in, zoom-out, or not to zoom at a computer interface. This user intent discrimination procedure can have broad application in disability aids and telerobotic control. Eye-gaze was collected from 10 subjects in a controlled experiment, requiring zoom decisions. The eye-gaze data were clustered, then fed into a multiple discriminant analysis (MDA) for optimal definition of heuristics separating the zoom-in, zoom-out, and no-zoom conditions. Confusion matrix analyses showed that a number of variable combinations classified at amore » statistically significant level, but practical significance was more difficult to establish. Composite contour plots demonstrated the regions in parameter space consistently assigned by the MDA to unique zoom conditions. Peak classification occurred at about 1200--1600 msec. Improvements in the methodology to achieve practical real-time zoom control are considered.« less

Manipulating the Alpha Level Cannot Cure Significance Testing

PubMed Central

Trafimow, David; Amrhein, Valentin; Areshenkoff, Corson N.; Barrera-Causil, Carlos J.; Beh, Eric J.; Bilgiç, Yusuf K.; Bono, Roser; Bradley, Michael T.; Briggs, William M.; Cepeda-Freyre, Héctor A.; Chaigneau, Sergio E.; Ciocca, Daniel R.; Correa, Juan C.; Cousineau, Denis; de Boer, Michiel R.; Dhar, Subhra S.; Dolgov, Igor; Gómez-Benito, Juana; Grendar, Marian; Grice, James W.; Guerrero-Gimenez, Martin E.; Gutiérrez, Andrés; Huedo-Medina, Tania B.; Jaffe, Klaus; Janyan, Armina; Karimnezhad, Ali; Korner-Nievergelt, Fränzi; Kosugi, Koji; Lachmair, Martin; Ledesma, Rubén D.; Limongi, Roberto; Liuzza, Marco T.; Lombardo, Rosaria; Marks, Michael J.; Meinlschmidt, Gunther; Nalborczyk, Ladislas; Nguyen, Hung T.; Ospina, Raydonal; Perezgonzalez, Jose D.; Pfister, Roland; Rahona, Juan J.; Rodríguez-Medina, David A.; Romão, Xavier; Ruiz-Fernández, Susana; Suarez, Isabel; Tegethoff, Marion; Tejo, Mauricio; van de Schoot, Rens; Vankov, Ivan I.; Velasco-Forero, Santiago; Wang, Tonghui; Yamada, Yuki; Zoppino, Felipe C. M.; Marmolejo-Ramos, Fernando

2018-01-01

We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from p = 0.05 to p = 0.005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable alpha levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and sample size much more directly than significance testing does; but none of the statistical tools should be taken as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, and implications for applications. To boil all this down to a binary decision based on a p-value threshold of 0.05, 0.01, 0.005, or anything else, is not acceptable. PMID:29867666

Bad apples, bad cases, and bad barrels: meta-analytic evidence about sources of unethical decisions at work.

PubMed

Kish-Gephart, Jennifer J; Harrison, David A; Treviño, Linda Klebe

2010-01-01

As corporate scandals proliferate, practitioners and researchers alike need a cumulative, quantitative understanding of the antecedents associated with unethical decisions in organizations. In this meta-analysis, the authors draw from over 30 years of research and multiple literatures to examine individual ("bad apple"), moral issue ("bad case"), and organizational environment ("bad barrel") antecedents of unethical choice. Findings provide empirical support for several foundational theories and paint a clearer picture of relationships characterized by mixed results. Structural equation modeling revealed the complexity (multidetermined nature) of unethical choice, as well as a need for research that simultaneously examines different sets of antecedents. Moderator analyses unexpectedly uncovered better prediction of unethical behavior than of intention for several variables. This suggests a need to more strongly consider a new "ethical impulse" perspective in addition to the traditional "ethical calculus" perspective. Results serve as a data-based foundation and guide for future theoretical and empirical development in the domain of behavioral ethics. Copyright 2009 APA, all rights reserved.

Mesolimbic Dopamine Signals the Value of Work

PubMed Central

Hamid, Arif A.; Pettibone, Jeffrey R.; Mabrouk, Omar S.; Hetrick, Vaughn L.; Schmidt, Robert; Vander Weele, Caitlin M.; Kennedy, Robert T.; Aragona, Brandon J.; Berke, Joshua D.

2015-01-01

Dopamine cell firing can encode errors in reward prediction, providing a learning signal to guide future behavior. Yet dopamine is also a key modulator of motivation, invigorating current behavior. Existing theories propose that fast (“phasic”) dopamine fluctuations support learning, while much slower (“tonic”) dopamine changes are involved in motivation. We examined dopamine release in the nucleus accumbens across multiple time scales, using complementary microdialysis and voltammetric methods during adaptive decision-making. We first show that minute-by-minute dopamine levels covary with reward rate and motivational vigor. We then show that second-by-second dopamine release encodes an estimate of temporally-discounted future reward (a value function). We demonstrate that changing dopamine immediately alters willingness to work, and reinforces preceding action choices by encoding temporal-difference reward prediction errors. Our results indicate that dopamine conveys a single, rapidly-evolving decision variable, the available reward for investment of effort, that is employed for both learning and motivational functions. PMID:26595651

Objective consensus from decision trees.

PubMed

Putora, Paul Martin; Panje, Cedric M; Papachristofilou, Alexandros; Dal Pra, Alan; Hundsberger, Thomas; Plasswilm, Ludwig

2014-12-05

Consensus-based approaches provide an alternative to evidence-based decision making, especially in situations where high-level evidence is limited. Our aim was to demonstrate a novel source of information, objective consensus based on recommendations in decision tree format from multiple sources. Based on nine sample recommendations in decision tree format a representative analysis was performed. The most common (mode) recommendations for each eventuality (each permutation of parameters) were determined. The same procedure was applied to real clinical recommendations for primary radiotherapy for prostate cancer. Data was collected from 16 radiation oncology centres, converted into decision tree format and analyzed in order to determine the objective consensus. Based on information from multiple sources in decision tree format, treatment recommendations can be assessed for every parameter combination. An objective consensus can be determined by means of mode recommendations without compromise or confrontation among the parties. In the clinical example involving prostate cancer therapy, three parameters were used with two cut-off values each (Gleason score, PSA, T-stage) resulting in a total of 27 possible combinations per decision tree. Despite significant variations among the recommendations, a mode recommendation could be found for specific combinations of parameters. Recommendations represented as decision trees can serve as a basis for objective consensus among multiple parties.

Regionalization of land-use impacts on streamflow using a network of paired catchments

NASA Astrophysics Data System (ADS)

Ochoa-Tocachi, Boris F.; Buytaert, Wouter; De Bièvre, Bert

2016-09-01

Quantifying the impact of land use and cover (LUC) change on catchment hydrological response is essential for land-use planning and management. Yet hydrologists are often not able to present consistent and reliable evidence to support such decision-making. The issue tends to be twofold: a scarcity of relevant observations, and the difficulty of regionalizing any existing observations. This study explores the potential of a paired catchment monitoring network to provide statistically robust, regionalized predictions of LUC change impact in an environment of high hydrological variability. We test the importance of LUC variables to explain hydrological responses and to improve regionalized predictions using 24 catchments distributed along the Tropical Andes. For this, we calculate first 50 physical catchment properties, and then select a subset based on correlation analysis. The reduced set is subsequently used to regionalize a selection of hydrological indices using multiple linear regression. Contrary to earlier studies, we find that incorporating LUC variables in the regional model structures increases significantly regression performance and predictive capacity for 66% of the indices. For the runoff ratio, baseflow index, and slope of the flow duration curve, the mean absolute error reduces by 53% and the variance of the residuals by 79%, on average. We attribute the explanatory capacity of LUC in the regional model to the pairwise monitoring setup, which increases the contrast of the land-use signal in the data set. As such, it may be a useful strategy to optimize data collection to support watershed management practices and improve decision-making in data-scarce regions.

Computer Based Behavioral Biometric Authentication via Multi-Modal Fusion

DTIC Science & Technology

2013-03-01

the decisions made by each individual modality. Fusion of features is the simple concatenation of feature vectors from multiple modalities to be...of Features BayesNet MDL 330 LibSVM PCA 80 J48 Wrapper Evaluator 11 3.5.3 Ensemble Based Decision Level Fusion. In ensemble learning multiple ...The high fusion percentages validate our hypothesis that by combining features from multiple modalities, classification accuracy can be improved. As

Developing Health-Based Pre-Planning Clearance Goals for Airport Remediation Following a Chemical Terrorist Attack: Decision Criteria for Multipathway Exposure Routes

PubMed Central

Watson, Annetta; Dolislager, Fredrick; Hall, Linda; Raber, Ellen; Hauschild, Veronique D.; Love, Adam H.

2011-01-01

In the event of a chemical terrorist attack on a transportation hub, post-event remediation and restoration activities necessary to attain unrestricted facility re-use and re-entry could require hours to multiple days. While timeframes are dependent on numerous variables, a primary controlling factor is the level of pre-planning and decision-making completed prior to chemical release. What follows is the second of a two-part analysis identifying key considerations, critical information and decision criteria to facilitate post-attack and post-decontamination consequence management activities. Decision criteria analysis presented here provides first-time, open-literature documentation of multi-pathway, health-based remediation exposure guidelines for selected toxic industrial compounds, chemical warfare agents, and agent degradation products for pre-planning application in anticipation of a chemical terrorist attack. Guideline values are provided for inhalation and direct ocular vapor exposure routes as well as percutaneous vapor, surface contact, and ingestion. Target populations include various employees as well as transit passengers. This work has been performed as a national case study conducted in partnership with the Los Angeles International Airport and The Bradley International Terminal. All recommended guidelines have been selected for consistency with airport scenario release parameters of a one-time, short-duration, finite airborne release from a single source followed by compound-specific decontamination. PMID:21399674

Developing health-based pre-planning clearance goals for airport remediation following a chemical terrorist attack: Decision criteria for multipathway exposure routes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Watson, Annetta Paule; Dolislager, Frederick; Hall, Dr. Linda

2011-01-01

In the event of a chemical terrorist attack on a transportation hub, post-event remediation and restoration activities necessary to attain unrestricted facility re-use and re-entry could require hours to multiple days. While timeframes are dependent on numerous variables, a primary controlling factor is the level of pre-planning and decision-making completed prior to chemical release. What follows is the second of a two-part analysis identifying key considerations, critical information and decision criteria to facilitate post-attack and post-decontamination consequence management activities. Decision criteria analysis presented here provides first-time, open-literature documentation of multi-pathway, health-based remediation exposure guidelines for selected toxic industrial compounds, chemicalmore » warfare agents, and agent degradation products for pre-planning application in anticipation of a chemical terrorist attack. Guideline values are provided for inhalation and direct ocular vapor exposure routes as well as percutaneous vapor, surface contact, and ingestion. Target populations include various employees as well as transit passengers. This work has been performed as a national case study conducted in partnership with the Los Angeles International Airport and The Bradley International Terminal. All recommended guidelines have been selected for consistency with airport scenario release parameters of a one-time, short-duration, finite airborne release from a single source followed by compound-specific decontamination.« less

The Decision Path Not Taken

PubMed Central

Connor, Charles E.; Stuphorn, Veit

2017-01-01

Real-life decisions often involve multiple intermediate choices among competing, interdependent options. Lorteije et al. (2015) introduce a new paradigm for dissecting the neural strategies underlying such decisions. PMID:26402598

Using histograms to introduce randomization in the generation of ensembles of decision trees

DOEpatents

Kamath, Chandrika; Cantu-Paz, Erick; Littau, David

2005-02-22

A system for decision tree ensembles that includes a module to read the data, a module to create a histogram, a module to evaluate a potential split according to some criterion using the histogram, a module to select a split point randomly in an interval around the best split, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method includes the steps of reading the data; creating a histogram; evaluating a potential split according to some criterion using the histogram, selecting a split point randomly in an interval around the best split, splitting the data, and combining multiple decision trees in ensembles.

Approaches to the management of agents used for the treatment of multiple sclerosis: consensus statements from a panel of U.S. managed care pharmacists and physicians.

PubMed

Miller, Ross M; Happe, Laura E; Meyer, Kellie L; Spear, Rachel J

2012-01-01

Multiple sclerosis (MS) is a chronic, disabling, and costly disease with several treatment options available; however, there is variability in evidence-based clinical guidelines. Therefore, payers are at a disadvantage when making management decisions without the benefit of definitive guidance from treatment guidelines. To outline approaches for the management of agents used to treat MS, as determined from a group of U.S. managed care pharmacists and physicians. A modified Delphi process was used to develop consensus statements regarding MS management approaches. The panel was composed of experts in managed care and included 8 pharmacy directors and 6 medical directors presently or previously involved in formulary decision making from 12 health plans, 1 specialty pharmacy, and 1 consulting company. These decision makers, who have experience designing health care benefits that include MS treatments, provided anonymous feedback through 2 rounds of web-based surveys and participated in 1 live panel meeting held in December 2010. Consensus was defined as a mean response of at least 3.3 or 100% of responses either "agree" or "strongly agree" (i.e., no panelist answered "disagree" or "strongly disagree") on a 4-item Likert scale (1=strongly disagree, 2=disagree, 3=agree, 4=strongly agree). After 3 phases, these managed care representatives reached consensus on 25 statements for management of patients with MS. Consistent with managed care principles, this group of managed care experts found that health plans should consider efficacy, effectiveness, and safety, as well as patient preference, when evaluating MS therapies for formulary placement. Cost and contracting should be considered if efficacy and safety are judged to be comparable between agents. The consensus statements developed by a panel of managed care representatives provide some insight into decision making in formulary and utilization management of MS therapies.

Information theoretic partitioning and confidence based weight assignment for multi-classifier decision level fusion in hyperspectral target recognition applications

NASA Astrophysics Data System (ADS)

Prasad, S.; Bruce, L. M.

2007-04-01

There is a growing interest in using multiple sources for automatic target recognition (ATR) applications. One approach is to take multiple, independent observations of a phenomenon and perform a feature level or a decision level fusion for ATR. This paper proposes a method to utilize these types of multi-source fusion techniques to exploit hyperspectral data when only a small number of training pixels are available. Conventional hyperspectral image based ATR techniques project the high dimensional reflectance signature onto a lower dimensional subspace using techniques such as Principal Components Analysis (PCA), Fisher's linear discriminant analysis (LDA), subspace LDA and stepwise LDA. While some of these techniques attempt to solve the curse of dimensionality, or small sample size problem, these are not necessarily optimal projections. In this paper, we present a divide and conquer approach to address the small sample size problem. The hyperspectral space is partitioned into contiguous subspaces such that the discriminative information within each subspace is maximized, and the statistical dependence between subspaces is minimized. We then treat each subspace as a separate source in a multi-source multi-classifier setup and test various decision fusion schemes to determine their efficacy. Unlike previous approaches which use correlation between variables for band grouping, we study the efficacy of higher order statistical information (using average mutual information) for a bottom up band grouping. We also propose a confidence measure based decision fusion technique, where the weights associated with various classifiers are based on their confidence in recognizing the training data. To this end, training accuracies of all classifiers are used for weight assignment in the fusion process of test pixels. The proposed methods are tested using hyperspectral data with known ground truth, such that the efficacy can be quantitatively measured in terms of target recognition accuracies.

Team clinician variability in return-to-play decisions.

PubMed

Shultz, Rebecca; Bido, Jennifer; Shrier, Ian; Meeuwisse, Willem H; Garza, Daniel; Matheson, Gordon O

2013-11-01

To describe the variability in the return-to-play (RTP) decisions of experienced team clinicians and to assess their clinical opinion as to the relevance of 19 factors described in a RTP decision-making model. Survey questionnaire. Advanced Team Physician Course. Sixty-seven of 101 sports medicine clinicians completed the questionnaire. Results were analyzed using descriptive statistics. For categorical variables, we report percentage and frequency. For continuous variables, we report mean (SD) if data were approximately normally distributed and frequencies for clinically relevant categories for skewed data. The average number of years of clinical sports medicine experience was 13.6 (9.8). Of the 62 clinicians who responded fully, 35% (n = 22) would "clear" (vs "not clear") an athlete to participate in sport even if the risk of an acute reinjury or long-term sequelae is increased. When respondents were given 6 different RTP options rather than binary choices, there were increased discrepancies across some injury risk scenarios. For example, 8.1% to 16.1% of respondents who chose to clear an athlete when presented with binary choices, later chose to "not clear" an athlete when given 6 graded RTP options. The respondents often considered factors of potential importance to athletes as nonimportant to the RTP decision process if risk of reinjury was unaffected (range, n = 4 [10%] to n = 19 [45%]). There is a high degree of variability in how different clinicians weight the different factors related to RTP decision making. More precise definitions decrease but do not eliminate this variability.

Female Literacy Rate is a Better Predictor of Birth Rate and Infant Mortality Rate in India.

PubMed

Saurabh, Suman; Sarkar, Sonali; Pandey, Dhruv K

2013-01-01

Educated women are known to take informed reproductive and healthcare decisions. These result in population stabilization and better infant care reflected by lower birth rates and infant mortality rates (IMRs), respectively. Our objective was to study the relationship of male and female literacy rates with crude birth rates (CBRs) and IMRs of the states and union territories (UTs) of India. The data were analyzed using linear regression. CBR and IMR were taken as the dependent variables; while the overall literacy rates, male, and female literacy rates were the independent variables. CBRs were inversely related to literacy rates (slope parameter = -0.402, P < 0.001). On multiple linear regression with male and female literacy rates, a significant inverse relationship emerged between female literacy rate and CBR (slope = -0.363, P < 0.001), while male literacy rate was not significantly related to CBR (P = 0.674). IMR of the states were also inversely related to their literacy rates (slope = -1.254, P < 0.001). Multiple linear regression revealed a significant inverse relationship between IMR and female literacy (slope = -0.816, P = 0.031), whereas male literacy rate was not significantly related (P = 0.630). Female literacy is relatively highly important for both population stabilization and better infant health.

Multiobjective evolutionary optimization of water distribution systems: Exploiting diversity with infeasible solutions.

PubMed

Tanyimboh, Tiku T; Seyoum, Alemtsehay G

2016-12-01

This article investigates the computational efficiency of constraint handling in multi-objective evolutionary optimization algorithms for water distribution systems. The methodology investigated here encourages the co-existence and simultaneous development including crossbreeding of subpopulations of cost-effective feasible and infeasible solutions based on Pareto dominance. This yields a boundary search approach that also promotes diversity in the gene pool throughout the progress of the optimization by exploiting the full spectrum of non-dominated infeasible solutions. The relative effectiveness of small and moderate population sizes with respect to the number of decision variables is investigated also. The results reveal the optimization algorithm to be efficient, stable and robust. It found optimal and near-optimal solutions reliably and efficiently. The real-world system based optimization problem involved multiple variable head supply nodes, 29 fire-fighting flows, extended period simulation and multiple demand categories including water loss. The least cost solutions found satisfied the flow and pressure requirements consistently. The best solutions achieved indicative savings of 48.1% and 48.2% based on the cost of the pipes in the existing network, for populations of 200 and 1000, respectively. The population of 1000 achieved slightly better results overall. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Time-Regularized, Multiple Gravity-Assist Low-Thrust, Bounded-Impulse Model for Trajectory Optimization

NASA Technical Reports Server (NTRS)

Ellison, Donald H.; Englander, Jacob A.; Conway, Bruce A.

2017-01-01

The multiple gravity assist low-thrust (MGALT) trajectory model combines the medium-fidelity Sims-Flanagan bounded-impulse transcription with a patched-conics flyby model and is an important tool for preliminary trajectory design. While this model features fast state propagation via Keplers equation and provides a pleasingly accurate estimation of the total mass budget for the eventual flight suitable integrated trajectory it does suffer from one major drawback, namely its temporal spacing of the control nodes. We introduce a variant of the MGALT transcription that utilizes the generalized anomaly from the universal formulation of Keplers equation as a decision variable in addition to the trajectory phase propagation time. This results in two improvements over the traditional model. The first is that the maneuver locations are equally spaced in generalized anomaly about the orbit rather than time. The second is that the Kepler propagator now has the generalized anomaly as its independent variable instead of time and thus becomes an iteration-free propagation method. The new algorithm is outlined, including the impact that this has on the computation of Jacobian entries for numerical optimization, and a motivating application problem is presented that illustrates the improvements that this model has over the traditional MGALT transcription.

A Time-Regularized Multiple Gravity-Assist Low-Thrust Bounded-Impulse Model for Trajectory Optimization

NASA Technical Reports Server (NTRS)

Ellison, Donald H.; Englander, Jacob A.; Conway, Bruce A.

2017-01-01

The multiple gravity assist low-thrust (MGALT) trajectory model combines the medium-fidelity Sims-Flanagan bounded-impulse transcription with a patched-conics flyby model and is an important tool for preliminary trajectory design. While this model features fast state propagation via Kepler's equation and provides a pleasingly accurate estimation of the total mass budget for the eventual flight-suitable integrated trajectory it does suffer from one major drawback, namely its temporal spacing of the control nodes. We introduce a variant of the MGALT transcription that utilizes the generalized anomaly from the universal formulation of Kepler's equation as a decision variable in addition to the trajectory phase propagation time. This results in two improvements over the traditional model. The first is that the maneuver locations are equally spaced in generalized anomaly about the orbit rather than time. The second is that the Kepler propagator now has the generalized anomaly as its independent variable instead of time and thus becomes an iteration-free propagation method. The new algorithm is outlined, including the impact that this has on the computation of Jacobian entries for numerical optimization, and a motivating application problem is presented that illustrates the improvements that this model has over the traditional MGALT transcription.

Use of multi-node wells in the Groundwater-Management Process of MODFLOW-2005 (GWM-2005)

USGS Publications Warehouse

Ahlfeld, David P.; Barlow, Paul M.

2013-01-01

Many groundwater wells are open to multiple aquifers or to multiple intervals within a single aquifer. These types of wells can be represented in numerical simulations of groundwater flow by use of the Multi-Node Well (MNW) Packages developed for the U.S. Geological Survey’s MODFLOW model. However, previous versions of the Groundwater-Management (GWM) Process for MODFLOW did not allow the use of multi-node wells in groundwater-management formulations. This report describes modifications to the MODFLOW–2005 version of the GWM Process (GWM–2005) to provide for such use with the MNW2 Package. Multi-node wells can be incorporated into a management formulation as flow-rate decision variables for which optimal withdrawal or injection rates will be determined as part of the GWM–2005 solution process. In addition, the heads within multi-node wells can be used as head-type state variables, and, in that capacity, be included in the objective function or constraint set of a management formulation. Simple head bounds also can be defined to constrain water levels at multi-node wells. The report provides instructions for including multi-node wells in the GWM–2005 data-input files and a sample problem that demonstrates use of multi-node wells in a typical groundwater-management problem.

Numeracy of multiple sclerosis patients: A comparison of patients from the PERCEPT study to a German probabilistic sample.

PubMed

Gaissmaier, Wolfgang; Giese, Helge; Galesic, Mirta; Garcia-Retamero, Rocio; Kasper, Juergen; Kleiter, Ingo; Meuth, Sven G; Köpke, Sascha; Heesen, Christoph

2018-01-01

A shared decision-making approach is suggested for multiple sclerosis (MS) patients. To properly evaluate benefits and risks of different treatment options accordingly, MS patients require sufficient numeracy - the ability to understand quantitative information. It is unknown whether MS affects numeracy. Therefore, we investigated whether patients' numeracy was impaired compared to a probabilistic national sample. As part of the larger prospective, observational, multicenter study PERCEPT, we assessed numeracy for a clinical study sample of German MS patients (N=725) with a standard test and compared them to a German probabilistic sample (N=1001), controlling for age, sex, and education. Within patients, we assessed whether disease variables (disease duration, disability, annual relapse rate, cognitive impairment) predicted numeracy beyond these demographics. MS patients showed a comparable level of numeracy as the probabilistic national sample (68.9% vs. 68.5% correct answers, P=0.831). In both samples, numeracy was higher for men and the highly educated. Disease variables did not predict numeracy beyond demographics within patients, and predictability was generally low. This sample of MS patients understood quantitative information on the same level as the general population. There is no reason to withhold quantitative information from MS patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Neuronal variability in orbitofrontal cortex during economic decisions.

PubMed

Conen, Katherine E; Padoa-Schioppa, Camillo

2015-09-01

Neuroeconomic models assume that economic decisions are based on the activity of offer value cells in the orbitofrontal cortex (OFC), but testing this assertion has proven difficult. In principle, the decision made on a given trial should correlate with the stochastic fluctuations of these cells. However, this correlation, measured as a choice probability (CP), is small. Importantly, a neuron's CP reflects not only its individual contribution to the decision (termed readout weight), but also the intensity and the structure of correlated variability across the neuronal population (termed noise correlation). A precise mathematical relation between CPs, noise correlations, and readout weights was recently derived by Haefner and colleagues (Haefner RM, Gerwinn S, Macke JH, Bethge M. Nat Neurosci 16: 235-242, 2013) for a linear decision model. In this framework, concurrent measurements of noise correlations and CPs can provide quantitative information on how a population of cells contributes to a decision. Here we examined neuronal variability in the OFC of rhesus monkeys during economic decisions. Noise correlations had similar structure but considerably lower strength compared with those typically measured in sensory areas during perceptual decisions. In contrast, variability in the activity of individual cells was high and comparable to that recorded in other cortical regions. Simulation analyses based on Haefner's equation showed that noise correlations measured in the OFC combined with a plausible readout of offer value cells reproduced the experimental measures of CPs. In other words, the results obtained for noise correlations and those obtained for CPs taken together support the hypothesis that economic decisions are primarily based on the activity of offer value cells. Copyright © 2015 the American Physiological Society.

Are We Telling Decision-makers the Wrong Things - and with Too Much Confidence?

NASA Astrophysics Data System (ADS)

Arnold, J.; Nowak, K. C.; Vano, J. A.; Newman, A. J.; Mizukami, N.; Mendoza, P. A.; Nijssen, B.; Wood, A.; Gutmann, E. D.; Clark, M. P.; Rasmussen, R.

2016-12-01

Water-resource management relies on decision-making over a wide range of space-time scales, nearly none of which maps cleanly onto the scales of current hydroclimatic scenarios of anthropogenic change. Myriad choices are made during vulnerability and impact assessments to quantify the changed-climate sensitivities of models used in that decision-making, including choices of hydrologic models, parameters, and parameterizations; their input forcings determined with various climate downscaling approaches; selected GCMs and output variables to be downscaled; and the forcing emissions scenarios, to name a few. Choosing alternative methods for producing gridded meteorological fields, for examples, can produce very different effects on the projected hydrologic outcomes they drive, with uncertainties across those methods revealed to be as large or larger than the climate change signal itself in some cases. Additionally, many popular climate downscaling methods simply rescale GCM precipitation, producing hydroclimatic projections with too much drizzle, incorrect representations of extreme events, and improper spatial scaling of variables crucial to water-resource vulnerability assessments and, importantly, the decisions they seek to inform. Real-world water-resource vulnerability and impacts assessments can be highly time-sensitive and resource limited, though, so they typically do not confront or even fully represent uncertainties associated with all choices. That deficiency results in assessments built on only partially revealed uncertainties which can misrepresent significant sensitivities and impacts in the final assessments of climate threats and hydrologic vulnerabilities. This talk will describe recent work by the U.S. Army Corps of Engineers, Bureau of Reclamation, University of Washington, and National Center for Atmospheric Research to develop and test methods to characterize more fully the uncertainties in the modeling chain for real-world uses. Examples will illustrate new implementations for communicating that fuller characterization in the ways most useful to inform water-resource management across multiple space-time scales under climate-changed futures.

Decision Making in Adults with ADHD

ERIC Educational Resources Information Center

Montyla, Timo; Still, Johanna; Gullberg, Stina; Del Missier, Fabio

2012-01-01

Objectives: This study examined decision-making competence in ADHD by using multiple decision tasks with varying demands on analytic versus affective processes. Methods: Adults with ADHD and healthy controls completed two tasks of analytic decision making, as measured by the Adult Decision-Making Competence (A-DMC) battery, and two affective…

Spatiotemporal dynamics of random stimuli account for trial-to-trial variability in perceptual decision making

PubMed Central

Park, Hame; Lueckmann, Jan-Matthis; von Kriegstein, Katharina; Bitzer, Sebastian; Kiebel, Stefan J.

2016-01-01

Decisions in everyday life are prone to error. Standard models typically assume that errors during perceptual decisions are due to noise. However, it is unclear how noise in the sensory input affects the decision. Here we show that there are experimental tasks for which one can analyse the exact spatio-temporal details of a dynamic sensory noise and better understand variability in human perceptual decisions. Using a new experimental visual tracking task and a novel Bayesian decision making model, we found that the spatio-temporal noise fluctuations in the input of single trials explain a significant part of the observed responses. Our results show that modelling the precise internal representations of human participants helps predict when perceptual decisions go wrong. Furthermore, by modelling precisely the stimuli at the single-trial level, we were able to identify the underlying mechanism of perceptual decision making in more detail than standard models. PMID:26752272

Shared Decision Making and Autonomy Among US Participants with Multiple Sclerosis in the NARCOMS Registry

PubMed Central

Thomas, Nina; Tyry, Tuula; Fox, Robert J.; Salter, Amber

2017-01-01

Background: Treatment decisions in multiple sclerosis (MS) are affected by many factors and are made by the patient, doctor, or both. With new disease-modifying therapies (DMTs) emerging, the complexity surrounding treatment decisions is increasing, further emphasizing the importance of understanding decision-making preferences. Methods: North American Research Committee on Multiple Sclerosis (NARCOMS) Registry participants completed the Fall 2014 Update survey, which included the Control Preferences Scale (CPS). The CPS consists of five images showing different patient/doctor roles in treatment decision making. The images were collapsed to three categories: patient-centered, shared, and physician-centered decision-making preferences. Associations between decision-making preferences and demographic and clinical factors were evaluated using multivariable logistic regression. Results: Of 7009 participants, 79.3% were women and 93.5% were white (mean [SD] age, 57.6 [10.3] years); 56.7% reported a history of relapses. Patient-centered decision making was most commonly preferred by participants (47.9%), followed by shared decision making (SDM; 42.8%). SDM preference was higher for women and those taking DMTs and increased with age and disease duration (all P < .05). Patient-centered decisions were most common for respondents not taking a DMT at the time of the survey and were preferred by those who had no DMT history compared with those who had previously taken a DMT (P < .0001). There was no difference in SDM preference by current MS disease course after adjusting for other disease-related factors. Conclusions: Responders reported most commonly considering their doctor's opinion before making a treatment decision and making decisions jointly with their doctor. DMT use, gender, and age were associated with decision-making preference. PMID:29270088

A Critical Meta-Analysis of Lens Model Studies in Human Judgment and Decision-Making

PubMed Central

Kaufmann, Esther; Reips, Ulf-Dietrich; Wittmann, Werner W.

2013-01-01

Achieving accurate judgment (‘judgmental achievement’) is of utmost importance in daily life across multiple domains. The lens model and the lens model equation provide useful frameworks for modeling components of judgmental achievement and for creating tools to help decision makers (e.g., physicians, teachers) reach better judgments (e.g., a correct diagnosis, an accurate estimation of intelligence). Previous meta-analyses of judgment and decision-making studies have attempted to evaluate overall judgmental achievement and have provided the basis for evaluating the success of bootstrapping (i.e., replacing judges by linear models that guide decision making). However, previous meta-analyses have failed to appropriately correct for a number of study design artifacts (e.g., measurement error, dichotomization), which may have potentially biased estimations (e.g., of the variability between studies) and led to erroneous interpretations (e.g., with regards to moderator variables). In the current study we therefore conduct the first psychometric meta-analysis of judgmental achievement studies that corrects for a number of study design artifacts. We identified 31 lens model studies (N = 1,151, k = 49) that met our inclusion criteria. We evaluated overall judgmental achievement as well as whether judgmental achievement depended on decision domain (e.g., medicine, education) and/or the level of expertise (expert vs. novice). We also evaluated whether using corrected estimates affected conclusions with regards to the success of bootstrapping with psychometrically-corrected models. Further, we introduce a new psychometric trim-and-fill method to estimate the effect sizes of potentially missing studies correct psychometric meta-analyses for effects of publication bias. Comparison of the results of the psychometric meta-analysis with the results of a traditional meta-analysis (which only corrected for sampling error) indicated that artifact correction leads to a) an increase in values of the lens model components, b) reduced heterogeneity between studies, and c) increases the success of bootstrapping. We argue that psychometric meta-analysis is useful for accurately evaluating human judgment and show the success of bootstrapping. PMID:24391781

A critical meta-analysis of lens model studies in human judgment and decision-making.

PubMed

Kaufmann, Esther; Reips, Ulf-Dietrich; Wittmann, Werner W

2013-01-01

Achieving accurate judgment ('judgmental achievement') is of utmost importance in daily life across multiple domains. The lens model and the lens model equation provide useful frameworks for modeling components of judgmental achievement and for creating tools to help decision makers (e.g., physicians, teachers) reach better judgments (e.g., a correct diagnosis, an accurate estimation of intelligence). Previous meta-analyses of judgment and decision-making studies have attempted to evaluate overall judgmental achievement and have provided the basis for evaluating the success of bootstrapping (i.e., replacing judges by linear models that guide decision making). However, previous meta-analyses have failed to appropriately correct for a number of study design artifacts (e.g., measurement error, dichotomization), which may have potentially biased estimations (e.g., of the variability between studies) and led to erroneous interpretations (e.g., with regards to moderator variables). In the current study we therefore conduct the first psychometric meta-analysis of judgmental achievement studies that corrects for a number of study design artifacts. We identified 31 lens model studies (N = 1,151, k = 49) that met our inclusion criteria. We evaluated overall judgmental achievement as well as whether judgmental achievement depended on decision domain (e.g., medicine, education) and/or the level of expertise (expert vs. novice). We also evaluated whether using corrected estimates affected conclusions with regards to the success of bootstrapping with psychometrically-corrected models. Further, we introduce a new psychometric trim-and-fill method to estimate the effect sizes of potentially missing studies correct psychometric meta-analyses for effects of publication bias. Comparison of the results of the psychometric meta-analysis with the results of a traditional meta-analysis (which only corrected for sampling error) indicated that artifact correction leads to a) an increase in values of the lens model components, b) reduced heterogeneity between studies, and c) increases the success of bootstrapping. We argue that psychometric meta-analysis is useful for accurately evaluating human judgment and show the success of bootstrapping.

Business process modeling in healthcare.

PubMed

Ruiz, Francisco; Garcia, Felix; Calahorra, Luis; Llorente, César; Gonçalves, Luis; Daniel, Christel; Blobel, Bernd

2012-01-01

The importance of the process point of view is not restricted to a specific enterprise sector. In the field of health, as a result of the nature of the service offered, health institutions' processes are also the basis for decision making which is focused on achieving their objective of providing quality medical assistance. In this chapter the application of business process modelling - using the Business Process Modelling Notation (BPMN) standard is described. Main challenges of business process modelling in healthcare are the definition of healthcare processes, the multi-disciplinary nature of healthcare, the flexibility and variability of the activities involved in health care processes, the need of interoperability between multiple information systems, and the continuous updating of scientific knowledge in healthcare.

A matter of tradeoffs: reintroduction as a multiple objective decision

USGS Publications Warehouse

Converse, Sarah J.; Moore, Clinton T.; Folk, Martin J.; Runge, Michael C.

2013-01-01

Decision making in guidance of reintroduction efforts is made challenging by the substantial scientific uncertainty typically involved. However, a less recognized challenge is that the management objectives are often numerous and complex. Decision makers managing reintroduction efforts are often concerned with more than just how to maximize the probability of reintroduction success from a population perspective. Decision makers are also weighing other concerns such as budget limitations, public support and/or opposition, impacts on the ecosystem, and the need to consider not just a single reintroduction effort, but conservation of the entire species. Multiple objective decision analysis is a powerful tool for formal analysis of such complex decisions. We demonstrate the use of multiple objective decision analysis in the case of the Florida non-migratory whooping crane reintroduction effort. In this case, the State of Florida was considering whether to resume releases of captive-reared crane chicks into the non-migratory whooping crane population in that state. Management objectives under consideration included maximizing the probability of successful population establishment, minimizing costs, maximizing public relations benefits, maximizing the number of birds available for alternative reintroduction efforts, and maximizing learning about the demographic patterns of reintroduced whooping cranes. The State of Florida engaged in a collaborative process with their management partners, first, to evaluate and characterize important uncertainties about system behavior, and next, to formally evaluate the tradeoffs between objectives using the Simple Multi-Attribute Rating Technique (SMART). The recommendation resulting from this process, to continue releases of cranes at a moderate intensity, was adopted by the State of Florida in late 2008. Although continued releases did not receive support from the International Whooping Crane Recovery Team, this approach does provide a template for the formal, transparent consideration of multiple, potentially competing, objectives in reintroduction decision making.

A decision tree for differentiating multiple system atrophy from Parkinson's disease using 3-T MR imaging.

PubMed

Nair, Shalini Rajandran; Tan, Li Kuo; Mohd Ramli, Norlisah; Lim, Shen Yang; Rahmat, Kartini; Mohd Nor, Hazman

2013-06-01

To develop a decision tree based on standard magnetic resonance imaging (MRI) and diffusion tensor imaging to differentiate multiple system atrophy (MSA) from Parkinson's disease (PD). 3-T brain MRI and DTI (diffusion tensor imaging) were performed on 26 PD and 13 MSA patients. Regions of interest (ROIs) were the putamen, substantia nigra, pons, middle cerebellar peduncles (MCP) and cerebellum. Linear, volumetry and DTI (fractional anisotropy and mean diffusivity) were measured. A three-node decision tree was formulated, with design goals being 100 % specificity at node 1, 100 % sensitivity at node 2 and highest combined sensitivity and specificity at node 3. Nine parameters (mean width, fractional anisotropy (FA) and mean diffusivity (MD) of MCP; anteroposterior diameter of pons; cerebellar FA and volume; pons and mean putamen volume; mean FA substantia nigra compacta-rostral) showed statistically significant (P < 0.05) differences between MSA and PD with mean MCP width, anteroposterior diameter of pons and mean FA MCP chosen for the decision tree. Threshold values were 14.6 mm, 21.8 mm and 0.55, respectively. Overall performance of the decision tree was 92 % sensitivity, 96 % specificity, 92 % PPV and 96 % NPV. Twelve out of 13 MSA patients were accurately classified. Formation of the decision tree using these parameters was both descriptive and predictive in differentiating between MSA and PD. • Parkinson's disease and multiple system atrophy can be distinguished on MR imaging. • Combined conventional MRI and diffusion tensor imaging improves the accuracy of diagnosis. • A decision tree is descriptive and predictive in differentiating between clinical entities. • A decision tree can reliably differentiate Parkinson's disease from multiple system atrophy.

Does the psychosocial quality of unpaid family work contribute to educational disparities in mental health among employed partnered mothers?

PubMed

Janzen, Bonnie; Hellsten, Laurie-Ann M

2018-04-24

The contribution of unpaid family work quality to understanding social inequalities in women's mental health has been understudied and further limited by a scarcity of psychometrically sound instruments available to measure family work. Therefore, using a multi-item scale of family work quality with evidence of validity and reliability, the overall aim of the present study was to determine whether psychosocial qualities of unpaid family work contribute to educational inequities in women's mental health. Study participants in this cross-sectional study were 512 employed partnered mothers living in a Canadian province and recruited from an online research panel. The dependent variable was psychological distress. In addition to a 28-item measure assessing five dimensions of unpaid family work quality, independent variables included material deprivation, job decision latitude, job demands and several measures of the work-family interface. Multiple linear regression was the primary analysis. Compared to women with high school or less, university educated women reported lower psychological distress [b = - 2.23 (SE = 0.50) p = 0.001]. The introduction of material deprivation into the model resulted in the largest reduction to the education disparity (51%), followed by equity in responsibility for unpaid family work (25%), family-to-work facilitation (22%), and decision latitude in paid work (21%). When entered simultaneously into the final model, the association between education and psychological distress was reduced by 70% and became statistically non-significant [b = - 0.68 (SE = 0.47) p = 0.10]. In addition to the more established mechanisms of material conditions and decision latitude to explain mental health disparities, inequity in responsibility for unpaid family work may also play a role.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oberije, Cary, E-mail: cary.oberije@maastro.nl; De Ruysscher, Dirk; Universitaire Ziekenhuizen Leuven, KU Leuven

Purpose: Although patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing andmore » validating a model that can provide physicians with a survival probability for an individual NSCLC patient. Methods and Materials: Data from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130). Results: The final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, and gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient's survival probability ( (www.predictcancer.org)). The data set can be downloaded at (https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048). Conclusions: The prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system.« less

Why bother with the brain? A role for decision neuroscience in understanding strategic variability.

PubMed

Venkatraman, Vinod

2013-01-01

Neuroscience, by its nature, seems to hold considerable promise for understanding the fundamental mechanisms of decision making. In recent years, several studies in the domain of "neuroeconomics" or "decision neuroscience" have provided important insights into brain function. Yet, the apparent success and value of each of these domains are frequently called into question by researchers in economics and behavioral decision making. Critics often charge that knowledge about the brain is unnecessary for understanding decision preferences. In this chapter, I contend that knowledge about underlying brain mechanisms helps in the development of biologically plausible models of behavior, which can then help elucidate the mechanisms underlying individual choice biases and strategic preferences. Using a novel risky choice paradigm, I will demonstrate that people vary in whether they adopt compensatory or noncompensatory rules in economic decision making. Importantly, neuroimaging studies using functional magnetic resonance imaging reveal that distinct neural mechanisms support variability in choices and variability in strategic preferences. Converging evidence from a study involving decisions between hypothetical stocks illustrates how knowledge about the underlying mechanisms can help inform neuroanatomical models of cognitive control. Last, I will demonstrate how knowledge about these underlying neural mechanisms can provide novel insights into the effects of decision states like sleep deprivation on decision preferences. Together, these findings suggest that neuroscience can play a critical role in creating robust and flexible models of real-world decision behavior. Copyright © 2013 Elsevier B.V. All rights reserved.

Variable Perceptions of Decision: An Operationalization of Four Models.

ERIC Educational Resources Information Center

Benjamin, Beverly P.; Kerchner, Charles T.

Decision-making and the models of decision-making that people carry in their minds were assessed. Participants in a public policy decision involving early childhood education were mapped onto four frequently used models of decision making: the rational, the bureaucratic, organizational process (Allison, 1971) and the garbage can or organized…

Model-based choices involve prospective neural activity

PubMed Central

Doll, Bradley B.; Duncan, Katherine D.; Simon, Dylan A.; Shohamy, Daphna; Daw, Nathaniel D.

2015-01-01

Decisions may arise via “model-free” repetition of previously reinforced actions, or by “model-based” evaluation, which is widely thought to follow from prospective anticipation of action consequences using a learned map or model. While choices and neural correlates of decision variables sometimes reflect knowledge of their consequences, it remains unclear whether this actually arises from prospective evaluation. Using functional MRI and a sequential reward-learning task in which paths contained decodable object categories, we found that humans’ model-based choices were associated with neural signatures of future paths observed at decision time, suggesting a prospective mechanism for choice. Prospection also covaried with the degree of model-based influences on neural correlates of decision variables, and was inversely related to prediction error signals thought to underlie model-free learning. These results dissociate separate mechanisms underlying model-based and model-free evaluation and support the hypothesis that model-based influences on choices and neural decision variables result from prospection. PMID:25799041

Decision-making and planning in full recovery of anorexia nervosa.

PubMed

Lindner, Susanne E; Fichter, Manfred M; Quadflieg, Norbert

2012-11-01

Based on findings of persisting neuropsychological impairments in women recovered from anorexia nervosa (rec AN), this study examined decision-making and planning, for achieving a desired goal, as central executive functions in a large sample of rec AN. The definition of recovery included physiological, behavioral, and psychological variables. A total of 100 rec AN women were compared to 100 healthy women, 1:1 matched for age and educational level. Decision-making was assessed with the Iowa Gambling Task and planning with the Tower of London. Expert interviews and self-ratings were used for assessing the inclusion/exclusion criteria and control variables. Compared to healthy controls, rec AN women were better in decision-making and worse in planning even after considering control variables. This study does not support results from other studies showing that rec AN participants perform better in decision-making. Results from this study show that planning is impaired even after full recovery from AN. Copyright © 2012 Wiley Periodicals, Inc.

Functional Freedom: A Psychological Model of Freedom in Decision-Making

PubMed Central

Lau, Stephan; Hiemisch, Anette

2017-01-01

The freedom of a decision is not yet sufficiently described as a psychological variable. We present a model of functional decision freedom that aims to fill that role. The model conceptualizes functional freedom as a capacity of people that varies depending on certain conditions of a decision episode. It denotes an inner capability to consciously shape complex decisions according to one’s own values and needs. Functional freedom depends on three compensatory dimensions: it is greatest when the decision-maker is highly rational, when the structure of the decision is highly underdetermined, and when the decision process is strongly based on conscious thought and reflection. We outline possible research questions, argue for psychological benefits of functional decision freedom, and explicate the model’s implications on current knowledge and research. In conclusion, we show that functional freedom is a scientific variable, permitting an additional psychological foothold in research on freedom, and that is compatible with a deterministic worldview. PMID:28678165

Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making.

PubMed

Owen, Rhiannon K; Cooper, Nicola J; Quinn, Terence J; Lees, Rosalind; Sutton, Alex J

2018-07-01

Network meta-analyses (NMA) have extensively been used to compare the effectiveness of multiple interventions for health care policy and decision-making. However, methods for evaluating the performance of multiple diagnostic tests are less established. In a decision-making context, we are often interested in comparing and ranking the performance of multiple diagnostic tests, at varying levels of test thresholds, in one simultaneous analysis. Motivated by an example of cognitive impairment diagnosis following stroke, we synthesized data from 13 studies assessing the efficiency of two diagnostic tests: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), at two test thresholds: MMSE <25/30 and <27/30, and MoCA <22/30 and <26/30. Using Markov chain Monte Carlo (MCMC) methods, we fitted a bivariate network meta-analysis model incorporating constraints on increasing test threshold, and accounting for the correlations between multiple test accuracy measures from the same study. We developed and successfully fitted a model comparing multiple tests/threshold combinations while imposing threshold constraints. Using this model, we found that MoCA at threshold <26/30 appeared to have the best true positive rate, whereas MMSE at threshold <25/30 appeared to have the best true negative rate. The combined analysis of multiple tests at multiple thresholds allowed for more rigorous comparisons between competing diagnostics tests for decision making. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Analytical group decision making in natural resources: Methodology and application

USGS Publications Warehouse

Schmoldt, D.L.; Peterson, D.L.

2000-01-01

Group decision making is becoming increasingly important in natural resource management and associated scientific applications, because multiple values are treated coincidentally in time and space, multiple resource specialists are needed, and multiple stakeholders must be included in the decision process. Decades of social science research on decision making in groups have provided insights into the impediments to effective group processes and on techniques that can be applied in a group context. Nevertheless, little integration and few applications of these results have occurred in resource management decision processes, where formal groups are integral, either directly or indirectly. A group decision-making methodology is introduced as an effective approach for temporary, formal groups (e.g., workshops). It combines the following three components: (1) brainstorming to generate ideas; (2) the analytic hierarchy process to produce judgments, manage conflict, enable consensus, and plan for implementation; and (3) a discussion template (straw document). Resulting numerical assessments of alternative decision priorities can be analyzed statistically to indicate where group member agreement occurs and where priority values are significantly different. An application of this group process to fire research program development in a workshop setting indicates that the process helps focus group deliberations; mitigates groupthink, nondecision, and social loafing pitfalls; encourages individual interaction; identifies irrational judgments; and provides a large amount of useful quantitative information about group preferences. This approach can help facilitate scientific assessments and other decision-making processes in resource management.

Integrated Forecast-Decision Systems For River Basin Planning and Management

NASA Astrophysics Data System (ADS)

Georgakakos, A. P.

2005-12-01

A central application of climatology, meteorology, and hydrology is the generation of reliable forecasts for water resources management. In principle, effective use of forecasts could improve water resources management by providing extra protection against floods, mitigating the adverse effects of droughts, generating more hydropower, facilitating recreational activities, and minimizing the impacts of extreme events on the environment and the ecosystems. In practice, however, realization of these benefits depends on three requisite elements. First is the skill and reliability of forecasts. Second is the existence of decision support methods/systems with the ability to properly utilize forecast information. And third is the capacity of the institutional infrastructure to incorporate the information provided by the decision support systems into the decision making processes. This presentation discusses several decision support systems (DSS) using ensemble forecasting that have been developed by the Georgia Water Resources Institute for river basin management. These DSS are currently operational in Africa, Europe, and the US and address integrated water resources and energy planning and management in river basins with multiple water uses, multiple relevant temporal and spatial scales, and multiple decision makers. The article discusses the methods used and advocates that the design, development, and implementation of effective forecast-decision support systems must bring together disciplines, people, and institutions necessary to address today's complex water resources challenges.

Principal Selection Decisions Made by Teachers: The Influence of Principal Candidate Experience

ERIC Educational Resources Information Center

Winter, Paul A.; Jaeger, Mary Grace

2004-01-01

Public school teachers (N = 189) role-played as members of school councils making principal selection decisions by rating simulated candidates for principal vacancies. The independent variables were principal candidate job experience, candidate person characteristics, and teacher school level. The dependent variable was teacher rating of the job…

Selection Practices of Group Leaders: A National Survey.

ERIC Educational Resources Information Center

Riva, Maria T.; Lippert, Laurel; Tackett, M. Jan

2000-01-01

Study surveys the selection practices of group leaders. Explores methods of selection, variables used to make selection decisions, and the types of selection errors that leaders have experienced. Results suggest that group leaders use clinical judgment to make selection decisions and endorse using some specific variables in selection. (Contains 22…

Multiple Criteria Evaluation of Quality and Optimisation of e-Learning System Components

ERIC Educational Resources Information Center

Kurilovas, Eugenijus; Dagiene, Valentina

2010-01-01

The main research object of the paper is investigation and proposal of the comprehensive Learning Object Repositories (LORs) quality evaluation tool suitable for their multiple criteria decision analysis, evaluation and optimisation. Both LORs "internal quality" and "quality in use" evaluation (decision making) criteria are analysed in the paper.…

Evaluator-blinded trial evaluating nurse-led immunotherapy DEcision Coaching In persons with relapsing-remitting Multiple Sclerosis (DECIMS) and accompanying process evaluation: study protocol for a cluster randomised controlled trial.

PubMed

Rahn, Anne Christin; Köpke, Sascha; Kasper, Jürgen; Vettorazzi, Eik; Mühlhauser, Ingrid; Heesen, Christoph

2015-03-21

Multiple sclerosis is a chronic neurological condition usually starting in early adulthood and regularly leading to severe disability. Immunotherapy options are growing in number and complexity, while costs of treatments are high and adherence rates remain low. Therefore, treatment decision-making has become more complex for patients. Structured decision coaching, based on the principles of evidence-based patient information and shared decision-making, has the potential to facilitate participation of individuals in the decision-making process. This cluster randomised controlled trial follows the assumption that decision coaching by trained nurses, using evidence-based patient information and preference elicitation, will facilitate informed choices and induce higher decision quality, as well as better decisional adherence. The decision coaching programme will be evaluated through an evaluator-blinded superiority cluster randomised controlled trial, including 300 patients with suspected or definite relapsing-remitting multiple sclerosis, facing an immunotherapy decision. The clusters are 12 multiple sclerosis outpatient clinics in Germany. Further, the trial will be accompanied by a mixed-methods process evaluation and a cost-effectiveness study. Nurses in the intervention group will be trained in shared decision-making, coaching, and evidence-based patient information principles. Patients who meet the inclusion criteria will receive decision coaching (intervention group) with up to three face-to-face coaching sessions with a trained nurse (decision coach) or counselling as usual (control group). Patients in both groups will be given access to an evidence-based online information tool. The primary outcome is 'informed choice' after six months, assessed with the multi-dimensional measure of informed choice including the sub-dimensions risk knowledge (questionnaire), attitude concerning immunotherapy (questionnaire), and immunotherapy uptake (telephone survey). Secondary outcomes include decisional conflict, adherence to immunotherapy decisions, autonomy preference, planned behaviour, coping self-efficacy, and perceived involvement in coaching and decisional encounters. Safety outcomes are comprised of anxiety and depression and disease-specific quality of life. This trial will assess the effectiveness of a new model of patient decision support concerning MS-immunotherapy options. The delegation of treatment information provision from physicians to trained nurses bears the potential to change current doctor-focused practice in Germany. Current Controlled Trials (identifier: ISRCTN37929939 ), May 27, 2014.

A framework for multi-stakeholder decision-making and ...

EPA Pesticide Factsheets

We propose a decision-making framework to compute compromise solutions that balance conflicting priorities of multiple stakeholders on multiple objectives. In our setting, we shape the stakeholder dis-satisfaction distribution by solving a conditional-value-at-risk (CVaR) minimization problem. The CVaR problem is parameterized by a probability level that shapes the tail of the dissatisfaction distribution. The proposed approach allows us to compute a family of compromise solutions and generalizes multi-stakeholder settings previously proposed in the literature that minimize average and worst-case dissatisfactions. We use the concept of the CVaR norm to give a geometric interpretation to this problem +and use the properties of this norm to prove that the CVaR minimization problem yields Pareto optimal solutions for any choice of the probability level. We discuss a broad range of potential applications of the framework that involve complex decision-making processes. We demonstrate the developments using a biowaste facility location case study in which we seek to balance stakeholder priorities on transportation, safety, water quality, and capital costs. This manuscript describes the methodology of a new decision-making framework that computes compromise solutions that balance conflicting priorities of multiple stakeholders on multiple objectives as needed for SHC Decision Science and Support Tools project. A biowaste facility location is employed as the case study

A web-based decision support tool for prognosis simulation in multiple sclerosis.

PubMed

Veloso, Mário

2014-09-01

A multiplicity of natural history studies of multiple sclerosis provides valuable knowledge of the disease progression but individualized prognosis remains elusive. A few decision support tools that assist the clinician in such task have emerged but have not received proper attention from clinicians and patients. The objective of the current work is to implement a web-based tool, conveying decision relevant prognostic scientific evidence, which will help clinicians discuss prognosis with individual patients. Data were extracted from a set of reference studies, especially those dealing with the natural history of multiple sclerosis. The web-based decision support tool for individualized prognosis simulation was implemented with NetLogo, a program environment suited for the development of complex adaptive systems. Its prototype has been launched online; it enables clinicians to predict both the likelihood of CIS to CDMS conversion, and the long-term prognosis of disability level and SPMS conversion, as well as assess and monitor the effects of treatment. More robust decision support tools, which convey scientific evidence and satisfy the needs of clinical practice by helping clinicians discuss prognosis expectations with individual patients, are required. The web-based simulation model herein introduced proposes to be a step forward toward this purpose. Copyright © 2014 Elsevier B.V. All rights reserved.

Perspectives of Patients, Clinicians, and Health System Leaders on Changes Needed to Improve the Health Care and Outcomes of Older Adults With Multiple Chronic Conditions.

PubMed

Ferris, Rosie; Blaum, Caroline; Kiwak, Eliza; Austin, Janet; Esterson, Jessica; Harkless, Gene; Oftedahl, Gary; Parchman, Michael; Van Ness, Peter H; Tinetti, Mary E

2018-06-01

To ascertain perspectives of multiple stakeholders on contributors to inappropriate care for older adults with multiple chronic conditions. Perspectives of 36 purposively sampled patients, clinicians, health systems, and payers were elicited. Data analysis followed a constant comparative method. Structural factors triggering burden and fragmentation include disease-based quality metrics and need to interact with multiple clinicians. The key cultural barrier identified is the assumption that "physicians know best." Inappropriate decision making may result from inattention to trade-offs and adherence to multiple disease guidelines. Stakeholders recommended changes in culture, structure, and decision making. Care options and quality metrics should reflect a focus on patients' priorities. Clinician-patient partnerships should reflect patients knowing their health goals and clinicians knowing how to achieve them. Access to specialty expertise should not require visits. Stakeholders' recommendations suggest health care redesigns that incorporate patients' health priorities into care decisions and realign relationships across patients and clinicians.

The Challenges of Measuring Glycemic Variability

PubMed Central

Rodbard, David

2012-01-01

This commentary reviews several of the challenges encountered when attempting to quantify glycemic variability and correlate it with risk of diabetes complications. These challenges include (1) immaturity of the field, including problems of data accuracy, precision, reliability, cost, and availability; (2) larger relative error in the estimates of glycemic variability than in the estimates of the mean glucose; (3) high correlation between glycemic variability and mean glucose level; (4) multiplicity of measures; (5) correlation of the multiple measures; (6) duplication or reinvention of methods; (7) confusion of measures of glycemic variability with measures of quality of glycemic control; (8) the problem of multiple comparisons when assessing relationships among multiple measures of variability and multiple clinical end points; and (9) differing needs for routine clinical practice and clinical research applications. PMID:22768904

Altruistic decisions following penetrating traumatic brain injury.

PubMed

Moll, Jorge; de Oliveira-Souza, Ricardo; Basilio, Rodrigo; Bramati, Ivanei Edson; Gordon, Barry; Rodríguez-Nieto, Geraldine; Zahn, Roland; Krueger, Frank; Grafman, Jordan

2018-05-01

The cerebral correlates of altruistic decisions have increasingly attracted the interest of neuroscientists. To date, investigations on the neural underpinnings of altruistic decisions have primarily been conducted in healthy adults undergoing functional neuroimaging as they engaged in decisions to punish third parties. The chief purpose of the present study was to investigate altruistic decisions following focal brain damage with a novel altruistic decision task. In contrast to studies that have focused either on altruistic punishment or donation, the Altruistic Decision Task allows players to anonymously punish or donate to 30 charitable organizations involved with salient societal issues such as abortion, nuclear energy and civil rights. Ninety-four Vietnam War veterans with variable patterns of penetrating traumatic brain injury and 28 healthy veterans who also served in combat participated in the study as normal controls. Participants were asked to invest $1 to punish or reward real societal organizations, or keep the money for themselves. Associations between lesion distribution and performance on the task were analysed with multivariate support vector regression, which enables the assessment of the joint contribution of multiple regions in the determination of a given behaviour of interest. Our main findings were: (i) bilateral dorsomedial prefrontal lesions increased altruistic punishment, whereas lesions of the right perisylvian region and left temporo-insular cortex decreased punishment; (ii) altruistic donations were increased by bilateral lesions of the dorsomedial parietal cortex, whereas lesions of the right posterior superior temporal sulcus and middle temporal gyri decreased donations; (iii) altruistic punishment and donation were only weakly correlated, emphasizing their dissociable neuroanatomical associations; and (iv) altruistic decisions were not related to post-traumatic personality changes. These findings indicate that altruistic punishment and donation are determined by largely non-overlapping cerebral regions, which have previously been implicated in social cognition and moral experience such as evaluations of intentionality and intuitions of justice and morality.10.1093/brain/awy064_video1awy064media15758316955001.

Improving Water Resources System Operation by Direct Use of Hydroclimatic Information

NASA Astrophysics Data System (ADS)

Castelletti, A.; Pianosi, F.

2011-12-01

It is generally agreed that more information translates into better decisions. For instance, the availability of inflow predictions can improve reservoir operation; soil moisture data can be exploited to increase irrigation efficiency; etc. However, beyond this general statement, many theoretical and practical questions remain open. Provided that not all information sources are equally relevant, how does their value depend on the physical features of the water system and on the purposes of the system operation? What is the minimum lead time needed for anticipatory management to be effective? How does uncertainty in the information propagates through the modelling chain from hydroclimatic data through descriptive and decision models, and finally affect the decision? Is the data-predictions-decision paradigm truly effective or would it be better to directly use hydroclimatic data to take optimal decisions, skipping the intermediate step of hydrological forecasting? In this work we investigate these issues by application to the management of a complex water system in Northern Vietnam, characterized by multiple, conflicting objectives including hydropower production, flood control and water supply. First, we quantify the value of hydroclimatic information as the improvement in the system performances that could be attained under the (ideal) assumption of perfect knowledge of all future meteorological and hydrological input. Then, we assess and compare the relevance of different candidate information (meteorological or hydrological observations; ground or remote data; etc.) for the purpose of system operation by novel Input Variable Selection techniques. Finally, we evaluate the performance improvement made possible by the use of such information in re-designing the system operation.

Assessments of the extent to which health-care providers involve patients in decision making: a systematic review of studies using the OPTION instrument.

PubMed

Couët, Nicolas; Desroches, Sophie; Robitaille, Hubert; Vaillancourt, Hugues; Leblanc, Annie; Turcotte, Stéphane; Elwyn, Glyn; Légaré, France

2015-08-01

We have no clear overview of the extent to which health-care providers involve patients in the decision-making process during consultations. The Observing Patient Involvement in Decision Making instrument (OPTION) was designed to assess this. To systematically review studies that used the OPTION instrument to observe the extent to which health-care providers involve patients in decision making across a range of clinical contexts, including different health professions and lengths of consultation. We conducted online literature searches in multiple databases (2001-12) and gathered further data through networking. (i) OPTION scores as reported outcomes and (ii) health-care providers and patients as study participants. For analysis, we only included studies using the revised scale. Extracted data included: (i) study and participant characteristics and (ii) OPTION outcomes (scores, statistical associations and reported psychometric results). We also assessed the quality of OPTION outcomes reporting. We found 33 eligible studies, 29 of which used the revised scale. Overall, we found low levels of patient-involving behaviours: in cases where no intervention was used to implement shared decision making (SDM), the mean OPTION score was 23 ± 14 (0-100 scale). When assessed, the variables most consistently associated with higher OPTION scores were interventions to implement SDM (n = 8/9) and duration of consultations (n = 8/15). Whatever the clinical context, few health-care providers consistently attempt to facilitate patient involvement, and even fewer adjust care to patient preferences. However, both SDM interventions and longer consultations could improve this. © 2013 John Wiley & Sons Ltd.

A multi-objective decision-making approach to the journal submission problem.

PubMed

Wong, Tony E; Srikrishnan, Vivek; Hadka, David; Keller, Klaus

2017-01-01

When researchers complete a manuscript, they need to choose a journal to which they will submit the study. This decision requires to navigate trade-offs between multiple objectives. One objective is to share the new knowledge as widely as possible. Citation counts can serve as a proxy to quantify this objective. A second objective is to minimize the time commitment put into sharing the research, which may be estimated by the total time from initial submission to final decision. A third objective is to minimize the number of rejections and resubmissions. Thus, researchers often consider the trade-offs between the objectives of (i) maximizing citations, (ii) minimizing time-to-decision, and (iii) minimizing the number of resubmissions. To complicate matters further, this is a decision with multiple, potentially conflicting, decision-maker rationalities. Co-authors might have different preferences, for example about publishing fast versus maximizing citations. These diverging preferences can lead to conflicting trade-offs between objectives. Here, we apply a multi-objective decision analytical framework to identify the Pareto-front between these objectives and determine the set of journal submission pathways that balance these objectives for three stages of a researcher's career. We find multiple strategies that researchers might pursue, depending on how they value minimizing risk and effort relative to maximizing citations. The sequences that maximize expected citations within each strategy are generally similar, regardless of time horizon. We find that the "conditional impact factor"-impact factor times acceptance rate-is a suitable heuristic method for ranking journals, to strike a balance between minimizing effort objectives and maximizing citation count. Finally, we examine potential co-author tension resulting from differing rationalities by mapping out each researcher's preferred Pareto front and identifying compromise submission strategies. The explicit representation of trade-offs, especially when multiple decision-makers (co-authors) have different preferences, facilitates negotiations and can support the decision process.

A multi-objective decision-making approach to the journal submission problem

PubMed Central

Hadka, David; Keller, Klaus

2017-01-01

When researchers complete a manuscript, they need to choose a journal to which they will submit the study. This decision requires to navigate trade-offs between multiple objectives. One objective is to share the new knowledge as widely as possible. Citation counts can serve as a proxy to quantify this objective. A second objective is to minimize the time commitment put into sharing the research, which may be estimated by the total time from initial submission to final decision. A third objective is to minimize the number of rejections and resubmissions. Thus, researchers often consider the trade-offs between the objectives of (i) maximizing citations, (ii) minimizing time-to-decision, and (iii) minimizing the number of resubmissions. To complicate matters further, this is a decision with multiple, potentially conflicting, decision-maker rationalities. Co-authors might have different preferences, for example about publishing fast versus maximizing citations. These diverging preferences can lead to conflicting trade-offs between objectives. Here, we apply a multi-objective decision analytical framework to identify the Pareto-front between these objectives and determine the set of journal submission pathways that balance these objectives for three stages of a researcher’s career. We find multiple strategies that researchers might pursue, depending on how they value minimizing risk and effort relative to maximizing citations. The sequences that maximize expected citations within each strategy are generally similar, regardless of time horizon. We find that the “conditional impact factor”—impact factor times acceptance rate—is a suitable heuristic method for ranking journals, to strike a balance between minimizing effort objectives and maximizing citation count. Finally, we examine potential co-author tension resulting from differing rationalities by mapping out each researcher’s preferred Pareto front and identifying compromise submission strategies. The explicit representation of trade-offs, especially when multiple decision-makers (co-authors) have different preferences, facilitates negotiations and can support the decision process. PMID:28582430

On Bayesian methods of exploring qualitative interactions for targeted treatment.

PubMed

Chen, Wei; Ghosh, Debashis; Raghunathan, Trivellore E; Norkin, Maxim; Sargent, Daniel J; Bepler, Gerold

2012-12-10

Providing personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction but different magnitudes are called quantitative interactions, whereas those having opposite directions in subgroups are called qualitative interactions (QIs). Identifying QIs is challenging because they are rare and usually unknown among many potential biomarkers. Meanwhile, subgroup analysis reduces the power of hypothesis testing and multiple subgroup analyses inflate the type I error rate. We propose a new Bayesian approach to search for QI in a multiple regression setting with adaptive decision rules. We consider various regression models for the outcome. We illustrate this method in two examples of phase III clinical trials. The algorithm is straightforward and easy to implement using existing software packages. We provide a sample code in Appendix A. Copyright © 2012 John Wiley & Sons, Ltd.

Substance Use and Self-Harm: Case Studies From Patients Admitted to an Urban Hospital Following Medically Serious Self-Harm.

PubMed

Breet, Elsie; Bantjes, Jason

2017-12-01

Few qualitative studies have explored the relationship between substance use and self-harm. We employed a multiple-case study research design to analyze data from 80 patients who were admitted to a hospital in South Africa following self-harm. Our analysis revealed, from the perspective of patients, a number of distinct ways in which substance use is implicated in self-harm. Some patients reported that substance intoxication resulted in poor decision making and impulsivity, which led to self-harm. Others said substance use facilitated their self-harm. Some participants detailed how in the past their chronic substance use had served an adaptive function helping them to cope with distress, but more recently, this coping mechanism had failed which precipitated their self-harm. Some participants reported that substance use by someone else triggered their self-harm. Findings suggest that there are multiple pathways and a host of variables which mediate the relationship between substance use and self-harm.

Recursive feature elimination for biomarker discovery in resting-state functional connectivity.

PubMed

Ravishankar, Hariharan; Madhavan, Radhika; Mullick, Rakesh; Shetty, Teena; Marinelli, Luca; Joel, Suresh E

2016-08-01

Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate analysis suffers from the problem of multiple comparisons. Here, we adopt an alternative data-driven method for identifying population differences in functional connectivity. We propose a machine-learning approach to down-select functional connectivity features associated with symptom severity in mild traumatic brain injury (mTBI). Using this approach, we identified functional regions with altered connectivity in mTBI. including the executive control, visual and precuneus networks. We compared functional connections at multiple resolutions to determine which scale would be more sensitive to changes related to patient recovery. These modular network-level features can be used as diagnostic tools for predicting disease severity and recovery profiles.

Modelling Concentrating Solar Power with Thermal Energy Storage for Integration Studies (Presentation)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hummon, M.; Jorgenson, J.; Denholm, P.

2013-10-01

Concentrating solar power with thermal energy storage (CSP-TES) can provide multiple benefits to the grid, including low marginal cost energy and the ability to levelize load, provide operating reserves, and provide firm capacity. It is challenging to properly value the integration of CSP because of the complicated nature of this technology. Unlike completely dispatchable fossil sources, CSP is a limited energy resource, depending on the hourly and daily supply of solar energy. To optimize the use of this limited energy, CSP-TES must be implemented in a production cost model with multiple decision variables for the operation of the CSP-TES plant.more » We develop and implement a CSP-TES plant in a production cost model that accurately characterizes the three main components of the plant: solar field, storage tank, and power block. We show the effect of various modelling simplifications on the value of CSP, including: scheduled versus optimized dispatch from the storage tank and energy-only operation versus co-optimization with ancillary services.« less

Modelling Concentrating Solar Power with Thermal Energy Storage for Integration Studies: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hummon, M.; Denholm, P.; Jorgenson, J.

2013-10-01

Concentrating solar power with thermal energy storage (CSP-TES) can provide multiple benefits to the grid, including low marginal cost energy and the ability to levelize load, provide operating reserves, and provide firm capacity. It is challenging to properly value the integration of CSP because of the complicated nature of this technology. Unlike completely dispatchable fossil sources, CSP is a limited energy resource, depending on the hourly and daily supply of solar energy. To optimize the use of this limited energy, CSP-TES must be implemented in a production cost model with multiple decision variables for the operation of the CSP-TES plant.more » We develop and implement a CSP-TES plant in a production cost model that accurately characterizes the three main components of the plant: solar field, storage tank, and power block. We show the effect of various modelling simplifications on the value of CSP, including: scheduled versus optimized dispatch from the storage tank and energy-only operation versus co-optimization with ancillary services.« less

A collaborative scheduling model for the supply-hub with multiple suppliers and multiple manufacturers.

PubMed

Li, Guo; Lv, Fei; Guan, Xu

2014-01-01

This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment.

A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers

PubMed Central

Lv, Fei; Guan, Xu

2014-01-01

This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment. PMID:24892104

Evaluation of Second-Level Inference in fMRI Analysis

PubMed Central

Roels, Sanne P.; Loeys, Tom; Moerkerke, Beatrijs

2016-01-01

We investigate the impact of decisions in the second-level (i.e., over subjects) inferential process in functional magnetic resonance imaging on (1) the balance between false positives and false negatives and on (2) the data-analytical stability, both proxies for the reproducibility of results. Second-level analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects. We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability. Second, one proceeds via inference based on parametrical assumptions or via permutation-based inference. Third, we evaluate 3 commonly used procedures to address the multiple testing problem: familywise error rate correction, False Discovery Rate (FDR) correction, and a two-step procedure with minimal cluster size. Based on a simulation study and real data we find that the two-step procedure with minimal cluster size results in most stable results, followed by the familywise error rate correction. The FDR results in most variable results, for both permutation-based inference and parametrical inference. Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference. PMID:26819578

Dissociating neural variability related to stimulus quality and response times in perceptual decision-making.

PubMed

Bode, Stefan; Bennett, Daniel; Sewell, David K; Paton, Bryan; Egan, Gary F; Smith, Philip L; Murawski, Carsten

2018-03-01

According to sequential sampling models, perceptual decision-making is based on accumulation of noisy evidence towards a decision threshold. The speed with which a decision is reached is determined by both the quality of incoming sensory information and random trial-by-trial variability in the encoded stimulus representations. To investigate those decision dynamics at the neural level, participants made perceptual decisions while functional magnetic resonance imaging (fMRI) was conducted. On each trial, participants judged whether an image presented under conditions of high, medium, or low visual noise showed a piano or a chair. Higher stimulus quality (lower visual noise) was associated with increased activation in bilateral medial occipito-temporal cortex and ventral striatum. Lower stimulus quality was related to stronger activation in posterior parietal cortex (PPC) and dorsolateral prefrontal cortex (DLPFC). When stimulus quality was fixed, faster response times were associated with a positive parametric modulation of activation in medial prefrontal and orbitofrontal cortex, while slower response times were again related to more activation in PPC, DLPFC and insula. Our results suggest that distinct neural networks were sensitive to the quality of stimulus information, and to trial-to-trial variability in the encoded stimulus representations, but that reaching a decision was a consequence of their joint activity. Copyright © 2018 Elsevier Ltd. All rights reserved.

How accurate are interpretations of curriculum-based measurement progress monitoring data? Visual analysis versus decision rules.

PubMed

Van Norman, Ethan R; Christ, Theodore J

2016-10-01

Curriculum based measurement of oral reading (CBM-R) is used to monitor the effects of academic interventions for individual students. Decisions to continue, modify, or terminate these interventions are made by interpreting time series CBM-R data. Such interpretation is founded upon visual analysis or the application of decision rules. The purpose of this study was to compare the accuracy of visual analysis and decision rules. Visual analysts interpreted 108 CBM-R progress monitoring graphs one of three ways: (a) without graphic aids, (b) with a goal line, or (c) with a goal line and a trend line. Graphs differed along three dimensions, including trend magnitude, variability of observations, and duration of data collection. Automated trend line and data point decision rules were also applied to each graph. Inferential analyses permitted the estimation of the probability of a correct decision (i.e., the student is improving - continue the intervention, or the student is not improving - discontinue the intervention) for each evaluation method as a function of trend magnitude, variability of observations, and duration of data collection. All evaluation methods performed better when students made adequate progress. Visual analysis and decision rules performed similarly when observations were less variable. Results suggest that educators should collect data for more than six weeks, take steps to control measurement error, and visually analyze graphs when data are variable. Implications for practice and research are discussed. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions

NASA Astrophysics Data System (ADS)

Fenicia, Fabrizio; Kavetski, Dmitri; Savenije, Hubert H. G.; Pfister, Laurent

2016-02-01

This paper explores the development and application of distributed hydrological models, focusing on the key decisions of how to discretize the landscape, which model structures to use in each landscape element, and how to link model parameters across multiple landscape elements. The case study considers the Attert catchment in Luxembourg—a 300 km2 mesoscale catchment with 10 nested subcatchments that exhibit clearly different streamflow dynamics. The research questions are investigated using conceptual models applied at hydrologic response unit (HRU) scales (1-4 HRUs) on 6 hourly time steps. Multiple model structures are hypothesized and implemented using the SUPERFLEX framework. Following calibration, space/time model transferability is tested using a split-sample approach, with evaluation criteria including streamflow prediction error metrics and hydrological signatures. Our results suggest that: (1) models using geology-based HRUs are more robust and capture the spatial variability of streamflow time series and signatures better than models using topography-based HRUs; this finding supports the hypothesis that, in the Attert, geology exerts a stronger control than topography on streamflow generation, (2) streamflow dynamics of different HRUs can be represented using distinct and remarkably simple model structures, which can be interpreted in terms of the perceived dominant hydrologic processes in each geology type, and (3) the same maximum root zone storage can be used across the three dominant geological units with no loss in model transferability; this finding suggests that the partitioning of water between streamflow and evaporation in the study area is largely independent of geology and can be used to improve model parsimony. The modeling methodology introduced in this study is general and can be used to advance our broader understanding and prediction of hydrological behavior, including the landscape characteristics that control hydrologic response, the dominant processes associated with different landscape types, and the spatial relations of catchment processes. This article was corrected on 14 MAR 2016. See the end of the full text for details.

Stochastic Multi-Timescale Power System Operations With Variable Wind Generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Hongyu; Krad, Ibrahim; Florita, Anthony

This paper describes a novel set of stochastic unit commitment and economic dispatch models that consider stochastic loads and variable generation at multiple operational timescales. The stochastic model includes four distinct stages: stochastic day-ahead security-constrained unit commitment (SCUC), stochastic real-time SCUC, stochastic real-time security-constrained economic dispatch (SCED), and deterministic automatic generation control (AGC). These sub-models are integrated together such that they are continually updated with decisions passed from one to another. The progressive hedging algorithm (PHA) is applied to solve the stochastic models to maintain the computational tractability of the proposed models. Comparative case studies with deterministic approaches are conductedmore » in low wind and high wind penetration scenarios to highlight the advantages of the proposed methodology, one with perfect forecasts and the other with current state-of-the-art but imperfect deterministic forecasts. The effectiveness of the proposed method is evaluated with sensitivity tests using both economic and reliability metrics to provide a broader view of its impact.« less

Visualization of Time-Series Sensor Data to Inform the Design of Just-In-Time Adaptive Stress Interventions.

PubMed

Sharmin, Moushumi; Raij, Andrew; Epstien, David; Nahum-Shani, Inbal; Beck, J Gayle; Vhaduri, Sudip; Preston, Kenzie; Kumar, Santosh

2015-09-01

We investigate needs, challenges, and opportunities in visualizing time-series sensor data on stress to inform the design of just-in-time adaptive interventions (JITAIs). We identify seven key challenges: massive volume and variety of data, complexity in identifying stressors, scalability of space, multifaceted relationship between stress and time, a need for representation at multiple granularities, interperson variability, and limited understanding of JITAI design requirements due to its novelty. We propose four new visualizations based on one million minutes of sensor data (n=70). We evaluate our visualizations with stress researchers (n=6) to gain first insights into its usability and usefulness in JITAI design. Our results indicate that spatio-temporal visualizations help identify and explain between- and within-person variability in stress patterns and contextual visualizations enable decisions regarding the timing, content, and modality of intervention. Interestingly, a granular representation is considered informative but noise-prone; an abstract representation is the preferred starting point for designing JITAIs.

Visualization of Time-Series Sensor Data to Inform the Design of Just-In-Time Adaptive Stress Interventions

PubMed Central

Sharmin, Moushumi; Raij, Andrew; Epstien, David; Nahum-Shani, Inbal; Beck, J. Gayle; Vhaduri, Sudip; Preston, Kenzie; Kumar, Santosh

2015-01-01

We investigate needs, challenges, and opportunities in visualizing time-series sensor data on stress to inform the design of just-in-time adaptive interventions (JITAIs). We identify seven key challenges: massive volume and variety of data, complexity in identifying stressors, scalability of space, multifaceted relationship between stress and time, a need for representation at multiple granularities, interperson variability, and limited understanding of JITAI design requirements due to its novelty. We propose four new visualizations based on one million minutes of sensor data (n=70). We evaluate our visualizations with stress researchers (n=6) to gain first insights into its usability and usefulness in JITAI design. Our results indicate that spatio-temporal visualizations help identify and explain between- and within-person variability in stress patterns and contextual visualizations enable decisions regarding the timing, content, and modality of intervention. Interestingly, a granular representation is considered informative but noise-prone; an abstract representation is the preferred starting point for designing JITAIs. PMID:26539566

An innovative index for evaluating water quality in streams.

PubMed

Said, Ahmend; Stevens, David K; Sehlke, Gerald

2004-09-01

A water quality index expressed as a single number is developed to describe overall water quality conditions using multiple water quality variables. The index consists of water quality variables: dissolved oxygen, specific conductivity, turbidity, total phosphorus, and fecal coliform. The objectives of this study were to describe the preexisting indices and to define a new water quality index that has advantages over these indices. The new index was applied to the Big Lost River Watershed in Idaho, and the results gave a quantitative picture for the water quality situation. If the new water quality index for the impaired water is less than a certain number, remediation-likely in the form of total maximum daily loads or changing the management practices-may be needed. The index can be used to assess water quality for general beneficial uses. Nevertheless, the index cannot be used in making regulatory decisions, indicate water quality for specific beneficial uses, or indicate contamination from trace metals, organic contaminants, and toxic substances.

The use of intelligent database systems in acute pancreatitis--a systematic review.

PubMed

van den Heever, Marc; Mittal, Anubhav; Haydock, Matthew; Windsor, John

2014-01-01

Acute pancreatitis (AP) is a complex disease with multiple aetiological factors, wide ranging severity, and multiple challenges to effective triage and management. Databases, data mining and machine learning algorithms (MLAs), including artificial neural networks (ANNs), may assist by storing and interpreting data from multiple sources, potentially improving clinical decision-making. 1) Identify database technologies used to store AP data, 2) collate and categorise variables stored in AP databases, 3) identify the MLA technologies, including ANNs, used to analyse AP data, and 4) identify clinical and non-clinical benefits and obstacles in establishing a national or international AP database. Comprehensive systematic search of online reference databases. The predetermined inclusion criteria were all papers discussing 1) databases, 2) data mining or 3) MLAs, pertaining to AP, independently assessed by two reviewers with conflicts resolved by a third author. Forty-three papers were included. Three data mining technologies and five ANN methodologies were reported in the literature. There were 187 collected variables identified. ANNs increase accuracy of severity prediction, one study showed ANNs had a sensitivity of 0.89 and specificity of 0.96 six hours after admission--compare APACHE II (cutoff score ≥8) with 0.80 and 0.85 respectively. Problems with databases were incomplete data, lack of clinical data, diagnostic reliability and missing clinical data. This is the first systematic review examining the use of databases, MLAs and ANNs in the management of AP. The clinical benefits these technologies have over current systems and other advantages to adopting them are identified. Copyright © 2013 IAP and EPC. Published by Elsevier B.V. All rights reserved.

More than just the mean: moving to a dynamic view of performance-based compensation.

PubMed

Barnes, Christopher M; Reb, Jochen; Ang, Dionysius

2012-05-01

Compensation decisions have important consequences for employees and organizations and affect factors such as retention, motivation, and recruitment. Past research has primarily focused on mean performance as a predictor of compensation, promoting the implicit assumption that alternative aspects of dynamic performance are not relevant. To address this gap in the literature, we examined the influence of dynamic performance characteristics on compensation decisions in the National Basketball Association (NBA). We predicted that, in addition to performance mean, performance trend and variability would also affect compensation decisions. Results revealed that performance mean and trend, but not variability, were significantly and positively related to changes in compensation levels of NBA players. Moreover, trend (but not mean or variability) predicted compensation when controlling for future performance, suggesting that organizations overweighted trend in their compensation decisions. Theoretical and practical implications are discussed. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Variability in visual working memory ability limits the efficiency of perceptual decision making.

PubMed

Ester, Edward F; Ho, Tiffany C; Brown, Scott D; Serences, John T

2014-04-02

The ability to make rapid and accurate decisions based on limited sensory information is a critical component of visual cognition. Available evidence suggests that simple perceptual discriminations are based on the accumulation and integration of sensory evidence over time. However, the memory system(s) mediating this accumulation are unclear. One candidate system is working memory (WM), which enables the temporary maintenance of information in a readily accessible state. Here, we show that individual variability in WM capacity is strongly correlated with the speed of evidence accumulation in speeded two-alternative forced choice tasks. This relationship generalized across different decision-making tasks, and could not be easily explained by variability in general arousal or vigilance. Moreover, we show that performing a difficult discrimination task while maintaining a concurrent memory load has a deleterious effect on the latter, suggesting that WM storage and decision making are directly linked.

Improved Hydrological Decision Support System for the Lower Mekong River Basin Using Satellite-Based Earth Observations.

PubMed

Mohammed, Ibrahim Nourein; Bolten, John D; Srinivasan, Raghavan; Lakshmi, Venkat

2018-06-01

Multiple satellite-based earth observations and traditional station data along with the Soil & Water Assessment Tool (SWAT) hydrologic model were employed to enhance the Lower Mekong River Basin region's hydrological decision support system. A nearest neighbor approximation methodology was introduced to fill the Integrated Multi-satellite Retrieval for the Global Precipitation Measurement mission (IMERG) grid points from 2001 to 2014, together with the Tropical Rainfall Measurement Mission (TRMM) data points for continuous precipitation forcing for our hydrological decision support system. A software tool to access and format satellite-based earth observation systems of precipitation and minimum and maximum air temperatures was developed and is presented. Our results suggest that the model-simulated streamflow utilizing TRMM and IMERG forcing data was able to capture the variability of the observed streamflow patterns in the Lower Mekong better than model-simulated streamflow with in-situ precipitation station data. We also present satellite-based and in-situ precipitation adjustment maps that can serve to correct precipitation data for the Lower Mekong region for use in other applications. The inconsistency, scarcity, poor spatial representation, difficult access and incompleteness of the available in-situ precipitation data for the Mekong region make it imperative to adopt satellite-based earth observations to pursue hydrologic modeling.

Genetic data and the listing of species under the U.S. Endangered Species Act.

PubMed

Fallon, Sylvia M

2007-10-01

Genetic information is becoming an influential factor in determining whether species, subspecies, and distinct population segments qualify for protection under the U.S. Endangered Species Act. Nevertheless, there are currently no standards or guidelines that define how genetic information should be used by the federal agencies that administer the act. I examined listing decisions made over a 10-year period (February 1996-February 2006) that relied on genetic information. There was wide variation in the genetic data used to inform listing decisions in terms of which genomes (mitochondrial vs. nuclear) were sampled and the number of markers (or genetic techniques) and loci evaluated. In general, whether the federal agencies identified genetic distinctions between putative taxonomic units or populations depended on the type and amount of genetic data. Studies that relied on multiple genetic markers were more likely to detect distinctions, and those organisms were more likely to receive protection than studies that relied on a single genetic marker. Although the results may, in part, reflect the corresponding availability of genetic techniques over the given time frame, the variable use of genetic information for listing decisions has the potential to misguide conservation actions. Future management policy would benefit from guidelines for the critical evaluation of genetic information to list or delist organisms under the Endangered Species Act.

Improved Hydrological Decision Support System for the Lower Mekong River Basin Using Satellite-Based Earth Observations

PubMed Central

Mohammed, Ibrahim Nourein; Bolten, John D.; Srinivasan, Raghavan; Lakshmi, Venkat

2018-01-01

Multiple satellite-based earth observations and traditional station data along with the Soil & Water Assessment Tool (SWAT) hydrologic model were employed to enhance the Lower Mekong River Basin region’s hydrological decision support system. A nearest neighbor approximation methodology was introduced to fill the Integrated Multi-satellite Retrieval for the Global Precipitation Measurement mission (IMERG) grid points from 2001 to 2014, together with the Tropical Rainfall Measurement Mission (TRMM) data points for continuous precipitation forcing for our hydrological decision support system. A software tool to access and format satellite-based earth observation systems of precipitation and minimum and maximum air temperatures was developed and is presented. Our results suggest that the model-simulated streamflow utilizing TRMM and IMERG forcing data was able to capture the variability of the observed streamflow patterns in the Lower Mekong better than model-simulated streamflow with in-situ precipitation station data. We also present satellite-based and in-situ precipitation adjustment maps that can serve to correct precipitation data for the Lower Mekong region for use in other applications. The inconsistency, scarcity, poor spatial representation, difficult access and incompleteness of the available in-situ precipitation data for the Mekong region make it imperative to adopt satellite-based earth observations to pursue hydrologic modeling. PMID:29938116

Identifying Key Performance Indicators for Holistic Hospital Management with a Modified DEMATEL Approach.

PubMed

Si, Sheng-Li; You, Xiao-Yue; Liu, Hu-Chen; Huang, Jia

2017-08-19

Performance analysis is an important way for hospitals to achieve higher efficiency and effectiveness in providing services to their customers. The performance of the healthcare system can be measured by many indicators, but it is difficult to improve them simultaneously due to the limited resources. A feasible way is to identify the central and influential indicators to improve healthcare performance in a stepwise manner. In this paper, we propose a hybrid multiple criteria decision making (MCDM) approach to identify key performance indicators (KPIs) for holistic hospital management. First, through integrating evidential reasoning approach and interval 2-tuple linguistic variables, various assessments of performance indicators provided by healthcare experts are modeled. Then, the decision making trial and evaluation laboratory (DEMATEL) technique is adopted to build an interactive network and visualize the causal relationships between the performance indicators. Finally, an empirical case study is provided to demonstrate the proposed approach for improving the efficiency of healthcare management. The results show that "accidents/adverse events", "nosocomial infection", ''incidents/errors", "number of operations/procedures" are significant influential indicators. Also, the indicators of "length of stay", "bed occupancy" and "financial measures" play important roles in performance evaluation of the healthcare organization. The proposed decision making approach could be considered as a reference for healthcare administrators to enhance the performance of their healthcare institutions.

Beyond IQ: A Latent State-Trait Analysis of General Intelligence, Dynamic Decision Making, and Implicit Learning

ERIC Educational Resources Information Center

Danner, Daniel; Hagemann, Dirk; Schankin, Andrea; Hager, Marieke; Funke, Joachim

2011-01-01

The present study investigated cognitive performance measures beyond IQ. In particular, we investigated the psychometric properties of dynamic decision making variables and implicit learning variables and their relation with general intelligence and professional success. N = 173 employees from different companies and occupational groups completed…

To Commit or Not to Commit: The Psychiatry Resident as a Variable in Involuntary Commitment Decisions

ERIC Educational Resources Information Center

Sattar, S. Pirzada; Pinals, Debra A.; Din, Amad U.; Appelbaum, Paul S.

2006-01-01

Objective: To study whether psychiatry residents' personal variables (such as age, gender, level of training, previous experience with patient suicide, or lawsuits) and their temperamental predispositions have an impact on their decisions to seek involuntary commitment. Method: In a prospective pilot study, all psychiatry residents in…

Examining Decision Making Level of Wrestlers in Terms of Some Variable

ERIC Educational Resources Information Center

Yigit, Sihmehmet; Dalbudak, Ibrahim; Musa, Mihriay; Gürkan, Alper C.; Dalkiliç, Mehmet

2016-01-01

The aim of this research is to examine decision making level of wrestlers who joined Turkey inter university wrestling championship, according to variables as wrestlers' sex, age, grade, department, and education type. Study group consists of 34 females and 196 males, totally 230 athletes, who joined Turkey Inter University Wrestling Championship…

Purchasing a Used Car Using Multiple Criteria Decision Making

ERIC Educational Resources Information Center

Edwards, Thomas G.; Chelst, Kenneth R.

2007-01-01

When studying mathematics, students often ask the age-old question, "When will I ever use this in my future?" The activities described in this article demonstrate for students a process that brings the power of mathematical reasoning to bear on a difficult decision involving multiple criteria that is sure to resonate with the interests of many of…

An interactive approach based on a discrete differential evolution algorithm for a class of integer bilevel programming problems

NASA Astrophysics Data System (ADS)

Li, Hong; Zhang, Li; Jiao, Yong-Chang

2016-07-01

This paper presents an interactive approach based on a discrete differential evolution algorithm to solve a class of integer bilevel programming problems, in which integer decision variables are controlled by an upper-level decision maker and real-value or continuous decision variables are controlled by a lower-level decision maker. Using the Karush--Kuhn-Tucker optimality conditions in the lower-level programming, the original discrete bilevel formulation can be converted into a discrete single-level nonlinear programming problem with the complementarity constraints, and then the smoothing technique is applied to deal with the complementarity constraints. Finally, a discrete single-level nonlinear programming problem is obtained, and solved by an interactive approach. In each iteration, for each given upper-level discrete variable, a system of nonlinear equations including the lower-level variables and Lagrange multipliers is solved first, and then a discrete nonlinear programming problem only with inequality constraints is handled by using a discrete differential evolution algorithm. Simulation results show the effectiveness of the proposed approach.

[Is the critical patient competent for decision taking? Psychological and psychopathological reasons of cognitive impairment].

PubMed

Bernat-Adell, M D; Ballester-Arnal, R; Abizanda-Campos, R

2012-01-01

Emotional factors may lead to cognitive impairment that can adversely affect the capacity of patients to reason, and thereby, limit their participation in decision taking. To analyze critical patient aptitude for decision taking, and to identify variables that may influence competence. An observational descriptive study was carried out. Intensive care unit. Participants were 29 critically ill patients. Social, demographic and psychological variables were analyzed. Functional capacities and psychological reactions during stay in the ICU were assessed. The patients are of the firm opinion that they should have the last word in the taking of decisions; they prefer bad news to be given by the physician; and feel that the presence of a psychologist would make the process easier. Failure on the part of the professional to answer their questions is perceived as the greatest stress factor. Increased depression results in lesser cognitive capacity, and for patients with impaired cognitive capacity, participation in the decision taking process constitutes a burden. The variables anxiety and depression are significantly related to decision taking capacity. Copyright © 2011 Elsevier España, S.L. and SEMICYUC. All rights reserved.

Utilization of multiple-criteria decision analysis (MCDA) to support healthcare decision-making FIFARMA, 2016

PubMed Central

Drake, Julia I.; de Hart, Juan Carlos Trujillo; Monleón, Clara; Toro, Walter; Valentim, Joice

2017-01-01

ABSTRACT Background and objectives:   MCDA is a decision-making tool with increasing use in the healthcare sector, including HTA (Health Technology Assessment). By applying multiple criteria, including innovation, in a comprehensive, structured and explicit manner, MCDA fosters a transparent, participative, consistent decision-making process taking into consideration values of all stakeholders. This paper by FIFARMA (Latin American Federation of Pharmaceutical Industry) proposes the deliberative (partial) MCDA as a more pragmatic, agile approach, especially when newly implemented. Methods: Literature review including real-world examples of effective MCDA implementation in healthcare decision making in both the public and private sector worldwide and in LA. Results and conclusion: It is the view of FIFARMA that MCDA should strongly be considered as a tool to support HTA and broader healthcare decision making such as the contracts and tenders process in order to foster transparency, fairness, and collaboration amongst stakeholders. PMID:29081919

Predictions of the pathological response to neoadjuvant chemotherapy in patients with primary breast cancer using a data mining technique.

PubMed

Takada, M; Sugimoto, M; Ohno, S; Kuroi, K; Sato, N; Bando, H; Masuda, N; Iwata, H; Kondo, M; Sasano, H; Chow, L W C; Inamoto, T; Naito, Y; Tomita, M; Toi, M

2012-07-01

Nomogram, a standard technique that utilizes multiple characteristics to predict efficacy of treatment and likelihood of a specific status of an individual patient, has been used for prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients. The aim of this study was to develop a novel computational technique to predict the pathological complete response (pCR) to NAC in primary breast cancer patients. A mathematical model using alternating decision trees, an epigone of decision tree, was developed using 28 clinicopathological variables that were retrospectively collected from patients treated with NAC (n = 150), and validated using an independent dataset from a randomized controlled trial (n = 173). The model selected 15 variables to predict the pCR with yielding area under the receiver operating characteristics curve (AUC) values of 0.766 [95 % confidence interval (CI)], 0.671-0.861, P value < 0.0001) in cross-validation using training dataset and 0.787 (95 % CI 0.716-0.858, P value < 0.0001) in the validation dataset. Among three subtypes of breast cancer, the luminal subgroup showed the best discrimination (AUC = 0.779, 95 % CI 0.641-0.917, P value = 0.0059). The developed model (AUC = 0.805, 95 % CI 0.716-0.894, P value < 0.0001) outperformed multivariate logistic regression (AUC = 0.754, 95 % CI 0.651-0.858, P value = 0.00019) of validation datasets without missing values (n = 127). Several analyses, e.g. bootstrap analysis, revealed that the developed model was insensitive to missing values and also tolerant to distribution bias among the datasets. Our model based on clinicopathological variables showed high predictive ability for pCR. This model might improve the prediction of the response to NAC in primary breast cancer patients.

Altered Sleep Stage Transitions of REM Sleep: A Novel and Stable Biomarker of Narcolepsy.

PubMed

Liu, Yaping; Zhang, Jihui; Lam, Venny; Ho, Crover Kwok Wah; Zhou, Junying; Li, Shirley Xin; Lam, Siu Ping; Yu, Mandy Wai Man; Tang, Xiangdong; Wing, Yun-Kwok

2015-08-15

To determine the diagnostic values, longitudinal stability, and HLA association of the sleep stage transitions in narcolepsy. To compare the baseline differences in the sleep stage transition to REM sleep among 35 patients with type 1 narcolepsy, 39 patients with type 2 narcolepsy, 26 unaffected relatives, and 159 non-narcoleptic sleep patient controls, followed by a reassessment at a mean duration of 37.4 months. The highest prevalence of altered transition from stage non-N2/N3 to stage R in multiple sleep latency test (MSLT) and nocturnal polysomnography (NPSG) was found in patients with type 1 narcolepsy (92.0% and 57.1%), followed by patients with type 2 narcolepsy (69.4% and 12.8%), unaffected relatives (46.2% and 0%), and controls (39.3% and 1.3%). Individual sleep variables had varied sensitivity and specificity in diagnosing narcolepsy. By incorporating a combination of sleep variables, the decision tree analysis improved the sensitivity to 94.3% and 82.1% and enhanced specificity to 82.4% and 83% for the diagnosis of type 1 and type 2 narcolepsy, respectively. There was a significant association of DBQ1*0602 with the altered sleep stage transition (OR = 16.0, 95% CI: 1.7-149.8, p = 0.015). The persistence of the altered sleep stage transition in both MSLT and NPSG was high for both type 1 (90.5% and 64.7%) and type 2 narcolepsy (92.3% and 100%), respectively. Altered sleep stage transition is a significant and stable marker of narcolepsy, which suggests a vulnerable wake-sleep dysregulation trait in narcolepsy. Altered sleep stage transition has a significant diagnostic value in the differential diagnosis of hypersomnias, especially when combined with other diagnostic sleep variables in decision tree analysis. © 2015 American Academy of Sleep Medicine.

Flood Protection Decision Making Within a Coupled Human and Natural System

NASA Astrophysics Data System (ADS)

O'Donnell, Greg; O'Connell, Enda

2013-04-01

Due to the perceived threat from climate change, prediction under changing climatic and hydrological conditions has become a dominant theme of hydrological research. Much of this research has been climate model-centric, in which GCM/RCM climate projections have been used to drive hydrological system models to explore potential impacts that should inform adaptation decision-making. However, adaptation fundamentally involves how humans may respond to increasing flood and drought hazards by changing their strategies, activities and behaviours which are coupled in complex ways to the natural systems within which they live and work. Humans are major agents of change in hydrological systems, and representing human activities and behaviours in coupled human and natural hydrological system models is needed to gain insight into the complex interactions that take place, and to inform adaptation decision-making. Governments and their agencies are under pressure to make proactive investments to protect people living in floodplains from the perceived increasing flood hazard. However, adopting this as a universal strategy everywhere is not affordable, particularly in times of economic stringency and given uncertainty about future climatic conditions. It has been suggested that the assumption of stationarity, which has traditionally been invoked in making hydrological risk assessments, is no longer tenable. However, before the assumption of hydrologic nonstationarity is accepted, the ability to cope with the uncertain impacts of global warming on water management via the operational assumption of hydrologic stationarity should be carefully examined. Much can be learned by focussing on natural climate variability and its inherent changes in assessing alternative adaptation strategies. A stationary stochastic multisite flood hazard model has been developed that can exhibit increasing variability/persistence in annual maximum floods, starting with the traditional assumption of independence. This has been coupled to an agent based model of how various stakeholders interact in determining where and when flood protection investments are made in a hypothetical region with multiple sites at risk from flood hazard. Monte Carlo simulation is used to explore how government agencies with finite resources might best invest in flood protection infrastructure in a highly variable climate with a high degree of future uncertainty. Insight is provided into whether proactive or reactive strategies are to be preferred in an increasingly variable climate.

Should "Multiple Imputations" Be Treated as "Multiple Indicators"?

ERIC Educational Resources Information Center

Mislevy, Robert J.

1993-01-01

Multiple imputations for latent variables are constructed so that analyses treating them as true variables have the correct expectations for population characteristics. Analyzing multiple imputations in accordance with their construction yields correct estimates of population characteristics, whereas analyzing them as multiple indicators generally…

Healthcare decision-making in end stage renal disease-patient preferences and clinical correlates.

PubMed

Jayanti, Anuradha; Neuvonen, Markus; Wearden, Alison; Morris, Julie; Foden, Philip; Brenchley, Paul; Mitra, Sandip

2015-11-14

Medical decision-making is critical to patient survival and well-being. Patients with end stage renal disease (ESRD) are faced with incrementally complex decision-making throughout their treatment journey. The extent to which patients seek involvement in the decision-making process and factors which influence these in ESRD need to be understood. 535 ESRD patients were enrolled into the cross-sectional study arm and 30 patients who started dialysis were prospectively evaluated. Patients were enrolled into 3 groups- 'predialysis' (group A), 'in-centre' haemodialysis (HD) (group B) and self-care HD (93 % at home-group C) from across five tertiary UK renal centres. The Autonomy Preference Index (API) has been employed to study patient preferences for information-seeking (IS) and decision-making (DM). Demographic, psychosocial and neuropsychometric assessments are considered for analyses. 458 complete responses were available. API items have high internal consistency in the study population (Cronbach's alpha > 0.70). Overall and across individual study groups, the scores for information-seeking and decision-making are significantly different indicating that although patients had a strong preference to be well informed, they were more neutral in their preference to participate in DM (p < 0.05). In the age, education and study group adjusted multiple linear regression analysis, lower age, female gender, marital status; higher API IS scores and white ethnicity background were significant predictors of preference for decision-making. DM scores were subdivided into tertiles to identify variables associated with high (DM > 70: and low DM (≤30) scores. This shows association of higher DM scores with lower age, lower comorbidity index score, higher executive brain function, belonging in the self-caring cohort and being unemployed. In the prospectively studied cohort of predialysis patients, there was no change in decision-making preference scores after commencement of dialysis. ESRD patients prefer to receive information, but this does not always imply active involvement in decision-making. By understanding modifiable and non-modifiable factors which affect patient preferences for involvement in healthcare decision-making, health professionals may acknowledge the need to accommodate individual patient preferences to the extent determined by the individual patient factors.

The Teaching Decisions Simulation: An Interactive Vehicle for Mapping Teaching Decisions.

ERIC Educational Resources Information Center

Strang, Harold R.

1996-01-01

Describes the Teaching Decisions Simulation, a program that allows participants to make decisions regarding lesson plan activities and student and teacher spatial arrangement or interactions. Postlesson feedback includes variables such as completion time and performance measures. Experienced teachers exhibited more deliberation in completing the…

Model assessment using a multi-metric ranking technique

NASA Astrophysics Data System (ADS)

Fitzpatrick, P. J.; Lau, Y.; Alaka, G.; Marks, F.

2017-12-01

Validation comparisons of multiple models presents challenges when skill levels are similar, especially in regimes dominated by the climatological mean. Assessing skill separation will require advanced validation metrics and identifying adeptness in extreme events, but maintain simplicity for management decisions. Flexibility for operations is also an asset. This work postulates a weighted tally and consolidation technique which ranks results by multiple types of metrics. Variables include absolute error, bias, acceptable absolute error percentages, outlier metrics, model efficiency, Pearson correlation, Kendall's Tau, reliability Index, multiplicative gross error, and root mean squared differences. Other metrics, such as root mean square difference and rank correlation were also explored, but removed when the information was discovered to be generally duplicative to other metrics. While equal weights are applied, weights could be altered depending for preferred metrics. Two examples are shown comparing ocean models' currents and tropical cyclone products, including experimental products. The importance of using magnitude and direction for tropical cyclone track forecasts instead of distance, along-track, and cross-track are discussed. Tropical cyclone intensity and structure prediction are also assessed. Vector correlations are not included in the ranking process, but found useful in an independent context, and will be briefly reported.

In the Way of Peacemaker Guide Curve between Water Supply and Flood Control for Short Term Reservoir Operation

NASA Astrophysics Data System (ADS)

Uysal, G.; Sensoy, A.; Yavuz, O.; Sorman, A. A.; Gezgin, T.

2012-04-01

Effective management of a controlled reservoir system where it involves multiple and sometimes conflicting objectives is a complex problem especially in real time operations. Yuvacık Dam Reservoir, located in the Marmara region of Turkey, is built to supply annual demand of 142 hm3 water for Kocaeli city requires such a complex management strategy since it has relatively small (51 hm3) effective capacity. On the other hand, the drainage basin is fed by both rainfall and snowmelt since the elevation ranges between 80 - 1548 m. Excessive water must be stored behind the radial gates between February and May in terms of sustainability especially for summer and autumn periods. Moreover, the downstream channel physical conditions constraint the spillway releases up to 100 m3/s although the spillway is large enough to handle major floods. Thus, this situation makes short term release decisions the challenging task. Long term water supply curves, based on historical inflows and annual water demand, are in conflict with flood regulation (control) levels, based on flood attenuation and routing curves, for this reservoir. A guide curve, that is generated using both water supply and flood control of downstream channel, generally corresponds to upper elevation of conservation pool for simulation of a reservoir. However, sometimes current operation necessitates exceeding this target elevation. Since guide curves can be developed as a function of external variables, the water potential of a basin can be an indicator to explain current conditions and decide on the further strategies. Besides, releases with respect to guide curve are managed and restricted by user-defined rules. Although the managers operate the reservoir due to several variable conditions and predictions, still the simulation model using variable guide curve is an urgent need to test alternatives quickly. To that end, using HEC-ResSim, the several variable guide curves are defined to meet the requirements by taking inflow, elevation, precipitation and snow water equivalent into consideration to propose alternative simulations as a decision support system. After that, the releases are subjected to user-defined rules. Thus, previous year reservoir simulations are compared with observed reservoir levels and releases. Hypothetical flood scenarios are tested in case of different storm event timing and sizing. Numerical weather prediction data of Mesoscale Model 5 (MM5) can be used for temperature and precipitation forecasts that will form the inputs for a hydrological model. The estimated flows can be used for real time short term decisions for reservoir simulation based on variable guide curve and user defined rules.

Research on efficiency evaluation model of integrated energy system based on hybrid multi-attribute decision-making.

PubMed

Li, Yan

2017-05-25

The efficiency evaluation model of integrated energy system, involving many influencing factors, and the attribute values are heterogeneous and non-deterministic, usually cannot give specific numerical or accurate probability distribution characteristics, making the final evaluation result deviation. According to the characteristics of the integrated energy system, a hybrid multi-attribute decision-making model is constructed. The evaluation model considers the decision maker's risk preference. In the evaluation of the efficiency of the integrated energy system, the evaluation value of some evaluation indexes is linguistic value, or the evaluation value of the evaluation experts is not consistent. These reasons lead to ambiguity in the decision information, usually in the form of uncertain linguistic values and numerical interval values. In this paper, the risk preference of decision maker is considered when constructing the evaluation model. Interval-valued multiple-attribute decision-making method and fuzzy linguistic multiple-attribute decision-making model are proposed. Finally, the mathematical model of efficiency evaluation of integrated energy system is constructed.

Using real options analysis to support strategic management decisions

NASA Astrophysics Data System (ADS)

Kabaivanov, Stanimir; Markovska, Veneta; Milev, Mariyan

2013-12-01

Decision making is a complex process that requires taking into consideration multiple heterogeneous sources of uncertainty. Standard valuation and financial analysis techniques often fail to properly account for all these sources of risk as well as for all sources of additional flexibility. In this paper we explore applications of a modified binomial tree method for real options analysis (ROA) in an effort to improve decision making process. Usual cases of use of real options are analyzed with elaborate study on the applications and advantages that company management can derive from their application. A numeric results based on extending simple binomial tree approach for multiple sources of uncertainty are provided to demonstrate the improvement effects on management decisions.

Public funding of pharmaceuticals in The Netherlands: investigating the effect of evidence, process and context on CVZ decision-making.

PubMed

Cerri, Karin H; Knapp, Martin; Fernandez, Jose-Luis

2014-09-01

The College Voor Zorgverzekeringen (CVZ) provides guidance to the Dutch healthcare system on funding and use of new pharmaceutical technologies. This study examined the impact of evidence, process and context factors on CVZ decisions in 2004-2009. A data set of CVZ decisions pertaining to pharmaceutical technologies was created, including 29 variables extracted from published information. A three-category outcome variable was used, defined as the decision to 'recommend', 'restrict' or 'not recommend' a technology. Technologies included in list 1A/1B or on the expensive drug list were considered recommended; those included in list 2 or for which patient co-payment is required were considered restricted; technologies not included on any reimbursement list were classified as 'not recommended'. Using multinomial logistic regression, the relative contribution of explanatory variables on CVZ decisions was assessed. In all, 244 technology appraisals (256 technologies) were analysed, with 51%, of technologies recommended, 33% restricted and 16% not recommended by CVZ for funding. The multinomial model showed significant associations (p ≤ 0.10) between CVZ outcome and several variables, including: (1) use of an active comparator and demonstration of statistical superiority of the primary endpoint in clinical trials, (2) pharmaceutical budget impact associated with introduction of the technology, (3) therapeutic indication and (4) prevalence of the target population. Results confirm the value of a comprehensive and multivariate approach to understanding CVZ decision-making.

How do medical students form impressions of the effectiveness of classroom teachers?

PubMed

Rannelli, Luke; Coderre, Sylvain; Paget, Michael; Woloschuk, Wayne; Wright, Bruce; McLaughlin, Kevin

2014-08-01

Teaching effectiveness ratings (TERs) are used to provide feedback to teachers on their performance and to guide decisions on academic promotion. However, exactly how raters make decisions on teaching effectiveness is unclear. The objectives of this study were to identify variables that medical students appraise when rating the effectiveness of a classroom teacher, and to explore whether the relationships among these variables and TERs are modified by the physical attractiveness of the teacher. We asked 48 Year 1 medical students to listen to 2-minute audio clips of 10 teachers and to describe their impressions of these teachers and rate their teaching effectiveness. During each clip, we displayed either an attractive or an unattractive photograph of an unrelated third party. We used qualitative analysis followed by factor analysis to identify the principal components of teaching effectiveness, and multiple linear regression to study the associations among these components, type of photograph displayed, and TER. We identified two principal components of teaching effectiveness: charisma and intellect. There was no association between rating of intellect and TER. Rating of charisma and the display of an attractive photograph were both positively associated with TER and a significant interaction between these two variables was apparent (p < 0.001). The regression coefficient for the association between charisma and TER was 0.26 (95% confidence interval [CI] 0.10-0.41) when an attractive picture was displayed and 0.83 (95% CI 0.66-1.00) when an unattractive picture was displayed (p < 0.001). When medical students rate classroom teachers, they consider the degree to which the teacher is charismatic, although the relationship between this attribute and TER appears to be modified by the perceived physical attractiveness of the teacher. Further studies are needed to identify other variables that may influence subjective ratings of teaching effectiveness and to evaluate alternative strategies for rating teaching effectiveness. © 2014 John Wiley & Sons Ltd.

Thresholds for conservation and management: structured decision making as a conceptual framework

USGS Publications Warehouse

Nichols, James D.; Eaton, Mitchell J.; Martin, Julien; Edited by Guntenspergen, Glenn R.

2014-01-01

changes in system dynamics. They are frequently incorporated into ecological models used to project system responses to management actions. Utility thresholds are components of management objectives and are values of state or performance variables at which small changes yield substantial changes in the value of the management outcome. Decision thresholds are values of system state variables at which small changes prompt changes in management actions in order to reach specified management objectives. Decision thresholds are derived from the other components of the decision process.We advocate a structured decision making (SDM) approach within which the following components are identified: objectives (possibly including utility thresholds), potential actions, models (possibly including ecological thresholds), monitoring program, and a solution algorithm (which produces decision thresholds). Adaptive resource management (ARM) is described as a special case of SDM developed for recurrent decision problems that are characterized by uncertainty. We believe that SDM, in general, and ARM, in particular, provide good approaches to conservation and management. Use of SDM and ARM also clarifies the distinct roles of ecological thresholds, utility thresholds, and decision thresholds in informed decision processes.

Linking Theoretical Decision-making Mechanisms in the Simon Task with Electrophysiological Data: A Model-based Neuroscience Study in Humans.

PubMed

Servant, Mathieu; White, Corey; Montagnini, Anna; Burle, Borís

2016-10-01

A current challenge for decision-making research is in extending models of simple decisions to more complex and ecological choice situations. Conflict tasks (e.g., Simon, Stroop, Eriksen flanker) have been the focus of much interest, because they provide a decision-making context representative of everyday life experiences. Modeling efforts have led to an elaborated drift diffusion model for conflict tasks (DMC), which implements a superimposition of automatic and controlled decision activations. The DMC has proven to capture the diversity of behavioral conflict effects across various task contexts. This study combined DMC predictions with EEG and EMG measurements to test a set of linking propositions that specify the relationship between theoretical decision-making mechanisms involved in the Simon task and brain activity. Our results are consistent with a representation of the superimposed decision variable in the primary motor cortices. The decision variable was also observed in the EMG activity of response agonist muscles. These findings provide new insight into the neurophysiology of human decision-making. In return, they provide support for the DMC model framework.

Multiple flood vulnerability assessment approach based on fuzzy comprehensive evaluation method and coordinated development degree model.

PubMed

Yang, Weichao; Xu, Kui; Lian, Jijian; Bin, Lingling; Ma, Chao

2018-05-01

Flood is a serious challenge that increasingly affects the residents as well as policymakers. Flood vulnerability assessment is becoming gradually relevant in the world. The purpose of this study is to develop an approach to reveal the relationship between exposure, sensitivity and adaptive capacity for better flood vulnerability assessment, based on the fuzzy comprehensive evaluation method (FCEM) and coordinated development degree model (CDDM). The approach is organized into three parts: establishment of index system, assessment of exposure, sensitivity and adaptive capacity, and multiple flood vulnerability assessment. Hydrodynamic model and statistical data are employed for the establishment of index system; FCEM is used to evaluate exposure, sensitivity and adaptive capacity; and CDDM is applied to express the relationship of the three components of vulnerability. Six multiple flood vulnerability types and four levels are proposed to assess flood vulnerability from multiple perspectives. Then the approach is applied to assess the spatiality of flood vulnerability in Hainan's eastern area, China. Based on the results of multiple flood vulnerability, a decision-making process for rational allocation of limited resources is proposed and applied to the study area. The study shows that multiple flood vulnerability assessment can evaluate vulnerability more completely, and help decision makers learn more information about making decisions in a more comprehensive way. In summary, this study provides a new way for flood vulnerability assessment and disaster prevention decision. Copyright © 2018 Elsevier Ltd. All rights reserved.

The Relationships of Self-Esteem, Future Time Perspective, Positive Affect, Social Support, and Career Decision: A Longitudinal Multilevel Study

PubMed Central

Park, In-Jo; Kim, Minhee; Kwon, Seungwoo; Lee, Hae-Gyoung

2018-01-01

This study aimed, first, to determine whether the intra-individual variability in positive affect was related to the intra-individual variability in career decision-making self-efficacy, and career choice anxiety. The second objective was to examine whether social support moderates the relationship between affect and these outcome variables. The third objective was to find out how career decision-making self-efficacy and career choice anxiety change according to self-esteem and future time perspective. We conducted a study using the daily diary method in which participants were asked to rate their affect or attitudes for 21 consecutive days. In total, 128 university students participated in this study. The main results were as follows. First, positive affect was associated positively with career decision-making self-efficacy and negatively with career choice anxiety. Second, social support had a synergy effect with positive affect to influence career choice anxiety. Third, self-esteem was related positively to career decision-making self-efficacy and negatively to career choice anxiety. We discuss theoretical and practical implications. PMID:29755381

The Relationships of Self-Esteem, Future Time Perspective, Positive Affect, Social Support, and Career Decision: A Longitudinal Multilevel Study.

PubMed

Park, In-Jo; Kim, Minhee; Kwon, Seungwoo; Lee, Hae-Gyoung

2018-01-01

This study aimed, first, to determine whether the intra-individual variability in positive affect was related to the intra-individual variability in career decision-making self-efficacy, and career choice anxiety. The second objective was to examine whether social support moderates the relationship between affect and these outcome variables. The third objective was to find out how career decision-making self-efficacy and career choice anxiety change according to self-esteem and future time perspective. We conducted a study using the daily diary method in which participants were asked to rate their affect or attitudes for 21 consecutive days. In total, 128 university students participated in this study. The main results were as follows. First, positive affect was associated positively with career decision-making self-efficacy and negatively with career choice anxiety. Second, social support had a synergy effect with positive affect to influence career choice anxiety. Third, self-esteem was related positively to career decision-making self-efficacy and negatively to career choice anxiety. We discuss theoretical and practical implications.

Development of Automated Aids for Decision Analysis

DTIC Science & Technology

1976-05-01

21 7. Resources Affected by a Decision .. ................... 22 8. Scope of Decision ..................................... 22 9. Urgency...24 t . Resources Available for Analysis . . . .. . . . 26 Expeiene anTrininginAayigDcsos2 C. Chrceisiso heDcso Poes2 -I. II TYPES OF DECISION...Assessment . . . . . . . . . ......... . 62 a. Assessing State Variables .... ........... ... 63 b. Assessing Dependencics .. . .. ... . 65 c. Assessing

Logit Estimation of a Gravity Model of the College Enrollment Decision.

ERIC Educational Resources Information Center

Leppel, Karen

1993-01-01

A study investigated the factors influencing students' decisions about attending a college to which they had been admitted. Logit analysis confirmed gravity model predictions that geographic distance and student ability would most influence the enrollment decision and found other variables, although affecting earlier stages of decision making, did…

Making Good Decisions in Healthcare with Multi-Criteria Decision Analysis: The Use, Current Research and Future Development of MCDA.

PubMed

Mühlbacher, Axel C; Kaczynski, Anika

2016-02-01

Healthcare decision making is usually characterized by a low degree of transparency. The demand for transparent decision processes can be fulfilled only when assessment, appraisal and decisions about health technologies are performed under a systematic construct of benefit assessment. The benefit of an intervention is often multidimensional and, thus, must be represented by several decision criteria. Complex decision problems require an assessment and appraisal of various criteria; therefore, a decision process that systematically identifies the best available alternative and enables an optimal and transparent decision is needed. For that reason, decision criteria must be weighted and goal achievement must be scored for all alternatives. Methods of multi-criteria decision analysis (MCDA) are available to analyse and appraise multiple clinical endpoints and structure complex decision problems in healthcare decision making. By means of MCDA, value judgments, priorities and preferences of patients, insurees and experts can be integrated systematically and transparently into the decision-making process. This article describes the MCDA framework and identifies potential areas where MCDA can be of use (e.g. approval, guidelines and reimbursement/pricing of health technologies). A literature search was performed to identify current research in healthcare. The results showed that healthcare decision making is addressing the problem of multiple decision criteria and is focusing on the future development and use of techniques to weight and score different decision criteria. This article emphasizes the use and future benefit of MCDA.

Adolescent Decision-Making Processes regarding University Entry: A Model Incorporating Cultural Orientation, Motivation and Occupational Variables

ERIC Educational Resources Information Center

Jung, Jae Yup

2013-01-01

This study tested a newly developed model of the cognitive decision-making processes of senior high school students related to university entry. The model incorporated variables derived from motivation theory (i.e. expectancy-value theory and the theory of reasoned action), literature on cultural orientation and occupational considerations. A…

Factors Influencing the Adoption of Cloud Storage by Information Technology Decision Makers

ERIC Educational Resources Information Center

Wheelock, Michael D.

2013-01-01

This dissertation uses a survey methodology to determine the factors behind the decision to adopt cloud storage. The dependent variable in the study is the intent to adopt cloud storage. Four independent variables are utilized including need, security, cost-effectiveness and reliability. The survey includes a pilot test, field test and statistical…

A method for integrating multiple components in a decision support system

Treesearch

Donald Nute; Walter D. Potter; Zhiyuan Cheng; Mayukh Dass; Astrid Glende; Frederick Maierv; Cy Routh; Hajime Uchiyama; Jin Wang; Sarah Witzig; Mark Twery; Peter Knopp; Scott Thomasma; H. Michael Rauscher

2005-01-01

We present a flexible, extensible method for integrating multiple tools into a single large decision support system (DSS) using a forest ecosystem management DSS (NED-2) as an example. In our approach, a rich ontology for the target domain is developed and implemented in the internal data model for the DSS. Semi-autonomous agents control external components and...

NOAA's Approach to Community Building and Governance for Data Integration and Standards Within IOOS

NASA Astrophysics Data System (ADS)

Willis, Z.; Shuford, R.

2007-12-01

This presentation will review NOAA's current approach to the Integrated Ocean Observing System (IOOS) at a national and regional level within the context of our United States Federal and Non-Federal partners. Further, it will discuss the context of integrating data and the necessary standards definition that must be done not only within the United States but in a larger global context. IOOS is the U.S. contribution to the Global Ocean Observing System (GOOS), which itself is the ocean contribution to the Global Earth Observation System of Systems (GEOSS). IOOS is a nationally important network of distributed systems that forms an infrastructure providing many different users with the diverse information they require to characterize, understand, predict, and monitor changes in dynamic coastal and open ocean environments. NOAA recently established an IOOS Program Office to provide a focal point for its ocean observation programs and assist with coordination of regional and national IOOS activities. One of the Program's initial priorities is the development of a data integration framework (DIF) proof-of-concept for IOOS data. The initial effort will focus on NOAA sources of data and be implemented incrementally over the course of three years. The first phase will focus on the integration of five core IOOS variables being collected, and disseminated, for independent purposes and goals by multiple NOAA observing sources. The goal is to ensure that data from different sources is interoperable to enable rapid and routine use by multiple NOAA decision-support tool developers and other end users. During the second phase we expect to ingest these integrated variables into four specific NOAA data products used for decision-support. Finally, we will systematically test and evaluate enhancements to these products, and verify, validate, and benchmark new performance specifications. The outcome will be an extensible product for operational use that allows for broader community applicability to include additional variables, applications, and non-NOAA sources of data. NOAA is working with Ocean.US to implement an interagency process for the submission, proposal, and recommendation of IOOS data standards. In order to achieve the broader goals of data interoperability of GEOSS, communication of this process and the identified standards needs to be coordinated at the international level. NOAA is participating in the development of a series of IODE workshops with the objective to achieve broad agreement and commitment to ocean data management and exchange standards. The first of these meetings will use the five core variables identified by the NOAA DIF as a focus.

Justifying scale type for a latent variable: Formative or reflective?

NASA Astrophysics Data System (ADS)

Liu, Hao; Bahron, Arsiah; Bagul, Awangku Hassanal Bahar Pengiran

2015-12-01

The study attempted to explore the possibilities to create a procedure at the experimental level to double confirm whether manifest variables scale type is formative or reflective. Now, the criteria of making such a decision are heavily depended on researchers' judgment at the conceptual and operational level. The study created an experimental procedure that seems could double confirm the decisions from the conceptual and operational level judgments. The experimental procedure includes the following tests, Variance Inflation Factor (VIF), Tolerance (TOL), Ridge Regression, Cronbach's alpha, Dillon-Goldstein's rho, and first and second eigenvalue. The procedure considers manifest variables' both multicollinearity and consistency. As the result, the procedure received the same judgment with the carefully established decision making at the concept and operational level.

Outsourcing decision factors in publicly owned electric utilities

NASA Astrophysics Data System (ADS)

Gonzales, James Edward

Purpose. The outsourcing of services in publicly owned electric utilities has generated some controversy. The purpose of this study was to explore this controversy by investigating the relationships between eight key independent variables and a dependent variable, "manager perceptions of overall value of outsourced services." The intent was to provide data so that utilities could make better decisions regarding outsourcing efforts. Theoretical framework. Decision theory was used as the framework for analyzing variables and alternatives used to support the outsourcing decision-making process. By reviewing these eight variables and the projected outputs and outcomes, a more predictive and potentially successful outsourcing effort can be realized. Methodology. A survey was distributed to a sample of 323 publicly owned electric utilities randomly selected from a population of 2,020 in the United States. Analysis of the data was made using statistical techniques including the Chi-Square, Lambda, Spearman's coefficient of rank correlation, as well as the Hypothesis Test, Rank Correlation, to test for relationships among the variables. Findings. Relationships among the eight key variables and perceptions of the overall value of outsourced services were generally weak. The notable exception was with the driving force (reason) for outsourcing decisions where the relationship was strongly positive. Conclusions and recommendations. The data in support of the research questions suggest that seven of the eight key variables may be weakly predictive of perceptions of the overall value of outsourced services. However, the primary driving force for outsourcing was strongly predictive. The data also suggest that many of the sampled utilities did not formally address these variables and alternatives, and therefore may not be achieving maximal results. Further studies utilizing customer perceptions rather than those of outsourcing service managers are recommended. In addition, it is recommended that a smaller sample population be analyzed after identifying one or more champions to ensure cooperation and legitimacy of data. Finally, this study supports the position that a manager's ability to identify and understand the relationships between these eight key variables and desired outcomes and outputs may contribute to more successful outsourcing operations.

Lenses for Framing Decisions: Undergraduates' Decision Making about Stem Cell Research

ERIC Educational Resources Information Center

Halverson, Kristy Lynn; Siegel, Marcelle A.; Freyermuth, Sharyn K.

2009-01-01

Decision making is influenced by multiple factors, especially when approaching controversial socio-scientific issues, such as stem cell research. In the present study, we used qualitative data from 132 college student papers in a biotechnology course to investigate how students made decisions about stem cell research issues. Students indicated…

The Wildland Fire Decision Support System: Integrating science, technology, and fire management

Treesearch

Morgan Pence; Tom Zimmerman

2011-01-01

Federal agency policy requires documentation and analysis of all wildland fire response decisions. In the past, planning and decision documentation for fires were completed using multiple unconnected processes, yielding many limitations. In response, interagency fire management executives chartered the development of the Wildland Fire Decision Support System (WFDSS)....

A mixed integer program to model spatial wildfire behavior and suppression placement decisions

Treesearch

Erin J. Belval; Yu Wei; Michael Bevers

2015-01-01

Wildfire suppression combines multiple objectives and dynamic fire behavior to form a complex problem for decision makers. This paper presents a mixed integer program designed to explore integrating spatial fire behavior and suppression placement decisions into a mathematical programming framework. Fire behavior and suppression placement decisions are modeled using...

A Multi-criterial Decision Support System for Forest Management

Treesearch

Donald Nute; Geneho Kim; Walter D. Potter; Mark J. Twery; H. Michael Rauscher; Scott Thomasma; Deborah Bennett; Peter Kollasch

1999-01-01

We describe a research project that has as its goal development of a full-featured decision support system for managing forested land to satisfy multiple criteria represented as timber, wildlife, water, ecological, and wildlife objectives. The decision process proposed for what was originally conceived of as a Northeast Decision Model (NED) includes data acquisition,...

The value of decision models: Using ecologically based invasive plant management as an example

USDA-ARS?s Scientific Manuscript database

Humans have both fast and slow thought processes which influence our judgment and decision-making. The fast system is intuitive and valuable for decisions which do not require multiple steps or the application of logic or statistics. However, many decisions in natural resources are complex and req...

Toolbox or Adjustable Spanner? A Critical Comparison of Two Metaphors for Adaptive Decision Making

ERIC Educational Resources Information Center

Söllner, Anke; Bröder, Arndt

2016-01-01

For multiattribute decision tasks, different metaphors exist that describe the process of decision making and its adaptation to diverse problems and situations. Multiple strategy models (MSMs) assume that decision makers choose adaptively from a set of different strategies (toolbox metaphor), whereas evidence accumulation models (EAMs) hold that a…

Finding the numerical compensation in multiple criteria decision-making problems under fuzzy environment

NASA Astrophysics Data System (ADS)

Gupta, Mahima; Mohanty, B. K.

2017-04-01

In this paper, we have developed a methodology to derive the level of compensation numerically in multiple criteria decision-making (MCDM) problems under fuzzy environment. The degree of compensation is dependent on the tranquility and anxiety level experienced by the decision-maker while taking the decision. Higher tranquility leads to the higher realisation of the compensation whereas the increased level of anxiety reduces the amount of compensation in the decision process. This work determines the level of tranquility (or anxiety) using the concept of fuzzy sets and its various level sets. The concepts of indexing of fuzzy numbers, the risk barriers and the tranquility level of the decision-maker are used to derive his/her risk prone or risk averse attitude of decision-maker in each criterion. The aggregation of the risk levels in each criterion gives us the amount of compensation in the entire MCDM problem. Inclusion of the compensation leads us to model the MCDM problem as binary integer programming problem (BIP). The solution to BIP gives us the compensatory decision to MCDM. The proposed methodology is illustrated through a numerical example.

A Multi-Tiered Approach for Building Capacity in Hydrologic Modeling for Water Resource Management in Developing Regions

NASA Astrophysics Data System (ADS)

Markert, K. N.; Limaye, A. S.; Rushi, B. R.; Adams, E. C.; Anderson, E.; Ellenburg, W. L.; Mithieu, F.; Griffin, R.

2017-12-01

Water resource management is the process by which governments, businesses and/or individuals reach and implement decisions that are intended to address the future quantity and/or quality of water for societal benefit. The implementation of water resource management typically requires the understanding of the quantity and/or timing of a variety of hydrologic variables (e.g. discharge, soil moisture and evapotranspiration). Often times these variables for management are simulated using hydrologic models particularly in data sparse regions. However, there are several large barriers to entry in learning how to use models, applying best practices during the modeling process, and selecting and understanding the most appropriate model for diverse applications. This presentation focuses on a multi-tiered approach to bring the state-of-the-art hydrologic modeling capabilities and methods to developing regions through the SERVIR program, a joint NASA and USAID initiative that builds capacity of regional partners and their end users on the use of Earth observations for environmental decision making. The first tier is a series of trainings on the use of multiple hydrologic models, including the Variable Infiltration Capacity (VIC) and Ensemble Framework For Flash Flood Forecasting (EF5), which focus on model concepts and steps to successfully implement the models. We present a case study for this in a pilot area, the Nyando Basin in Kenya. The second tier is focused on building a community of practice on applied hydrology modeling aimed at creating a support network for hydrologists in SERVIR regions and promoting best practices. The third tier is a hydrologic inter-comparison project under development in the SERVIR regions. The objective of this step is to understand model performance under specific decision-making scenarios, and to share knowledge among hydrologists in SERVIR regions. The results of these efforts include computer programs, training materials, and new scientific understanding, all of which are shared in an open and collaborative environment for transparency and subsequent capacity building in SERVIR regions and beyond. The outcome of this work is increased awareness and capacity on the use of hydrologic models in developing regions to support water resource management and water security.

Associations between self-rated health and personality.

PubMed

Aiken-Morgan, Adrienne T; Bichsel, Jacqueline; Savla, Jyoti; Edwards, Christopher L; Whitfield, Keith E

2014-01-01

The goal of our study was to examine how Big Five personality factors predict variability in self-rated health in a sample of older African Americans from the Baltimore Study of Black Aging. Personality was measured by the NEO Personality Inventory-Revised, and self-rated health was assessed by the Health Problems Checklist. The study sample had 202 women and 87 men. Ages ranged from 49 to 90 years (M = 67.2 years, SD = 8.55), and average years of formal education was 10.8 (SD = 3.3). Multiple linear regressions showed that neuroticism and extraversion were significant regression predictors of self-rated health, after controlling for demographic factors. These findings suggest individual personality traits may influence health ratings, behaviors, and decision-making among older African Americans.

Patterns and correlates of serostatus disclosure to sexual partners by perinatally-infected adolescents and young adults

PubMed Central

Weintraub, Amy; Mellins, Claude; Warne, Patricia; Dolezal, Curtis; Elkington, Katherine; Bucek, Amelia; Leu, Cheng-Shiun; Bamji, Mahrukh; Wiznia, Andrew; Abrams, Elaine J

2017-01-01

Similar to same-age peers, perinatally HIV-infected (PHIV+) youth in the US are engaging in sex, including condomless sex. Understanding decisions about serostatus disclosure to sexual partners is important to domestic and global HIV prevention efforts, since large numbers of PHIV+ children are entering adolescence and becoming sexually active. Using Social Action Theory (SAT) to inform variable selection, we examined correlates of disclosure among 98 PHIV+ adolescents/young adults in New York City. Over half of these youth reported not disclosing to any casual partners (59%) and to any partners when using condoms (55%). In simple regression analyses, increased disclosure was associated with older age; being female; earlier age of learning one’s serostatus; and increased STD knowledge, disclosure intentions, and parent-child communication. Multiple regression analyses revealed a strong fit with the SAT model. As with adults, disclosure to sexual partners is difficult for PHIV+ youth and challenges prevention efforts. Effective interventions that help youth with disclosure decisions are needed to curb the epidemic. PMID:26874846

[Economic factors and gender differences in the prevalence of smoking among adults].

PubMed

Paes, Nelson Leitão

2016-01-01

This article presents a study that seeks to identify the relevant economic variables in the prevalence of smoking in a group of 37 countries. The chosen methodology was to estimate multiple linear regression using the least square approach. The econometric exercise is performed by gender, seeking to examine whether there are different motivations for cigarette smoking among the adult population of men and women. The results show that although taxation is a common element in the decision of both sexes, the decision to smoke among women is also sensitive to price and other social and cultural factors. These factors were based on the fact that women who live in countries that are part of the Organization for Economic Cooperation and Development reveal a significantly higher prevalence of cigarette consumption. The evidence presented in this study, therefore, reinforces the perception that taxation is in fact a crucial tool in the control of smoking, but in the specific case of women, higher prices and the promotion of greater equality with men, are also important.

Patterns of Cognitive Strengths and Weaknesses: Identification Rates, Agreement, and Validity for Learning Disabilities Identification

PubMed Central

Miciak, Jeremy; Fletcher, Jack M.; Stuebing, Karla; Vaughn, Sharon; Tolar, Tammy D.

2014-01-01

Purpose Few empirical investigations have evaluated LD identification methods based on a pattern of cognitive strengths and weaknesses (PSW). This study investigated the reliability and validity of two proposed PSW methods: the concordance/discordance method (C/DM) and cross battery assessment (XBA) method. Methods Cognitive assessment data for 139 adolescents demonstrating inadequate response to intervention was utilized to empirically classify participants as meeting or not meeting PSW LD identification criteria using the two approaches, permitting an analysis of: (1) LD identification rates; (2) agreement between methods; and (3) external validity. Results LD identification rates varied between the two methods depending upon the cut point for low achievement, with low agreement for LD identification decisions. Comparisons of groups that met and did not meet LD identification criteria on external academic variables were largely null, raising questions of external validity. Conclusions This study found low agreement and little evidence of validity for LD identification decisions based on PSW methods. An alternative may be to use multiple measures of academic achievement to guide intervention. PMID:24274155

Projected 2050 Model Simulations for the Chesapeake Bay ...

EPA Pesticide Factsheets

The Chesapeake Bay Program as has been tasked with assessing how changes in climate systems are expected to alter key variables and processes within the Watershed in concurrence with land use changes. EPA’s Office of Research and Development will be conducting historic and future, 2050, Weather Research and Forecast (WRF) metrological and Community Multiscale Air Quality (CMAQ) chemical transport model simulations to provide meteorological and nutrient deposition estimates for inclusion of the Chesapeake Bay Program’s assessment of how climate and land use change may impact water quality and ecosystem health. This presentation will present the timeline and research updates. The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.

Building a Computer Program to Support Children, Parents, and Distraction during Healthcare Procedures

PubMed Central

McCarthy, Ann Marie; Kleiber, Charmaine; Ataman, Kaan; Street, W. Nick; Zimmerman, M. Bridget; Ersig, Anne L.

2012-01-01

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children’s responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, the Children, Parents and Distraction (CPaD), is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure. PMID:22805121

Arctic BioMap: Building Participatory Technologies for Community-Specific Environmental Monitoring and Decision Making in the North

NASA Astrophysics Data System (ADS)

Murray, M. S.; Panikkar, B.; Liang, S.; Kutz, S.

2016-12-01

The Arctic continues to undergo unprecedented and accelerated system-wide environmental change. For people who live in the north this presents challenges to resource management, subsistence, health and well-being, and yet, there is very little community-specific data on wildlife (including wildlife health), local environmental conditions and emerging hazards in Northern Canada. A novel approach that integrates community expertise with developing technologies can simplify data collection and improve understanding of current and future conditions. It can also improve our ability to manage and adapt to the rapidly transforming Arctic. Arctic BioMap is a data platform for real-time monitoring and a geospatial informational database of wildlife and environmental information useful for assessment, research, management, and education. It enables monitoring of wildlife and environmental variables including hazards to inform decision-making at multiples scales. Using participatory technologies Arctic BioMap incorporates indigenous research needs and the ensuing data can be used to inform policy making. Arctic BioMap provides a forum for continuous exchange and communication among community members, scientists, resources managers, and other stakeholders.

Using Fuzzy Multiple Criteria Decision-Making Approach for Assessing the Risk of Railway Reconstruction Project in Taiwan

PubMed Central

Yu, Shih-Heng; Chang, Dong-Shang

2014-01-01

This study investigates the risk factors in railway reconstruction project through complete literature reviews on construction project risks and scrutinizing experiences and challenges of railway reconstructions in Taiwan. Based on the identified risk factors, an assessing framework based on the fuzzy multicriteria decision-making (fuzzy MCDM) approach to help construction agencies build awareness of the critical risk factors on the execution of railway reconstruction project, measure the impact and occurrence likelihood for these risk factors. Subjectivity, uncertainty and vagueness within the assessment process are dealt with using linguistic variables parameterized by trapezoid fuzzy numbers. By multiplying the degree of impact and the occurrence likelihood of risk factors, estimated severity values of each identified risk factor are determined. Based on the assessment results, the construction agencies were informed of what risks should be noticed and what they should do to avoid the risks. That is, it enables construction agencies of railway reconstruction to plan the appropriate risk responses/strategies to increase the opportunity of project success and effectiveness. PMID:24772014

Repetitive flood victims and acceptance of FEMA mitigation offers: an analysis with community-system policy implications.

PubMed

Kick, Edward L; Fraser, James C; Fulkerson, Gregory M; McKinney, Laura A; De Vries, Daniel H

2011-07-01

Of all natural disasters, flooding causes the greatest amount of economic and social damage. The United States' Federal Emergency Management Agency (FEMA) uses a number of hazard mitigation grant programmes for flood victims, including mitigation offers to relocate permanently repetitive flood loss victims. This study examines factors that help to explain the degree of difficulty repetitive flood loss victims experience when they make decisions about relocating permanently after multiple flood losses. Data are drawn from interviews with FEMA officials and a survey of flood victims from eight repetitive flooding sites. The qualitative and quantitative results show the importance of rational choices by flood victims in their mitigation decisions, as they relate to financial variables, perceptions of future risk, attachments to home and community, and the relationships between repetitive flood loss victims and the local flood management officials who help them. The results offer evidence to suggest the value of a more community-system approach to FEMA relocation practices. © 2011 The Author(s). Disasters © Overseas Development Institute, 2011.

A visualization tool to support decision making in environmental and biological planning

USGS Publications Warehouse

Romañach, Stephanie S.; McKelvy, James M.; Conzelmann, Craig; Suir, Kevin J.

2014-01-01

Large-scale ecosystem management involves consideration of many factors for informed decision making. The EverVIEW Data Viewer is a cross-platform desktop decision support tool to help decision makers compare simulation model outputs from competing plans for restoring Florida's Greater Everglades. The integration of NetCDF metadata conventions into EverVIEW allows end-users from multiple institutions within and beyond the Everglades restoration community to share information and tools. Our development process incorporates continuous interaction with targeted end-users for increased likelihood of adoption. One of EverVIEW's signature features is side-by-side map panels, which can be used to simultaneously compare species or habitat impacts from alternative restoration plans. Other features include examination of potential restoration plan impacts across multiple geographic or tabular displays, and animation through time. As a result of an iterative, standards-driven approach, EverVIEW is relevant to large-scale planning beyond Florida, and is used in multiple biological planning efforts in the United States.

Mixture-based gatekeeping procedures in adaptive clinical trials.

PubMed

Kordzakhia, George; Dmitrienko, Alex; Ishida, Eiji

2018-01-01

Clinical trials with data-driven decision rules often pursue multiple clinical objectives such as the evaluation of several endpoints or several doses of an experimental treatment. These complex analysis strategies give rise to "multivariate" multiplicity problems with several components or sources of multiplicity. A general framework for defining gatekeeping procedures in clinical trials with adaptive multistage designs is proposed in this paper. The mixture method is applied to build a gatekeeping procedure at each stage and inferences at each decision point (interim or final analysis) are performed using the combination function approach. An advantage of utilizing the mixture method is that it enables powerful gatekeeping procedures applicable to a broad class of settings with complex logical relationships among the hypotheses of interest. Further, the combination function approach supports flexible data-driven decisions such as a decision to increase the sample size or remove a treatment arm. The paper concludes with a clinical trial example that illustrates the methodology by applying it to develop an adaptive two-stage design with a mixture-based gatekeeping procedure.

Clustering and group selection of multiple criteria alternatives with application to space-based networks.

PubMed

Malakooti, Behnam; Yang, Ziyong

2004-02-01

In many real-world problems, the range of consequences of different alternatives are considerably different. In addition, sometimes, selection of a group of alternatives (instead of only one best alternative) is necessary. Traditional decision making approaches treat the set of alternatives with the same method of analysis and selection. In this paper, we propose clustering alternatives into different groups so that different methods of analysis, selection, and implementation for each group can be applied. As an example, consider the selection of a group of functions (or tasks) to be processed by a group of processors. The set of tasks can be grouped according to their similar criteria, and hence, each cluster of tasks to be processed by a processor. The selection of the best alternative for each clustered group can be performed using existing methods; however, the process of selecting groups is different than the process of selecting alternatives within a group. We develop theories and procedures for clustering discrete multiple criteria alternatives. We also demonstrate how the set of alternatives is clustered into mutually exclusive groups based on 1) similar features among alternatives; 2) ideal (or most representative) alternatives given by the decision maker; and 3) other preferential information of the decision maker. The clustering of multiple criteria alternatives also has the following advantages. 1) It decreases the set of alternatives to be considered by the decision maker (for example, different decision makers are assigned to different groups of alternatives). 2) It decreases the number of criteria. 3) It may provide a different approach for analyzing multiple decision makers problems. Each decision maker may cluster alternatives differently, and hence, clustering of alternatives may provide a basis for negotiation. The developed approach is applicable for solving a class of telecommunication networks problems where a set of objects (such as routers, processors, or intelligent autonomous vehicles) are to be clustered into similar groups. Objects are clustered based on several criteria and the decision maker's preferences.

Female Literacy Rate is a Better Predictor of Birth Rate and Infant Mortality Rate in India

PubMed Central

Saurabh, Suman; Sarkar, Sonali; Pandey, Dhruv K.

2013-01-01

Background: Educated women are known to take informed reproductive and healthcare decisions. These result in population stabilization and better infant care reflected by lower birth rates and infant mortality rates (IMRs), respectively. Materials and Methods: Our objective was to study the relationship of male and female literacy rates with crude birth rates (CBRs) and IMRs of the states and union territories (UTs) of India. The data were analyzed using linear regression. CBR and IMR were taken as the dependent variables; while the overall literacy rates, male, and female literacy rates were the independent variables. Results: CBRs were inversely related to literacy rates (slope parameter = −0.402, P < 0.001). On multiple linear regression with male and female literacy rates, a significant inverse relationship emerged between female literacy rate and CBR (slope = −0.363, P < 0.001), while male literacy rate was not significantly related to CBR (P = 0.674). IMR of the states were also inversely related to their literacy rates (slope = −1.254, P < 0.001). Multiple linear regression revealed a significant inverse relationship between IMR and female literacy (slope = −0.816, P = 0.031), whereas male literacy rate was not significantly related (P = 0.630). Conclusion: Female literacy is relatively highly important for both population stabilization and better infant health. PMID:26664840

Search for Supersymmetry in Hadronic Final States

NASA Astrophysics Data System (ADS)

Mulholland, Troy

We present a search for supersymmetry in purely hadronic final states with large missing transverse momentum using data collected by the CMS detector at the CERN LHC. The data were produced in proton-proton collisions with center-of-mass energy of 13 TeV and correspond to an integrated luminosity of 35.9 fb -1. Data are analyzed with variables defined in terms of jet multiplicity, bottom quark tagged jet multiplicity, the scalar sum of jet transverse momentum, the magnitude of the vector sum of jet transverse momentum, and angular separation between jets and the vector sum of transverse momentum. We perform the search on the data using two analysis techniques: a boosted decision tree trained on simulated data using the above variables as features and a four-dimensional fit with rectangular search regions. In both analyses, standard model background estimations are derived from data-driven techniques and the signal data are separated into exclusive search regions. The observed yields in the search regions agree with background expectations. We derive upper limits on the production cross sections of pairs of gluinos and pairs of top squarks at 95% confidence using simplified models with the lightest supersymmetric particle assumed to be a weakly interacting neutralino. Gluinos as heavy as 1960 GeV and top squarks as heavy as 980 GeV are excluded. The limits significantly extend the exclusions obtained from previous results.

Do research studies in the UK reporting child neurodevelopment adjust for the variability of assessors: a systematic review.

PubMed

Khalid, Rahila; Willatts, Peter; Williams, Fiona L R

2016-02-01

Neurodevelopment is a key outcome for many childhood trials and observational studies. Clinically important decisions may rest on finding relatively small differences in neurodevelopment between groups receiving complex and costly interventions. Our purpose was to determine whether studies which measure neurodevelopment report the numbers, training, and auditing of assessors and, for multiple assessor studies, whether the results were adjusted and if so by which method? Electronic searches were conducted using Medline, Embase, Cinahl, PsycINFO, and the Cochrane Library. A study was eligible if it reported neurodevelopmental outcome in children resident in the UK, less than or equal to 18 years and was published between 2000 and 2015. Trials and observational studies were included. Three hundred and seven full papers were reviewed: 52% of papers did not report the number of assessors used; 21% used a single assessor; and 27% used multiple assessors. Thirty-five per cent mentioned that assessors were trained in the use of the neurodevelopmental tool; 13% of assessors were audited; and only 1% of studies adjusted statistically for the number of assessors. At the very least, the quality of reporting the use of assessors in these research publications is poor, while at worst, the variability of assessors may mask the true relationship between an intervention/observation and neurodevelopmental outcome. © 2015 Mac Keith Press.

Computational mate choice: theory and empirical evidence.

PubMed

Castellano, Sergio; Cadeddu, Giorgia; Cermelli, Paolo

2012-06-01

The present review is based on the thesis that mate choice results from information-processing mechanisms governed by computational rules and that, to understand how females choose their mates, we should identify which are the sources of information and how they are used to make decisions. We describe mate choice as a three-step computational process and for each step we present theories and review empirical evidence. The first step is a perceptual process. It describes the acquisition of evidence, that is, how females use multiple cues and signals to assign an attractiveness value to prospective mates (the preference function hypothesis). The second step is a decisional process. It describes the construction of the decision variable (DV), which integrates evidence (private information by direct assessment), priors (public information), and value (perceived utility) of prospective mates into a quantity that is used by a decision rule (DR) to produce a choice. We make the assumption that females are optimal Bayesian decision makers and we derive a formal model of DV that can explain the effects of preference functions, mate copying, social context, and females' state and condition on the patterns of mate choice. The third step of mating decision is a deliberative process that depends on the DRs. We identify two main categories of DRs (absolute and comparative rules), and review the normative models of mate sampling tactics associated to them. We highlight the limits of the normative approach and present a class of computational models (sequential-sampling models) that are based on the assumption that DVs accumulate noisy evidence over time until a decision threshold is reached. These models force us to rethink the dichotomy between comparative and absolute decision rules, between discrimination and recognition, and even between rational and irrational choice. Since they have a robust biological basis, we think they may represent a useful theoretical tool for behavioural ecologist interested in integrating proximate and ultimate causes of mate choice. Copyright © 2012 Elsevier B.V. All rights reserved.

Pilot study of a point-of-use decision support tool for cancer clinical trials eligibility.

PubMed

Breitfeld, P P; Weisburd, M; Overhage, J M; Sledge, G; Tierney, W M

1999-01-01

Many adults with cancer are not enrolled in clinical trials because caregivers do not have the time to match the patient's clinical findings with varying eligibility criteria associated with multiple trials for which the patient might be eligible. The authors developed a point-of-use portable decision support tool (DS-TRIEL) to automate this matching process. The support tool consists of a hand-held computer with a programmable relational database. A two-level hierarchic decision framework was used for the identification of eligible subjects for two open breast cancer clinical trials. The hand-held computer also provides protocol consent forms and schemas to further help the busy oncologist. This decision support tool and the decision framework on which it is based could be used for multiple trials and different cancer sites.

Pilot Study of a Point-of-use Decision Support Tool for Cancer Clinical Trials Eligibility

PubMed Central

Breitfeld, Philip P.; Weisburd, Marina; Overhage, J. Marc; Sledge, George; Tierney, William M.

1999-01-01

Many adults with cancer are not enrolled in clinical trials because caregivers do not have the time to match the patient's clinical findings with varying eligibility criteria associated with multiple trials for which the patient might be eligible. The authors developed a point-of-use portable decision support tool (DS-TRIEL) to automate this matching process. The support tool consists of a hand-held computer with a programmable relational database. A two-level hierarchic decision framework was used for the identification of eligible subjects for two open breast cancer clinical trials. The hand-held computer also provides protocol consent forms and schemas to further help the busy oncologist. This decision support tool and the decision framework on which it is based could be used for multiple trials and different cancer sites. PMID:10579605

The desire for shared decision making among patients with solid and hematological cancer.

PubMed

Ernst, Jochen; Kuhnt, Susanne; Schwarzer, Andreas; Aldaoud, Ali; Niederwieser, Dietger; Mantovani-Löffler, Luisa; Kuchenbecker, Doris; Schröder, Christina

2011-02-01

The desire for shared decision making arises especially for frequently occurring cases of solid cancer. For hematological cancer conditions, there are no analogous results. This study compares the participation patients' desires concerning medical decisions dealing with their solid and hematological tumors. The 533 inpatients with solid cancer (age<65: 61.0%; female: 39.6 %) and 177 patients with hematological cancer (inpatient: 62.1%, outpatient: 37.9%; age<65: 63.3%; female: 42.4%) were given a questionnaire after admission to a hospital or medical practice. The dependent variable was patient preference for control in decision making for eight different medical areas of decision. Descriptive results showed that patients with solid cancer had a stronger desire to participate in the decisions in six of a total of eight survey fields (p<0.01). When considering medical and socio-demographic control variables, the multivariate regression shows that the differences between the patient groups remain in all areas (p<0.01). Further predictor variables are educational background and age (p<0.05). No influence resulted from the factors of gender, medical or treatment characteristics. The results show differences between patients with hematological cancer and patients with solid tumors, and these differences concern the preference to participate in medical decisions. Hemato-oncological patients desire less active participation and prefer a more dominant role of the physician in the various areas requiring decisions. Physicians should respect this in the course of the treatment. Copyright © 2010 John Wiley & Sons, Ltd.

Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models.

PubMed

Garibaldi, Jonathan M; Zhou, Shang-Ming; Wang, Xiao-Ying; John, Robert I; Ellis, Ian O

2012-06-01

It has been often demonstrated that clinicians exhibit both inter-expert and intra-expert variability when making difficult decisions. In contrast, the vast majority of computerized models that aim to provide automated support for such decisions do not explicitly recognize or replicate this variability. Furthermore, the perfect consistency of computerized models is often presented as a de facto benefit. In this paper, we describe a novel approach to incorporate variability within a fuzzy inference system using non-stationary fuzzy sets in order to replicate human variability. We apply our approach to a decision problem concerning the recommendation of post-operative breast cancer treatment; specifically, whether or not to administer chemotherapy based on assessment of five clinical variables: NPI (the Nottingham Prognostic Index), estrogen receptor status, vascular invasion, age and lymph node status. In doing so, we explore whether such explicit modeling of variability provides any performance advantage over a more conventional fuzzy approach, when tested on a set of 1310 unselected cases collected over a fourteen year period at the Nottingham University Hospitals NHS Trust, UK. The experimental results show that the standard fuzzy inference system (that does not model variability) achieves overall agreement to clinical practice around 84.6% (95% CI: 84.1-84.9%), while the non-stationary fuzzy model can significantly increase performance to around 88.1% (95% CI: 88.0-88.2%), p<0.001. We conclude that non-stationary fuzzy models provide a valuable new approach that may be applied to clinical decision support systems in any application domain. Copyright © 2012 Elsevier Inc. All rights reserved.

Repeatability and Reproducibility of Decisions by Latent Fingerprint Examiners

PubMed Central

Ulery, Bradford T.; Hicklin, R. Austin; Buscaglia, JoAnn; Roberts, Maria Antonia

2012-01-01

The interpretation of forensic fingerprint evidence relies on the expertise of latent print examiners. We tested latent print examiners on the extent to which they reached consistent decisions. This study assessed intra-examiner repeatability by retesting 72 examiners on comparisons of latent and exemplar fingerprints, after an interval of approximately seven months; each examiner was reassigned 25 image pairs for comparison, out of total pool of 744 image pairs. We compare these repeatability results with reproducibility (inter-examiner) results derived from our previous study. Examiners repeated 89.1% of their individualization decisions, and 90.1% of their exclusion decisions; most of the changed decisions resulted in inconclusive decisions. Repeatability of comparison decisions (individualization, exclusion, inconclusive) was 90.0% for mated pairs, and 85.9% for nonmated pairs. Repeatability and reproducibility were notably lower for comparisons assessed by the examiners as “difficult” than for “easy” or “moderate” comparisons, indicating that examiners' assessments of difficulty may be useful for quality assurance. No false positive errors were repeated (n = 4); 30% of false negative errors were repeated. One percent of latent value decisions were completely reversed (no value even for exclusion vs. of value for individualization). Most of the inter- and intra-examiner variability concerned whether the examiners considered the information available to be sufficient to reach a conclusion; this variability was concentrated on specific image pairs such that repeatability and reproducibility were very high on some comparisons and very low on others. Much of the variability appears to be due to making categorical decisions in borderline cases. PMID:22427888

Dissociating sensory from decision processes in human perceptual decision making.

PubMed

Mostert, Pim; Kok, Peter; de Lange, Floris P

2015-12-15

A key question within systems neuroscience is how the brain translates physical stimulation into a behavioral response: perceptual decision making. To answer this question, it is important to dissociate the neural activity underlying the encoding of sensory information from the activity underlying the subsequent temporal integration into a decision variable. Here, we adopted a decoding approach to empirically assess this dissociation in human magnetoencephalography recordings. We used a functional localizer to identify the neural signature that reflects sensory-specific processes, and subsequently traced this signature while subjects were engaged in a perceptual decision making task. Our results revealed a temporal dissociation in which sensory processing was limited to an early time window and consistent with occipital areas, whereas decision-related processing became increasingly pronounced over time, and involved parietal and frontal areas. We found that the sensory processing accurately reflected the physical stimulus, irrespective of the eventual decision. Moreover, the sensory representation was stable and maintained over time when it was required for a subsequent decision, but unstable and variable over time when it was task-irrelevant. In contrast, decision-related activity displayed long-lasting sustained components. Together, our approach dissects neuro-anatomically and functionally distinct contributions to perceptual decisions.

Dissociating sensory from decision processes in human perceptual decision making

PubMed Central

Mostert, Pim; Kok, Peter; de Lange, Floris P.

2015-01-01

A key question within systems neuroscience is how the brain translates physical stimulation into a behavioral response: perceptual decision making. To answer this question, it is important to dissociate the neural activity underlying the encoding of sensory information from the activity underlying the subsequent temporal integration into a decision variable. Here, we adopted a decoding approach to empirically assess this dissociation in human magnetoencephalography recordings. We used a functional localizer to identify the neural signature that reflects sensory-specific processes, and subsequently traced this signature while subjects were engaged in a perceptual decision making task. Our results revealed a temporal dissociation in which sensory processing was limited to an early time window and consistent with occipital areas, whereas decision-related processing became increasingly pronounced over time, and involved parietal and frontal areas. We found that the sensory processing accurately reflected the physical stimulus, irrespective of the eventual decision. Moreover, the sensory representation was stable and maintained over time when it was required for a subsequent decision, but unstable and variable over time when it was task-irrelevant. In contrast, decision-related activity displayed long-lasting sustained components. Together, our approach dissects neuro-anatomically and functionally distinct contributions to perceptual decisions. PMID:26666393

Implementation of Data Mining to Analyze Drug Cases Using C4.5 Decision Tree

NASA Astrophysics Data System (ADS)

Wahyuni, Sri

2018-03-01

Data mining was the process of finding useful information from a large set of databases. One of the existing techniques in data mining was classification. The method used was decision tree method and algorithm used was C4.5 algorithm. The decision tree method was a method that transformed a very large fact into a decision tree which was presenting the rules. Decision tree method was useful for exploring data, as well as finding a hidden relationship between a number of potential input variables with a target variable. The decision tree of the C4.5 algorithm was constructed with several stages including the selection of attributes as roots, created a branch for each value and divided the case into the branch. These stages would be repeated for each branch until all the cases on the branch had the same class. From the solution of the decision tree there would be some rules of a case. In this case the researcher classified the data of prisoners at Labuhan Deli prison to know the factors of detainees committing criminal acts of drugs. By applying this C4.5 algorithm, then the knowledge was obtained as information to minimize the criminal acts of drugs. From the findings of the research, it was found that the most influential factor of the detainee committed the criminal act of drugs was from the address variable.

Clinical reasoning: concept analysis.

PubMed

Simmons, Barbara

2010-05-01

This paper is a report of a concept analysis of clinical reasoning in nursing. Clinical reasoning is an ambiguous term that is often used synonymously with decision-making and clinical judgment. Clinical reasoning has not been clearly defined in the literature. Healthcare settings are increasingly filled with uncertainty, risk and complexity due to increased patient acuity, multiple comorbidities, and enhanced use of technology, all of which require clinical reasoning. Data sources. Literature for this concept analysis was retrieved from several databases, including CINAHL, PubMed, PsycINFO, ERIC and OvidMEDLINE, for the years 1980 to 2008. Rodgers's evolutionary method of concept analysis was used because of its applicability to concepts that are still evolving. Multiple terms have been used synonymously to describe the thinking skills that nurses use. Research in the past 20 years has elucidated differences among these terms and identified the cognitive processes that precede judgment and decision-making. Our concept analysis defines one of these terms, 'clinical reasoning,' as a complex process that uses cognition, metacognition, and discipline-specific knowledge to gather and analyse patient information, evaluate its significance, and weigh alternative actions. This concept analysis provides a middle-range descriptive theory of clinical reasoning in nursing that helps clarify meaning and gives direction for future research. Appropriate instruments to operationalize the concept need to be developed. Research is needed to identify additional variables that have an impact on clinical reasoning and what are the consequences of clinical reasoning in specific situations.

Decision making from economic and signal detection perspectives: development of an integrated framework

PubMed Central

Lynn, Spencer K.; Wormwood, Jolie B.; Barrett, Lisa F.; Quigley, Karen S.

2015-01-01

Behavior is comprised of decisions made from moment to moment (i.e., to respond one way or another). Often, the decision maker cannot be certain of the value to be accrued from the decision (i.e., the outcome value). Decisions made under outcome value uncertainty form the basis of the economic framework of decision making. Behavior is also based on perception—perception of the external physical world and of the internal bodily milieu, which both provide cues that guide decision making. These perceptual signals are also often uncertain: another person's scowling facial expression may indicate threat or intense concentration, alternatives that require different responses from the perceiver. Decisions made under perceptual uncertainty form the basis of the signals framework of decision making. Traditional behavioral economic approaches to decision making focus on the uncertainty that comes from variability in possible outcome values, and typically ignore the influence of perceptual uncertainty. Conversely, traditional signal detection approaches to decision making focus on the uncertainty that arises from variability in perceptual signals and typically ignore the influence of outcome value uncertainty. Here, we compare and contrast the economic and signals frameworks that guide research in decision making, with the aim of promoting their integration. We show that an integrated framework can expand our ability to understand a wider variety of decision-making behaviors, in particular the complexly determined real-world decisions we all make every day. PMID:26217275

What Can We Expect from Value-Based Funding of Medicines? A Retrospective Study.

PubMed

Harris, Anthony; Li, Jing Jing; Yong, Karen

2016-04-01

Deciding on public funding for pharmaceuticals on the basis of value for money is now widespread. We suggest that evidence-based assessment of value has restricted the availability of medicines in Australia in a way that reflects the relative bargaining power of government and the pharmaceutical industry. We propose a simple informal game-theoretic model of bargaining between the funding agency and industry and test its predictions using a logistic multiple regression model of past funding decisions made by the Pharmaceutical Benefits Advisory Committee in Australia. The model estimates the probability of a drug being recommended for subsidy as a function of incremental cost per quality-adjusted life-year (QALY), as well as other drug and market characteristics. Data are major submissions or resubmissions from 1993 to 2009 where there was a claim of superiority and evidence of a difference in quality of life. Independent variables measure the incremental cost per QALY, the cost to the public budget, the strength and quality of the clinical and economic evidence, need as measured by severity of illness and the availability of alternative treatments, whether or not a resubmission, and newspaper reports as a measure of public pressure. We report the odds ratio for each variable and calculate the ratio of the marginal effect of each variable to the marginal effect of the cost per QALY as a measure of the revealed willingness to pay for each of the variables that influence the decision. The results are consistent with a bargaining model where a 10,000 Australian dollar ($A) fall in value (increase in cost per QALY) reduces the average probability of public funding from 37 to 33% (95% CI 34-32). If the condition is life threatening or the drug has no active comparator, then the odds of a positive recommendation are 3.18 (95% CI 1.00-10.11) and 2.14 (95% CI 0.95-4.83) greater, equivalent to a $A33,000 and a $A21,000 increase in value (fall in cost per QALY). If both conditions are met, the odds are increased by 4.41 (95% CI 1.28-15.24) times, equivalent to an increase in value of $A46,000. Funding is more likely as time elapses and price falls, but we did not find clear evidence that public or corporate pressure influences decisions. Evidence from Australia suggests that the determinants of public funding and pricing decisions for medicines reflect the relative bargaining power of government and drug companies. Value for money depends on the quality of evidence, timing, patient need, perceived benefit and opportunity cost; these factors reflect the potential gains from striking a bargain and the risk of loss from not doing so.

Interactions between large-scale modes of climate and their relationship with Australian climate and hydrology

NASA Astrophysics Data System (ADS)

Whan, K. R.; Lindesay, J. A.; Timbal, B.; Raupach, M. R.; Williams, E.

2010-12-01

Australia’s natural environment is adapted to low rainfall availability and high variability but human systems are less able to adapt to variability in the hydrological cycle. Understanding the mechanisms underlying drought persistence and severity is vital to contextualising future climate change. Multiple external forcings mean the mechanisms of drought occurrence in south-eastern Australian are complex. The key influences on SEA climate are El Niño-Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), the Southern Annular Mode (SAM) and the sub-tropical ridge (STR); each of these large-scale climate modes (LSCM) has been studied widely. The need for research into the interactions among the modes has been noted [1], although to date this has received limited attention. Relationships between LSCM and hydrometeorological variability are nonlinear, making linearity assumptions underlying usual statistical techniques (e.g. correlation, principle components analysis) questionable. In the current research a statistical technique that can deal with nonlinear interactions is applied to a new dataset enabling a full examination of the Australian water balance. The Australian Water Availability Project (AWAP) dataset models the Australian water balance on a fine grid [2]. Hydrological parameters (e.g. soil moisture, evaporation, runoff) are modelled from meteorological data, allowing the complete Australian water balance (climate and hydrology) to be examined and the mechanisms of drought to be studied holistically. Classification and regression trees (CART) are a powerful regression-based technique that is capable of accounting for nonlinear effects. Although it has limited previous application in climate research [3] this methodology is particularly informative in cases with multiple predictors and nonlinear relationships such as climate variability. Statistical relationships between variables are the basis for the decision rules in CART that are used to split the data into increasingly homogeneous groups. CART is applied to the AWAP dataset to identify the hydroclimatic regimes associated with various combinations of LSCM and the importance of each mode in producing the regime. Analysis of the LSCM is conducted on a range of hydroclimatic variables to assess the relative and combined influences of these LSCM on the Australian water balance. This gives information about interactions between LSCM that are vital for specific hydroclimatic states (e.g. drought) and about which combinations of LSCM result in specific regimes. The dominant LSCM in different seasons and the relationships among the climate drivers have been identified. 1. Ummenhofer, C., et al., What causes southeast Australia's worst droughts? Geophysical Research Letters, 2009. 36: p. L04706. 2. Raupach, M., et al., Australian Water Availability Project (AWAP). CSIRO Marine and Atmospheric Research Component: Final Report for Phase 3. 2008. 3. Burrows, W., et al., CART Decision-Tree Statistical Analysis and Prediction of Summer Season Maximum Surface Ozone for the Vancouver, Montreal and Atlantic Regions of Canada. Journal of Applied Meteorology, 1995. 34: p. 1848-1862.

Estimating the Growth of Internal Evidence Guiding Perceptual Decisions

ERIC Educational Resources Information Center

Ludwig, Casimir J. H.; Davies, J. Rhys

2011-01-01

Perceptual decision-making is thought to involve a gradual accrual of noisy evidence. Temporal integration of the evidence reduces the relative contribution of dynamic internal noise to the decision variable, thereby boosting its signal-to-noise ratio. We aimed to estimate the internal evidence guiding perceptual decisions over time, using a novel…

[The framing effect: medical implications].

PubMed

Mazzocco, Ketti; Cherubini, Paolo; Rumiati, Rino

2005-01-01

Over the last 20 years, many studies explored how the way information is presented modifies choices. This sort of effect, referred to as "framing effects", typically consists of the inversion of choices when presenting structurally identical decision problems in different ways. It is a common assumption that physicians are unaffected (or less affected) by the surface description of a decision problem, because they are formally trained in medical decision making. However, several studies showed that framing effects occur even in the medical field. The complexity and variability of these effects are remarkable, making it necessary to distinguish among different framing effects, depending on whether the effect is obtained by modifying adjectives (attribute framing), goals of a behavior (goal framing), or the probability of an outcome (risky choice framing). A further reason for the high variability of the framing effects seems to be the domain of the decision problem, with different effects occurring in prevention decisions, disease-detection decisions, and treatment decisions. The present work reviews the studies on framing effects, in order to summarize them and clarify their possible role in medical decision making.

Automatically updating predictive modeling workflows support decision-making in drug design.

PubMed

Muegge, Ingo; Bentzien, Jörg; Mukherjee, Prasenjit; Hughes, Robert O

2016-09-01

Using predictive models for early decision-making in drug discovery has become standard practice. We suggest that model building needs to be automated with minimum input and low technical maintenance requirements. Models perform best when tailored to answering specific compound optimization related questions. If qualitative answers are required, 2-bin classification models are preferred. Integrating predictive modeling results with structural information stimulates better decision making. For in silico models supporting rapid structure-activity relationship cycles the performance deteriorates within weeks. Frequent automated updates of predictive models ensure best predictions. Consensus between multiple modeling approaches increases the prediction confidence. Combining qualified and nonqualified data optimally uses all available information. Dose predictions provide a holistic alternative to multiple individual property predictions for reaching complex decisions.

MICROPIK: A Multiple-Alternatives, Criterion-Referenced Decisioning Model for Evaluating CAI Software and Microcomputer Hardware Against Selected Curriculum Instructional Objectives. Paper and Report Series No. 73.

ERIC Educational Resources Information Center

Wholeben, Brent Edward

This report describing the use of operations research techniques to determine which courseware packages or what microcomputer systems best address varied instructional objectives focuses on the MICROPIK model, a highly structured evaluation technique for making such complex instructional decisions. MICROPIK is a multiple alternatives model (MAA)…

Applying decision tree for identification of a low risk population for type 2 diabetes. Tehran Lipid and Glucose Study.

PubMed

Ramezankhani, Azra; Pournik, Omid; Shahrabi, Jamal; Khalili, Davood; Azizi, Fereidoun; Hadaegh, Farzad

2014-09-01

The aim of this study was to create a prediction model using data mining approach to identify low risk individuals for incidence of type 2 diabetes, using the Tehran Lipid and Glucose Study (TLGS) database. For a 6647 population without diabetes, aged ≥20 years, followed for 12 years, a prediction model was developed using classification by the decision tree technique. Seven hundred and twenty-nine (11%) diabetes cases occurred during the follow-up. Predictor variables were selected from demographic characteristics, smoking status, medical and drug history and laboratory measures. We developed the predictive models by decision tree using 60 input variables and one output variable. The overall classification accuracy was 90.5%, with 31.1% sensitivity, 97.9% specificity; and for the subjects without diabetes, precision and f-measure were 92% and 0.95, respectively. The identified variables included fasting plasma glucose, body mass index, triglycerides, mean arterial blood pressure, family history of diabetes, educational level and job status. In conclusion, decision tree analysis, using routine demographic, clinical, anthropometric and laboratory measurements, created a simple tool to predict individuals at low risk for type 2 diabetes. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Perceiving affordances in rugby union.

PubMed

Passos, Pedro; Cordovil, Rita; Fernandes, Orlando; Barreiros, João

2012-01-01

To succeed in competitive environments, players need to continuously adjust their decisions and actions to the behaviour of relevant others. Players' interactions demand ongoing decisions that are constrained by what is previously defined (e.g., coaches' prescriptions that establish 'what' to do) and by information that is available in the context and specifies not only 'what' the player should do, but also 'how', 'when' and 'where'. We describe what affordances emerge to the ball carrier as a consequence of changes in kinematic variables, such as interpersonal distances or distances to the nearest sideline. Changes in these variables determine whether and when different actions are possible. The ball carrier tended to perform a pass when the tackler was farthest from the sideline and the velocity of approach to the tackler did not seem to effect the ball carrier's decision. In the few episodes where the ball carrier moved forward instead of passing the ball, he was mainly influenced by contextual information, such as the variability of the players' distance to the nearest sideline. In sum, actors must be aware of the affordances of others that are specified by particular variables that become available just before decision-making.

Managing wildfire events: risk-based decision making among a group of federal fire managers

Treesearch

Robyn S. Wilson; Patricia L. Winter; Lynn A. Maguire; Timothy Ascher

2011-01-01

Managing wildfire events to achieve multiple management objectives involves a high degree of decision complexity and uncertainty, increasing the likelihood that decisions will be informed by experience-based heuristics triggered by available cues at the time of the decision. The research reported here tests the prevalence of three risk-based biases among 206...

Decision making.

PubMed

Chambers, David W

2011-01-01

A decision is a commitment of resources under conditions of risk in expectation of the best future outcome. The smart decision is always the strategy with the best overall expected value-the best combination of facts and values. Some of the special circumstances involved in decision making are discussed, including decisions where there are multiple goals, those where more than one person is involved in making the decision, using trigger points, framing decisions correctly, commitments to lost causes, and expert decision makers. A complex example of deciding about removal of asymptomatic third molars, with and without an EBD search, is discussed.

Combined model of intrinsic and extrinsic variability for computational network design with application to synthetic biology.

PubMed

Toni, Tina; Tidor, Bruce

2013-01-01

Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA--for example, on the same transcript--was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady state. Our model and findings provide a general framework to guide design principles in synthetic biology.

Combined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology

PubMed Central

Toni, Tina; Tidor, Bruce

2013-01-01

Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA – for example, on the same transcript – was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady state. Our model and findings provide a general framework to guide design principles in synthetic biology. PMID:23555205

Development of a real-time clinical decision support system upon the web mvc-based architecture for prostate cancer treatment

PubMed Central

2011-01-01

Background A real-time clinical decision support system (RTCDSS) with interactive diagrams enables clinicians to instantly and efficiently track patients' clinical records (PCRs) and improve their quality of clinical care. We propose a RTCDSS to process online clinical informatics from multiple databases for clinical decision making in the treatment of prostate cancer based on Web Model-View-Controller (MVC) architecture, by which the system can easily be adapted to different diseases and applications. Methods We designed a framework upon the Web MVC-based architecture in which the reusable and extractable models can be conveniently adapted to other hospital information systems and which allows for efficient database integration. Then, we determined the clinical variables of the prostate cancer treatment based on participating clinicians' opinions and developed a computational model to determine the pretreatment parameters. Furthermore, the components of the RTCDSS integrated PCRs and decision factors for real-time analysis to provide evidence-based diagrams upon the clinician-oriented interface for visualization of treatment guidance and health risk assessment. Results The resulting system can improve quality of clinical treatment by allowing clinicians to concurrently analyze and evaluate the clinical markers of prostate cancer patients with instantaneous clinical data and evidence-based diagrams which can automatically identify pretreatment parameters. Moreover, the proposed RTCDSS can aid interactions between patients and clinicians. Conclusions Our proposed framework supports online clinical informatics, evaluates treatment risks, offers interactive guidance, and provides real-time reference for decision making in the treatment of prostate cancer. The developed clinician-oriented interface can assist clinicians in conveniently presenting evidence-based information to patients and can be readily adapted to an existing hospital information system and be easily applied in other chronic diseases. PMID:21385459

Development of a real-time clinical decision support system upon the Web MVC-based architecture for prostate cancer treatment.

PubMed

Lin, Hsueh-Chun; Wu, Hsi-Chin; Chang, Chih-Hung; Li, Tsai-Chung; Liang, Wen-Miin; Wang, Jong-Yi Wang

2011-03-08

A real-time clinical decision support system (RTCDSS) with interactive diagrams enables clinicians to instantly and efficiently track patients' clinical records (PCRs) and improve their quality of clinical care. We propose a RTCDSS to process online clinical informatics from multiple databases for clinical decision making in the treatment of prostate cancer based on Web Model-View-Controller (MVC) architecture, by which the system can easily be adapted to different diseases and applications. We designed a framework upon the Web MVC-based architecture in which the reusable and extractable models can be conveniently adapted to other hospital information systems and which allows for efficient database integration. Then, we determined the clinical variables of the prostate cancer treatment based on participating clinicians' opinions and developed a computational model to determine the pretreatment parameters. Furthermore, the components of the RTCDSS integrated PCRs and decision factors for real-time analysis to provide evidence-based diagrams upon the clinician-oriented interface for visualization of treatment guidance and health risk assessment. The resulting system can improve quality of clinical treatment by allowing clinicians to concurrently analyze and evaluate the clinical markers of prostate cancer patients with instantaneous clinical data and evidence-based diagrams which can automatically identify pretreatment parameters. Moreover, the proposed RTCDSS can aid interactions between patients and clinicians. Our proposed framework supports online clinical informatics, evaluates treatment risks, offers interactive guidance, and provides real-time reference for decision making in the treatment of prostate cancer. The developed clinician-oriented interface can assist clinicians in conveniently presenting evidence-based information to patients and can be readily adapted to an existing hospital information system and be easily applied in other chronic diseases.

Vulnerability-based evaluation of water supply design under climate change

NASA Astrophysics Data System (ADS)

Umit Taner, Mehmet; Ray, Patrick; Brown, Casey

2015-04-01

Long-lived water supply infrastructures are strategic investments in the developing world, serving the purpose of balancing water deficits compounded by both population growth and socio-economic development. Robust infrastructure design under climate change is compelling, and often addressed by focusing on the outcomes of climate model projections ('scenario-led' planning), or by identifying design options that are less vulnerable to a wide range of plausible futures ('vulnerability-based' planning). Decision-Scaling framework combines these two approaches by first applying a climate stress test on the system to explore vulnerabilities across many traces of the future, and then employing climate projections to inform the decision-making process. In this work, we develop decision scaling's nascent risk management concepts further, directing actions on vulnerabilities identified during the climate stress test. In the process, we present a new way to inform climate vulnerability space using climate projections, and demonstrate the use of multiple decision criteria to guide to a final design recommendation. The concepts are demonstrated for a water supply project in the Mombasa Province of Kenya, planned to provide domestic and irrigation supply. Six storage design capacities (from 40 to 140 million cubic meters) are explored through a stress test, under a large number climate traces representing both natural climate variability and plausible climate changes. Design outcomes are simulated over a 40-year planning period with a coupled hydrologic-water resources systems model and using standard reservoir operation rules. Resulting performance is expressed in terms of water supply reliability and economic efficiency. Ensemble climate projections are used for assigning conditional likelihoods to the climate traces using a statistical distance measure. The final design recommendations are presented and discussed for the decision criteria of expected regret, satisficing, and conditional value-at-risk (CVaR).

C-fuzzy variable-branch decision tree with storage and classification error rate constraints

NASA Astrophysics Data System (ADS)

Yang, Shiueng-Bien

2009-10-01

The C-fuzzy decision tree (CFDT), which is based on the fuzzy C-means algorithm, has recently been proposed. The CFDT is grown by selecting the nodes to be split according to its classification error rate. However, the CFDT design does not consider the classification time taken to classify the input vector. Thus, the CFDT can be improved. We propose a new C-fuzzy variable-branch decision tree (CFVBDT) with storage and classification error rate constraints. The design of the CFVBDT consists of two phases-growing and pruning. The CFVBDT is grown by selecting the nodes to be split according to the classification error rate and the classification time in the decision tree. Additionally, the pruning method selects the nodes to prune based on the storage requirement and the classification time of the CFVBDT. Furthermore, the number of branches of each internal node is variable in the CFVBDT. Experimental results indicate that the proposed CFVBDT outperforms the CFDT and other methods.

Artificial neural networks in mammography interpretation and diagnostic decision making.

PubMed

Ayer, Turgay; Chen, Qiushi; Burnside, Elizabeth S

2013-01-01

Screening mammography is the most effective means for early detection of breast cancer. Although general rules for discriminating malignant and benign lesions exist, radiologists are unable to perfectly detect and classify all lesions as malignant and benign, for many reasons which include, but are not limited to, overlap of features that distinguish malignancy, difficulty in estimating disease risk, and variability in recommended management. When predictive variables are numerous and interact, ad hoc decision making strategies based on experience and memory may lead to systematic errors and variability in practice. The integration of computer models to help radiologists increase the accuracy of mammography examinations in diagnostic decision making has gained increasing attention in the last two decades. In this study, we provide an overview of one of the most commonly used models, artificial neural networks (ANNs), in mammography interpretation and diagnostic decision making and discuss important features in mammography interpretation. We conclude by discussing several common limitations of existing research on ANN-based detection and diagnostic models and provide possible future research directions.

Preferences and actual chemotherapy decision-making in the greater plains collaborative breast cancer study.

PubMed

Berger, Ann M; Buzalko, Russell J; Kupzyk, Kevin A; Gardner, Bret J; Djalilova, Dilorom M; Otte, Julie L

2017-12-01

There is renewed interest in identifying breast cancer patients' participation in decision-making about adjuvant chemotherapy. There is a gap in the literature regarding the impact of these decisions on quality of life (QOL) and quality of care (QOC). Our aims were to determine similarities and differences in how patients diagnosed with breast cancer preferred to make decisions with providers about cancer treatment, to examine the patient's recall of her role when the decision was made about chemotherapy and to determine how preferred and actual roles, as well as congruence between them, relate to QOL and perceived QOC. Greater Plains Collaborative clinical data research network of PCORnet conducted the 'Share Thoughts on Breast Cancer' survey among women 12-18 months post-diagnosis at eight sites in seven Midwestern United States. Patients recalled their preferred and actual treatment decision-making roles and three new shared decision-making (SDM) variables were created. Patients completed QOL and QOC measurements. Correlations and t-tests were used. Of 1235 returned surveys, 873 (full sample) and 329 (subsample who received chemotherapy) were used. About one-half of women in both the full (50.7%) and subsample (49.8%,) preferred SDM with providers about treatment decisions, but only 41.2% (full) and 42.6% (subsample) reported experiencing SDM. Significant differences were found between preferred versus actual roles in the full (p < .001) and subsample (p < .004). In the full sample, there were no relationships between five decision-making variables with QOL, but there was an association with QOC. The subsample's decision-making variables related to several QOL scales and QOC items, with a more patient-centered decision than originally preferred related to higher physical and social/family well-being, overall QOL and QOC. Patients benefit from providers' efforts to identify patient preferences, encourage an active role in SDM, and tailor decision making to their desired choice.

Effects of electrofishing gear type on spatial and temporal variability in fish community sampling

USGS Publications Warehouse

Meador, M.R.; McIntyre, J.P.

2003-01-01

Fish community data collected from 24 major river basins between 1993 and 1998 as part of the U.S. Geological Survey's National Water-Quality Assessment Program were analyzed to assess multiple-reach (three consecutive reaches) and multiple-year (three consecutive years) variability in samples collected at a site. Variability was assessed using the coefficient of variation (CV; SD/mean) of species richness, the Jaccard index (JI), and the percent similarity index (PSI). Data were categorized by three electrofishing sample collection methods: backpack, towed barge, and boat. Overall, multiple-reach CV values were significantly lower than those for multiple years, whereas multiple-reach JI and PSI values were significantly greater than those for multiple years. Multiple-reach and multiple-year CV values did not vary significantly among electrofishing methods, although JI and PSI values were significantly greatest for backpack electrofishing across multiple reaches and multiple years. The absolute difference between mean species richness for multiple-reach samples and mean species richness for multiple-year samples was 0.8 species (9.5% of total species richness) for backpack samples, 1.7 species (10.1%) for towed-barge samples, and 4.5 species (24.4%) for boat-collected samples. Review of boat-collected fish samples indicated that representatives of four taxonomic families - Catostomidae, Centrarchidae, Cyprinidae, and Ictaluridae - were collected at all sites. Of these, catostomids exhibited greater interannual variability than centrarchids, cyprinids, or ictalurids. Caution should be exercised when combining boat-collected fish community data from different years because of relatively high interannual variability, which is primarily due to certain relatively mobile species. Such variability may obscure longer-term trends.

Multiple perspectives on shared decision-making and interprofessional collaboration in mental healthcare.

PubMed

Chong, Wei Wen; Aslani, Parisa; Chen, Timothy F

2013-05-01

Shared decision-making is an essential element of patient-centered care in mental health. Since mental health services involve healthcare providers from different professions, a multiple perspective to shared decision-making may be valuable. The objective of this study was to explore the perceptions of different healthcare professionals on shared decision-making and current interprofessional collaboration in mental healthcare. Semi-structured interviews were conducted with 31 healthcare providers from a range of professions, which included medical practitioners (psychiatrists, general practitioners), pharmacists, nurses, occupational therapists, psychologists and social workers. Findings indicated that healthcare providers supported the notion of shared decision-making in mental health, but felt that it should be condition dependent. Medical practitioners advocated a more active participation from consumers in treatment decision-making; whereas other providers (e.g. pharmacists, occupational therapists) focused more toward acknowledging consumers' needs in decisions, perceiving themselves to be in an advisory role in supporting consumers' decision-making. Although healthcare providers acknowledged the importance of interprofessional collaboration, only a minority discussed it within the context of shared decision-making. In conclusion, healthcare providers appeared to have differing perceptions on the level of consumer involvement in shared decision-making. Interprofessional roles to facilitate shared decision-making in mental health need to be acknowledged, understood and strengthened, before an interprofessional approach to shared decision-making in mental health can be effectively implemented.

Rethinking Dental School Admission Criteria: Correlation Between Pre-Admission Variables and First-Year Performance for Six Classes at One Dental School.

PubMed

Rowland, Kevin C; Rieken, Susan

2018-04-01

Admissions committees in dental schools are charged with the responsibility of selecting candidates who will succeed in school and become successful members of the profession. Identifying students who will have academic difficulty is challenging. The aim of this study was to determine the predictive value of pre-admission variables for the first-year performance of six classes at one U.S. dental school. The authors hypothesized that the variables undergraduate grade point average (GPA), undergraduate science GPA (biology, chemistry, and physics), and Dental Admission Test (DAT) scores would predict the level of performance achieved in the first year of dental school, measured by year-end GPA. Data were collected in 2015 from school records for all 297 students in the six cohorts who completed the first year (Classes of 2007 through 2013). In the results, statistically significant correlations existed between all pre-admission variables and first-year GPA, but the associations were only weak to moderate. Lower performing students at the end of the first year (lowest 10% of GPA) had, on average, lower pre-admission variables than the other students, but the differences were small (≤10.8% in all categories). When all the pre-admission variables were considered together in a multiple regression analysis, a significant association was found between pre-admission variables and first-year GPA, but the association was weak (adjusted R 2 =0.238). This weak association suggests that these students' first-year dental school GPAs were mostly determined by factors other than the pre-admission variables studied and has resulted in the school's placing greater emphasis on other factors for admission decisions.

Key External Influences Affecting Consumers’ Decisions Regarding Food

PubMed Central

Martínez-Ruiz, María Pilar; Gómez-Cantó, Carmen María

2016-01-01

Among the numerous internal and external forces that compete for consumers’ attention in the context in which they buy their food, this paper will seek to provide a review of the most important external influences, such as the variables related to food itself. To this end, in addition to the food attributes traditionally identified in fields such as consumer behavior, it will give special consideration to the classification of food values. Although the influence of these variables on consumer decisions depends on the individual, analyzing them will undoubtedly increase understanding of consumers’ decisions. Additionally, identifying and describing these variables will enable subsequent research on how they influence both consumer behavior and other key outcomes for producers, manufacturers, and retailers in the food industry, such as satisfaction, trust, and loyalty. PMID:27803686

Focus of attention in an activity-based scheduler

NASA Technical Reports Server (NTRS)

Sadeh, Norman; Fox, Mark S.

1989-01-01

Earlier research in job shop scheduling has demonstrated the advantages of opportunistically combining order-based and resource-based scheduling techniques. An even more flexible approach is investigated where each activity is considered a decision point by itself. Heuristics to opportunistically select the next decision point on which to focus attention (i.e., variable ordering heuristics) and the next decision to be tried at this point (i.e., value ordering heuristics) are described that probabilistically account for both activity precedence and resource requirement interactions. Preliminary experimental results indicate that the variable ordering heuristic greatly increases search efficiency. While least constraining value ordering heuristics have been advocated in the literature, the experimental results suggest that other value ordering heuristics combined with our variable-ordering heuristic can produce much better schedules without significantly increasing search.

Do physicians’ recommendations pull patients away from their preferred treatment options?

PubMed Central

Mendel, Rosmarie; Traut‐Mattausch, Eva; Frey, Dieter; Bühner, Markus; Berthele, Achim; Kissling, Werner; Hamann, Johannes

2011-01-01

Abstract Context and objective  Shared decision making is especially advocated for preference‐sensitive decisions. We investigated whether physicians’ recommendations pull patients away from their preferred treatment option when making a preference‐sensitive decision. Design, participants and methods  Inpatients (N = 102 with schizophrenia, N = 101 with multiple sclerosis) were presented with a hypothetical scenario (the choice between two drugs). They were first asked about their preferences concerning the two drugs and then they received a (fictitious) clinician’s recommendation that was contrary to their preferences. Subsequently they made a final choice between the two drugs. Main outcome measures  The main outcome measure was whether the patient followed the physician’s advice in the hypothetical scenario. Thereby patient’s (pre‐recommendation) preferences served as a baseline. Results  In the decision scenario, about 48% of the patients with schizophrenia and 26% of the patients with multiple sclerosis followed the advice of their physician and thus chose the treatment option that went against their initial preferences. Patients who followed their physician’s advice were less satisfied with their decision than patients not following their physician’s advice (schizophrenia: t = 2.61, P = 0.01; multiple sclerosis: t = 2.67, P = 0.009). Discussion and conclusions  When sharing decisions with patients, physicians should be aware that their advice might influence patients’ decisions away from their preferred treatment option. They should encourage their patients to identify their own preferences and help to find the treatment option most consistent with them. PMID:21323824

Modeling Confidence Judgments, Response Times, and Multiple Choices in Decision Making: Recognition Memory and Motion Discrimination

PubMed Central

Ratcliff, Roger; Starns, Jeffrey J.

2014-01-01

Confidence in judgments is a fundamental aspect of decision making, and tasks that collect confidence judgments are an instantiation of multiple-choice decision making. We present a model for confidence judgments in recognition memory tasks that uses a multiple-choice diffusion decision process with separate accumulators of evidence for the different confidence choices. The accumulator that first reaches its decision boundary determines which choice is made. Five algorithms for accumulating evidence were compared, and one of them produced proportions of responses for each of the choices and full response time distributions for each choice that closely matched empirical data. With this algorithm, an increase in the evidence in one accumulator is accompanied by a decrease in the others so that the total amount of evidence in the system is constant. Application of the model to the data from an earlier experiment (Ratcliff, McKoon, & Tindall, 1994) uncovered a relationship between the shapes of z-transformed receiver operating characteristics and the behavior of response time distributions. Both are explained in the model by the behavior of the decision boundaries. For generality, we also applied the decision model to a 3-choice motion discrimination task and found it accounted for data better than a competing class of models. The confidence model presents a coherent account of confidence judgments and response time that cannot be explained with currently popular signal detection theory analyses or dual-process models of recognition. PMID:23915088

How Variables Uncorrelated with the Dependent Variable Can Actually Make Excellent Predictors: The Important Suppressor Variable Case.

ERIC Educational Resources Information Center

Woolley, Kristin K.

Many researchers are unfamiliar with suppressor variables and how they operate in multiple regression analyses. This paper describes the role suppressor variables play in a multiple regression model and provides practical examples that explain how they can change research results. A variable that when added as another predictor increases the total…

Embedding Human Expert Cognition Into Autonomous UAS Trajectory Planning.

PubMed

Narayan, Pritesh; Meyer, Patrick; Campbell, Duncan

2013-04-01

This paper presents a new approach for the inclusion of human expert cognition into autonomous trajectory planning for unmanned aerial systems (UASs) operating in low-altitude environments. During typical UAS operations, multiple objectives may exist; therefore, the use of multicriteria decision aid techniques can potentially allow for convergence to trajectory solutions which better reflect overall mission requirements. In that context, additive multiattribute value theory has been applied to optimize trajectories with respect to multiple objectives. A graphical user interface was developed to allow for knowledge capture from a human decision maker (HDM) through simulated decision scenarios. The expert decision data gathered are converted into value functions and corresponding criteria weightings using utility additive theory. The inclusion of preferences elicited from HDM data within an automated decision system allows for the generation of trajectories which more closely represent the candidate HDM decision preferences. This approach has been demonstrated in this paper through simulation using a fixed-wing UAS operating in low-altitude environments.

A new approach to hazardous materials transportation risk analysis: decision modeling to identify critical variables.

PubMed

Clark, Renee M; Besterfield-Sacre, Mary E

2009-03-01

We take a novel approach to analyzing hazardous materials transportation risk in this research. Previous studies analyzed this risk from an operations research (OR) or quantitative risk assessment (QRA) perspective by minimizing or calculating risk along a transport route. Further, even though the majority of incidents occur when containers are unloaded, the research has not focused on transportation-related activities, including container loading and unloading. In this work, we developed a decision model of a hazardous materials release during unloading using actual data and an exploratory data modeling approach. Previous studies have had a theoretical perspective in terms of identifying and advancing the key variables related to this risk, and there has not been a focus on probability and statistics-based approaches for doing this. Our decision model empirically identifies the critical variables using an exploratory methodology for a large, highly categorical database involving latent class analysis (LCA), loglinear modeling, and Bayesian networking. Our model identified the most influential variables and countermeasures for two consequences of a hazmat incident, dollar loss and release quantity, and is one of the first models to do this. The most influential variables were found to be related to the failure of the container. In addition to analyzing hazmat risk, our methodology can be used to develop data-driven models for strategic decision making in other domains involving risk.

Evaluating the decision accuracy and speed of clinical data visualizations.

PubMed

Pieczkiewicz, David S; Finkelstein, Stanley M

2010-01-01

Clinicians face an increasing volume of biomedical data. Assessing the efficacy of systems that enable accurate and timely clinical decision making merits corresponding attention. This paper discusses the multiple-reader multiple-case (MRMC) experimental design and linear mixed models as means of assessing and comparing decision accuracy and latency (time) for decision tasks in which clinician readers must interpret visual displays of data. These tools can assess and compare decision accuracy and latency (time). These experimental and statistical techniques, used extensively in radiology imaging studies, offer a number of practical and analytic advantages over more traditional quantitative methods such as percent-correct measurements and ANOVAs, and are recommended for their statistical efficiency and generalizability. An example analysis using readily available, free, and commercial statistical software is provided as an appendix. While these techniques are not appropriate for all evaluation questions, they can provide a valuable addition to the evaluative toolkit of medical informatics research.

A model for making project funding decisions at the National Cancer Institute.

PubMed

Hall, N G; Hershey, J C; Kessler, L G; Stotts, R C

1992-01-01

This paper describes the development of a model for making project funding decisions at The National Cancer Institute (NCI). The American Stop Smoking Intervention Study (ASSIST) is a multiple-year, multiple-site demonstration project, aimed at reducing smoking prevalence. The initial request for ASSIST proposals was answered by about twice as many states as could be funded. Scientific peer review of the proposals was the primary criterion used for funding decisions. However, a modified Delphi process made explicit several criteria of secondary importance. A structured questionnaire identified the relative importance of these secondary criteria, some of which we incorporated into a composite preference function. We modeled the proposal funding decision as a zero-one program, and adjusted the preference function and available budget parametrically to generate many suitable outcomes. The actual funding decision, identified by our model, offers significant advantages over manually generated solutions found by experts at NCI.

Fuzzy decision-making framework for treatment selection based on the combined QUALIFLEX-TODIM method

NASA Astrophysics Data System (ADS)

Ji, Pu; Zhang, Hong-yu; Wang, Jian-qiang

2017-10-01

Treatment selection is a multi-criteria decision-making problem of significant concern in the medical field. In this study, a fuzzy decision-making framework is established for treatment selection. The framework mitigates information loss by introducing single-valued trapezoidal neutrosophic numbers to denote evaluation information. Treatment selection has multiple criteria that remarkably exceed the alternatives. In consideration of this characteristic, the framework utilises the idea of the qualitative flexible multiple criteria method. Furthermore, it considers the risk-averse behaviour of a decision maker by employing a concordance index based on TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method. A sensitivity analysis is performed to illustrate the robustness of the framework. Finally, a comparative analysis is conducted to compare the framework with several extant methods. Results indicate the advantages of the framework and its better performance compared with the extant methods.

A Benefit-Risk Analysis Approach to Capture Regulatory Decision-Making: Multiple Myeloma.

PubMed

Raju, G K; Gurumurthi, Karthik; Domike, Reuben; Kazandjian, Dickran; Landgren, Ola; Blumenthal, Gideon M; Farrell, Ann; Pazdur, Richard; Woodcock, Janet

2018-01-01

Drug regulators around the world make decisions about drug approvability based on qualitative benefit-risk analysis. In this work, a quantitative benefit-risk analysis approach captures regulatory decision-making about new drugs to treat multiple myeloma (MM). MM assessments have been based on endpoints such as time to progression (TTP), progression-free survival (PFS), and objective response rate (ORR) which are different than benefit-risk analysis based on overall survival (OS). Twenty-three FDA decisions on MM drugs submitted to FDA between 2003 and 2016 were identified and analyzed. The benefits and risks were quantified relative to comparators (typically the control arm of the clinical trial) to estimate whether the median benefit-risk was positive or negative. A sensitivity analysis was demonstrated using ixazomib to explore the magnitude of uncertainty. FDA approval decision outcomes were consistent and logical using this benefit-risk framework. © 2017 American Society for Clinical Pharmacology and Therapeutics.

Decision-analytic modeling studies: An overview for clinicians using multiple myeloma as an example.

PubMed

Rochau, U; Jahn, B; Qerimi, V; Burger, E A; Kurzthaler, C; Kluibenschaedl, M; Willenbacher, E; Gastl, G; Willenbacher, W; Siebert, U

2015-05-01

The purpose of this study was to provide a clinician-friendly overview of decision-analytic models evaluating different treatment strategies for multiple myeloma (MM). We performed a systematic literature search to identify studies evaluating MM treatment strategies using mathematical decision-analytic models. We included studies that were published as full-text articles in English, and assessed relevant clinical endpoints, and summarized methodological characteristics (e.g., modeling approaches, simulation techniques, health outcomes, perspectives). Eleven decision-analytic modeling studies met our inclusion criteria. Five different modeling approaches were adopted: decision-tree modeling, Markov state-transition modeling, discrete event simulation, partitioned-survival analysis and area-under-the-curve modeling. Health outcomes included survival, number-needed-to-treat, life expectancy, and quality-adjusted life years. Evaluated treatment strategies included novel agent-based combination therapies, stem cell transplantation and supportive measures. Overall, our review provides a comprehensive summary of modeling studies assessing treatment of MM and highlights decision-analytic modeling as an important tool for health policy decision making. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A trainable decisions-in decision-out (DEI-DEO) fusion system

NASA Astrophysics Data System (ADS)

Dasarathy, Belur V.

1998-03-01

Most of the decision fusion systems proposed hitherto in the literature for multiple data source (sensor) environments operate on the basis of pre-defined fusion logic, be they crisp (deterministic), probabilistic, or fuzzy in nature, with no specific learning phase. The fusion systems that are trainable, i.e., ones that have a learning phase, mostly operate in the features-in-decision-out mode, which essentially reduces the fusion process functionally to a pattern classification task in the joint feature space. In this study, a trainable decisions-in-decision-out fusion system is described which estimates a fuzzy membership distribution spread across the different decision choices based on the performance of the different decision processors (sensors) corresponding to each training sample (object) which is associated with a specific ground truth (true decision). Based on a multi-decision space histogram analysis of the performance of the different processors over the entire training data set, a look-up table associating each cell of the histogram with a specific true decision is generated which forms the basis for the operational phase. In the operational phase, for each set of decision inputs, a pointer to the look-up table learnt previously is generated from which a fused decision is derived. This methodology, although primarily designed for fusing crisp decisions from the multiple decision sources, can be adapted for fusion of fuzzy decisions as well if such are the inputs from these sources. Examples, which illustrate the benefits and limitations of the crisp and fuzzy versions of the trainable fusion systems, are also included.

Expert-novice differences in cognitive and execution skills during tennis competition.

PubMed

Del Villar, Fernando; García González, Luis; Iglesias, Damián; Perla Moreno, M; Cervelló, Eduardo M

2007-04-01

This study deals with decision and execution behavior of tennis players during competition. The study is based on the expert-novice paradigm and aims to identify differences between both groups in the decision-making and execution variables in serve and shot actions in tennis. Six expert players (elite Spanish tennis players) and six novice players (grade school tennis players) took part in this study. To carry out this study, the observation protocol defined by McPherson and Thomas in 1989, in which control, decision-making and execution variables were included, was used, where it was applied to the performance of the tennis player in a real match situation. In the analysis, significant differences between experts and novices in decision-making and execution variables are found wherein it can be observed that experts display a greater ability to make the appropriate decisions, selecting the most tactical responses to put pressure on the opponent. Expert tennis players were also able to carry out forceful executions to their opponent with greater efficiency, making the opponent's response to a large extent more difficult. These findings are in accordance with those of McPherson and colleagues.

Semantic size does not matter: "bigger" words are not recognized faster.

PubMed

Kang, Sean H K; Yap, Melvin J; Tse, Chi-Shing; Kurby, Christopher A

2011-06-01

Sereno, O'Donnell, and Sereno (2009) reported that words are recognized faster in a lexical decision task when their referents are physically large than when they are small, suggesting that "semantic size" might be an important variable that should be considered in visual word recognition research and modelling. We sought to replicate their size effect, but failed to find a significant latency advantage in lexical decision for "big" words (cf. "small" words), even though we used the same word stimuli as Sereno et al. and had almost three times as many subjects. We also examined existing data from visual word recognition megastudies (e.g., English Lexicon Project) and found that semantic size is not a significant predictor of lexical decision performance after controlling for the standard lexical variables. In summary, the null results from our lab experiment--despite a much larger subject sample size than Sereno et al.--converged with our analysis of megastudy lexical decision performance, leading us to conclude that semantic size does not matter for word recognition. Discussion focuses on why semantic size (unlike some other semantic variables) is unlikely to play a role in lexical decision.

Motivational antecedents to contraceptive method change following a pregnancy scare: a couple analysis.

PubMed

Miller, W B; Pasta, D J

2001-01-01

In this study we develop and then test a couple model of contraceptive method choice decision-making following a pregnancy scare. The central constructs in our model are satisfaction with one's current method and confidence in the use of it. Downstream in the decision sequence, satisfaction and confidence predict desires and intentions to change methods. Upstream they are predicted by childbearing motivations, contraceptive attitudes, and the residual effects of the couples' previous method decisions. We collected data from 175 mostly unmarried and racially/ethnically diverse couples who were seeking pregnancy tests. We used LISREL and its latent variable capacity to estimate a structural equation model of the couple decision-making sequence leading to a change (or not) in contraceptive method. Results confirm most elements in our model and demonstrate a number of important cross-partner effects. Almost one-half of the sample had positive pregnancy tests and the base model fitted to this subsample indicates less accuracy in partner perception and greater influence of the female partner on method change decision-making. The introduction of some hypothesis-generating exogenous variables to our base couple model, together with some unexpected findings for the contraceptive attitude variables, suggest interesting questions that require further exploration.