Multicriteria decision model for retrofitting existing buildings

NASA Astrophysics Data System (ADS)

Bostenaru Dan, B.

2003-04-01

In this paper a model to decide which buildings from an urban area should be retrofitted is presented. The model has been cast into existing ones by choosing the decision rule, criterion weighting and decision support system types most suitable for the spatial problem of reducing earthquake risk in urban areas, considering existing spatial multiatributive and multiobjective decision methods and especially collaborative issues. Due to the participative character of the group decision problem "retrofitting existing buildings" the decision making model is based on interactivity. Buildings have been modeled following the criteria of spatial decision support systems. This includes identifying the corresponding spatial elements of buildings according to the information needs of actors from different sphaeres like architects, construction engineers and economists. The decision model aims to facilitate collaboration between this actors. The way of setting priorities interactivelly will be shown, by detailing the two phases: judgemental and computational, in this case site analysis, collection and evaluation of the unmodified data and converting survey data to information with computational methods using additional expert support. Buildings have been divided into spatial elements which are characteristic for the survey, present typical damages in case of an earthquake and are decisive for a better seismic behaviour in case of retrofitting. The paper describes the architectural and engineering characteristics as well as the structural damage for constuctions of different building ages on the example of building types in Bucharest, Romania in compressible and interdependent charts, based on field observation, reports from the 1977 earthquake and detailed studies made by the author together with a local engineer for the EERI Web Housing Encyclopedia. On this base criteria for setting priorities flow into the expert information contained in the system.

The Role of Integrated Modelling and Assessment for Decision-Making: Lessons from Water Allocation Issues in Australia

NASA Astrophysics Data System (ADS)

Jakeman, A. J.; Guillaume, J. H. A.; El Sawah, S.; Hamilton, S.

2014-12-01

Integrated modelling and assessment (IMA) is best regarded as a process that can support environmental decision-making when issues are strongly contested and uncertainties pervasive. To be most useful, the process must be multi-dimensional and phased. Principally, it must be tailored to the problem context to encompass diverse issues of concern, management settings and stakeholders. This in turn requires the integration of multiple processes and components of natural and human systems and their corresponding spatial and temporal scales. Modellers therefore need to be able to integrate multiple disciplines, methods, models, tools and data, and many sources and types of uncertainty. These dimensions are incorporated into iteration between the various phases of the IMA process, including scoping, problem framing and formulation, assessing options and communicating findings. Two case studies in Australia are employed to share the lessons of how integration can be achieved in these IMA phases using a mix of stakeholder participation processes and modelling tools. One case study aims to improve the relevance of modelling by incorporating stakeholder's views of irrigated viticulture and water management decision making. It used a novel methodology with the acronym ICTAM, consisting of Interviews to elicit mental models, Cognitive maps to represent and analyse individual and group mental models, Time-sequence diagrams to chronologically structure the decision making process, an All-encompassing conceptual model, and computational Models of stakeholder decision making. The second case uses a hydro-economic river network model to examine basin-wide impacts of water allocation cuts and adoption of farm innovations. The knowledge exchange approach used in each case was designed to integrate data and knowledge bearing in mind the contextual dimensions of the problem at hand, and the specific contributions that environmental modelling was thought to be able to make.

A methodology for eliciting, representing, and analysing stakeholder knowledge for decision making on complex socio-ecological systems: from cognitive maps to agent-based models.

PubMed

Elsawah, Sondoss; Guillaume, Joseph H A; Filatova, Tatiana; Rook, Josefine; Jakeman, Anthony J

2015-03-15

This paper aims to contribute to developing better ways for incorporating essential human elements in decision making processes for modelling of complex socio-ecological systems. It presents a step-wise methodology for integrating perceptions of stakeholders (qualitative) into formal simulation models (quantitative) with the ultimate goal of improving understanding and communication about decision making in complex socio-ecological systems. The methodology integrates cognitive mapping and agent based modelling. It cascades through a sequence of qualitative/soft and numerical methods comprising: (1) Interviews to elicit mental models; (2) Cognitive maps to represent and analyse individual and group mental models; (3) Time-sequence diagrams to chronologically structure the decision making process; (4) All-encompassing conceptual model of decision making, and (5) computational (in this case agent-based) Model. We apply the proposed methodology (labelled ICTAM) in a case study of viticulture irrigation in South Australia. Finally, we use strengths-weakness-opportunities-threats (SWOT) analysis to reflect on the methodology. Results show that the methodology leverages the use of cognitive mapping to capture the richness of decision making and mental models, and provides a combination of divergent and convergent analysis methods leading to the construction of an Agent Based Model. Copyright © 2014 Elsevier Ltd. All rights reserved.

A public health decision support system model using reasoning methods.

PubMed

Mera, Maritza; González, Carolina; Blobel, Bernd

2015-01-01

Public health programs must be based on the real health needs of the population. However, the design of efficient and effective public health programs is subject to availability of information that can allow users to identify, at the right time, the health issues that require special attention. The objective of this paper is to propose a case-based reasoning model for the support of decision-making in public health. The model integrates a decision-making process and case-based reasoning, reusing past experiences for promptly identifying new population health priorities. A prototype implementation of the model was performed, deploying the case-based reasoning framework jColibri. The proposed model contributes to solve problems found today when designing public health programs in Colombia. Current programs are developed under uncertain environments, as the underlying analyses are carried out on the basis of outdated and unreliable data.

A multiple biomarker risk score for guiding clinical decisions using a decision curve approach.

PubMed

Hughes, Maria F; Saarela, Olli; Blankenberg, Stefan; Zeller, Tanja; Havulinna, Aki S; Kuulasmaa, Kari; Yarnell, John; Schnabel, Renate B; Tiret, Laurence; Salomaa, Veikko; Evans, Alun; Kee, Frank

2012-08-01

We assessed whether a cardiovascular risk model based on classic risk factors (e.g. cholesterol, blood pressure) could refine disease prediction if it included novel biomarkers (C-reactive protein, N-terminal pro-B-type natriuretic peptide, troponin I) using a decision curve approach which can incorporate clinical consequences. We evaluated whether a model including biomarkers and classic risk factors could improve prediction of 10 year risk of cardiovascular disease (CVD; chronic heart disease and ischaemic stroke) against a classic risk factor model using a decision curve approach in two prospective MORGAM cohorts. This included 7739 men and women with 457 CVD cases from the FINRISK97 cohort; and 2524 men with 259 CVD cases from PRIME Belfast. The biomarker model improved disease prediction in FINRISK across the high-risk group (20-40%) but not in the intermediate risk group, at the 23% risk threshold net benefit was 0.0033 (95% CI 0.0013-0.0052). However, in PRIME Belfast the net benefit of decisions guided by the decision curve was improved across intermediate risk thresholds (10-20%). At p(t) = 10% in PRIME, the net benefit was 0.0059 (95% CI 0.0007-0.0112) with a net increase in 6 true positive cases per 1000 people screened and net decrease of 53 false positive cases per 1000 potentially leading to 5% fewer treatments in patients not destined for an event. The biomarker model improves 10-year CVD prediction at intermediate and high-risk thresholds and in particular, could be clinically useful at advising middle-aged European males of their CVD risk.

Some Results of Weak Anticipative Concept Applied in Simulation Based Decision Support in Enterprise

NASA Astrophysics Data System (ADS)

Kljajić, Miroljub; Kofjač, Davorin; Kljajić Borštnar, Mirjana; Škraba, Andrej

2010-11-01

The simulation models are used as for decision support and learning in enterprises and in schools. Tree cases of successful applications demonstrate usefulness of weak anticipative information. Job shop scheduling production with makespan criterion presents a real case customized flexible furniture production optimization. The genetic algorithm for job shop scheduling optimization is presented. Simulation based inventory control for products with stochastic lead time and demand describes inventory optimization for products with stochastic lead time and demand. Dynamic programming and fuzzy control algorithms reduce the total cost without producing stock-outs in most cases. Values of decision making information based on simulation were discussed too. All two cases will be discussed from optimization, modeling and learning point of view.

Scalable methodology for large scale building energy improvement: Relevance of calibration in model-based retrofit analysis

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

Heo, Yeonsook; Augenbroe, Godfried; Graziano, Diane

2015-05-01

The increasing interest in retrofitting of existing buildings is motivated by the need to make a major contribution to enhancing building energy efficiency and reducing energy consumption and CO2 emission by the built environment. This paper examines the relevance of calibration in model-based analysis to support decision-making for energy and carbon efficiency retrofits of individual buildings and portfolios of buildings. The authors formulate a set of real retrofit decision-making situations and evaluate the role of calibration by using a case study that compares predictions and decisions from an uncalibrated model with those of a calibrated model. The case study illustratesmore » both the mechanics and outcomes of a practical alternative to the expert- and time-intense application of dynamic energy simulation models for large-scale retrofit decision-making under uncertainty.« less

Applying a contingency model of strategic decision making to the implementation of smoking bans: a case study.

PubMed

Willemsen, M C; Meijer, A; Jannink, M

1999-08-01

A model of strategic decision making was applied to study the implementation of worksite smoking policy. This model assumes there is no best way of implementing smoking policies, but that 'the best way' depends on how decision making fits specific content and context factors. A case study at Wehkamp, a mail-order company, is presented to illustrate the usefulness of this model to understand how organizations implement smoking policies. Interview data were collected from representatives of Wehkamp, and pre- and post-ban survey data were collected from employees. After having failed to solve the smoking problem in a more democratic way, Wehkamp's top management choose a highly confrontational and decentralized decision-making approach to implement a complete smoking ban. This resulted in an effective smoking ban, but was to some extent at the cost of employees' satisfaction with the policy and with how the policy was implemented. The choice of implementation approach was contingent upon specific content and context factors, such as managers' perception of the problem, leadership style and legislation. More case studies from different types of companies are needed to better understand how organizational factors affect decision making about smoking bans and other health promotion innovations.

Integrating info-gap decision theory with robust population management: a case study using the Mountain Plover.

PubMed

van der Burg, Max Post; Tyre, Andrew J

2011-01-01

Wildlife managers often make decisions under considerable uncertainty. In the most extreme case, a complete lack of data leads to uncertainty that is unquantifiable. Information-gap decision theory deals with assessing management decisions under extreme uncertainty, but it is not widely used in wildlife management. So too, robust population management methods were developed to deal with uncertainties in multiple-model parameters. However, the two methods have not, as yet, been used in tandem to assess population management decisions. We provide a novel combination of the robust population management approach for matrix models with the information-gap decision theory framework for making conservation decisions under extreme uncertainty. We applied our model to the problem of nest survival management in an endangered bird species, the Mountain Plover (Charadrius montanus). Our results showed that matrix sensitivities suggest that nest management is unlikely to have a strong effect on population growth rate, confirming previous analyses. However, given the amount of uncertainty about adult and juvenile survival, our analysis suggested that maximizing nest marking effort was a more robust decision to maintain a stable population. Focusing on the twin concepts of opportunity and robustness in an information-gap model provides a useful method of assessing conservation decisions under extreme uncertainty.

CorRECTreatment: A Web-based Decision Support Tool for Rectal Cancer Treatment that Uses the Analytic Hierarchy Process and Decision Tree

PubMed Central

Karakülah, G.; Dicle, O.; Sökmen, S.; Çelikoğlu, C.C.

2015-01-01

Summary Background The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians’ decision making. Objective The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer. Methods The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set. Results In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step. Conclusions The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options. PMID:25848413

CorRECTreatment: a web-based decision support tool for rectal cancer treatment that uses the analytic hierarchy process and decision tree.

PubMed

Suner, A; Karakülah, G; Dicle, O; Sökmen, S; Çelikoğlu, C C

2015-01-01

The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians' decision making. The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer. The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set. In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step. The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options.

Decision support systems and the healthcare strategic planning process: a case study.

PubMed

Lundquist, D L; Norris, R M

1991-01-01

The repertoire of applications that comprises health-care decision support systems (DSS) includes analyses of clinical, financial, and operational activities. As a whole, these applications facilitate developing comprehensive and interrelated business and medical models that support the complex decisions required to successfully manage today's health-care organizations. Kennestone Regional Health Care System's use of DSS to facilitate strategic planning has precipitated marked changes in the organization's method of determining capital allocations. This case study discusses Kennestone's use of DSS in the strategic planning process, including profiles of key DSS modeling components.

It’s Personal: Biology Instructors Prioritize Personal Evidence over Empirical Evidence in Teaching Decisions

PubMed Central

Andrews, Tessa C.; Lemons, Paula P.

2015-01-01

Despite many calls for undergraduate biology instructors to incorporate active learning into lecture courses, few studies have focused on what it takes for instructors to make this change. We sought to investigate the process of adopting and sustaining active-learning instruction. As a framework for our research, we used the innovation-decision model, a generalized model of how individuals adopt innovations. We interviewed 17 biology instructors who were attempting to implement case study teaching and conducted qualitative text analysis on interview data. The overarching theme that emerged from our analysis was that instructors prioritized personal experience—rather than empirical evidence—in decisions regarding case study teaching. We identified personal experiences that promote case study teaching, such as anecdotal observations of student outcomes, and those that hinder case study teaching, such as insufficient teaching skills. By analyzing the differences between experienced and new case study instructors, we discovered that new case study instructors need support to deal with unsupportive colleagues and to develop the skill set needed for an active-learning classroom. We generated hypotheses that are grounded in our data about effectively supporting instructors in adopting and sustaining active-learning strategies. We also synthesized our findings with existing literature to tailor the innovation-decision model. PMID:25713092

Community College Presidents' Decision-Making Processes during a Potential Crisis

ERIC Educational Resources Information Center

Berry, Judith Kaye

2013-01-01

This case study addressed how community college presidents make decisions under conditions that can escalate to full-scale crises. The purpose of this study was to gather data to support the development of alternative models or refinement of existing models for crisis decision making on community college campuses, using an abbreviated…

How to use multi-criteria decision analysis methods for reimbursement decision-making in healthcare: a step-by-step guide.

PubMed

Diaby, Vakaramoko; Goeree, Ron

2014-02-01

In recent years, the quest for more comprehensiveness, structure and transparency in reimbursement decision-making in healthcare has prompted the research into alternative decision-making frameworks. In this environment, multi-criteria decision analysis (MCDA) is arising as a valuable tool to support healthcare decision-making. In this paper, we present the main MCDA decision support methods (elementary methods, value-based measurement models, goal programming models and outranking models) using a case study approach. For each family of methods, an example of how an MCDA model would operate in a real decision-making context is presented from a critical perspective, highlighting the parameters setting, the selection of the appropriate evaluation model as well as the role of sensitivity and robustness analyses. This study aims to provide a step-by-step guide on how to use MCDA methods for reimbursement decision-making in healthcare.

Quality Assurance Decisions with Air Models: A Case Study of ImputatIon of Missing Input Data Using EPA's Multi-Layer Model

EPA Science Inventory

Abstract Environmental models are frequently used within regulatory and policy frameworks to estimate environmental metrics that are difficult or impossible to physically measure. As important decision tools, the uncertainty associated with the model outputs should impact their ...

The Integrated Medical Model: A Decision Support Tool for In-flight Crew Health Care

NASA Technical Reports Server (NTRS)

Butler, Doug

2009-01-01

This viewgraph presentation reviews the development of an Integrated Medical Model (IMM) decision support tool for in-flight crew health care safety. Clinical methods, resources, and case scenarios are also addressed.

Trusted Advisors, Decision Models and Other Keys to Communicating Science to Decision Makers

NASA Astrophysics Data System (ADS)

Webb, E.

2006-12-01

Water resource management decisions often involve multiple parties engaged in contentious negotiations that try to navigate through complex combinations of legal, social, hydrologic, financial, and engineering considerations. The standard approach for resolving these issues is some form of multi-party negotiation, a formal court decision, or a combination of the two. In all these cases, the role of the decision maker(s) is to choose and implement the best option that fits the needs and wants of the community. However, each path to a decision carries the risk of technical and/or financial infeasibility as well as the possibility of unintended consequences. To help reduce this risk, decision makers often rely on some type of predictive analysis from which they can evaluate the projected consequences of their decisions. Typically, decision makers are supported in the analysis process by trusted advisors who engage in the analysis as well as the day to day tasks associated with multi-party negotiations. In the case of water resource management, the analysis is frequently a numerical model or set of models that can simulate various management decisions across multiple systems and output results that illustrate the impact on areas of concern. Thus, in order to communicate scientific knowledge to the decision makers, the quality of the communication between the analysts, the trusted advisor, and the decision maker must be clear and direct. To illustrate this concept, a multi-attribute decision analysis matrix will be used to outline the value of computer model-based collaborative negotiation approaches to guide water resources decision making and communication with decision makers. In addition, the critical role of the trusted advisor and other secondary participants in the decision process will be discussed using examples from recent water negotiations.

Optimal policy for value-based decision-making.

PubMed

Tajima, Satohiro; Drugowitsch, Jan; Pouget, Alexandre

2016-08-18

For decades now, normative theories of perceptual decisions, and their implementation as drift diffusion models, have driven and significantly improved our understanding of human and animal behaviour and the underlying neural processes. While similar processes seem to govern value-based decisions, we still lack the theoretical understanding of why this ought to be the case. Here, we show that, similar to perceptual decisions, drift diffusion models implement the optimal strategy for value-based decisions. Such optimal decisions require the models' decision boundaries to collapse over time, and to depend on the a priori knowledge about reward contingencies. Diffusion models only implement the optimal strategy under specific task assumptions, and cease to be optimal once we start relaxing these assumptions, by, for example, using non-linear utility functions. Our findings thus provide the much-needed theory for value-based decisions, explain the apparent similarity to perceptual decisions, and predict conditions under which this similarity should break down.

Optimal policy for value-based decision-making

PubMed Central

Tajima, Satohiro; Drugowitsch, Jan; Pouget, Alexandre

2016-01-01

For decades now, normative theories of perceptual decisions, and their implementation as drift diffusion models, have driven and significantly improved our understanding of human and animal behaviour and the underlying neural processes. While similar processes seem to govern value-based decisions, we still lack the theoretical understanding of why this ought to be the case. Here, we show that, similar to perceptual decisions, drift diffusion models implement the optimal strategy for value-based decisions. Such optimal decisions require the models' decision boundaries to collapse over time, and to depend on the a priori knowledge about reward contingencies. Diffusion models only implement the optimal strategy under specific task assumptions, and cease to be optimal once we start relaxing these assumptions, by, for example, using non-linear utility functions. Our findings thus provide the much-needed theory for value-based decisions, explain the apparent similarity to perceptual decisions, and predict conditions under which this similarity should break down. PMID:27535638

Research on Bidding Decision-making of International Public-Private Partnership Projects

NASA Astrophysics Data System (ADS)

Hu, Zhen Yu; Zhang, Shui Bo; Liu, Xin Yan

2018-06-01

In order to select the optimal quasi-bidding project for an investment enterprise, a bidding decision-making model for international PPP projects was established in this paper. Firstly, the literature frequency statistics method was adopted to screen out the bidding decision-making indexes, and accordingly the bidding decision-making index system for international PPP projects was constructed. Then, the group decision-making characteristic root method, the entropy weight method, and the optimization model based on least square method were used to set the decision-making index weights. The optimal quasi-bidding project was thus determined by calculating the consistent effect measure of each decision-making index value and the comprehensive effect measure of each quasi-bidding project. Finally, the bidding decision-making model for international PPP projects was further illustrated by a hypothetical case. This model can effectively serve as a theoretical foundation and technical support for the bidding decision-making of international PPP projects.

Ethics and rationality in information-enriched decisions: A model for technical communication

NASA Astrophysics Data System (ADS)

Dressel, S. B.; Carlson, P.; Killingsworth, M. J.

1993-12-01

In a technological culture, information has a crucial impact upon decisions, but exactly how information plays into decisions is not always clear. Decisions that are effective, efficient, and ethical must be rational. That is, we must be able to determine and present good reasons for our actions. The topic in this paper is how information relates to good reasons and thereby affects the best decisions. A brief sketch of a model for decision-making, is presented which offers a synthesis of theoretical approaches to argument and to information analysis. Then the model is applied to a brief hypothetical case. The main purpose is to put the model before an interested audience in hopes of stimulating discussion and further research.

M&S Decision/Role-Behavior Decompositions

DTIC Science & Technology

2007-10-17

M &S Decision/Role-Behavior Decompositions Wargaming and Analysis Workshop Military Operations Research Society 17 October 2007 Paul Works, Methods...number. 1. REPORT DATE 17 OCT 2007 2. REPORT TYPE 3. DATES COVERED 00-00-2007 to 00-00-2007 4. TITLE AND SUBTITLE M &S Decision/Role-Behavior...transmission. • Combat models and simulations ( M &S) continue, in most cases, to model “effects-level” representations of SA, decisions, and behaviors. – M &S

Simple model for multiple-choice collective decision making

NASA Astrophysics Data System (ADS)

Lee, Ching Hua; Lucas, Andrew

2014-11-01

We describe a simple model of heterogeneous, interacting agents making decisions between n ≥2 discrete choices. For a special class of interactions, our model is the mean field description of random field Potts-like models and is effectively solved by finding the extrema of the average energy E per agent. In these cases, by studying the propagation of decision changes via avalanches, we argue that macroscopic dynamics is well captured by a gradient flow along E . We focus on the permutation symmetric case, where all n choices are (on average) the same, and spontaneous symmetry breaking (SSB) arises purely from cooperative social interactions. As examples, we show that bimodal heterogeneity naturally provides a mechanism for the spontaneous formation of hierarchies between decisions and that SSB is a preferred instability to discontinuous phase transitions between two symmetric points. Beyond the mean field limit, exponentially many stable equilibria emerge when we place this model on a graph of finite mean degree. We conclude with speculation on decision making with persistent collective oscillations. Throughout the paper, we emphasize analogies between methods of solution to our model and common intuition from diverse areas of physics, including statistical physics and electromagnetism.

Development and pilot testing of an online case-based approach to shared decision making skills training for clinicians.

PubMed

Volk, Robert J; Shokar, Navkiran K; Leal, Viola B; Bulik, Robert J; Linder, Suzanne K; Mullen, Patricia Dolan; Wexler, Richard M; Shokar, Gurjeet S

2014-11-01

Although research suggests that patients prefer a shared decision making (SDM) experience when making healthcare decisions, clinicians do not routinely implement SDM into their practice and training programs are needed. Using a novel case-based strategy, we developed and pilot tested an online educational program to promote shared decision making (SDM) by primary care clinicians. A three-phased approach was used: 1) development of a conceptual model of the SDM process; 2) development of an online teaching case utilizing the Design A Case (DAC) authoring template, a well-tested process used to create peer-reviewed web-based clinical cases across all levels of healthcare training; and 3) pilot testing of the case. Participants were clinician members affiliated with several primary care research networks across the United States who answered an invitation email. The case used prostate cancer screening as the clinical context and was delivered online. Post-intervention ratings of clinicians' general knowledge of SDM, knowledge of specific SDM steps, confidence in and intention to perform SDM steps were also collected online. Seventy-nine clinicians initially volunteered to participate in the study, of which 49 completed the case and provided evaluations. Forty-three clinicians (87.8%) reported the case met all the learning objectives, and 47 (95.9%) indicated the case was relevant for other equipoise decisions. Thirty-one clinicians (63.3%) accessed supplementary information via links provided in the case. After viewing the case, knowledge of SDM was high (over 90% correctly identified the steps in a SDM process). Determining a patient's preferred role in making the decision (62.5% very confident) and exploring a patient's values (65.3% very confident) about the decisions were areas where clinician confidence was lowest. More than 70% of the clinicians intended to perform SDM in the future. A comprehensive model of the SDM process was used to design a case-based approach to teaching SDM skills to primary care clinicians. The case was favorably rated in this pilot study. Clinician skills training for helping patients clarify their values and for assessing patients' desire for involvement in decision making remain significant challenges and should be a focus of future comparative studies.

Dynamic Educational e-Content Selection Using Multiple Criteria in Web-Based Personalized Learning Environments.

ERIC Educational Resources Information Center

Manouselis, Nikos; Sampson, Demetrios

This paper focuses on the way a multi-criteria decision making methodology is applied in the case of agent-based selection of offered learning objects. The problem of selection is modeled as a decision making one, with the decision variables being the learner model and the learning objects' educational description. In this way, selection of…

Psychological model for judicial decision making in emergency or temporary child placement.

PubMed

Ballou, M; Barry, J; Billingham, K; Boorstein, B W; Butler, C; Gershberg, R; Heim, J; Lirianio, D; McGovern, S; Nicastro, S; Romaniello, J; Vazquez-Nuttall, K; White, C

2001-10-01

In emergencies, family court judges must often make rapid decisions, without benefit of thorough information, that have significant impact on people's lives. Action-oriented research was used to develop a model that would bring psychosocial factors to the legal system for the purpose of enhancing the judicial decision-making process in emergency and temporary child placement cases.

Rationality Validation of a Layered Decision Model for Network Defense

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

Wei, Huaqiang; Alves-Foss, James; Zhang, Du

2007-08-31

We propose a cost-effective network defense strategy built on three key: three decision layers: security policies, defense strategies, and real-time defense tactics for countering immediate threats. A layered decision model (LDM) can be used to capture this decision process. The LDM helps decision-makers gain insight into the hierarchical relationships among inter-connected entities and decision types, and supports the selection of cost-effective defense mechanisms to safeguard computer networks. To be effective as a business tool, it is first necessary to validate the rationality of model before applying it to real-world business cases. This paper describes our efforts in validating the LDMmore » rationality through simulation.« less

A Layered Decision Model for Cost-Effective System Security

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

Wei, Huaqiang; Alves-Foss, James; Soule, Terry

System security involves decisions in at least three areas: identification of well-defined security policies, selection of cost-effective defence strategies, and implementation of real-time defence tactics. Although choices made in each of these areas affect the others, existing decision models typically handle these three decision areas in isolation. There is no comprehensive tool that can integrate them to provide a single efficient model for safeguarding a network. In addition, there is no clear way to determine which particular combinations of defence decisions result in cost-effective solutions. To address these problems, this paper introduces a Layered Decision Model (LDM) for use inmore » deciding how to address defence decisions based on their cost-effectiveness. To validate the LDM and illustrate how it is used, we used simulation to test model rationality and applied the LDM to the design of system security for an e-commercial business case.« less

Enrollment Planning Using Computer Decision Model: A Case Study at Grambling State University.

ERIC Educational Resources Information Center

Ghosh, Kalyan; Lundy, Harold W.

Achieving enrollment goals continues to be a major administrative concern in higher education. Enrollment management can be assisted through the use of computerized planning and forecast models. Although commercially available Markov transition type curve fitting models have been developed and used, a microcomputer-based decision planning model…

Decision Support for Renewal of Wastewater Collection and Water Distribution Systems

EPA Science Inventory

The objective of this study was to identify the current decision support methodologies, models and approaches being used for determining how to rehabilitate or replace underground utilities; identify the critical gaps of these current models through comparison with case history d...

[Diagnostic rationalism. Views of general practitioners on fibromyalgia].

PubMed

Daehli, B

1993-09-20

Clinical practice is characterized by having to make numerous important decisions, including the diagnosis. In this study, general practitioners were asked to agree or to disagree with statements of fibromyalgia. The main purpose was to test the usefulness of two well-known models for decision-making when studying diagnosis in cases of uncertainty and scepticism. The results show that the models are inadequate to explain the decisions.

Decision curve analysis revisited: overall net benefit, relationships to ROC curve analysis, and application to case-control studies.

PubMed

Rousson, Valentin; Zumbrunn, Thomas

2011-06-22

Decision curve analysis has been introduced as a method to evaluate prediction models in terms of their clinical consequences if used for a binary classification of subjects into a group who should and into a group who should not be treated. The key concept for this type of evaluation is the "net benefit", a concept borrowed from utility theory. We recall the foundations of decision curve analysis and discuss some new aspects. First, we stress the formal distinction between the net benefit for the treated and for the untreated and define the concept of the "overall net benefit". Next, we revisit the important distinction between the concept of accuracy, as typically assessed using the Youden index and a receiver operating characteristic (ROC) analysis, and the concept of utility of a prediction model, as assessed using decision curve analysis. Finally, we provide an explicit implementation of decision curve analysis to be applied in the context of case-control studies. We show that the overall net benefit, which combines the net benefit for the treated and the untreated, is a natural alternative to the benefit achieved by a model, being invariant with respect to the coding of the outcome, and conveying a more comprehensive picture of the situation. Further, within the framework of decision curve analysis, we illustrate the important difference between the accuracy and the utility of a model, demonstrating how poor an accurate model may be in terms of its net benefit. Eventually, we expose that the application of decision curve analysis to case-control studies, where an accurate estimate of the true prevalence of a disease cannot be obtained from the data, is achieved with a few modifications to the original calculation procedure. We present several interrelated extensions to decision curve analysis that will both facilitate its interpretation and broaden its potential area of application.

Decision curve analysis revisited: overall net benefit, relationships to ROC curve analysis, and application to case-control studies

PubMed Central

2011-01-01

Background Decision curve analysis has been introduced as a method to evaluate prediction models in terms of their clinical consequences if used for a binary classification of subjects into a group who should and into a group who should not be treated. The key concept for this type of evaluation is the "net benefit", a concept borrowed from utility theory. Methods We recall the foundations of decision curve analysis and discuss some new aspects. First, we stress the formal distinction between the net benefit for the treated and for the untreated and define the concept of the "overall net benefit". Next, we revisit the important distinction between the concept of accuracy, as typically assessed using the Youden index and a receiver operating characteristic (ROC) analysis, and the concept of utility of a prediction model, as assessed using decision curve analysis. Finally, we provide an explicit implementation of decision curve analysis to be applied in the context of case-control studies. Results We show that the overall net benefit, which combines the net benefit for the treated and the untreated, is a natural alternative to the benefit achieved by a model, being invariant with respect to the coding of the outcome, and conveying a more comprehensive picture of the situation. Further, within the framework of decision curve analysis, we illustrate the important difference between the accuracy and the utility of a model, demonstrating how poor an accurate model may be in terms of its net benefit. Eventually, we expose that the application of decision curve analysis to case-control studies, where an accurate estimate of the true prevalence of a disease cannot be obtained from the data, is achieved with a few modifications to the original calculation procedure. Conclusions We present several interrelated extensions to decision curve analysis that will both facilitate its interpretation and broaden its potential area of application. PMID:21696604

Effectiveness of a Case-Based Computer Program on Students' Ethical Decision Making.

PubMed

Park, Eun-Jun; Park, Mihyun

2015-11-01

The aim of this study was to test the effectiveness of a case-based computer program, using an integrative ethical decision-making model, on the ethical decision-making competency of nursing students in South Korea. This study used a pre- and posttest comparison design. Students in the intervention group used a computer program for case analysis assignments, whereas students in the standard group used a traditional paper assignment for case analysis. The findings showed that using the case-based computer program as a complementary tool for the ethics courses offered at the university enhanced students' ethical preparedness and satisfaction with the course. On the basis of the findings, it is recommended that nurse educators use a case-based computer program as a complementary self-study tool in ethics courses to supplement student learning without an increase in course hours, particularly in terms of analyzing ethics cases with dilemma scenarios and exercising ethical decision making. Copyright 2015, SLACK Incorporated.

Controversies in water management: Frames and mental models

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

Kolkman, M.J.; Department of Civil Engineering and Management, Faculty of Engineering Technology; Veen, A. van der

Controversies in decision and policy-making processes can be analysed using frame reflection and mental model mapping techniques. The purpose of the method presented in this paper is to improve the quality of the information and interpretations available to decision makers, by surfacing and juxtaposing the different frames of decision makers, experts, and special interests groups. The research provides a new method to analyse frames. It defines a frame to consist of perspectives and a mental model, which are in close interaction (through second order learning processes). The mental model acts like a 'filter' through which the problem situation is observed.more » Five major perspective types guide the construction of meaning out of the information delivered by the mental model, and determine what actors see as their interests. The perspective types are related to an actor's institutional and personal position in the decision making process. The method was applied to a case, in order to test its viability. The case concerns the decision making process and environmental impact assessment procedure for the improvement of dike ring 53 in the Netherlands, which was initiated by the Dutch 'Flood Defences Act 1996'. In this specific case the perspectives and mental models of stakeholders were elicited to explain controversies. The case was analysed with regard to the conflicts emerging between stakeholders, on an individual level. The influence of institutional embedding of individuals on the use of information and the construction of meaning, and the limits of a participatory approach were analysed within the details of controversies that emerged during the case analysis. Complicating factor appeared to be the interaction between national dike safety norms (short term) and local water management problems (long term). Revealed controversies mainly concerned disputes between an organisational and a technical perspective. But also disputes on distribution of responsibilities between different institutes, on legal and political liability, and on funding issues, involving persons of both perspectives, were found. The case reveals a lack of possibilities to search for an integrated solution which involves all levels of authority, and a lack of possibilities to discuss the additional problems that were raised by the integrated approach in the initial phase of the case project. The complex and unstructured nature of the problem situation caused the traditional substantive approach to fail to deliver a good solution. Legal, socio-economic and institutional factors ultimately dominated the decision making process.« less

Rational Decisionmaking in Higher Education. An NCHEMS Executive Overview.

ERIC Educational Resources Information Center

Chaffee, Ellen Earle

Five models of organizational decision-making are described, and a case study of the rational model as seen in the budget process at Stanford University during the 1970s is presented. Several issues are addressed to help administrators who are interested in increasing the organization's rational decision-making. The five models are as follows: the…

A Case-Based Learning Model in Orthodontics.

ERIC Educational Resources Information Center

Engel, Francoise E.; Hendricson, William D.

1994-01-01

A case-based, student-centered instructional model designed to mimic orthodontic problem solving and decision making in dental general practice is described. Small groups of students analyze case data, then record and discuss their diagnoses and treatments. Students and instructors rated the seminars positively, and students reported improved…

Embodied Choice: How Action Influences Perceptual Decision Making

PubMed Central

Lepora, Nathan F.; Pezzulo, Giovanni

2015-01-01

Embodied Choice considers action performance as a proper part of the decision making process rather than merely as a means to report the decision. The central statement of embodied choice is the existence of bidirectional influences between action and decisions. This implies that for a decision expressed by an action, the action dynamics and its constraints (e.g. current trajectory and kinematics) influence the decision making process. Here we use a perceptual decision making task to compare three types of model: a serial decision-then-action model, a parallel decision-and-action model, and an embodied choice model where the action feeds back into the decision making. The embodied model incorporates two key mechanisms that together are lacking in the other models: action preparation and commitment. First, action preparation strategies alleviate delays in enacting a choice but also modify decision termination. Second, action dynamics change the prospects and create a commitment effect to the initially preferred choice. Our results show that these two mechanisms make embodied choice models better suited to combine decision and action appropriately to achieve suitably fast and accurate responses, as usually required in ecologically valid situations. Moreover, embodied choice models with these mechanisms give a better account of trajectory tracking experiments during decision making. In conclusion, the embodied choice framework offers a combined theory of decision and action that gives a clear case that embodied phenomena such as the dynamics of actions can have a causal influence on central cognition. PMID:25849349

Embodied choice: how action influences perceptual decision making.

PubMed

Lepora, Nathan F; Pezzulo, Giovanni

2015-04-01

Embodied Choice considers action performance as a proper part of the decision making process rather than merely as a means to report the decision. The central statement of embodied choice is the existence of bidirectional influences between action and decisions. This implies that for a decision expressed by an action, the action dynamics and its constraints (e.g. current trajectory and kinematics) influence the decision making process. Here we use a perceptual decision making task to compare three types of model: a serial decision-then-action model, a parallel decision-and-action model, and an embodied choice model where the action feeds back into the decision making. The embodied model incorporates two key mechanisms that together are lacking in the other models: action preparation and commitment. First, action preparation strategies alleviate delays in enacting a choice but also modify decision termination. Second, action dynamics change the prospects and create a commitment effect to the initially preferred choice. Our results show that these two mechanisms make embodied choice models better suited to combine decision and action appropriately to achieve suitably fast and accurate responses, as usually required in ecologically valid situations. Moreover, embodied choice models with these mechanisms give a better account of trajectory tracking experiments during decision making. In conclusion, the embodied choice framework offers a combined theory of decision and action that gives a clear case that embodied phenomena such as the dynamics of actions can have a causal influence on central cognition.

Research on the decision-making model of land-use spatial optimization

NASA Astrophysics Data System (ADS)

He, Jianhua; Yu, Yan; Liu, Yanfang; Liang, Fei; Cai, Yuqiu

2009-10-01

Using the optimization result of landscape pattern and land use structure optimization as constraints of CA simulation results, a decision-making model of land use spatial optimization is established coupled the landscape pattern model with cellular automata to realize the land use quantitative and spatial optimization simultaneously. And Huangpi district is taken as a case study to verify the rationality of the model.

Group decisions in biodiversity conservation: implications from game theory.

PubMed

Frank, David M; Sarkar, Sahotra

2010-05-27

Decision analysis and game theory have proved useful tools in various biodiversity conservation planning and modeling contexts. This paper shows how game theory may be used to inform group decisions in biodiversity conservation scenarios by modeling conflicts between stakeholders to identify Pareto-inefficient Nash equilibria. These are cases in which each agent pursuing individual self-interest leads to a worse outcome for all, relative to other feasible outcomes. Three case studies from biodiversity conservation contexts showing this feature are modeled to demonstrate how game-theoretical representation can inform group decision-making. The mathematical theory of games is used to model three biodiversity conservation scenarios with Pareto-inefficient Nash equilibria: (i) a two-agent case involving wild dogs in South Africa; (ii) a three-agent raptor and grouse conservation scenario from the United Kingdom; and (iii) an n-agent fish and coral conservation scenario from the Philippines. In each case there is reason to believe that traditional mechanism-design solutions that appeal to material incentives may be inadequate, and the game-theoretical analysis recommends a resumption of further deliberation between agents and the initiation of trust--and confidence--building measures. Game theory can and should be used as a normative tool in biodiversity conservation contexts: identifying scenarios with Pareto-inefficient Nash equilibria enables constructive action in order to achieve (closer to) optimal conservation outcomes, whether by policy solutions based on mechanism design or otherwise. However, there is mounting evidence that formal mechanism-design solutions may backfire in certain cases. Such scenarios demand a return to group deliberation and the creation of reciprocal relationships of trust.

Model of the best-of-N nest-site selection process in honeybees.

PubMed

Reina, Andreagiovanni; Marshall, James A R; Trianni, Vito; Bose, Thomas

2017-05-01

The ability of a honeybee swarm to select the best nest site plays a fundamental role in determining the future colony's fitness. To date, the nest-site selection process has mostly been modeled and theoretically analyzed for the case of binary decisions. However, when the number of alternative nests is larger than two, the decision-process dynamics qualitatively change. In this work, we extend previous analyses of a value-sensitive decision-making mechanism to a decision process among N nests. First, we present the decision-making dynamics in the symmetric case of N equal-quality nests. Then, we generalize our findings to a best-of-N decision scenario with one superior nest and N-1 inferior nests, previously studied empirically in bees and ants. Whereas previous binary models highlighted the crucial role of inhibitory stop-signaling, the key parameter in our new analysis is the relative time invested by swarm members in individual discovery and in signaling behaviors. Our new analysis reveals conflicting pressures on this ratio in symmetric and best-of-N decisions, which could be solved through a time-dependent signaling strategy. Additionally, our analysis suggests how ecological factors determining the density of suitable nest sites may have led to selective pressures for an optimal stable signaling ratio.

Model of the best-of-N nest-site selection process in honeybees

NASA Astrophysics Data System (ADS)

Reina, Andreagiovanni; Marshall, James A. R.; Trianni, Vito; Bose, Thomas

2017-05-01

The ability of a honeybee swarm to select the best nest site plays a fundamental role in determining the future colony's fitness. To date, the nest-site selection process has mostly been modeled and theoretically analyzed for the case of binary decisions. However, when the number of alternative nests is larger than two, the decision-process dynamics qualitatively change. In this work, we extend previous analyses of a value-sensitive decision-making mechanism to a decision process among N nests. First, we present the decision-making dynamics in the symmetric case of N equal-quality nests. Then, we generalize our findings to a best-of-N decision scenario with one superior nest and N -1 inferior nests, previously studied empirically in bees and ants. Whereas previous binary models highlighted the crucial role of inhibitory stop-signaling, the key parameter in our new analysis is the relative time invested by swarm members in individual discovery and in signaling behaviors. Our new analysis reveals conflicting pressures on this ratio in symmetric and best-of-N decisions, which could be solved through a time-dependent signaling strategy. Additionally, our analysis suggests how ecological factors determining the density of suitable nest sites may have led to selective pressures for an optimal stable signaling ratio.

Assessing electronic health record systems in emergency departments: Using a decision analytic Bayesian model.

PubMed

Ben-Assuli, Ofir; Leshno, Moshe

2016-09-01

In the last decade, health providers have implemented information systems to improve accuracy in medical diagnosis and decision-making. This article evaluates the impact of an electronic health record on emergency department physicians' diagnosis and admission decisions. A decision analytic approach using a decision tree was constructed to model the admission decision process to assess the added value of medical information retrieved from the electronic health record. Using a Bayesian statistical model, this method was evaluated on two coronary artery disease scenarios. The results show that the cases of coronary artery disease were better diagnosed when the electronic health record was consulted and led to more informed admission decisions. Furthermore, the value of medical information required for a specific admission decision in emergency departments could be quantified. The findings support the notion that physicians and patient healthcare can benefit from implementing electronic health record systems in emergency departments. © The Author(s) 2015.

Decision analysis to address extreme weather : extreme weather effects on ridership and modeling the decision to invest in canopy coverage.

DOT National Transportation Integrated Search

2016-09-01

This project applies a decision analytic methodology that takes considerations of extreme weather events to quantify and assess canopy investment options. The project collected data for two cases studies in two different transit agencies: Chicago Tra...

Helping Students Make Decisions with the Help of Egan's Model.

ERIC Educational Resources Information Center

Stephens, Ginny Lee; Reynolds, JoLynne

1992-01-01

Discusses using Gerald Egan's model for creative decision making as a career counseling tool. Explains why to use this model and how it was adapted to meet career counseling issues. Describes its successful use in three case studies with a college sophomore in search of a major, a new graduate in search of a first job, and a homemaker. (Author/ABL)

An interval-parameter mixed integer multi-objective programming for environment-oriented evacuation management

NASA Astrophysics Data System (ADS)

Wu, C. Z.; Huang, G. H.; Yan, X. P.; Cai, Y. P.; Li, Y. P.

2010-05-01

Large crowds are increasingly common at political, social, economic, cultural and sports events in urban areas. This has led to attention on the management of evacuations under such situations. In this study, we optimise an approximation method for vehicle allocation and route planning in case of an evacuation. This method, based on an interval-parameter multi-objective optimisation model, has potential for use in a flexible decision support system for evacuation management. The modeling solutions are obtained by sequentially solving two sub-models corresponding to lower- and upper-bounds for the desired objective function value. The interval solutions are feasible and stable in the given decision space, and this may reduce the negative effects of uncertainty, thereby improving decision makers' estimates under different conditions. The resulting model can be used for a systematic analysis of the complex relationships among evacuation time, cost and environmental considerations. The results of a case study used to validate the proposed model show that the model does generate useful solutions for planning evacuation management and practices. Furthermore, these results are useful for evacuation planners, not only in making vehicle allocation decisions but also for providing insight into the tradeoffs among evacuation time, environmental considerations and economic objectives.

Effects of child interview tactics on prospective jurors' decisions.

PubMed

Johnson, Jonni L; Shelley, Alexandra E

2014-01-01

Although decisions in child sexual abuse (CSA) cases are influenced by many factors (e.g., child age, juror gender), case and trial characteristics (e.g., interview quality) can strongly influence legal outcomes. In the present study, 319 prospective jurors read about a CSA investigation in which the alleged victim was interviewed at a child advocacy center (CAC) or traditional police setting. The prospective jurors then provided legally relevant ratings (e.g., child credibility, interview quality, defendant guilt). Structural equation modeling techniques revealed that child credibility predicted greater confidence in guilt decisions and also mediated all associations with such decisions. Having fewer negative prior opinions and rating the interview as of better quality were associated with higher child credibility ratings. Mitigating factors (e.g., interview quality), as opposed to proxy indicators (e.g., participant gender), better predicted CSA case outcomes. Similar associations across groups (e.g., CAC interviews did not make child victims more or less credible) permit a tentative conclusion that CACs do not positively or negatively affect decisions made in hypothetical CSA cases. Ideas for future studies examining factors influencing decisions in CSA cases are discussed. Copyright © 2014 John Wiley & Sons, Ltd.

The Decision Module Working Paper

DTIC Science & Technology

1973-12-01

and goal change has received very little attention In the litera- ture on the analysis of choice situations. It has generally been the case that the...Decision Making: Approach and Prototype" (197:0, done In context of the Mesarovlc - Pestel World Model Projet’ The Issues dealing with «-he cho ce...Nelson, Winder, and Schuette (1973) on evolutionary economic growth models. The discussion of the two components of the decision module that follows

Neural network modeling for surgical decisions on traumatic brain injury patients.

PubMed

Li, Y C; Liu, L; Chiu, W T; Jian, W S

2000-01-01

Computerized medical decision support systems have been a major research topic in recent years. Intelligent computer programs were implemented to aid physicians and other medical professionals in making difficult medical decisions. This report compares three different mathematical models for building a traumatic brain injury (TBI) medical decision support system (MDSS). These models were developed based on a large TBI patient database. This MDSS accepts a set of patient data such as the types of skull fracture, Glasgow Coma Scale (GCS), episode of convulsion and return the chance that a neurosurgeon would recommend an open-skull surgery for this patient. The three mathematical models described in this report including a logistic regression model, a multi-layer perceptron (MLP) neural network and a radial-basis-function (RBF) neural network. From the 12,640 patients selected from the database. A randomly drawn 9480 cases were used as the training group to develop/train our models. The other 3160 cases were in the validation group which we used to evaluate the performance of these models. We used sensitivity, specificity, areas under receiver-operating characteristics (ROC) curve and calibration curves as the indicator of how accurate these models are in predicting a neurosurgeon's decision on open-skull surgery. The results showed that, assuming equal importance of sensitivity and specificity, the logistic regression model had a (sensitivity, specificity) of (73%, 68%), compared to (80%, 80%) from the RBF model and (88%, 80%) from the MLP model. The resultant areas under ROC curve for logistic regression, RBF and MLP neural networks are 0.761, 0.880 and 0.897, respectively (P < 0.05). Among these models, the logistic regression has noticeably poorer calibration. This study demonstrated the feasibility of applying neural networks as the mechanism for TBI decision support systems based on clinical databases. The results also suggest that neural networks may be a better solution for complex, non-linear medical decision support systems than conventional statistical techniques such as logistic regression.

Predicting species distributions for conservation decisions

PubMed Central

Guisan, Antoine; Tingley, Reid; Baumgartner, John B; Naujokaitis-Lewis, Ilona; Sutcliffe, Patricia R; Tulloch, Ayesha I T; Regan, Tracey J; Brotons, Lluis; McDonald-Madden, Eve; Mantyka-Pringle, Chrystal; Martin, Tara G; Rhodes, Jonathan R; Maggini, Ramona; Setterfield, Samantha A; Elith, Jane; Schwartz, Mark W; Wintle, Brendan A; Broennimann, Olivier; Austin, Mike; Ferrier, Simon; Kearney, Michael R; Possingham, Hugh P; Buckley, Yvonne M

2013-01-01

Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of ‘translators’ between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes. PMID:24134332

The need for consumer behavior analysis in health care coverage decisions.

PubMed

Thompson, A M; Rao, C P

1990-01-01

Demographic analysis has been the primary form of analysis connected with health care coverage decisions. This paper reviews past demographic research and shows the need to use behavioral analyses for health care coverage policy decisions. A behavioral model based research study is presented and a case is made for integrated study into why consumers make health care coverage decisions.

The Evolution of an Interprofessional Shared Decision-Making Research Program: Reflective Case Study of an Emerging Paradigm

PubMed Central

Menear, Matthew; Stacey, Dawn; Brière, Nathalie; Légaré, France

2016-01-01

Introduction: Healthcare research increasingly focuses on interprofessional collaboration and on shared decision making, but knowledge gaps remain about effective strategies for implementing interprofessional collaboration and shared decision-making together in clinical practice. We used Kuhn’s theory of scientific revolutions to reflect on how an integrated interprofessional shared decision-making approach was developed and implemented over time. Methods: In 2007, an interdisciplinary team initiated a new research program to promote the implementation of an interprofessional shared decision-making approach in clinical settings. For this reflective case study, two new team members analyzed the team’s four projects, six research publications, one unpublished and two published protocols and organized them into recognizable phases according to Kuhn’s theory. Results: The merging of two young disciplines led to challenges characteristic of emerging paradigms. Implementation of interprofessional shared-decision making was hindered by a lack of conceptual clarity, a dearth of theories and models, little methodological guidance, and insufficient evaluation instruments. The team developed a new model, identified new tools, and engaged knowledge users in a theory-based approach to implementation. However, several unresolved challenges remain. Discussion: This reflective case study sheds light on the evolution of interdisciplinary team science. It offers new approaches to implementing emerging knowledge in the clinical context. PMID:28435417

The Evolution of an Interprofessional Shared Decision-Making Research Program: Reflective Case Study of an Emerging Paradigm.

PubMed

Dogba, Maman Joyce; Menear, Matthew; Stacey, Dawn; Brière, Nathalie; Légaré, France

2016-07-19

Healthcare research increasingly focuses on interprofessional collaboration and on shared decision making, but knowledge gaps remain about effective strategies for implementing interprofessional collaboration and shared decision-making together in clinical practice. We used Kuhn's theory of scientific revolutions to reflect on how an integrated interprofessional shared decision-making approach was developed and implemented over time. In 2007, an interdisciplinary team initiated a new research program to promote the implementation of an interprofessional shared decision-making approach in clinical settings. For this reflective case study, two new team members analyzed the team's four projects, six research publications, one unpublished and two published protocols and organized them into recognizable phases according to Kuhn's theory. The merging of two young disciplines led to challenges characteristic of emerging paradigms. Implementation of interprofessional shared-decision making was hindered by a lack of conceptual clarity, a dearth of theories and models, little methodological guidance, and insufficient evaluation instruments. The team developed a new model, identified new tools, and engaged knowledge users in a theory-based approach to implementation. However, several unresolved challenges remain. This reflective case study sheds light on the evolution of interdisciplinary team science. It offers new approaches to implementing emerging knowledge in the clinical context.

Bayesian Decision Support

NASA Astrophysics Data System (ADS)

Berliner, M.

2017-12-01

Bayesian statistical decision theory offers a natural framework for decision-policy making in the presence of uncertainty. Key advantages of the approach include efficient incorporation of information and observations. However, in complicated settings it is very difficult, perhaps essentially impossible, to formalize the mathematical inputs needed in the approach. Nevertheless, using the approach as a template is useful for decision support; that is, organizing and communicating our analyses. Bayesian hierarchical modeling is valuable in quantifying and managing uncertainty such cases. I review some aspects of the idea emphasizing statistical model development and use in the context of sea-level rise.

A Conceptual Framework for Decision-making Support in Uncertainty- and Risk-based Diagnosis of Rare Clinical Cases by Specialist Physicians.

PubMed

Santos, Adriano A; Moura, J Antão B; de Araújo, Joseana Macêdo Fechine Régis

2015-01-01

Mitigating uncertainty and risks faced by specialist physicians in analysis of rare clinical cases is something desired by anyone who needs health services. The number of clinical cases never seen by these experts, with little documentation, may introduce errors in decision-making. Such errors negatively affect well-being of patients, increase procedure costs, rework, health insurance premiums, and impair the reputation of specialists and medical systems involved. In this context, IT and Clinical Decision Support Systems (CDSS) play a fundamental role, supporting decision-making process, making it more efficient and effective, reducing a number of avoidable medical errors and enhancing quality of treatment given to patients. An investigation has been initiated to look into characteristics and solution requirements of this problem, model it, propose a general solution in terms of a conceptual risk-based, automated framework to support rare-case medical diagnostics and validate it by means of case studies. A preliminary validation study of the proposed framework has been carried out by interviews conducted with experts who are practicing professionals, academics, and researchers in health care. This paper summarizes the investigation and its positive results. These results motivate continuation of research towards development of the conceptual framework and of a software tool that implements the proposed model.

Revisiting the generation and interpretation of climate models experiments for adaptation decision-making (Invited)

NASA Astrophysics Data System (ADS)

Ranger, N.; Millner, A.; Niehoerster, F.

2010-12-01

Traditionally, climate change risk assessments have taken a roughly four-stage linear ‘chain’ of moving from socioeconomic projections, to climate projections, to primary impacts and then finally onto economic and social impact assessment. Adaptation decisions are then made on the basis of these outputs. The escalation of uncertainty through this chain is well known; resulting in an ‘explosion’ of uncertainties in the final risk and adaptation assessment. The space of plausible future risk scenarios is growing ever wider with the application of new techniques which aim to explore uncertainty ever more deeply; such as those used in the recent ‘probabilistic’ UK Climate Projections 2009, and the stochastic integrated assessment models, for example PAGE2002. This explosion of uncertainty can make decision-making problematic, particularly given that the uncertainty information communicated can not be treated as strictly probabilistic and therefore, is not an easy fit with standard decision-making under uncertainty approaches. Additional problems can arise from the fact that the uncertainty estimated for different components of the ‘chain’ is rarely directly comparable or combinable. Here, we explore the challenges and limitations of using current projections for adaptation decision-making. We report the findings of a recent report completed for the UK Adaptation Sub-Committee on approaches to deal with these challenges and make robust adaptation decisions today. To illustrate these approaches, we take a number of illustrative case studies, including a case of adaptation to hurricane risk on the US Gulf Coast. This is a particularly interesting case as it involves urgent adaptation of long-lived infrastructure but requires interpreting highly uncertain climate change science and modelling; i.e. projections of Atlantic basin hurricane activity. An approach we outline is reversing the linear chain of assessments to put the economics and decision-making first. Such an approach forces one to focus on the information of greatest value for the specific decision. We suggest that such an approach will help to accommodate the uncertainties in the chain and facilitate robust decision-making. Initial findings of these case studies will be presented with the aim of raising open questions and promoting discussion of the methodology. Finally, we reflect on the implications for the design of climate model experiments.

How Do Cultural Producers Make Creative Decisions? Lessons from the Catwalk

ERIC Educational Resources Information Center

Godart, Frederic C. Mears, Ashley

2009-01-01

Faced with high uncertainty, how do producers in the cultural economy make creative decisions? We present a case study of the fashion modeling industry. Using participant observation, interviews and network analysis of the Spring/Summer 2007 Fashion Week collections, we explain how producers select models for fashion shows. While fashion producers…

The value of models in informing resource allocation in colorectal cancer screening – 1 the case of the Netherlands

PubMed Central

van Hees, Frank; Zauber, Ann G.; van Veldhuizen, Harriët; Heijnen, Marie-Louise A.; Penning, Corine; de Koning, Harry J.; van Ballegooijen, Marjolein; Lansdorp-Vogelaar, Iris

2015-01-01

In May 2011, the Dutch government decided to implement a national programme for colorectal cancer (CRC) screening using biennial faecal immunochemical test (FIT) screening between ages 55 and 75.[1] Decision modelling played an important role in informing this decision, as well as in the planning and implementation of the programme afterwards. In this overview, we illustrate the value of models in informing resource allocation in CRC screening, using the role that decision modelling has played in the Dutch CRC screening programme as an example. PMID:26063755

The Vroom and Yetton Normative Leadership Model Applied to Public School Case Examples.

ERIC Educational Resources Information Center

Sample, John

This paper seeks to familiarize school administrators with the Vroom and Yetton Normative Leadership model by presenting its essential components and providing original case studies for its application to school settings. The five decision-making methods of the Vroom and Yetton model, including two "autocratic," two…

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.

Modeling uncertainty in producing natural gas from tight sands

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

Chermak, J.M.; Dahl, C.A.; Patrick, R.H

1995-12-31

Since accurate geologic, petroleum engineering, and economic information are essential ingredients in making profitable production decisions for natural gas, we combine these ingredients in a dynamic framework to model natural gas reservoir production decisions. We begin with the certainty case before proceeding to consider how uncertainty might be incorporated in the decision process. Our production model uses dynamic optimal control to combine economic information with geological constraints to develop optimal production decisions. To incorporate uncertainty into the model, we develop probability distributions on geologic properties for the population of tight gas sand wells and perform a Monte Carlo study tomore » select a sample of wells. Geological production factors, completion factors, and financial information are combined into the hybrid economic-petroleum reservoir engineering model to determine the optimal production profile, initial gas stock, and net present value (NPV) for an individual well. To model the probability of the production abandonment decision, the NPV data is converted to a binary dependent variable. A logit model is used to model this decision as a function of the above geological and economic data to give probability relationships. Additional ways to incorporate uncertainty into the decision process include confidence intervals and utility theory.« less

RELATING ACCUMULATOR MODEL PARAMETERS AND NEURAL DYNAMICS

PubMed Central

Purcell, Braden A.; Palmeri, Thomas J.

2016-01-01

Accumulator models explain decision-making as an accumulation of evidence to a response threshold. Specific model parameters are associated with specific model mechanisms, such as the time when accumulation begins, the average rate of evidence accumulation, and the threshold. These mechanisms determine both the within-trial dynamics of evidence accumulation and the predicted behavior. Cognitive modelers usually infer what mechanisms vary during decision-making by seeing what parameters vary when a model is fitted to observed behavior. The recent identification of neural activity with evidence accumulation suggests that it may be possible to directly infer what mechanisms vary from an analysis of how neural dynamics vary. However, evidence accumulation is often noisy, and noise complicates the relationship between accumulator dynamics and the underlying mechanisms leading to those dynamics. To understand what kinds of inferences can be made about decision-making mechanisms based on measures of neural dynamics, we measured simulated accumulator model dynamics while systematically varying model parameters. In some cases, decision- making mechanisms can be directly inferred from dynamics, allowing us to distinguish between models that make identical behavioral predictions. In other cases, however, different parameterized mechanisms produce surprisingly similar dynamics, limiting the inferences that can be made based on measuring dynamics alone. Analyzing neural dynamics can provide a powerful tool to resolve model mimicry at the behavioral level, but we caution against drawing inferences based solely on neural analyses. Instead, simultaneous modeling of behavior and neural dynamics provides the most powerful approach to understand decision-making and likely other aspects of cognition and perception. PMID:28392584

Diagnostic classification scheme in Iranian breast cancer patients using a decision tree.

PubMed

Malehi, Amal Saki

2014-01-01

The objective of this study was to determine a diagnostic classification scheme using a decision tree based model. The study was conducted as a retrospective case-control study in Imam Khomeini hospital in Tehran during 2001 to 2009. Data, including demographic and clinical-pathological characteristics, were uniformly collected from 624 females, 312 of them were referred with positive diagnosis of breast cancer (cases) and 312 healthy women (controls). The decision tree was implemented to develop a diagnostic classification scheme using CART 6.0 Software. The AUC (area under curve), was measured as the overall performance of diagnostic classification of the decision tree. Five variables as main risk factors of breast cancer and six subgroups as high risk were identified. The results indicated that increasing age, low age at menarche, single and divorced statues, irregular menarche pattern and family history of breast cancer are the important diagnostic factors in Iranian breast cancer patients. The sensitivity and specificity of the analysis were 66% and 86.9% respectively. The high AUC (0.82) also showed an excellent classification and diagnostic performance of the model. Decision tree based model appears to be suitable for identifying risk factors and high or low risk subgroups. It can also assists clinicians in making a decision, since it can identify underlying prognostic relationships and understanding the model is very explicit.

Predicting Sentencing for Low-Level Crimes: Comparing Models of Human Judgment

ERIC Educational Resources Information Center

von Helversen, Bettina; Rieskamp, Jorg

2009-01-01

Laws and guidelines regulating legal decision making are often imposed without taking the cognitive processes of the legal decision maker into account. In the case of sentencing, this raises the question of whether the sentencing decisions of prosecutors and judges are consistent with legal policy. Especially in handling low-level crimes, legal…

Independence and interdependence in collective decision making: an agent-based model of nest-site choice by honeybee swarms

PubMed Central

List, Christian; Elsholtz, Christian; Seeley, Thomas D.

2008-01-01

Condorcet's jury theorem shows that when the members of a group have noisy but independent information about what is best for the group as a whole, majority decisions tend to outperform dictatorial ones. When voting is supplemented by communication, however, the resulting interdependencies between decision makers can strengthen or undermine this effect: they can facilitate information pooling, but also amplify errors. We consider an intriguing non-human case of independent information pooling combined with communication: the case of nest-site choice by honeybee (Apis mellifera) swarms. It is empirically well documented that when there are different nest sites that vary in quality, the bees usually choose the best one. We develop a new agent-based model of the bees' decision process and show that its remarkable reliability stems from a particular interplay of independence and interdependence between the bees. PMID:19073474

P.C. disposal decisions: a banking industry case study

NASA Astrophysics Data System (ADS)

Shah, Sejal P.; Sarkis, Joseph

2002-02-01

The service industry and the manufacturing industry are interlinked in a supply chain situation. Part of the effectiveness of some manufacturing industry environmental performance based on remanufacturing and recycling is dependent on service industry decisions. In the information technology arena, personal computers (PCs) are the hard equipment of the service industry. The end-of-life decisions made by the service industry, and in this case the banking industry will have implications for the amount of systems within the waste or reverse logistics stream for manufacturers. Looking at some of the issues (and presenting a model for evaluation) related to decision making concerning end-of-life disposition for PCs is something this paper investigates. The analytical hierarchy process (AHP) is applied in this circumstance. The development of the model, its application, and results, provide the basis for much of the discussion in this paper.

A Comparison of Juror Decision Making in Race-Based and Sexual Orientation-Based Hate Crime Cases.

PubMed

Gamblin, Bradlee W; Kehn, Andre; Vanderzanden, Karen; Ruthig, Joelle C; Jones, Kelly M; Long, Brittney L

2018-05-01

Several constructs have been identified as relevant to the juror decision-making process in hate crime cases. However, there is a lack of research on the relationships between these constructs and their variable influence across victim group. The purpose of the current study was to reexamine factors relevant to the juror decision-making process in hate crime cases within a structural model, and across victim group, to gauge the relative strength and explanatory power of various predictors. In the current study, 313 participants sentenced a perpetrator found guilty of a hate crime committed against either a Black man or a gay man; participants also responded to individual difference measures relevant to mock juror hate crime decision making, including prejudice toward the victim's social group. Using path analysis, we explored the role of juror prejudice on sentencing decisions in hate crime cases as well as similarities and differences based on the victimized group. Results indicated that, when the victim was a Black man, modern racism influenced sentencing both directly and indirectly through perpetrator blame attributions, explaining 18% of the variance in sentencing. In contrast, when the victim was a gay man, modern homophobia did not directly predict sentencing, and the overall model explained only 4% of the variance in sentencing, suggesting variables beyond juror prejudice may be better suited to explain juror decision making in sexual orientation-based hate crimes. The current study suggests that the role of juror prejudice in hate crime cases varies as a function of the victimized group and raises questions about the importance of juror prejudice in the sentencing of hate crime cases, particularly antigay prejudice. The importance of blame attributions, social dominance orientation, and juror beliefs regarding penalty enhancements for hate crime cases, as well as policy implications, are also addressed.

Inside the black box: starting to uncover the underlying decision rules used in one-by-one expert assessment of occupational exposure in case-control studies

PubMed Central

Wheeler, David C.; Burstyn, Igor; Vermeulen, Roel; Yu, Kai; Shortreed, Susan M.; Pronk, Anjoeka; Stewart, Patricia A.; Colt, Joanne S.; Baris, Dalsu; Karagas, Margaret R.; Schwenn, Molly; Johnson, Alison; Silverman, Debra T.; Friesen, Melissa C.

2014-01-01

Objectives Evaluating occupational exposures in population-based case-control studies often requires exposure assessors to review each study participants' reported occupational information job-by-job to derive exposure estimates. Although such assessments likely have underlying decision rules, they usually lack transparency, are time-consuming and have uncertain reliability and validity. We aimed to identify the underlying rules to enable documentation, review, and future use of these expert-based exposure decisions. Methods Classification and regression trees (CART, predictions from a single tree) and random forests (predictions from many trees) were used to identify the underlying rules from the questionnaire responses and an expert's exposure assignments for occupational diesel exhaust exposure for several metrics: binary exposure probability and ordinal exposure probability, intensity, and frequency. Data were split into training (n=10,488 jobs), testing (n=2,247), and validation (n=2,248) data sets. Results The CART and random forest models' predictions agreed with 92–94% of the expert's binary probability assignments. For ordinal probability, intensity, and frequency metrics, the two models extracted decision rules more successfully for unexposed and highly exposed jobs (86–90% and 57–85%, respectively) than for low or medium exposed jobs (7–71%). Conclusions CART and random forest models extracted decision rules and accurately predicted an expert's exposure decisions for the majority of jobs and identified questionnaire response patterns that would require further expert review if the rules were applied to other jobs in the same or different study. This approach makes the exposure assessment process in case-control studies more transparent and creates a mechanism to efficiently replicate exposure decisions in future studies. PMID:23155187

Optimal allocation model of construction land based on two-level system optimization theory

NASA Astrophysics Data System (ADS)

Liu, Min; Liu, Yanfang; Xia, Yuping; Lei, Qihong

2007-06-01

The allocation of construction land is an important task in land-use planning. Whether implementation of planning decisions is a success or not, usually depends on a reasonable and scientific distribution method. Considering the constitution of land-use planning system and planning process in China, multiple levels and multiple objective decision problems is its essence. Also, planning quantity decomposition is a two-level system optimization problem and an optimal resource allocation decision problem between a decision-maker in the topper and a number of parallel decision-makers in the lower. According the characteristics of the decision-making process of two-level decision-making system, this paper develops an optimal allocation model of construction land based on two-level linear planning. In order to verify the rationality and the validity of our model, Baoan district of Shenzhen City has been taken as a test case. Under the assistance of the allocation model, construction land is allocated to ten townships of Baoan district. The result obtained from our model is compared to that of traditional method, and results show that our model is reasonable and usable. In the end, the paper points out the shortcomings of the model and further research directions.

Generation Expansion Planning With Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

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

Zhan, Yiduo; Zheng, Qipeng P.; Wang, Jianhui

Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studies of generation assets' planning (e.g., thermal versus renewable), new findings show that the investment decisions could affect the future uncertainties as well. To this end, this paper proposes a multistage decision-dependent stochastic optimization model for long-term large-scale generation expansion planning, where large amounts of windmore » power are involved. In the decision-dependent model, the future uncertainties are not only affecting but also affected by the current decisions. In particular, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming model. The wind penetration, investment decisions, and the optimality of the decision-dependent model are evaluated in a series of multistage case studies. The results show that the proposed decision-dependent model provides effective optimization solutions for long-term generation expansion planning.« less

The role of decision analytic modeling in the health economic assessment of spinal intervention.

PubMed

Edwards, Natalie C; Skelly, Andrea C; Ziewacz, John E; Cahill, Kevin; McGirt, Matthew J

2014-10-15

Narrative review. To review the common tenets, strengths, and weaknesses of decision modeling for health economic assessment and to review the use of decision modeling in the spine literature to date. For the majority of spinal interventions, well-designed prospective, randomized, pragmatic cost-effectiveness studies that address the specific decision-in-need are lacking. Decision analytic modeling allows for the estimation of cost-effectiveness based on data available to date. Given the rising demands for proven value in spine care, the use of decision analytic modeling is rapidly increasing by clinicians and policy makers. This narrative review discusses the general components of decision analytic models, how decision analytic models are populated and the trade-offs entailed, makes recommendations for how users of spine intervention decision models might go about appraising the models, and presents an overview of published spine economic models. A proper, integrated, clinical, and economic critical appraisal is necessary in the evaluation of the strength of evidence provided by a modeling evaluation. As is the case with clinical research, all options for collecting health economic or value data are not without their limitations and flaws. There is substantial heterogeneity across the 20 spine intervention health economic modeling studies summarized with respect to study design, models used, reporting, and general quality. There is sparse evidence for populating spine intervention models. Results mostly showed that interventions were cost-effective based on $100,000/quality-adjusted life-year threshold. Spine care providers, as partners with their health economic colleagues, have unique clinical expertise and perspectives that are critical to interpret the strengths and weaknesses of health economic models. Health economic models must be critically appraised for both clinical validity and economic quality before altering health care policy, payment strategies, or patient care decisions. 4.

Modeling vs. Coaching of Argumentation in a Case-Based Learning Environment.

ERIC Educational Resources Information Center

Li, Tiancheng; And Others

The major purposes of this study are: (1) to investigate and compare the effectiveness of two instructional strategies, modeling and coaching on helping students to articulate and support their decisions in a case-based learning environment; (2) to compare the effectiveness of modeling and coaching on helping students address essential criteria in…

Interactive modelling with stakeholders in two cases in flood management

NASA Astrophysics Data System (ADS)

Leskens, Johannes; Brugnach, Marcela

2013-04-01

New policies on flood management called Multi-Level Safety (MLS), demand for an integral and collaborative approach. The goal of MLS is to minimize flood risks by a coherent package of protection measures, crisis management and flood resilience measures. To achieve this, various stakeholders, such as water boards, municipalities and provinces, have to collaborate in composing these measures. Besides the many advances this integral and collaborative approach gives, the decision-making environment becomes also more complex. Participants have to consider more criteria than they used to do and have to take a wide network of participants into account, all with specific perspectives, cultures and preferences. In response, sophisticated models are developed to support decision-makers in grasping this complexity. These models provide predictions of flood events and offer the opportunity to test the effectiveness of various measures under different criteria. Recent model advances in computation speed and model flexibility allow stakeholders to directly interact with a hydrological hydraulic model during meetings. Besides a better understanding of the decision content, these interactive models are supposed to support the incorporation of stakeholder knowledge in modelling and to support mutual understanding of different perspectives of stakeholders To explore the support of interactive modelling in integral and collaborate policies, such as MLS, we tested a prototype of an interactive flood model (3Di) with respect to a conventional model (Sobek) in two cases. The two cases included the designing of flood protection measures in Amsterdam and a flood event exercise in Delft. These case studies yielded two main results. First, we observed that in the exploration phase of a decision-making process, stakeholders participated actively in interactive modelling sessions. This increased the technical understanding of complex problems and the insight in the effectiveness of various integral measures. Second, when measures became more concrete, the model played a minor role, as stakeholders were still bounded to goals, responsibilities and budgets of their own organization. Model results in this phase are mainly used in a political way to maximize the goals of particular organizations.

Audit method suited for DSS in clinical environment.

PubMed

Vicente, Javier

2015-01-01

This chapter presents a novel online method to audit predictive models using a Bayesian perspective. The auditing model has been specifically designed for Decision Support Systems (DSSs) suited for clinical or research environments. Taking as starting point the working diagnosis supplied by the clinician, this method compares and evaluates the predictive skills of those models able to answer to that diagnosis. The approach consists in calculating the posterior odds of a model through the composition of a prior odds, a static odds, and a dynamic odds. To do so, this method estimates the posterior odds from the cases that the comparing models had in common during the design stage and from the cases already viewed by the DSS after deployment in the clinical site. In addition, if an ontology of the classes is available, this method can audit models answering related questions, which offers a reinforcement to the decisions the user already took and gives orientation on further diagnostic steps.The main technical novelty of this approach lies in the design of an audit model adapted to suit the decision workflow of a clinical environment. The audit model allows deciding what is the classifier that best suits each particular case under evaluation and allows the detection of possible misbehaviours due to population differences or data shifts in the clinical site. We show the efficacy of our method for the problem of brain tumor diagnosis with Magnetic Resonance Spectroscopy (MRS).

Optimal insemination and replacement decisions to minimize the cost of pathogen-specific clinical mastitis in dairy cows.

PubMed

Cha, E; Kristensen, A R; Hertl, J A; Schukken, Y H; Tauer, L W; Welcome, F L; Gröhn, Y T

2014-01-01

Mastitis is a serious production-limiting disease, with effects on milk yield, milk quality, and conception rate, and an increase in the risk of mortality and culling. The objective of this study was 2-fold: (1) to develop an economic optimization model that incorporates all the different types of pathogens that cause clinical mastitis (CM) categorized into 8 classes of culture results, and account for whether the CM was a first, second, or third case in the current lactation and whether the cow had a previous case or cases of CM in the preceding lactation; and (2) to develop this decision model to be versatile enough to add additional pathogens, diseases, or other cow characteristics as more information becomes available without significant alterations to the basic structure of the model. The model provides economically optimal decisions depending on the individual characteristics of the cow and the specific pathogen causing CM. The net returns for the basic herd scenario (with all CM included) were $507/cow per year, where the incidence of CM (cases per 100 cow-years) was 35.6, of which 91.8% of cases were recommended for treatment under an optimal replacement policy. The cost per case of CM was $216.11. The CM cases comprised (incidences, %) Staphylococcus spp. (1.6), Staphylococcus aureus (1.8), Streptococcus spp. (6.9), Escherichia coli (8.1), Klebsiella spp. (2.2), other treated cases (e.g., Pseudomonas; 1.1), other not treated cases (e.g., Trueperella pyogenes; 1.2), and negative culture cases (12.7). The average cost per case, even under optimal decisions, was greatest for Klebsiella spp. ($477), followed by E. coli ($361), other treated cases ($297), and other not treated cases ($280). This was followed by the gram-positive pathogens; among these, the greatest cost per case was due to Staph. aureus ($266), followed by Streptococcus spp. ($174) and Staphylococcus spp. ($135); negative culture had the lowest cost ($115). The model recommended treatment for most CM cases (>85%); the range was 86.2% (Klebsiella spp.) to 98.5% (Staphylococcus spp.). In general, the optimal recommended time for replacement was up to 5 mo earlier for cows with CM compared with cows without CM. Furthermore, although the parameter estimates implemented in this model are applicable to the dairy farms in this study, the parameters may be altered to be specific to other dairy farms. Cow rankings and values based on disease status, pregnancy status, and milk production can be extracted; these provide guidance when determining which cows to keep or cull. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Using Decision Trees for Estimating Mode Choice of Trips in Buca-Izmir

NASA Astrophysics Data System (ADS)

Oral, L. O.; Tecim, V.

2013-05-01

Decision makers develop transportation plans and models for providing sustainable transport systems in urban areas. Mode Choice is one of the stages in transportation modelling. Data mining techniques can discover factors affecting the mode choice. These techniques can be applied with knowledge process approach. In this study a data mining process model is applied to determine the factors affecting the mode choice with decision trees techniques by considering individual trip behaviours from household survey data collected within Izmir Transportation Master Plan. From this perspective transport mode choice problem is solved on a case in district of Buca-Izmir, Turkey with CRISP-DM knowledge process model.

The application of a decision tree to establish the parameters associated with hypertension.

PubMed

Tayefi, Maryam; Esmaeili, Habibollah; Saberi Karimian, Maryam; Amirabadi Zadeh, Alireza; Ebrahimi, Mahmoud; Safarian, Mohammad; Nematy, Mohsen; Parizadeh, Seyed Mohammad Reza; Ferns, Gordon A; Ghayour-Mobarhan, Majid

2017-02-01

Hypertension is an important risk factor for cardiovascular disease (CVD). The goal of this study was to establish the factors associated with hypertension by using a decision-tree algorithm as a supervised classification method of data mining. Data from a cross-sectional study were used in this study. A total of 9078 subjects who met the inclusion criteria were recruited. 70% of these subjects (6358 cases) were randomly allocated to the training dataset for the constructing of the decision-tree. The remaining 30% (2720 cases) were used as the testing dataset to evaluate the performance of decision-tree. Two models were evaluated in this study. In model I, age, gender, body mass index, marital status, level of education, occupation status, depression and anxiety status, physical activity level, smoking status, LDL, TG, TC, FBG, uric acid and hs-CRP were considered as input variables and in model II, age, gender, WBC, RBC, HGB, HCT MCV, MCH, PLT, RDW and PDW were considered as input variables. The validation of the model was assessed by constructing a receiver operating characteristic (ROC) curve. The prevalence rates of hypertension were 32% in our population. For the decision-tree model I, the accuracy, sensitivity, specificity and area under the ROC curve (AUC) value for identifying the related risk factors of hypertension were 73%, 63%, 77% and 0.72, respectively. The corresponding values for model II were 70%, 61%, 74% and 0.68, respectively. We have developed a decision tree model to identify the risk factors associated with hypertension that maybe used to develop programs for hypertension management. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Abstraction of an Affective-Cognitive Decision Making Model Based on Simulated Behaviour and Perception Chains

NASA Astrophysics Data System (ADS)

Sharpanskykh, Alexei; Treur, Jan

Employing rich internal agent models of actors in large-scale socio-technical systems often results in scalability issues. The problem addressed in this paper is how to improve computational properties of a complex internal agent model, while preserving its behavioral properties. The problem is addressed for the case of an existing affective-cognitive decision making model instantiated for an emergency scenario. For this internal decision model an abstracted behavioral agent model is obtained, which ensures a substantial increase of the computational efficiency at the cost of approximately 1% behavioural error. The abstraction technique used can be applied to a wide range of internal agent models with loops, for example, involving mutual affective-cognitive interactions.

Safety Assessment of Dangerous Goods Transport Enterprise Based on the Relative Entropy Aggregation in Group Decision Making Model

PubMed Central

Wu, Jun; Li, Chengbing; Huo, Yueying

2014-01-01

Safety of dangerous goods transport is directly related to the operation safety of dangerous goods transport enterprise. Aiming at the problem of the high accident rate and large harm in dangerous goods logistics transportation, this paper took the group decision making problem based on integration and coordination thought into a multiagent multiobjective group decision making problem; a secondary decision model was established and applied to the safety assessment of dangerous goods transport enterprise. First of all, we used dynamic multivalue background and entropy theory building the first level multiobjective decision model. Secondly, experts were to empower according to the principle of clustering analysis, and combining with the relative entropy theory to establish a secondary rally optimization model based on relative entropy in group decision making, and discuss the solution of the model. Then, after investigation and analysis, we establish the dangerous goods transport enterprise safety evaluation index system. Finally, case analysis to five dangerous goods transport enterprises in the Inner Mongolia Autonomous Region validates the feasibility and effectiveness of this model for dangerous goods transport enterprise recognition, which provides vital decision making basis for recognizing the dangerous goods transport enterprises. PMID:25477954

Safety assessment of dangerous goods transport enterprise based on the relative entropy aggregation in group decision making model.

PubMed

Wu, Jun; Li, Chengbing; Huo, Yueying

2014-01-01

Safety of dangerous goods transport is directly related to the operation safety of dangerous goods transport enterprise. Aiming at the problem of the high accident rate and large harm in dangerous goods logistics transportation, this paper took the group decision making problem based on integration and coordination thought into a multiagent multiobjective group decision making problem; a secondary decision model was established and applied to the safety assessment of dangerous goods transport enterprise. First of all, we used dynamic multivalue background and entropy theory building the first level multiobjective decision model. Secondly, experts were to empower according to the principle of clustering analysis, and combining with the relative entropy theory to establish a secondary rally optimization model based on relative entropy in group decision making, and discuss the solution of the model. Then, after investigation and analysis, we establish the dangerous goods transport enterprise safety evaluation index system. Finally, case analysis to five dangerous goods transport enterprises in the Inner Mongolia Autonomous Region validates the feasibility and effectiveness of this model for dangerous goods transport enterprise recognition, which provides vital decision making basis for recognizing the dangerous goods transport enterprises.

Intelligent Case Based Decision Support System for Online Diagnosis of Automated Production System

NASA Astrophysics Data System (ADS)

Ben Rabah, N.; Saddem, R.; Ben Hmida, F.; Carre-Menetrier, V.; Tagina, M.

2017-01-01

Diagnosis of Automated Production System (APS) is a decision-making process designed to detect, locate and identify a particular failure caused by the control law. In the literature, there are three major types of reasoning for industrial diagnosis: the first is model-based, the second is rule-based and the third is case-based. The common and major limitation of the first and the second reasonings is that they do not have automated learning ability. This paper presents an interactive and effective Case Based Decision Support System for online Diagnosis (CB-DSSD) of an APS. It offers a synergy between the Case Based Reasoning (CBR) and the Decision Support System (DSS) in order to support and assist Human Operator of Supervision (HOS) in his/her decision process. Indeed, the experimental evaluation performed on an Interactive Training System for PLC (ITS PLC) that allows the control of a Programmable Logic Controller (PLC), simulating sensors or/and actuators failures and validating the control algorithm through a real time interactive experience, showed the efficiency of our approach.

Decision science: a scientific approach to enhance public health budgeting.

PubMed

Honoré, Peggy A; Fos, Peter J; Smith, Torney; Riley, Michael; Kramarz, Kim

2010-01-01

The allocation of resources for public health programming is a complicated and daunting responsibility. Financial decision-making processes within public health agencies are especially difficult when not supported with techniques for prioritizing and ranking alternatives. This article presents a case study of a decision analysis software model that was applied to the process of identifying funding priorities for public health services in the Spokane Regional Health District. Results on the use of this decision support system provide insights into how decision science models, which have been used for decades in business and industry, can be successfully applied to public health budgeting as a means of strengthening agency financial management processes.

A Strategic Model for the Business Communication Field Training Decision in the Commercial Enterprises

NASA Astrophysics Data System (ADS)

Ioannis, Seimenis; Damianos, Sakas P.; Nikolaos, Konstantopoulos

2009-08-01

This article examines the factors that affect the decision making of the training managers responsible in case of business communication field as they have emerged from the study of the decision that have taken place in the commercial sector in this specific Greek market. Previous researches have indicated the participation of a number of variables in this kind of decision. The aim of this article is to locate the main factors which determine, in the commercial sector the decision for the training of the employees in the field of business communication. On the basis of quality research, dynamic simulation model have been created for some of this main factors.

Personalized Clinical Diagnosis in Data Bases for Treatment Support in Phthisiology.

PubMed

Lugovkina, T K; Skornyakov, S N; Golubev, D N; Egorov, E A; Medvinsky, I D

2016-01-01

The decision-making is a key event in the clinical practice. The program products with clinical decision support models in electronic data-base as well as with fixed decision moments of the real clinical practice and treatment results are very actual instruments for improving phthisiological practice and may be useful in the severe cases caused by the resistant strains of Mycobacterium tuberculosis. The methodology for gathering and structuring of useful information (critical clinical signals for decisions) is described. Additional coding of clinical diagnosis characteristics was implemented for numeric reflection of the personal situations. The created methodology for systematization and coding Clinical Events allowed to improve the clinical decision models for better clinical results.

A design process for using normative models in shared decision making: a case study in the context of prenatal testing.

PubMed

Rapaport, Sivan; Leshno, Moshe; Fink, Lior

2014-12-01

Shared decision making (SDM) encourages the patient to play a more active role in the process of medical consultation and its primary objective is to find the best treatment for a specific patient. Recent findings, however, show that patient preferences cannot be easily or accurately judged on the basis of communicative exchange during routine office visits, even for patients who seek to expand their role in medical decision making (MDM). The objective of this study is to improve the quality of patient-physician communication by developing a novel design process for SDM and then demonstrating, through a case study, the applicability of this process in enabling the use of a normative model for a specific medical situation. Our design process goes through the following stages: definition of medical situation and decision problem, development/identification of normative model, adaptation of normative model, empirical analysis and development of decision support systems (DSS) tools that facilitate the SDM process in the specific medical situation. This study demonstrates the applicability of the process through the implementation of the general normative theory of MDM under uncertainty for the medical-financial dilemma of choosing a physician to perform amniocentesis. The use of normative models in SDM raises several issues, such as the goal of the normative model, the relation between the goals of prediction and recommendation, and the general question of whether it is valid to use a normative model for people who do not behave according to the model's assumptions. © 2012 John Wiley & Sons Ltd.

Knowledge mobilisation for policy development: implementing systems approaches through participatory dynamic simulation modelling.

PubMed

Freebairn, Louise; Rychetnik, Lucie; Atkinson, Jo-An; Kelly, Paul; McDonnell, Geoff; Roberts, Nick; Whittall, Christine; Redman, Sally

2017-10-02

Evidence-based decision-making is an important foundation for health policy and service planning decisions, yet there remain challenges in ensuring that the many forms of available evidence are considered when decisions are being made. Mobilising knowledge for policy and practice is an emergent process, and one that is highly relational, often messy and profoundly context dependent. Systems approaches, such as dynamic simulation modelling can be used to examine both complex health issues and the context in which they are embedded, and to develop decision support tools. This paper reports on the novel use of participatory simulation modelling as a knowledge mobilisation tool in Australian real-world policy settings. We describe how this approach combined systems science methodology and some of the core elements of knowledge mobilisation best practice. We describe the strategies adopted in three case studies to address both technical and socio-political issues, and compile the experiential lessons derived. Finally, we consider the implications of these knowledge mobilisation case studies and provide evidence for the feasibility of this approach in policy development settings. Participatory dynamic simulation modelling builds on contemporary knowledge mobilisation approaches for health stakeholders to collaborate and explore policy and health service scenarios for priority public health topics. The participatory methods place the decision-maker at the centre of the process and embed deliberative methods and co-production of knowledge. The simulation models function as health policy and programme dynamic decision support tools that integrate diverse forms of evidence, including research evidence, expert knowledge and localised contextual information. Further research is underway to determine the impact of these methods on health service decision-making.

Quantifying human behavior uncertainties in a coupled agent-based model for water resources management

NASA Astrophysics Data System (ADS)

Hyun, J. Y.; Yang, Y. C. E.; Tidwell, V. C.; Macknick, J.

2017-12-01

Modeling human behaviors and decisions in water resources management is a challenging issue due to its complexity and uncertain characteristics that affected by both internal (such as stakeholder's beliefs on any external information) and external factors (such as future policies and weather/climate forecast). Stakeholders' decision regarding how much water they need is usually not entirely rational in the real-world cases, so it is not quite suitable to model their decisions with a centralized (top-down) approach that assume everyone in a watershed follow the same order or pursue the same objective. Agent-based modeling (ABM) uses a decentralized approach (bottom-up) that allow each stakeholder to make his/her own decision based on his/her own objective and the belief of information acquired. In this study, we develop an ABM which incorporates the psychological human decision process by the theory of risk perception. The theory of risk perception quantifies human behaviors and decisions uncertainties using two sequential methodologies: the Bayesian Inference and the Cost-Loss Problem. The developed ABM is coupled with a regulation-based water system model: Riverware (RW) to evaluate different human decision uncertainties in water resources management. The San Juan River Basin in New Mexico (Figure 1) is chosen as a case study area, while we define 19 major irrigation districts as water use agents and their primary decision is to decide the irrigated area on an annual basis. This decision will be affected by three external factors: 1) upstream precipitation forecast (potential amount of water availability), 2) violation of the downstream minimum flow (required to support ecosystems), and 3) enforcement of a shortage sharing plan (a policy that is currently undertaken in the region for drought years). Three beliefs (as internal factors) that correspond to these three external factors will also be considered in the modeling framework. The objective of this study is to use the two-way coupling between ABM and RW to mimic how stakeholders' uncertain decisions that have been made through the theory of risk perception will affect local and basin-wide water uses.

Using ecosystem services in decision-making to support sustainable development: Critiques, model development, a case study, and perspectives.

PubMed

Zagonari, Fabio

2016-04-01

In this paper, I propose a general, consistent, and operational approach that accounts for ecosystem services in a decision-making context: I link ecosystem services to sustainable development criteria; adopt multi-criteria analysis to measure ecosystem services, with weights provided by stakeholders used to account for equity issues; apply both temporal and spatial discount rates; and adopt a technique to order performance of the possible solutions based on their similarity to an ideal solution (TOPSIS) to account for uncertainty about the parameters and functions. Applying this approach in a case study of an offshore research platform in Italy (CNR Acqua Alta) revealed that decisions depend non-linearly on the degree of loss aversion, to a smaller extent on a global focus (as opposed to a local focus), and to the smallest extent on social concerns (as opposed to economic or environmental concerns). Application of the general model to the case study leads to the conclusion that the ecosystem services framework is likely to be less useful in supporting decisions than in identifying the crucial features on which decisions depend, unless experts from different disciplines are involved, stakeholders are represented, and experts and stakeholders achieve mutual understanding. Copyright © 2016 Elsevier B.V. All rights reserved.

The application of data mining techniques to oral cancer prognosis.

PubMed

Tseng, Wan-Ting; Chiang, Wei-Fan; Liu, Shyun-Yeu; Roan, Jinsheng; Lin, Chun-Nan

2015-05-01

This study adopted an integrated procedure that combines the clustering and classification features of data mining technology to determine the differences between the symptoms shown in past cases where patients died from or survived oral cancer. Two data mining tools, namely decision tree and artificial neural network, were used to analyze the historical cases of oral cancer, and their performance was compared with that of logistic regression, the popular statistical analysis tool. Both decision tree and artificial neural network models showed superiority to the traditional statistical model. However, as to clinician, the trees created by the decision tree models are relatively easier to interpret compared to that of the artificial neural network models. Cluster analysis also discovers that those stage 4 patients whose also possess the following four characteristics are having an extremely low survival rate: pN is N2b, level of RLNM is level I-III, AJCC-T is T4, and cells mutate situation (G) is moderate.

Searching for solutions to mitigate greenhouse gas emissions by agricultural policy decisions--Application of system dynamics modeling for the case of Latvia.

PubMed

Dace, Elina; Muizniece, Indra; Blumberga, Andra; Kaczala, Fabio

2015-09-15

European Union (EU) Member States have agreed to limit their greenhouse gas (GHG) emissions from sectors not covered by the EU Emissions Trading Scheme (non-ETS). That includes also emissions from agricultural sector. Although the Intergovernmental Panel on Climate Change (IPCC) has established a methodology for assessment of GHG emissions from agriculture, the forecasting options are limited, especially when policies and their interaction with the agricultural system are tested. Therefore, an advanced tool, a system dynamics model, was developed that enables assessment of effects various decisions and measures have on agricultural GHG emissions. The model is based on the IPCC guidelines and includes the main elements of an agricultural system, i.e. land management, livestock farming, soil fertilization and crop production, as well as feedback mechanisms between the elements. The case of Latvia is selected for simulations, as agriculture generates 22% of the total anthropogenic GHG emissions in the country. The results demonstrate that there are very limited options for GHG mitigation in the agricultural sector. Thereby, reaching the non-ETS GHG emission targets will be very challenging for Latvia, as the level of agricultural GHG emissions will be exceeded considerably above the target levels. Thus, other non-ETS sectors will have to reduce their emissions drastically to "neutralize" the agricultural sector's emissions for reaching the EU's common ambition to move towards low-carbon economy. The developed model may serve as a decision support tool for impact assessment of various measures and decisions on the agricultural system's GHG emissions. Although the model is applied to the case of Latvia, the elements and structure of the model developed are similar to agricultural systems in many countries. By changing numeric values of certain parameters, the model can be applied to analyze decisions and measures in other countries. Copyright © 2015 Elsevier B.V. All rights reserved.

Decision making generalized by a cumulative probability weighting function

NASA Astrophysics Data System (ADS)

dos Santos, Lindomar Soares; Destefano, Natália; Martinez, Alexandre Souto

2018-01-01

Typical examples of intertemporal decision making involve situations in which individuals must choose between a smaller reward, but more immediate, and a larger one, delivered later. Analogously, probabilistic decision making involves choices between options whose consequences differ in relation to their probability of receiving. In Economics, the expected utility theory (EUT) and the discounted utility theory (DUT) are traditionally accepted normative models for describing, respectively, probabilistic and intertemporal decision making. A large number of experiments confirmed that the linearity assumed by the EUT does not explain some observed behaviors, as nonlinear preference, risk-seeking and loss aversion. That observation led to the development of new theoretical models, called non-expected utility theories (NEUT), which include a nonlinear transformation of the probability scale. An essential feature of the so-called preference function of these theories is that the probabilities are transformed by decision weights by means of a (cumulative) probability weighting function, w(p) . We obtain in this article a generalized function for the probabilistic discount process. This function has as particular cases mathematical forms already consecrated in the literature, including discount models that consider effects of psychophysical perception. We also propose a new generalized function for the functional form of w. The limiting cases of this function encompass some parametric forms already proposed in the literature. Far beyond a mere generalization, our function allows the interpretation of probabilistic decision making theories based on the assumption that individuals behave similarly in the face of probabilities and delays and is supported by phenomenological models.

Use of travel cost models in planning: A case study

Treesearch

Allan Marsinko; William T. Zawacki; J. Michael Bowker

2002-01-01

This article examines the use of the travel cost, method in tourism-related decision making in the area of nonconsumptive wildlife-associated recreation. A travel cost model of nonconsumptive wildlife-associated recreation, developed by Zawacki, Maninko, and Bowker, is used as a case study for this analysis. The travel cost model estimates the demand for the activity...

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.

Extending BPM Environments of Your Choice with Performance Related Decision Support

NASA Astrophysics Data System (ADS)

Fritzsche, Mathias; Picht, Michael; Gilani, Wasif; Spence, Ivor; Brown, John; Kilpatrick, Peter

What-if Simulations have been identified as one solution for business performance related decision support. Such support is especially useful in cases where it can be automatically generated out of Business Process Management (BPM) Environments from the existing business process models and performance parameters monitored from the executed business process instances. Currently, some of the available BPM Environments offer basic-level performance prediction capabilities. However, these functionalities are normally too limited to be generally useful for performance related decision support at business process level. In this paper, an approach is presented which allows the non-intrusive integration of sophisticated tooling for what-if simulations, analytic performance prediction tools, process optimizations or a combination of such solutions into already existing BPM environments. The approach abstracts from process modelling techniques which enable automatic decision support spanning processes across numerous BPM Environments. For instance, this enables end-to-end decision support for composite processes modelled with the Business Process Modelling Notation (BPMN) on top of existing Enterprise Resource Planning (ERP) processes modelled with proprietary languages.

Bayesian techniques for analyzing group differences in the Iowa Gambling Task: A case study of intuitive and deliberate decision-makers.

PubMed

Steingroever, Helen; Pachur, Thorsten; Šmíra, Martin; Lee, Michael D

2018-06-01

The Iowa Gambling Task (IGT) is one of the most popular experimental paradigms for comparing complex decision-making across groups. Most commonly, IGT behavior is analyzed using frequentist tests to compare performance across groups, and to compare inferred parameters of cognitive models developed for the IGT. Here, we present a Bayesian alternative based on Bayesian repeated-measures ANOVA for comparing performance, and a suite of three complementary model-based methods for assessing the cognitive processes underlying IGT performance. The three model-based methods involve Bayesian hierarchical parameter estimation, Bayes factor model comparison, and Bayesian latent-mixture modeling. We illustrate these Bayesian methods by applying them to test the extent to which differences in intuitive versus deliberate decision style are associated with differences in IGT performance. The results show that intuitive and deliberate decision-makers behave similarly on the IGT, and the modeling analyses consistently suggest that both groups of decision-makers rely on similar cognitive processes. Our results challenge the notion that individual differences in intuitive and deliberate decision styles have a broad impact on decision-making. They also highlight the advantages of Bayesian methods, especially their ability to quantify evidence in favor of the null hypothesis, and that they allow model-based analyses to incorporate hierarchical and latent-mixture structures.

Is There a Conjunction Fallacy in Legal Probabilistic Decision Making?

PubMed

Wojciechowski, Bartosz W; Pothos, Emmanuel M

2018-01-01

Classical probability theory (CPT) has represented the rational standard for decision making in human cognition. Even though CPT has provided many descriptively excellent decision models, there have also been some empirical results persistently problematic for CPT accounts. The tension between the normative prescription of CPT and human behavior is particularly acute in cases where we have higher expectations for rational decisions. One such case concerns legal decision making from legal experts, such as attorneys and prosecutors and, more so, judges. In the present research we explore one of the most influential CPT decision fallacies, the conjunction fallacy (CF), in a legal decision making task, involving assessing evidence that the same suspect had committed two separate crimes. The information for the two crimes was presented consecutively. Each participant was asked to provide individual ratings for the two crimes in some cases and conjunctive probability rating for both crimes in other cases, after all information had been presented. Overall, 360 probability ratings for guilt were collected from 120 participants, comprised of 40 judges, 40 attorneys and prosecutors, and 40 individuals without legal education. Our results provide evidence for a double conjunction fallacy (in this case, a higher probability of committing both crimes than the probability of committing either crime individually), in the group of individuals without legal education. These results are discussed in terms of their applied implications and in relation to a recent framework for understanding such results, quantum probability theory (QPT).

Is There a Conjunction Fallacy in Legal Probabilistic Decision Making?

PubMed Central

Wojciechowski, Bartosz W.; Pothos, Emmanuel M.

2018-01-01

Classical probability theory (CPT) has represented the rational standard for decision making in human cognition. Even though CPT has provided many descriptively excellent decision models, there have also been some empirical results persistently problematic for CPT accounts. The tension between the normative prescription of CPT and human behavior is particularly acute in cases where we have higher expectations for rational decisions. One such case concerns legal decision making from legal experts, such as attorneys and prosecutors and, more so, judges. In the present research we explore one of the most influential CPT decision fallacies, the conjunction fallacy (CF), in a legal decision making task, involving assessing evidence that the same suspect had committed two separate crimes. The information for the two crimes was presented consecutively. Each participant was asked to provide individual ratings for the two crimes in some cases and conjunctive probability rating for both crimes in other cases, after all information had been presented. Overall, 360 probability ratings for guilt were collected from 120 participants, comprised of 40 judges, 40 attorneys and prosecutors, and 40 individuals without legal education. Our results provide evidence for a double conjunction fallacy (in this case, a higher probability of committing both crimes than the probability of committing either crime individually), in the group of individuals without legal education. These results are discussed in terms of their applied implications and in relation to a recent framework for understanding such results, quantum probability theory (QPT). PMID:29674983

Implementing interactive decision support: A case for combining cyberinfrastructure, data fusion, and social process to mobilize scientific knowledge in sustainability problems

NASA Astrophysics Data System (ADS)

Pierce, S. A.

2014-12-01

Geosciences are becoming increasingly data intensive, particularly in relation to sustainability problems, which are multi-dimensional, weakly structured and characterized by high levels of uncertainty. In the case of complex resource management problems, the challenge is to extract meaningful information from data and make sense of it. Simultaneously, scientific knowledge alone is insufficient to change practice. Creating tools, and group decision support processes for end users to interact with data are key challenges to transforming science-based information into actionable knowledge. The ENCOMPASS project began as a multi-year case study in the Atacama Desert of Chile to design and implement a knowledge transfer model for energy-water-mining conflicts in the region. ENCOMPASS combines the use of cyberinfrastructure (CI), automated data collection, interactive interfaces for dynamic decision support, and participatory modelling to support social learning. A pilot version of the ENCOMPASS CI uses open source systems and serves as a structure to integrate and store multiple forms of data and knowledge, such as DEM, meteorological, water quality, geomicrobiological, energy demand, and groundwater models. In the case study, informatics and data fusion needs related to scientific uncertainty around deep groundwater flowpaths and energy-water connections. Users may upload data from field sites with handheld devices or desktops. Once uploaded, data assets are accessible for a variety of uses. To address multi-attributed decision problems in the Atacama region a standalone application with touch-enabled interfaces was created to improve real-time interactions with datasets by groups. The tool was used to merge datasets from the ENCOMPASS CI to support exploration among alternatives and build shared understanding among stakeholders. To date, the project has increased technical capacity among stakeholders, resulted in the creation of both for-profit and non-profit entities, enabled cross-sector collaboration with mining-indigenous stakeholders, and produced an interactive application for group decision support. ENCOMPASS leverages advances in computational tools to deliver data and models for group decision support applied to sustainability science problems.

Individual vision and peak distribution in collective actions

NASA Astrophysics Data System (ADS)

Lu, Peng

2017-06-01

People make decisions on whether they should participate as participants or not as free riders in collective actions with heterogeneous visions. Besides of the utility heterogeneity and cost heterogeneity, this work includes and investigates the effect of vision heterogeneity by constructing a decision model, i.e. the revised peak model of participants. In this model, potential participants make decisions under the joint influence of utility, cost, and vision heterogeneities. The outcomes of simulations indicate that vision heterogeneity reduces the values of peaks, and the relative variance of peaks is stable. Under normal distributions of vision heterogeneity and other factors, the peaks of participants are normally distributed as well. Therefore, it is necessary to predict distribution traits of peaks based on distribution traits of related factors such as vision heterogeneity and so on. We predict the distribution of peaks with parameters of both mean and standard deviation, which provides the confident intervals and robust predictions of peaks. Besides, we validate the peak model of via the Yuyuan Incident, a real case in China (2014), and the model works well in explaining the dynamics and predicting the peak of real case.

[Cost-conscious medical decisions. Normative guidance within the conflicting demands of ethics and economics].

PubMed

Marckmann, G; In der Schmitten, J

2014-05-01

Under the current conditions in the health care system, physicians inevitably have to take responsibility for the cost dimension of their decisions on the level of single cases. This article, therefore, discusses the question how physicians can integrate cost considerations into their clinical decisions at the microlevel in a medically rational and ethically justified way. We propose a four-step model for "ethical cost-consciousness": (1) forego ineffective interventions as required by good evidence-based medicine, (2) respect individual patient preferences, (3) minimize the diagnostic and therapeutic effort to achieve a certain treatment goal, and (4) forego expensive interventions that have only a small or unlikely (net) benefit for the patient. Steps 1-3 are ethically justified by the principles of beneficence, nonmaleficence, and respect for autonomy, step 4 by the principles of justice. For decisions on step 4, explicit cost-conscious guidelines should be developed locally or regionally. Following the four-step model can contribute to ethically defensible, cost-conscious decision-making at the microlevel. In addition, physicians' rationing decisions should meet basic standards of procedural fairness. Regular cost-case discussions and clinical ethics consultation should be available as decision support. Implementing step 4, however, requires first of all a clear political legitimation with the corresponding legal framework.

The influence of differential response on decision-making in child protective service agencies.

PubMed

Janczewski, Colleen E

2015-01-01

Differential response (DR) profoundly changes the decision pathways of public child welfare systems, yet little is known about how DR shapes the experiences of children whose reports receive an investigation rather than an alternate response. Using data from the National Child Abuse and Neglect Data System (NCANDS), this study examined the relationship between DR implementation and decision outcomes in neglect cases, as measured by investigation, substantiation, and removal rates in 297 U.S. counties. Multivariate regression models included county-level measures of child poverty and proportions of African American children. Path analyses were also conducted to identify mediating effects of prior decision points and moderating effects of DR on poverty and race's influence on decision outcomes. Results indicate that compared to non-DR counties, those implementing DR have significantly lower investigation and substantiation rates within county populations but higher substantiation rates among investigated cases. Regression models showed significant reductions in removal rates associated with DR implementation, but these effects became insignificant in path models that accounted for mediation effects of previous decision points. Findings also suggest that DR implementation may reduce the positive association between child poverty rates and investigation rates, but additional studies with larger samples are needed to confirm this moderation effect. Two methods of calculating decision outcomes, population- and decision-based enumeration, were used, and policy and research implications of each are discussed. This study demonstrates that despite their inherit complexity, large administrative datasets such as NCANDS can be used to assess the impact of wide-scale system change across jurisdictions. Copyright © 2014 Elsevier Ltd. All rights reserved.

How old is old in allegations of age discrimination? The limitations of existing law.

PubMed

Wiener, Richard L; Farnum, Katlyn S

2016-10-01

Under Title VII, courts may give a mixed motive instruction allowing jurors to determine that defendants are liable for discrimination if an illegal factor (here: race, color, religion, sex, or national origin) contributed to an adverse decision. Recently, the Supreme Court held that to conclude that an employer discriminated against a worker because of age, the Age Discrimination in Employment Act, unlike Title VII of the Civil Rights Act of 1964, requires "but for" causality, necessitating jurors to find that age was the determinative factor in an employer's adverse decision regarding that worker. Using a national online sample (N = 392) and 2 study phases, 1 to measure stereotypes, and a second to present experimental manipulations, this study tested whether older worker stereotypes as measured through the lens of the Stereotype Content Model, instruction type (but for vs. mixed motive causality), and plaintiff age influenced mock juror verdicts in an age discrimination case. Decision modeling in Phase 2 with 3 levels of case orientation (i.e., proplaintiff, prodefendant, and neutral) showed that participants relied on multiple factors when making a decision, as opposed to just 1, suggesting that mock jurors favor a mixed model approach to discrimination verdict decisions. In line with previous research, instruction effects showed that mock jurors found in favor of plaintiffs under mixed motive instructions but not under "but for" instructions especially for older plaintiffs (64- and 74-year-old as opposed to 44- and 54-year-old-plaintiffs). Most importantly, in accordance with the Stereotype Content Model theory, competence and warmth stereotypes moderated the instruction effects found for specific judgments. The results of this study show the importance of the type of legal causality required for age discrimination cases. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Monte Carlo Simulation of Effective Coordination Mechanisms for e-Commerce

NASA Astrophysics Data System (ADS)

Sakas, D. P.; Vlachos, D. S.; Simos, T. E.

2008-11-01

Making decisions in a dynamic environment is considered extremely important in today's market. Decision trees which can be used to model these systems, are not easily constructed and solved, especially in the case of infinite sets of consequences (for example, consider the case where only the mean and the variance of an outcome is known). In this work, discrete approximation and Monte Carlo techniques are used to overcome the aforementioned difficulties.

Quantitative Systems Pharmacology: A Case for Disease Models

PubMed Central

Ramanujan, S; Schmidt, BJ; Ghobrial, OG; Lu, J; Heatherington, AC

2016-01-01

Quantitative systems pharmacology (QSP) has emerged as an innovative approach in model‐informed drug discovery and development, supporting program decisions from exploratory research through late‐stage clinical trials. In this commentary, we discuss the unique value of disease‐scale “platform” QSP models that are amenable to reuse and repurposing to support diverse clinical decisions in ways distinct from other pharmacometrics strategies. PMID:27709613

Probabilistic Risk Assessment for Decision Making During Spacecraft Operations

NASA Technical Reports Server (NTRS)

Meshkat, Leila

2009-01-01

Decisions made during the operational phase of a space mission often have significant and immediate consequences. Without the explicit consideration of the risks involved and their representation in a solid model, it is very likely that these risks are not considered systematically in trade studies. Wrong decisions during the operational phase of a space mission can lead to immediate system failure whereas correct decisions can help recover the system even from faulty conditions. A problem of special interest is the determination of the system fault protection strategies upon the occurrence of faults within the system. Decisions regarding the fault protection strategy also heavily rely on a correct understanding of the state of the system and an integrated risk model that represents the various possible scenarios and their respective likelihoods. Probabilistic Risk Assessment (PRA) modeling is applicable to the full lifecycle of a space mission project, from concept development to preliminary design, detailed design, development and operations. The benefits and utilities of the model, however, depend on the phase of the mission for which it is used. This is because of the difference in the key strategic decisions that support each mission phase. The focus of this paper is on describing the particular methods used for PRA modeling during the operational phase of a spacecraft by gleaning insight from recently conducted case studies on two operational Mars orbiters. During operations, the key decisions relate to the commands sent to the spacecraft for any kind of diagnostics, anomaly resolution, trajectory changes, or planning. Often, faults and failures occur in the parts of the spacecraft but are contained or mitigated before they can cause serious damage. The failure behavior of the system during operations provides valuable data for updating and adjusting the related PRA models that are built primarily based on historical failure data. The PRA models, in turn, provide insight into the effect of various faults or failures on the risk and failure drivers of the system and the likelihood of possible end case scenarios, thereby facilitating the decision making process during operations. This paper describes the process of adjusting PRA models based on observed spacecraft data, on one hand, and utilizing the models for insight into the future system behavior on the other hand. While PRA models are typically used as a decision aid during the design phase of a space mission, we advocate adjusting them based on the observed behavior of the spacecraft and utilizing them for decision support during the operations phase.

Likelihood Ratio, Optimal Decision Rules, and Relationship between Proportion Correct and d' in the Dual-Pair AB vs BA identification Paradigm

PubMed Central

Micheyl, Christophe; Dai, Huanping

2010-01-01

The equal-variance Gaussian signal-detection-theory (SDT) decision model for the dual-pair change-detection (or “4IAX”) paradigm has been described in earlier publications. In this note, we consider the equal-variance Gaussian SDT model for the related dual-pair AB vs BA identification paradigm. The likelihood ratios, optimal decision rules, receiver operating characteristics (ROCs), and relationships between d' and proportion-correct (PC) are analyzed for two special cases: that of statistically independent observations, which is likely to apply in constant-stimuli experiments, and that of highly correlated observations, which is likely to apply in experiments where stimuli are roved widely across trials or pairs. A surprising outcome of this analysis is that although these two situations lead to different optimal decision rules, the predicted ROCs and proportions of correct responses (PCs) for these two cases are not substantially different, and are either identical or similar to those observed in the basic Yes-No paradigm. PMID:19633356

Death Penalty Decisions: Instruction Comprehension, Attitudes, and Decision Mediators.

PubMed

Patry, Marc W; Penrod, Steven D

2013-01-01

A primary goal of this research was to empirically evaluate a set of assumptions, advanced in the Supreme Court's ruling in Buchanan v. Angelone (1998), about jury comprehension of death penalty instructions. Further, this research examined the use of evidence in capital punishment decision making by exploring underlying mediating factors upon which death penalty decisions may be based. Manipulated variables included the type of instructions and several variations of evidence. Study 1 was a paper and pencil study of 245 undergraduate mock jurors. The experimental design was an incomplete 4×2×2×2×2 factorial model resulting in 56 possible conditions. Manipulations included four different types of instructions, presence of a list of case-specific mitigators to accompany the instructions, and three variations in the case facts: age of the defendant, bad prior record, and defendant history of emotional abuse. Study 2 was a fully-crossed 2×2×2×2×2 experiment with four deliberating mock juries per cell. Manipulations included jury instructions (original or revised), presence of a list of case-specific mitigators, defendant history of emotional abuse, bad prior record, and heinousness of the crime. The sample of 735 jury-eligible participants included 130 individuals who identified themselves as students. Participants watched one of 32 stimulus videotapes based on a replication of a capital sentencing hearing. The present findings support previous research showing low comprehension of capital penalty instructions. Further, we found that higher instruction comprehension was associated with higher likelihood of issuing life sentence decisions. The importance of instruction comprehension is emphasized in a social cognitive model of jury decision making at the sentencing phase of capital cases.

Death Penalty Decisions: Instruction Comprehension, Attitudes, and Decision Mediators

PubMed Central

Patry, Marc W.; Penrod, Steven D.

2013-01-01

A primary goal of this research was to empirically evaluate a set of assumptions, advanced in the Supreme Court’s ruling in Buchanan v. Angelone (1998), about jury comprehension of death penalty instructions. Further, this research examined the use of evidence in capital punishment decision making by exploring underlying mediating factors upon which death penalty decisions may be based. Manipulated variables included the type of instructions and several variations of evidence. Study 1 was a paper and pencil study of 245 undergraduate mock jurors. The experimental design was an incomplete 4×2×2×2×2 factorial model resulting in 56 possible conditions. Manipulations included four different types of instructions, presence of a list of case-specific mitigators to accompany the instructions, and three variations in the case facts: age of the defendant, bad prior record, and defendant history of emotional abuse. Study 2 was a fully-crossed 2×2×2×2×2 experiment with four deliberating mock juries per cell. Manipulations included jury instructions (original or revised), presence of a list of case-specific mitigators, defendant history of emotional abuse, bad prior record, and heinousness of the crime. The sample of 735 jury-eligible participants included 130 individuals who identified themselves as students. Participants watched one of 32 stimulus videotapes based on a replication of a capital sentencing hearing. The present findings support previous research showing low comprehension of capital penalty instructions. Further, we found that higher instruction comprehension was associated with higher likelihood of issuing life sentence decisions. The importance of instruction comprehension is emphasized in a social cognitive model of jury decision making at the sentencing phase of capital cases. PMID:24072981

A preference aggregation model and application in AHP-group decision making

NASA Astrophysics Data System (ADS)

Yang, Taiyi; Yang, De; Chao, Xiangrui

2018-04-01

Group decision making process integrate individual preferences to obtain the group preference by applying aggregation rules and preference relations. The two most useful approaches, the aggregation of individual judgements and the aggregation of individual priorities, traditionally are employed in the Analytic Hierarchy Process to deal with group decision making problems. In both cases, it is assumed that the group preference is approximate weighted mathematical expectation of individual judgements and individual priorities. We propose new preference aggregation methods using optimization models in order to obtain group preference which is close to all individual priorities. Some illustrative examples are finally examined to demonstrate proposed models for application.

Based on a multi-agent system for multi-scale simulation and application of household's LUCC: a case study for Mengcha village, Mizhi county, Shaanxi province.

PubMed

Chen, Hai; Liang, Xiaoying; Li, Rui

2013-01-01

Multi-Agent Systems (MAS) offer a conceptual approach to include multi-actor decision making into models of land use change. Through the simulation based on the MAS, this paper tries to show the application of MAS in the micro scale LUCC, and reveal the transformation mechanism of difference scale. This paper starts with a description of the context of MAS research. Then, it adopts the Nested Spatial Choice (NSC) method to construct the multi-scale LUCC decision-making model. And a case study for Mengcha village, Mizhi County, Shaanxi Province is reported. Finally, the potentials and drawbacks of the following approach is discussed and concluded. From our design and implementation of the MAS in multi-scale model, a number of observations and conclusions can be drawn on the implementation and future research directions. (1) The use of the LUCC decision-making and multi-scale transformation framework provides, according to us, a more realistic modeling of multi-scale decision making process. (2) By using continuous function, rather than discrete function, to construct the decision-making of the households is more realistic to reflect the effect. (3) In this paper, attempts have been made to give a quantitative analysis to research the household interaction. And it provides the premise and foundation for researching the communication and learning among the households. (4) The scale transformation architecture constructed in this paper helps to accumulate theory and experience for the interaction research between the micro land use decision-making and the macro land use landscape pattern. Our future research work will focus on: (1) how to rational use risk aversion principle, and put the rule on rotation between household parcels into model. (2) Exploring the methods aiming at researching the household decision-making over a long period, it allows us to find the bridge between the long-term LUCC data and the short-term household decision-making. (3) Researching the quantitative method and model, especially the scenario analysis model which may reflect the interaction among different household types.

Application of an interactive water simulation model in urban water management: a case study in Amsterdam.

PubMed

Leskens, J G; Brugnach, M; Hoekstra, A Y

2014-01-01

Water simulation models are available to support decision-makers in urban water management. To use current water simulation models, special expertise is required. Therefore, model information is prepared prior to work sessions, in which decision-makers weigh different solutions. However, this model information quickly becomes outdated when new suggestions for solutions arise and are therefore limited in use. We suggest that new model techniques, i.e. fast and flexible computation algorithms and realistic visualizations, allow this problem to be solved by using simulation models during work sessions. A new Interactive Water Simulation Model was applied for two case study areas in Amsterdam and was used in two workshops. In these workshops, the Interactive Water Simulation Model was positively received. It included non-specialist participants in the process of suggesting and selecting possible solutions and made them part of the accompanying discussions and negotiations. It also provided the opportunity to evaluate and enhance possible solutions more often within the time horizon of a decision-making process. Several preconditions proved to be important for successfully applying the Interactive Water Simulation Model, such as the willingness of the stakeholders to participate and the preparation of different general main solutions that can be used for further iterations during a work session.

Modeling in Real Time During the Ebola Response.

PubMed

Meltzer, Martin I; Santibanez, Scott; Fischer, Leah S; Merlin, Toby L; Adhikari, Bishwa B; Atkins, Charisma Y; Campbell, Caresse; Fung, Isaac Chun-Hai; Gambhir, Manoj; Gift, Thomas; Greening, Bradford; Gu, Weidong; Jacobson, Evin U; Kahn, Emily B; Carias, Cristina; Nerlander, Lina; Rainisch, Gabriel; Shankar, Manjunath; Wong, Karen; Washington, Michael L

2016-07-08

To aid decision-making during CDC's response to the 2014-2016 Ebola virus disease (Ebola) epidemic in West Africa, CDC activated a Modeling Task Force to generate estimates on various topics related to the response in West Africa and the risk for importation of cases into the United States. Analysis of eight Ebola response modeling projects conducted during August 2014-July 2015 provided insight into the types of questions addressed by modeling, the impact of the estimates generated, and the difficulties encountered during the modeling. This time frame was selected to cover the three phases of the West African epidemic curve. Questions posed to the Modeling Task Force changed as the epidemic progressed. Initially, the task force was asked to estimate the number of cases that might occur if no interventions were implemented compared with cases that might occur if interventions were implemented; however, at the peak of the epidemic, the focus shifted to estimating resource needs for Ebola treatment units. Then, as the epidemic decelerated, requests for modeling changed to generating estimates of the potential number of sexually transmitted Ebola cases. Modeling to provide information for decision-making during the CDC Ebola response involved limited data, a short turnaround time, and difficulty communicating the modeling process, including assumptions and interpretation of results. Despite these challenges, modeling yielded estimates and projections that public health officials used to make key decisions regarding response strategy and resources required. The impact of modeling during the Ebola response demonstrates the usefulness of modeling in future responses, particularly in the early stages and when data are scarce. Future modeling can be enhanced by planning ahead for data needs and data sharing, and by open communication among modelers, scientists, and others to ensure that modeling and its limitations are more clearly understood. The activities summarized in this report would not have been possible without collaboration with many U.S. and international partners (http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/partners.html).

Toward better public health reporting using existing off the shelf approaches: The value of medical dictionaries in automated cancer detection using plaintext medical data.

PubMed

Kasthurirathne, Suranga N; Dixon, Brian E; Gichoya, Judy; Xu, Huiping; Xia, Yuni; Mamlin, Burke; Grannis, Shaun J

2017-05-01

Existing approaches to derive decision models from plaintext clinical data frequently depend on medical dictionaries as the sources of potential features. Prior research suggests that decision models developed using non-dictionary based feature sourcing approaches and "off the shelf" tools could predict cancer with performance metrics between 80% and 90%. We sought to compare non-dictionary based models to models built using features derived from medical dictionaries. We evaluated the detection of cancer cases from free text pathology reports using decision models built with combinations of dictionary or non-dictionary based feature sourcing approaches, 4 feature subset sizes, and 5 classification algorithms. Each decision model was evaluated using the following performance metrics: sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. Decision models parameterized using dictionary and non-dictionary feature sourcing approaches produced performance metrics between 70 and 90%. The source of features and feature subset size had no impact on the performance of a decision model. Our study suggests there is little value in leveraging medical dictionaries for extracting features for decision model building. Decision models built using features extracted from the plaintext reports themselves achieve comparable results to those built using medical dictionaries. Overall, this suggests that existing "off the shelf" approaches can be leveraged to perform accurate cancer detection using less complex Named Entity Recognition (NER) based feature extraction, automated feature selection and modeling approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

Multi-Criteria Decision Making For Determining A Simple Model of Supplier Selection

NASA Astrophysics Data System (ADS)

Harwati

2017-06-01

Supplier selection is a decision with many criteria. Supplier selection model usually involves more than five main criteria and more than 10 sub-criteria. In fact many model includes more than 20 criteria. Too many criteria involved in supplier selection models sometimes make it difficult to apply in many companies. This research focuses on designing supplier selection that easy and simple to be applied in the company. Analytical Hierarchy Process (AHP) is used to weighting criteria. The analysis results there are four criteria that are easy and simple can be used to select suppliers: Price (weight 0.4) shipment (weight 0.3), quality (weight 0.2) and services (weight 0.1). A real case simulation shows that simple model provides the same decision with a more complex model.

The politics of participation in watershed modeling.

PubMed

Korfmacher, K S

2001-02-01

While researchers and decision-makers increasingly recognize the importance of public participation in environmental decision-making, there is less agreement about how to involve the public. One of the most controversial issues is how to involve citizens in producing scientific information. Although this question is relevant to many areas of environmental policy, it has come to the fore in watershed management. Increasingly, the public is becoming involved in the sophisticated computer modeling efforts that have been developed to inform watershed management decisions. These models typically have been treated as technical inputs to the policy process. However, model-building itself involves numerous assumptions, judgments, and decisions that are relevant to the public. This paper examines the politics of public involvement in watershed modeling efforts and proposes five guidelines for good practice for such efforts. Using these guidelines, I analyze four cases in which different approaches to public involvement in the modeling process have been attempted and make recommendations for future efforts to involve communities in watershed modeling. Copyright 2001 Springer-Verlag

Watershed Management Optimization Support Tool (WMOST) v2: User Manual and Case Studies

EPA Science Inventory

The Watershed Management Optimization Support Tool (WMOST) is a decision support tool that evaluates the relative cost-effectiveness of management practices at the local or watershed scale. WMOST models the environmental effects and costs of management decisions in a watershed c...

Multi-Agent Market Modeling of Foreign Exchange Rates

NASA Astrophysics Data System (ADS)

Zimmermann, Georg; Neuneier, Ralph; Grothmann, Ralph

A market mechanism is basically driven by a superposition of decisions of many agents optimizing their profit. The oeconomic price dynamic is a consequence of the cumulated excess demand/supply created on this micro level. The behavior analysis of a small number of agents is well understood through the game theory. In case of a large number of agents one may use the limiting case that an individual agent does not have an influence on the market, which allows the aggregation of agents by statistic methods. In contrast to this restriction, we can omit the assumption of an atomic market structure, if we model the market through a multi-agent approach. The contribution of the mathematical theory of neural networks to the market price formation is mostly seen on the econometric side: neural networks allow the fitting of high dimensional nonlinear dynamic models. Furthermore, in our opinion, there is a close relationship between economics and the modeling ability of neural networks because a neuron can be interpreted as a simple model of decision making. With this in mind, a neural network models the interaction of many decisions and, hence, can be interpreted as the price formation mechanism of a market.

The cost of different types of lameness in dairy cows calculated by dynamic programming.

PubMed

Cha, E; Hertl, J A; Bar, D; Gröhn, Y T

2010-10-01

Traditionally, studies which placed a monetary value on the effect of lameness have calculated the costs at the herd level and rarely have they been specific to different types of lameness. These costs which have been calculated from former studies are not particularly useful for farmers in making economically optimal decisions depending on individual cow characteristics. The objective of this study was to calculate the cost of different types of lameness at the individual cow level and thereby identify the optimal management decision for each of three representative lameness diagnoses. This model would provide a more informed decision making process in lameness management for maximal economic profitability. We made modifications to an existing dynamic optimization and simulation model, studying the effects of various factors (incidence of lameness, milk loss, pregnancy rate and treatment cost) on the cost of different types of lameness. The average cost per case (US$) of sole ulcer, digital dermatitis and foot rot were 216.07, 132.96 and 120.70, respectively. It was recommended that 97.3% of foot rot cases, 95.5% of digital dermatitis cases and 92.3% of sole ulcer cases be treated. The main contributor to the total cost per case of sole ulcer was milk loss (38%), treatment cost for digital dermatitis (42%) and the effect of decreased fertility for foot rot (50%). This model affords versatility as it allows for parameters such as production costs, economic values and disease frequencies to be altered. Therefore, cost estimates are the direct outcome of the farm specific parameters entered into the model. Thus, this model can provide farmers economically optimal guidelines specific to their individual cows suffering from different types of lameness. Copyright © 2010 Elsevier B.V. All rights reserved.

The Woodworker's Website: A Project Management Case Study

ERIC Educational Resources Information Center

Jance, Marsha

2014-01-01

A case study that focuses on building a website for a woodworking business is discussed. Project management and linear programming techniques can be used to determine the time required to complete the website project discussed in the case. This case can be assigned to students in an undergraduate or graduate decision modeling or management science…

Linguistic Multi-Attribute Group Decision Making with Risk Preferences and Its Use in Low-Carbon Tourism Destination Selection.

PubMed

Lin, Hui; Wang, Zhou-Jing

2017-09-17

Low-carbon tourism plays an important role in carbon emission reduction and environmental protection. Low-carbon tourism destination selection often involves multiple conflicting and incommensurate attributes or criteria and can be modelled as a multi-attribute decision-making problem. This paper develops a framework to solve multi-attribute group decision-making problems, where attribute evaluation values are provided as linguistic terms and the attribute weight information is incomplete. In order to obtain a group risk preference captured by a linguistic term set with triangular fuzzy semantic information, a nonlinear programming model is established on the basis of individual risk preferences. We first convert individual linguistic-term-based decision matrices to their respective triangular fuzzy decision matrices, which are then aggregated into a group triangular fuzzy decision matrix. Based on this group decision matrix and the incomplete attribute weight information, a linear program is developed to find an optimal attribute weight vector. A detailed procedure is devised for tackling linguistic multi-attribute group decision making problems. A low-carbon tourism destination selection case study is offered to illustrate how to use the developed group decision-making model in practice.

Linguistic Multi-Attribute Group Decision Making with Risk Preferences and Its Use in Low-Carbon Tourism Destination Selection

PubMed Central

Lin, Hui; Wang, Zhou-Jing

2017-01-01

Low-carbon tourism plays an important role in carbon emission reduction and environmental protection. Low-carbon tourism destination selection often involves multiple conflicting and incommensurate attributes or criteria and can be modelled as a multi-attribute decision-making problem. This paper develops a framework to solve multi-attribute group decision-making problems, where attribute evaluation values are provided as linguistic terms and the attribute weight information is incomplete. In order to obtain a group risk preference captured by a linguistic term set with triangular fuzzy semantic information, a nonlinear programming model is established on the basis of individual risk preferences. We first convert individual linguistic-term-based decision matrices to their respective triangular fuzzy decision matrices, which are then aggregated into a group triangular fuzzy decision matrix. Based on this group decision matrix and the incomplete attribute weight information, a linear program is developed to find an optimal attribute weight vector. A detailed procedure is devised for tackling linguistic multi-attribute group decision making problems. A low-carbon tourism destination selection case study is offered to illustrate how to use the developed group decision-making model in practice. PMID:28926985

Problem-Oriented Corporate Knowledge Base Models on the Case-Based Reasoning Approach Basis

NASA Astrophysics Data System (ADS)

Gluhih, I. N.; Akhmadulin, R. K.

2017-07-01

One of the urgent directions of efficiency enhancement of production processes and enterprises activities management is creation and use of corporate knowledge bases. The article suggests a concept of problem-oriented corporate knowledge bases (PO CKB), in which knowledge is arranged around possible problem situations and represents a tool for making and implementing decisions in such situations. For knowledge representation in PO CKB a case-based reasoning approach is encouraged to use. Under this approach, the content of a case as a knowledge base component has been defined; based on the situation tree a PO CKB knowledge model has been developed, in which the knowledge about typical situations as well as specific examples of situations and solutions have been represented. A generalized problem-oriented corporate knowledge base structural chart and possible modes of its operation have been suggested. The obtained models allow creating and using corporate knowledge bases for support of decision making and implementing, training, staff skill upgrading and analysis of the decisions taken. The universal interpretation of terms “situation” and “solution” adopted in the work allows using the suggested models to develop problem-oriented corporate knowledge bases in different subject domains. It has been suggested to use the developed models for making corporate knowledge bases of the enterprises that operate engineer systems and networks at large production facilities.

Quantitative Systems Pharmacology: A Case for Disease Models.

PubMed

Musante, C J; Ramanujan, S; Schmidt, B J; Ghobrial, O G; Lu, J; Heatherington, A C

2017-01-01

Quantitative systems pharmacology (QSP) has emerged as an innovative approach in model-informed drug discovery and development, supporting program decisions from exploratory research through late-stage clinical trials. In this commentary, we discuss the unique value of disease-scale "platform" QSP models that are amenable to reuse and repurposing to support diverse clinical decisions in ways distinct from other pharmacometrics strategies. © 2016 The Authors Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of The American Society for Clinical Pharmacology and Therapeutics.

Consulting as a Strategy for Knowledge Transfer

PubMed Central

Jacobson, Nora; Butterill, Dale; Goering, Paula

2005-01-01

Academic researchers who work on health policy and health services are expected to transfer knowledge to decision makers. Decision makers often do not, however, regard academics’ traditional ways of doing research and disseminating their findings as relevant or useful. This article argues that consulting can be a strategy for transferring knowledge between researchers and decision makers and is effective at promoting the “enlightenment” and “interactive” models of knowledge use. Based on three case studies, it develops a model of knowledge transfer–focused consulting that consists of six stages and four types of work. Finally, the article explores how knowledge is generated in consulting and identifies several classes of factors facilitating its use by decision makers. PMID:15960773

Applying multi-criteria decision-making to improve the waste reduction policy in Taiwan.

PubMed

Su, Jun-Pin; Hung, Ming-Lung; Chao, Chia-Wei; Ma, Hwong-wen

2010-01-01

Over the past two decades, the waste reduction problem has been a major issue in environmental protection. Both recycling and waste reduction policies have become increasingly important. As the complexity of decision-making has increased, it has become evident that more factors must be considered in the development and implementation of policies aimed at resource recycling and waste reduction. There are many studies focused on waste management excluding waste reduction. This study paid more attention to waste reduction. Social, economic, and management aspects of waste treatment policies were considered in this study. Further, a life-cycle assessment model was applied as an evaluation system for the environmental aspect. Results of both quantitative and qualitative analyses on the social, economic, and management aspects were integrated via the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method into the comprehensive decision-making support system of multi-criteria decision-making (MCDM). A case study evaluating the waste reduction policy in Taoyuan County is presented to demonstrate the feasibility of this model. In the case study, reinforcement of MSW sorting was shown to be the best practice. The model in this study can be applied to other cities faced with the waste reduction problems.

Measuring sustainable development using a multi-criteria model: a case study.

PubMed

Boggia, Antonio; Cortina, Carla

2010-11-01

This paper shows how Multi-criteria Decision Analysis (MCDA) can help in a complex process such as the assessment of the level of sustainability of a certain area. The paper presents the results of a study in which a model for measuring sustainability was implemented to better aid public policy decisions regarding sustainability. In order to assess sustainability in specific areas, a methodological approach based on multi-criteria analysis has been developed. The aim is to rank areas in order to understand the specific technical and/or financial support that they need to develop sustainable growth. The case study presented is an assessment of the level of sustainability in different areas of an Italian Region using the MCDA approach. Our results show that MCDA is a proper approach for sustainability assessment. The results are easy to understand and the evaluation path is clear and transparent. This is what decision makers need for having support to their decisions. The multi-criteria model for evaluation has been developed respecting the sustainable development economic theory, so that final results can have a clear meaning in terms of sustainability. Copyright 2010 Elsevier Ltd. All rights reserved.

Cargo Logistics Airlift Systems Study (CLASS). Volume 2: Case study approach and results

NASA Technical Reports Server (NTRS)

Burby, R. J.; Kuhlman, W. H.

1978-01-01

Models of transportation mode decision making were developed. The user's view of the present and future air cargo systems is discussed. Issues summarized include: (1) organization of the distribution function; (2) mode choice decision making; (3) air freight system; and (4) the future of air freight.

Integrating urban recharge uncertainty into standard groundwater modeling practice: A case study on water main break predictions for the Barton Springs segment of the Edwards Aquifer, Austin, Texas

NASA Astrophysics Data System (ADS)

Sinner, K.; Teasley, R. L.

2016-12-01

Groundwater models serve as integral tools for understanding flow processes and informing stakeholders and policy makers in management decisions. Historically, these models tended towards a deterministic nature, relying on historical data to predict and inform future decisions based on model outputs. This research works towards developing a stochastic method of modeling recharge inputs from pipe main break predictions in an existing groundwater model, which subsequently generates desired outputs incorporating future uncertainty rather than deterministic data. The case study for this research is the Barton Springs segment of the Edwards Aquifer near Austin, Texas. Researchers and water resource professionals have modeled the Edwards Aquifer for decades due to its high water quality, fragile ecosystem, and stakeholder interest. The original case study and model that this research is built upon was developed as a co-design problem with regional stakeholders and the model outcomes are generated specifically for communication with policy makers and managers. Recently, research in the Barton Springs segment demonstrated a significant contribution of urban, or anthropogenic, recharge to the aquifer, particularly during dry period, using deterministic data sets. Due to social and ecological importance of urban water loss to recharge, this study develops an evaluation method to help predicted pipe breaks and their related recharge contribution within the Barton Springs segment of the Edwards Aquifer. To benefit groundwater management decision processes, the performance measures captured in the model results, such as springflow, head levels, storage, and others, were determined by previous work in elicitation of problem framing to determine stakeholder interests and concerns. The results of the previous deterministic model and the stochastic model are compared to determine gains to stakeholder knowledge through the additional modeling

Spiking Phineas Gage: a neurocomputational theory of cognitive-affective integration in decision making.

PubMed

Wagar, Brandon M; Thagard, Paul

2004-01-01

The authors present a neurological theory of how cognitive information and emotional information are integrated in the nucleus accumbens during effective decision making. They describe how the nucleus accumbens acts as a gateway to integrate cognitive information from the ventromedial prefrontal cortex and the hippocampus with emotional information from the amygdala. The authors have modeled this integration by a network of spiking artificial neurons organized into separate areas and used this computational model to simulate 2 kinds of cognitive-affective integration. The model simulates successful performance by people with normal cognitive-affective integration. The model also simulates the historical case of Phineas Gage as well as subsequent patients whose ability to make decisions became impeded by damage to the ventromedial prefrontal cortex.

Screening of pollution control and clean-up materials for river chemical spills using the multiple case-based reasoning method with a difference-driven revision strategy.

PubMed

Liu, Rentao; Jiang, Jiping; Guo, Liang; Shi, Bin; Liu, Jie; Du, Zhaolin; Wang, Peng

2016-06-01

In-depth filtering of emergency disposal technology (EDT) and materials has been required in the process of environmental pollution emergency disposal. However, an urgent problem that must be solved is how to quickly and accurately select the most appropriate materials for treating a pollution event from the existing spill control and clean-up materials (SCCM). To meet this need, the following objectives were addressed in this study. First, the material base and a case base for environment pollution emergency disposal were established to build a foundation and provide material for SCCM screening. Second, the multiple case-based reasoning model method with a difference-driven revision strategy (DDRS-MCBR) was applied to improve the original dual case-based reasoning model method system, and screening and decision-making was performed for SCCM using this model. Third, an actual environmental pollution accident from 2012 was used as a case study to verify the material base, case base, and screening model. The results demonstrated that the DDRS-MCBR method was fast, efficient, and practical. The DDRS-MCBR method changes the passive situation in which the choice of SCCM screening depends only on the subjective experience of the decision maker and offers a new approach to screening SCCM.

IT vendor selection model by using structural equation model & analytical hierarchy process

NASA Astrophysics Data System (ADS)

Maitra, Sarit; Dominic, P. D. D.

2012-11-01

Selecting and evaluating the right vendors is imperative for an organization's global marketplace competitiveness. Improper selection and evaluation of potential vendors can dwarf an organization's supply chain performance. Numerous studies have demonstrated that firms consider multiple criteria when selecting key vendors. This research intends to develop a new hybrid model for vendor selection process with better decision making. The new proposed model provides a suitable tool for assisting decision makers and managers to make the right decisions and select the most suitable vendor. This paper proposes a Hybrid model based on Structural Equation Model (SEM) and Analytical Hierarchy Process (AHP) for long-term strategic vendor selection problems. The five steps framework of the model has been designed after the thorough literature study. The proposed hybrid model will be applied using a real life case study to assess its effectiveness. In addition, What-if analysis technique will be used for model validation purpose.

Estimating the impact of enterprise resource planning project management decisions on post-implementation maintenance costs: a case study using simulation modelling

NASA Astrophysics Data System (ADS)

Fryling, Meg

2010-11-01

Organisations often make implementation decisions with little consideration for the maintenance phase of an enterprise resource planning (ERP) system, resulting in significant recurring maintenance costs. Poor cost estimations are likely related to the lack of an appropriate framework for enterprise-wide pre-packaged software maintenance, which requires an ongoing relationship with the software vendor (Markus, M.L., Tanis, C., and Fenema, P.C., 2000. Multisite ERP implementation. CACM, 43 (4), 42-46). The end result is that critical project decisions are made with little empirical data, resulting in substantial long-term cost impacts. The product of this research is a formal dynamic simulation model that enables theory testing, scenario exploration and policy analysis. The simulation model ERPMAINT1 was developed by combining and extending existing frameworks in several research domains, and by incorporating quantitative and qualitative case study data. The ERPMAINT1 model evaluates tradeoffs between different ERP project management decisions and their impact on post-implementation total cost of ownership (TCO). Through model simulations a variety of dynamic insights were revealed that could assist ERP project managers. Major findings from the simulation show that upfront investments in mentoring and system exposure translate to long-term cost savings. The findings also indicate that in addition to customisations, add-ons have a significant impact on TCO.

LANL Institutional Decision Support By Process Modeling and Analysis Group (AET-2)

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

Booth, Steven Richard

2016-04-04

AET-2 has expertise in process modeling, economics, business case analysis, risk assessment, Lean/Six Sigma tools, and decision analysis to provide timely decision support to LANS leading to continuous improvement. This capability is critical during the current tight budgetary environment as LANS pushes to identify potential areas of cost savings and efficiencies. An important arena is business systems and operations, where processes can impact most or all laboratory employees. Lab-wide efforts are needed to identify and eliminate inefficiencies to accomplish Director McMillan’s charge of “doing more with less.” LANS faces many critical and potentially expensive choices that require sound decision supportmore » to ensure success. AET-2 is available to provide this analysis support to expedite the decisions at hand.« less

Integrating HL7 RIM and ontology for unified knowledge and data representation in clinical decision support systems.

PubMed

Zhang, Yi-Fan; Tian, Yu; Zhou, Tian-Shu; Araki, Kenji; Li, Jing-Song

2016-01-01

The broad adoption of clinical decision support systems within clinical practice has been hampered mainly by the difficulty in expressing domain knowledge and patient data in a unified formalism. This paper presents a semantic-based approach to the unified representation of healthcare domain knowledge and patient data for practical clinical decision making applications. A four-phase knowledge engineering cycle is implemented to develop a semantic healthcare knowledge base based on an HL7 reference information model, including an ontology to model domain knowledge and patient data and an expression repository to encode clinical decision making rules and queries. A semantic clinical decision support system is designed to provide patient-specific healthcare recommendations based on the knowledge base and patient data. The proposed solution is evaluated in the case study of type 2 diabetes mellitus inpatient management. The knowledge base is successfully instantiated with relevant domain knowledge and testing patient data. Ontology-level evaluation confirms model validity. Application-level evaluation of diagnostic accuracy reaches a sensitivity of 97.5%, a specificity of 100%, and a precision of 98%; an acceptance rate of 97.3% is given by domain experts for the recommended care plan orders. The proposed solution has been successfully validated in the case study as providing clinical decision support at a high accuracy and acceptance rate. The evaluation results demonstrate the technical feasibility and application prospect of our approach. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

When domestic goes capital: Juror decision making in capital murder trials involving domestic homicide.

PubMed

Richards, Tara N; Smith, M Dwayne; Fogel, Sondra J; Bjerregaard, Beth

2015-08-01

Prior research suggests that homicide cases involving familial offenders and victims are subject to a "domestic discount" that reduces sentencing severity. However, the operation of a domestic discount in regard to death penalty sentencing has been rarely examined. The current research uses a near-population of jury decisions in capital murder trials conducted in North Carolina from 1991 to 2009 (n = 800), and a series of logistic regression analyses to determine whether there is (a) a direct effect between offender-victim relationship (e.g., domestic, friend/acquaintance, and stranger) and jury decision making, and/or (b) whether domestic offender-victim relationship (as well as other offender-victim relationships) moderates the effect of legal and extralegal case characteristics on jury assessment of the death penalty. Our findings revealed no empirical support for a "domestic discount" whereby juries are less likely to impose death sentences in cases involving domestic homicides. However, substantial differences in predictors of death sentencing were found across offender-victim dyads; most notably, domestic homicide cases demonstrated the most legalistic model of jury decisions to impose death sentences. (c) 2015 APA, all rights reserved).

An integrated experiment for identification of best decision styles and teamworks with respect to HSE and ergonomics program: The case of a large oil refinery.

PubMed

Azadeh, A; Mokhtari, Z; Sharahi, Z Jiryaei; Zarrin, M

2015-12-01

Decision making failure is a predominant human error in emergency situations. To demonstrate the subject model, operators of an oil refinery were asked to answer a health, safety and environment HSE-decision styles (DS) questionnaire. In order to achieve this purpose, qualitative indicators in HSE and ergonomics domain have been collected. Decision styles, related to the questions, have been selected based on Driver taxonomy of human decision making approach. Teamwork efficiency has been assessed based on different decision style combinations. The efficiency has been ranked based on HSE performance. Results revealed that efficient decision styles resulted from data envelopment analysis (DEA) optimization model is consistent with the plant's dominant styles. Therefore, improvement in system performance could be achieved using the best operator for critical posts or in team arrangements. This is the first study that identifies the best decision styles with respect to HSE and ergonomics factors. Copyright © 2015 Elsevier Ltd. All rights reserved.

Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network

PubMed Central

Ho, Wen-Hsien; Lee, King-Teh; Chen, Hong-Yaw; Ho, Te-Wei; Chiu, Herng-Chia

2012-01-01

Background A database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group. Methods The three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80% of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20% of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models. Conclusions The ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection. PMID:22235270

Bridging the gap between evidence and policy for infectious diseases: How models can aid public health decision-making.

PubMed

Knight, Gwenan M; Dharan, Nila J; Fox, Gregory J; Stennis, Natalie; Zwerling, Alice; Khurana, Renuka; Dowdy, David W

2016-01-01

The dominant approach to decision-making in public health policy for infectious diseases relies heavily on expert opinion, which often applies empirical evidence to policy questions in a manner that is neither systematic nor transparent. Although systematic reviews are frequently commissioned to inform specific components of policy (such as efficacy), the same process is rarely applied to the full decision-making process. Mathematical models provide a mechanism through which empirical evidence can be methodically and transparently integrated to address such questions. However, such models are often considered difficult to interpret. In addition, models provide estimates that need to be iteratively re-evaluated as new data or considerations arise. Using the case study of a novel diagnostic for tuberculosis, a framework for improved collaboration between public health decision-makers and mathematical modellers that could lead to more transparent and evidence-driven policy decisions for infectious diseases in the future is proposed. The framework proposes that policymakers should establish long-term collaborations with modellers to address key questions, and that modellers should strive to provide clear explanations of the uncertainty of model structure and outputs. Doing so will improve the applicability of models and clarify their limitations when used to inform real-world public health policy decisions. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Analytic hierarchy process (AHP) as a tool in asset allocation

NASA Astrophysics Data System (ADS)

Zainol Abidin, Siti Nazifah; Mohd Jaffar, Maheran

2013-04-01

Allocation capital investment into different assets is the best way to balance the risk and reward. This can prevent from losing big amount of money. Thus, the aim of this paper is to help investors in making wise investment decision in asset allocation. This paper proposes modifying and adapting Analytic Hierarchy Process (AHP) model. The AHP model is widely used in various fields of study that are related in decision making. The results of the case studies show that the proposed model can categorize stocks and determine the portion of capital investment. Hence, it can assist investors in decision making process and reduce the risk of loss in stock market investment.

A Bridging Opportunities Work-frame to develop mobile applications for clinical decision making

PubMed Central

van Rooij, Tibor; Rix, Serena; Moore, James B; Marsh, Sharon

2015-01-01

Background: Mobile applications (apps) providing clinical decision support (CDS) may show the greatest promise when created by and for frontline clinicians. Our aim was to create a generic model enabling healthcare providers to direct the development of CDS apps. Methods: We combined Change Management with a three-tier information technology architecture to stimulate CDS app development. Results: A Bridging Opportunities Work-frame model was developed. A test case was used to successfully develop an app. Conclusion: Healthcare providers can re-use this globally applicable model to actively create and manage regional decision support applications to translate evidence-based medicine in the use of emerging medication or novel treatment regimens. PMID:28031883

3D printed renal cancer models derived from MRI data: application in pre-surgical planning.

PubMed

Wake, Nicole; Rude, Temitope; Kang, Stella K; Stifelman, Michael D; Borin, James F; Sodickson, Daniel K; Huang, William C; Chandarana, Hersh

2017-05-01

To determine whether patient-specific 3D printed renal tumor models change pre-operative planning decisions made by urological surgeons in preparation for complex renal mass surgical procedures. From our ongoing IRB approved study on renal neoplasms, ten renal mass cases were retrospectively selected based on Nephrometry Score greater than 5 (range 6-10). A 3D post-contrast fat-suppressed gradient-echo T1-weighted sequence was used to generate 3D printed models. The cases were evaluated by three experienced urologic oncology surgeons in a randomized fashion using (1) imaging data on PACS alone and (2) 3D printed model in addition to the imaging data. A questionnaire regarding surgical approach and planning was administered. The presumed pre-operative approaches with and without the model were compared. Any change between the presumed approaches and the actual surgical intervention was recorded. There was a change in planned approach with the 3D printed model for all ten cases with the largest impact seen regarding decisions on transperitoneal or retroperitoneal approach and clamping, with changes seen in 30%-50% of cases. Mean parenchymal volume loss for the operated kidney was 21.4%. Volume losses >20% were associated with increased ischemia times and surgeons tended to report a different approach with the use of the 3D model compared to that with imaging alone in these cases. The 3D printed models helped increase confidence regarding the chosen operative procedure in all cases. Pre-operative physical 3D models created from MRI data may influence surgical planning for complex kidney cancer.

Research on investment decisions model of trans-regional transmission network based on the theory of NPV

NASA Astrophysics Data System (ADS)

Zai, Wenjiao; Wang, Bo; Liu, Jichun; Shi, Haobo; Zeng, Pingliang

2018-02-01

The investment decision model of trans-regional transmission network in the context of Global Energy Internet was studied in this paper. The key factors affecting the trans-regional transmission network investment income: the income tax rate, the loan interest rate, the expected return on investment of the investment subject, the per capita GDP and so on were considered in the transmission network investment income model. First, according to the principle of system dynamics, the causality diagram of key factors was constructed. Then, the dynamic model of transmission investment decision was established. A case study of the power transmission network between China and Mongolia, through the simulation of the system dynamic model, the influence of the above key factors on the investment returns was analyzed, and the feasibility and effectiveness of the model was proved.

A conceptual evolutionary aseismic decision support framework for hospitals

NASA Astrophysics Data System (ADS)

Hu, Yufeng; Dargush, Gary F.; Shao, Xiaoyun

2012-12-01

In this paper, aconceptual evolutionary framework for aseismic decision support for hospitalsthat attempts to integrate a range of engineering and sociotechnical models is presented. Genetic algorithms are applied to find the optimal decision sets. A case study is completed to demonstrate how the frameworkmay applytoa specific hospital.The simulations show that the proposed evolutionary decision support framework is able to discover robust policy sets in either uncertain or fixed environments. The framework also qualitatively identifies some of the characteristicbehavior of the critical care organization. Thus, by utilizing the proposedframework, the decision makers are able to make more informed decisions, especially toenhance the seismic safety of the hospitals.

Issue a Boil-Water Advisory or Wait for Definitive Information? A Decision Analysis

PubMed Central

Wagner, Michael M.; Wallstrom, Garrick L.; Onisko, Agnieszka

2005-01-01

Objective Study the decision to issue a boil-water advisory in response to a spike in sales of diarrhea remedies or wait 72 hours for the results of definitive testing of water and people. Methods Decision analysis. Results In the base-case analysis, the optimal decision is test-and-wait. If the cost of issuing a boil-water advisory is less than 13.92 cents per person per day, the optimal decision is to issue the boil-water advisory immediately. Conclusions Decisions based on surveillance data that are suggestive but not conclusive about the existence of a disease outbreak can be modeled. PMID:16779145

FlooDSuM - a decision support methodology for assisting local authorities in flood situations

NASA Astrophysics Data System (ADS)

Schwanbeck, Jan; Weingartner, Rolf

2014-05-01

Decision making in flood situations is a difficult task, especially in small to medium-sized mountain catchments (30 - 500 km2) which are usually characterized by complex topography, high drainage density and quick runoff response to rainfall events. Operating hydrological models driven by numerical weather prediction systems, which have a lead-time of several hours up to few even days, would be beneficial in this case as time for prevention could be gained. However, the spatial and quantitative accuracy of such meteorological forecasts usually decrease with increasing lead-time. In addition, the sensitivity of rainfall-runoff models to inaccuracies in estimations of areal rainfall increases with decreasing catchment size. Accordingly, decisions on flood alerts should ideally be based on areal rainfall from high resolution and short-term numerical weather prediction, nowcasts or even real-time measurements, which is transformed into runoff by a hydrological model. In order to benefit from the best possible rainfall data while retaining enough time for alerting and for prevention, the hydrological model should be fast and easily applicable by decision makers within local authorities themselves. The proposed decision support methodology FlooDSuM (Flood Decision Support Methodology) aims to meet those requirements. Applying FlooDSuM, a few successive binary decisions of increasing complexity have to be processed following a flow-chart-like structure. Prepared data and straightforwardly applicable tools are provided for each of these decisions. Maps showing the current flood disposition are used for the first step. While danger of flooding cannot be excluded more and more complex and time consuming methods will be applied. For the final decision, a set of scatter-plots relating areal precipitation to peak flow is provided. These plots take also further decisive parameters into account such as storm duration, distribution of rainfall intensity in time as well as the catchment's antecedent moisture conditions. The proposed approach is currently tested in two catchments in the Swiss Pre-Alps and Alps. We will show the general setup and selected results. The findings of those case studies will lead to further improvements of the proposed approach.

A systematic approach to embedded biomedical decision making.

PubMed

Song, Zhe; Ji, Zhongkai; Ma, Jian-Guo; Sputh, Bernhard; Acharya, U Rajendra; Faust, Oliver

2012-11-01

An embedded decision making is a key feature for many biomedical systems. In most cases human life directly depends on correct decisions made by these systems, therefore they have to work reliably. This paper describes how we applied systems engineering principles to design a high performance embedded classification system in a systematic and well structured way. We introduce the structured design approach by discussing requirements capturing, specifications refinement, implementation and testing. Thereby, we follow systems engineering principles and execute each of these processes as formal as possible. The requirements, which motivate the system design, describe an automated decision making system for diagnostic support. These requirements are refined into the implementation of a support vector machine (SVM) algorithm which enables us to integrate automated decision making in embedded systems. With a formal model we establish functionality, stability and reliability of the system. Furthermore, we investigated different parallel processing configurations of this computationally complex algorithm. We found that, by adding SVM processes, an almost linear speedup is possible. Once we established these system properties, we translated the formal model into an implementation. The resulting implementation was tested using XMOS processors with both normal and failure cases, to build up trust in the implementation. Finally, we demonstrated that our parallel implementation achieves the speedup, predicted by the formal model. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Challenges surrounding return-to-play (RTP) for the sports clinician: a case highlighting the need for a thorough three-step RTP model.

PubMed

Menta, Roger; D'Angelo, Kevin

2016-12-01

Return-to-play (RTP) is a multifactorial process of retuning an injured athlete back to competition when risk for re-injury is minimized. Traditionally, these decisions are made by medical practitioners based on experience or anecdotal evidence. RTP decisions continue to be a challenging task for the medical practitioner. In the interest of advancing sports medicine for the betterment of athletes, improving the RTP decision-making process with a new paradigm has been suggested.1 It stands to clarify the intricacies used by clinicians when making RTP decisions by providing insight into the multiple factors that must be considered; not only by the athlete and medical practitioner, but all relevant parties (i.e., coaches, trainers, and organizations). This case describes a 19-year-old Ontario Junior Hockey League (OJHL) player who fractured his left clavicle during game play and consequently, suffered a more severe injury to the same clavicle 5½ weeks later by returning to competition against medical advice. This case highlights the potential issues that present when a RTP protocol is poorly executed and addresses the need to adopt a thorough decision-based RTP model proposed by Creighton et al.1 Further, the discussion will draw on current literature and issues surrounding RTP, and the potential legal implications associated with premature return to competition. Given the lack of consensus among sport medicine experts in regards to RTP criteria, the presented model stands to provide a pivotal framework upon which future research can be conducted, while improving the current criteria in place when returning an athlete to competition to aid medical practitioners.

Three-dimensional-printed cardiac prototypes aid surgical decision-making and preoperative planning in selected cases of complex congenital heart diseases: Early experience and proof of concept in a resource-limited environment.

PubMed

Kappanayil, Mahesh; Koneti, Nageshwara Rao; Kannan, Rajesh R; Kottayil, Brijesh P; Kumar, Krishna

2017-01-01

Three-dimensional. (3D) printing is an innovative manufacturing process that allows computer-assisted conversion of 3D imaging data into physical "printouts" Healthcare applications are currently in evolution. The objective of this study was to explore the feasibility and impact of using patient-specific 3D-printed cardiac prototypes derived from high-resolution medical imaging data (cardiac magnetic resonance imaging/computed tomography [MRI/CT]) on surgical decision-making and preoperative planning in selected cases of complex congenital heart diseases (CHDs). Five patients with complex CHD with previously unresolved management decisions were chosen. These included two patients with complex double-outlet right ventricle, two patients with criss-cross atrioventricular connections, and one patient with congenitally corrected transposition of great arteries with pulmonary atresia. Cardiac MRI was done for all patients, cardiac CT for one; specific surgical challenges were identified. Volumetric data were used to generate patient-specific 3D models. All cases were reviewed along with their 3D models, and the impact on surgical decision-making and preoperative planning was assessed. Accurate life-sized 3D cardiac prototypes were successfully created for all patients. The models enabled radically improved 3D understanding of anatomy, identification of specific technical challenges, and precise surgical planning. Augmentation of existing clinical and imaging data by 3D prototypes allowed successful execution of complex surgeries for all five patients, in accordance with the preoperative planning. 3D-printed cardiac prototypes can radically assist decision-making, planning, and safe execution of complex congenital heart surgery by improving understanding of 3D anatomy and allowing anticipation of technical challenges.

Make or buy decision model with multi-stage manufacturing process and supplier imperfect quality

NASA Astrophysics Data System (ADS)

Pratama, Mega Aria; Rosyidi, Cucuk Nur

2017-11-01

This research develops an make or buy decision model considering supplier imperfect quality. This model can be used to help companies make the right decision in case of make or buy component with the best quality and the least cost in multistage manufacturing process. The imperfect quality is one of the cost component that must be minimizing in this model. Component with imperfect quality, not necessarily defective. It still can be rework and used for assembly. This research also provide a numerical example and sensitivity analysis to show how the model work. We use simulation and help by crystal ball to solve the numerical problem. The sensitivity analysis result show that percentage of imperfect generally not affect to the model significantly, and the model is not sensitive to changes in these parameters. This is because the imperfect cost are smaller than overall total cost components.

Moral dilemmas and abortion decision-making: Lessons learnt from abortion research in England and Wales.

PubMed

Hoggart, Lesley

2018-05-21

This paper scrutinises the concepts of moral reasoning and personal reasoning, problematising the binary model by looking at young women's pregnancy decision-making. Data from two UK empirical studies are subjected to theoretically driven qualitative secondary analysis, and illustrative cases show how complex decision-making is characterised by an intertwining of the personal and the moral, and is thus best understood by drawing on moral relativism.

"Setting up for the Next Big Thing": Undergraduate Women Engineering Students' Postbaccalaureate Career Decisions

ERIC Educational Resources Information Center

Smith, Kathleen N.; Gayles, Joy Gaston

2017-01-01

Using social cognitive career theory and the cognitive information processing model as frameworks, in this constructivist case study we examined the career-related experiences and decisions of 10 women engineering undergraduate seniors who accepted full-time positions. From the data analysis 3 major themes emerged: critical undergraduate…

Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction.

PubMed

Kamphuis, C; Mollenhorst, H; Heesterbeek, J A P; Hogeveen, H

2010-08-01

The objective was to develop and validate a clinical mastitis (CM) detection model by means of decision-tree induction. For farmers milking with an automatic milking system (AMS), it is desirable that the detection model has a high level of sensitivity (Se), especially for more severe cases of CM, at a very high specificity (Sp). In addition, an alert for CM should be generated preferably at the quarter milking (QM) at which the CM infection is visible for the first time. Data were collected from 9 Dutch dairy herds milking automatically during a 2.5-yr period. Data included sensor data (electrical conductivity, color, and yield) at the QM level and visual observations of quarters with CM recorded by the farmers. Visual observations of quarters with CM were combined with sensor data of the most recent automatic milking recorded for that same quarter, within a 24-h time window before the visual assessment time. Sensor data of 3.5 million QM were collected, of which 348 QM were combined with a CM observation. Data were divided into a training set, including two-thirds of all data, and a test set. Cows in the training set were not included in the test set and vice versa. A decision-tree model was trained using only clear examples of healthy (n=24,717) or diseased (n=243) QM. The model was tested on 105 QM with CM and a random sample of 50,000 QM without CM. While keeping the Se at a level comparable to that of models currently used by AMS, the decision-tree model was able to decrease the number of false-positive alerts by more than 50%. At an Sp of 99%, 40% of the CM cases were detected. Sixty-four percent of the severe CM cases were detected and only 12.5% of the CM that were scored as watery milk. The Se increased considerably from 40% to 66.7% when the time window increased from less than 24h before the CM observation, to a time window from 24h before to 24h after the CM observation. Even at very wide time windows, however, it was impossible to reach an Se of 100%. This indicates the inability to detect all CM cases based on sensor data alone. Sensitivity levels varied largely when the decision tree was validated per herd. This trend was confirmed when decision trees were trained using data from 8 herds and tested on data from the ninth herd. This indicates that when using the decision tree as a generic CM detection model in practice, some herds will continue having difficulties in detecting CM using mastitis alert lists, whereas others will perform well. Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Using Decision Structures for Policy Analysis in Software Product-line Evolution - A Case Study

NASA Astrophysics Data System (ADS)

Sarang, Nita; Sanglikar, Mukund A.

Project management decisions are the primary basis for project success (or failure). Mostly, such decisions are based on an intuitive understanding of the underlying software engineering and management process and have a likelihood of being misjudged. Our problem domain is product-line evolution. We model the dynamics of the process by incorporating feedback loops appropriate to two decision structures: staffing policy, and the forces of growth associated with long-term software evolution. The model is executable and supports project managers to assess the long-term effects of possible actions. Our work also corroborates results from earlier studies of E-type systems, in particular the FEAST project and the rules for software evolution, planning and management.

Program Monitoring: Problems and Cases.

ERIC Educational Resources Information Center

Lundin, Edward; Welty, Gordon

Designed as the major component of a comprehensive model of educational management, a behavioral model of decision making is presented that approximates the synoptic model of neoclassical economic theory. The synoptic model defines all possible alternatives and provides a basis for choosing that alternative which maximizes expected utility. The…

A robust multi-objective global supplier selection model under currency fluctuation and price discount

NASA Astrophysics Data System (ADS)

Zarindast, Atousa; Seyed Hosseini, Seyed Mohamad; Pishvaee, Mir Saman

2017-06-01

Robust supplier selection problem, in a scenario-based approach has been proposed, when the demand and exchange rates are subject to uncertainties. First, a deterministic multi-objective mixed integer linear programming is developed; then, the robust counterpart of the proposed mixed integer linear programming is presented using the recent extension in robust optimization theory. We discuss decision variables, respectively, by a two-stage stochastic planning model, a robust stochastic optimization planning model which integrates worst case scenario in modeling approach and finally by equivalent deterministic planning model. The experimental study is carried out to compare the performances of the three models. Robust model resulted in remarkable cost saving and it illustrated that to cope with such uncertainties, we should consider them in advance in our planning. In our case study different supplier were selected due to this uncertainties and since supplier selection is a strategic decision, it is crucial to consider these uncertainties in planning approach.

Simulating Spatial Growth Patterns in Developing Countries: A Case of Shama in the Western Region of Ghana.

NASA Astrophysics Data System (ADS)

Inkoom, J. N.; Nyarko, B. K.

2014-12-01

The integration of geographic information systems (GIS) and agent-based modelling (ABM) can be an efficient tool to improve spatial planning practices. This paper utilizes GIS and ABM approaches to simulate spatial growth patterns of settlement structures in Shama. A preliminary household survey on residential location decision-making choice served as the behavioural rule for household agents in the model. Physical environment properties of the model were extracted from a 2005 image implemented in NetLogo. The resulting growth pattern model was compared with empirical growth patterns to ascertain the model's accuracy. The paper establishes that the development of unplanned structures and its evolving structural pattern are a function of land price, proximity to economic centres, household economic status and location decision-making patterns. The application of the proposed model underlines its potential for integration into urban planning policies and practices, and for understanding residential decision-making processes in emerging cities in developing countries. Key Words: GIS; Agent-based modelling; Growth patterns; NetLogo; Location decision making; Computational Intelligence.

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.

Achieving Robustness to Uncertainty for Financial Decision-making

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

Barnum, George M.; Van Buren, Kendra L.; Hemez, Francois M.

2014-01-10

This report investigates the concept of robustness analysis to support financial decision-making. Financial models, that forecast future stock returns or market conditions, depend on assumptions that might be unwarranted and variables that might exhibit large fluctuations from their last-known values. The analysis of robustness explores these sources of uncertainty, and recommends model settings such that the forecasts used for decision-making are as insensitive as possible to the uncertainty. A proof-of-concept is presented with the Capital Asset Pricing Model. The robustness of model predictions is assessed using info-gap decision theory. Info-gaps are models of uncertainty that express the “distance,” or gapmore » of information, between what is known and what needs to be known in order to support the decision. The analysis yields a description of worst-case stock returns as a function of increasing gaps in our knowledge. The analyst can then decide on the best course of action by trading-off worst-case performance with “risk”, which is how much uncertainty they think needs to be accommodated in the future. The report also discusses the Graphical User Interface, developed using the MATLAB® programming environment, such that the user can control the analysis through an easy-to-navigate interface. Three directions of future work are identified to enhance the present software. First, the code should be re-written using the Python scientific programming software. This change will achieve greater cross-platform compatibility, better portability, allow for a more professional appearance, and render it independent from a commercial license, which MATLAB® requires. Second, a capability should be developed to allow users to quickly implement and analyze their own models. This will facilitate application of the software to the evaluation of proprietary financial models. The third enhancement proposed is to add the ability to evaluate multiple models simultaneously. When two models reflect past data with similar accuracy, the more robust of the two is preferable for decision-making because its predictions are, by definition, less sensitive to the uncertainty.« less

Restoration of contaminated ecosystems: adaptive management in a changing climate

USGS Publications Warehouse

Farag, Aida; Larson, Diane L.; Stauber, Jenny; Stahl, Ralph; Isanhart, John; McAbee, Kevin T.; Walsh, Christopher J.

2017-01-01

Three case studies illustrate how adaptive management (AM) has been used in ecological restorations that involve contaminants. Contaminants addressed include mercury, selenium, and contaminants and physical disturbances delivered to streams by urban stormwater runoff. All three cases emphasize the importance of broad stakeholder input early and consistently throughout decision analysis for AM. Risk of contaminant exposure provided input to the decision analyses (e.g. selenium exposure to endangered razorback suckers, Stewart Lake; multiple contaminants in urban stormwater runoff, Melbourne) and was balanced with the protection of resources critical for a desired future state (e.g. preservation old growth trees, South River). Monitoring also played a critical role in the ability to conduct the decision analyses necessary for AM plans. For example, newer technologies in the Melbourne case provided a testable situation where contaminant concentrations and flow disturbance were reduced to support a return to good ecological condition. In at least one case (Stewart Lake), long-term monitoring data are being used to document the potential effects of climate change on a restoration trajectory. Decision analysis formalized the process by which stakeholders arrived at the priorities for the sites, which together constituted the desired future condition towards which each restoration is aimed. Alternative models were developed that described in mechanistic terms how restoration can influence the system towards the desired future condition. Including known and anticipated effects of future climate scenarios in these models will make them robust to the long-term exposure and effects of contaminants in restored ecosystems.

Evidence accumulation in decision making: unifying the "take the best" and the "rational" models.

PubMed

Lee, Michael D; Cummins, Tarrant D R

2004-04-01

An evidence accumulation model of forced-choice decision making is proposed to unify the fast and frugal take the best (TTB) model and the alternative rational (RAT) model with which it is usually contrasted. The basic idea is to treat the TTB model as a sequential-sampling process that terminates as soon as any evidence in favor of a decision is found and the rational approach as a sequential-sampling process that terminates only when all available information has been assessed. The unified TTB and RAT models were tested in an experiment in which participants learned to make correct judgments for a set of real-world stimuli on the basis of feedback, and were then asked to make additional judgments without feedback for cases in which the TTB and the rational models made different predictions. The results show that, in both experiments, there was strong intraparticipant consistency in the use of either the TTB or the rational model but large interparticipant differences in which model was used. The unified model is shown to be able to capture the differences in decision making across participants in an interpretable way and is preferred by the minimum description length model selection criterion.

Locally adaptive decision in detection of clustered microcalcifications in mammograms.

PubMed

Sainz de Cea, María V; Nishikawa, Robert M; Yang, Yongyi

2018-02-15

In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric method which is based on a stochastic neighbor graph model of the detected objects. We demonstrated the proposed decision approach with two existing MC detectors on a set of 188 full-field digital mammograms (95 cases). The results, evaluated using free response operating characteristic (FROC) analysis, showed a significant improvement in detection accuracy by the proposed outlier decision approach over traditional thresholding (the partial area under the FROC curve increased from 3.95 to 4.25, p-value  <10 -4 ). There was also a reduction in case-to-case variability in detected FPs at a given sensitivity level. The proposed adaptive decision approach could not only reduce the number of FPs in detected MCs but also improve case-to-case consistency in detection.

Locally adaptive decision in detection of clustered microcalcifications in mammograms

NASA Astrophysics Data System (ADS)

Sainz de Cea, María V.; Nishikawa, Robert M.; Yang, Yongyi

2018-02-01

In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric method which is based on a stochastic neighbor graph model of the detected objects. We demonstrated the proposed decision approach with two existing MC detectors on a set of 188 full-field digital mammograms (95 cases). The results, evaluated using free response operating characteristic (FROC) analysis, showed a significant improvement in detection accuracy by the proposed outlier decision approach over traditional thresholding (the partial area under the FROC curve increased from 3.95 to 4.25, p-value  <10-4). There was also a reduction in case-to-case variability in detected FPs at a given sensitivity level. The proposed adaptive decision approach could not only reduce the number of FPs in detected MCs but also improve case-to-case consistency in detection.

The attentional drift-diffusion model extends to simple purchasing decisions.

PubMed

Krajbich, Ian; Lu, Dingchao; Camerer, Colin; Rangel, Antonio

2012-01-01

How do we make simple purchasing decisions (e.g., whether or not to buy a product at a given price)? Previous work has shown that the attentional drift-diffusion model (aDDM) can provide accurate quantitative descriptions of the psychometric data for binary and trinary value-based choices, and of how the choice process is guided by visual attention. Here we extend the aDDM to the case of purchasing decisions, and test it using an eye-tracking experiment. We find that the model also provides a reasonably accurate quantitative description of the relationship between choice, reaction time, and visual fixations using parameters that are very similar to those that best fit the previous data. The only critical difference is that the choice biases induced by the fixations are about half as big in purchasing decisions as in binary choices. This suggests that a similar computational process is used to make binary choices, trinary choices, and simple purchasing decisions.

Normative evidence accumulation in unpredictable environments

PubMed Central

Glaze, Christopher M; Kable, Joseph W; Gold, Joshua I

2015-01-01

In our dynamic world, decisions about noisy stimuli can require temporal accumulation of evidence to identify steady signals, differentiation to detect unpredictable changes in those signals, or both. Normative models can account for learning in these environments but have not yet been applied to faster decision processes. We present a novel, normative formulation of adaptive learning models that forms decisions by acting as a leaky accumulator with non-absorbing bounds. These dynamics, derived for both discrete and continuous cases, depend on the expected rate of change of the statistics of the evidence and balance signal identification and change detection. We found that, for two different tasks, human subjects learned these expectations, albeit imperfectly, then used them to make decisions in accordance with the normative model. The results represent a unified, empirically supported account of decision-making in unpredictable environments that provides new insights into the expectation-driven dynamics of the underlying neural signals. DOI: http://dx.doi.org/10.7554/eLife.08825.001 PMID:26322383

The Attentional Drift-Diffusion Model Extends to Simple Purchasing Decisions

PubMed Central

Krajbich, Ian; Lu, Dingchao; Camerer, Colin; Rangel, Antonio

2012-01-01

How do we make simple purchasing decisions (e.g., whether or not to buy a product at a given price)? Previous work has shown that the attentional drift-diffusion model (aDDM) can provide accurate quantitative descriptions of the psychometric data for binary and trinary value-based choices, and of how the choice process is guided by visual attention. Here we extend the aDDM to the case of purchasing decisions, and test it using an eye-tracking experiment. We find that the model also provides a reasonably accurate quantitative description of the relationship between choice, reaction time, and visual fixations using parameters that are very similar to those that best fit the previous data. The only critical difference is that the choice biases induced by the fixations are about half as big in purchasing decisions as in binary choices. This suggests that a similar computational process is used to make binary choices, trinary choices, and simple purchasing decisions. PMID:22707945

Groundwater Model Validation

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

Ahmed E. Hassan

2006-01-24

Models have an inherent uncertainty. The difficulty in fully characterizing the subsurface environment makes uncertainty an integral component of groundwater flow and transport models, which dictates the need for continuous monitoring and improvement. Building and sustaining confidence in closure decisions and monitoring networks based on models of subsurface conditions require developing confidence in the models through an iterative process. The definition of model validation is postulated as a confidence building and long-term iterative process (Hassan, 2004a). Model validation should be viewed as a process not an end result. Following Hassan (2004b), an approach is proposed for the validation process ofmore » stochastic groundwater models. The approach is briefly summarized herein and detailed analyses of acceptance criteria for stochastic realizations and of using validation data to reduce input parameter uncertainty are presented and applied to two case studies. During the validation process for stochastic models, a question arises as to the sufficiency of the number of acceptable model realizations (in terms of conformity with validation data). Using a hierarchical approach to make this determination is proposed. This approach is based on computing five measures or metrics and following a decision tree to determine if a sufficient number of realizations attain satisfactory scores regarding how they represent the field data used for calibration (old) and used for validation (new). The first two of these measures are applied to hypothetical scenarios using the first case study and assuming field data consistent with the model or significantly different from the model results. In both cases it is shown how the two measures would lead to the appropriate decision about the model performance. Standard statistical tests are used to evaluate these measures with the results indicating they are appropriate measures for evaluating model realizations. The use of validation data to constrain model input parameters is shown for the second case study using a Bayesian approach known as Markov Chain Monte Carlo. The approach shows a great potential to be helpful in the validation process and in incorporating prior knowledge with new field data to derive posterior distributions for both model input and output.« less

Land Resources Allocation Strategies in an Urban Area Involving Uncertainty: A Case Study of Suzhou, in the Yangtze River Delta of China

NASA Astrophysics Data System (ADS)

Lu, Shasha; Guan, Xingliang; Zhou, Min; Wang, Yang

2014-05-01

A large number of mathematical models have been developed to support land resource allocation decisions and land management needs; however, few of them can address various uncertainties that exist in relation to many factors presented in such decisions (e.g., land resource availabilities, land demands, land-use patterns, and social demands, as well as ecological requirements). In this study, a multi-objective interval-stochastic land resource allocation model (MOISLAM) was developed for tackling uncertainty that presents as discrete intervals and/or probability distributions. The developed model improves upon the existing multi-objective programming and inexact optimization approaches. The MOISLAM not only considers economic factors, but also involves food security and eco-environmental constraints; it can, therefore, effectively reflect various interrelations among different aspects in a land resource management system. Moreover, the model can also help examine the reliability of satisfying (or the risk of violating) system constraints under uncertainty. In this study, the MOISLAM was applied to a real case of long-term urban land resource allocation planning in Suzhou, in the Yangtze River Delta of China. Interval solutions associated with different risk levels of constraint violation were obtained. The results are considered useful for generating a range of decision alternatives under various system conditions, and thus helping decision makers to identify a desirable land resource allocation strategy under uncertainty.

Decision analysis of emergency ventilation and evacuation strategies against suddenly released contaminant indoors by considering the uncertainty of source locations.

PubMed

Cai, Hao; Long, Weiding; Li, Xianting; Kong, Lingjuan; Xiong, Shuang

2010-06-15

In case hazardous contaminants are suddenly released indoors, the prompt and proper emergency responses are critical to protect occupants. This paper aims to provide a framework for determining the optimal combination of ventilation and evacuation strategies by considering the uncertainty of source locations. The certainty of source locations is classified as complete certainty, incomplete certainty, and complete uncertainty to cover all the possible situations. According to this classification, three types of decision analysis models are presented. A new concept, efficiency factor of contaminant source (EFCS), is incorporated in these models to evaluate the payoffs of the ventilation and evacuation strategies. A procedure of decision-making based on these models is proposed and demonstrated by numerical studies of one hundred scenarios with ten ventilation modes, two evacuation modes, and five source locations. The results show that the models can be useful to direct the decision analysis of both the ventilation and evacuation strategies. In addition, the certainty of the source locations has an important effect on the outcomes of the decision-making. Copyright 2010 Elsevier B.V. All rights reserved.

Modelling emergency decisions: recognition-primed decision making. The literature in relation to an ophthalmic critical incident.

PubMed

Bond, Susan; Cooper, Simon

2006-08-01

To review and reflect on the literature on recognition-primed decision (RPD) making and influences on emergency decisions with particular reference to an ophthalmic critical incident involving the sub-arachnoid spread of local anaesthesia following the peribulbar injection. This paper critics the literature on recognition-primed decision making, with particular reference to emergency situations. It illustrates the findings by focussing on an ophthalmic critical incident. Systematic literature review with critical incident reflection. Medline, CINAHL and PsychINFO databases were searched for papers on recognition-primed decision making (1996-2004) followed by the 'snowball method'. Studies were selected in accordance with preset criteria. A total of 12 papers were included identifying the recognition-primed decision making as a good theoretical description of acute emergency decisions. In addition, cognitive resources, situational awareness, stress, team support and task complexity were identified as influences on the decision process. Recognition-primed decision-making theory describes the decision processes of experts in time-bound emergency situations and is the foundation for a model of emergency decision making (Fig. 2). Decision theory and models, in this case related to emergency situations, inform practice and enhance clinical effectiveness. The critical incident described highlights the need for nurses to have a comprehensive and in-depth understanding of anaesthetic techniques as well as an ability to manage and resuscitate patients autonomously. In addition, it illustrates how the critical incidents should influence the audit cycle with improvements in patient safety.

The 2014 Sandia Verification and Validation Challenge: Problem statement

DOE PAGES

Hu, Kenneth; Orient, George

2016-01-18

This paper presents a case study in utilizing information from experiments, models, and verification and validation (V&V) to support a decision. It consists of a simple system with data and models provided, plus a safety requirement to assess. The goal is to pose a problem that is flexible enough to allow challengers to demonstrate a variety of approaches, but constrained enough to focus attention on a theme. This was accomplished by providing a good deal of background information in addition to the data, models, and code, but directing the participants' activities with specific deliverables. In this challenge, the theme ismore » how to gather and present evidence about the quality of model predictions, in order to support a decision. This case study formed the basis of the 2014 Sandia V&V Challenge Workshop and this resulting special edition of the ASME Journal of Verification, Validation, and Uncertainty Quantification.« less

Comparing multi-criteria decision analysis and integrated assessment to support long-term water supply planning

PubMed Central

Maurer, Max; Lienert, Judit

2017-01-01

We compare the use of multi-criteria decision analysis (MCDA)–or more precisely, models used in multi-attribute value theory (MAVT)–to integrated assessment (IA) models for supporting long-term water supply planning in a small town case study in Switzerland. They are used to evaluate thirteen system scale water supply alternatives in four future scenarios regarding forty-four objectives, covering technical, social, environmental, and economic aspects. The alternatives encompass both conventional and unconventional solutions and differ regarding technical, spatial and organizational characteristics. This paper focuses on the impact assessment and final evaluation step of the structured MCDA decision support process. We analyze the performance of the alternatives for ten stakeholders. We demonstrate the implications of model assumptions by comparing two IA and three MAVT evaluation model layouts of different complexity. For this comparison, we focus on the validity (ranking stability), desirability (value), and distinguishability (value range) of the alternatives given the five model layouts. These layouts exclude or include stakeholder preferences and uncertainties. Even though all five led us to identify the same best alternatives, they did not produce identical rankings. We found that the MAVT-type models provide higher distinguishability and a more robust basis for discussion than the IA-type models. The needed complexity of the model, however, should be determined based on the intended use of the model within the decision support process. The best-performing alternatives had consistently strong performance for all stakeholders and future scenarios, whereas the current water supply system was outperformed in all evaluation layouts. The best-performing alternatives comprise proactive pipe rehabilitation, adapted firefighting provisions, and decentralized water storage and/or treatment. We present recommendations for possible ways of improving water supply planning in the case study and beyond. PMID:28481881

The Marathi "taskonomy" of respiratory illnesses in children.

PubMed

Chand, A D; Bhattacharyya, K

1994-05-01

The reduction of childhood mortality from respiratory infections depends upon a case management strategy which encourages the decision to seek treatment. The research discussed, conducted in Pachod, Maharashtra, India, uses a "taskonomic" approach to understanding the diagnosis of illness and treatment decisions. This approach views treatment decisions as a result not only of local conceptual models of illness, but also of the specific circumstances of illness episodes involving different types of social relationships and control over resources.

Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016.

PubMed

Zeng, Qianglin; Li, Dandan; Huang, Gui; Xia, Jin; Wang, Xiaoming; Zhang, Yamei; Tang, Wanping; Zhou, Hui

2016-08-31

Short-term forecast of pertussis incidence is helpful for advanced warning and planning resource needs for future epidemics. By utilizing the Auto-Regressive Integrated Moving Average (ARIMA) model and Exponential Smoothing (ETS) model as alterative models with R software, this paper analyzed data from Chinese Center for Disease Control and Prevention (China CDC) between January 2005 and June 2016. The ARIMA (0,1,0)(1,1,1)12 model (AICc = 1342.2 BIC = 1350.3) was selected as the best performing ARIMA model and the ETS (M,N,M) model (AICc = 1678.6, BIC = 1715.4) was selected as the best performing ETS model, and the ETS (M,N,M) model with the minimum RMSE was finally selected for in-sample-simulation and out-of-sample forecasting. Descriptive statistics showed that the reported number of pertussis cases by China CDC increased by 66.20% from 2005 (4058 cases) to 2015 (6744 cases). According to Hodrick-Prescott filter, there was an apparent cyclicity and seasonality in the pertussis reports. In out of sample forecasting, the model forecasted a relatively high incidence cases in 2016, which predicates an increasing risk of ongoing pertussis resurgence in the near future. In this regard, the ETS model would be a useful tool in simulating and forecasting the incidence of pertussis, and helping decision makers to take efficient decisions based on the advanced warning of disease incidence.

Hybrid supply chain model for material requirement planning under financial constraints: A case study

NASA Astrophysics Data System (ADS)

Curci, Vita; Dassisti, Michele; Josefa, Mula Bru; Manuel, Díaz Madroñero

2014-10-01

Supply chain model (SCM) are potentially capable to integrate different aspects in supporting decision making for enterprise management tasks. The aim of the paper is to propose an hybrid mathematical programming model for optimization of production requirements resources planning. The preliminary model was conceived bottom-up from a real industrial case analysed oriented to maximize cash flow. Despite the intense computational effort required to converge to a solution, optimisation done brought good result in solving the objective function.

Decisons To Promote or Hire: The Case of University Administrative Appointments. ASHE Annual Meeting Paper.

ERIC Educational Resources Information Center

Johnsrud, Linda K.; Sagaria, Mary Ann D.

Internal labor market theory is extended to identify market domains that influence administrative staffing decisions, and a theoretical predictive model about the role of market domains in decisions to promote or hire is proposed and tested. Information is presented as follows: theoretical framework; labor markets within higher education; and the…

Decision Maker Perception of Information Quality: A Case Study of Military Command and Control

ERIC Educational Resources Information Center

Morgan, Grayson B.

2013-01-01

Decision maker perception of information quality cues from an "information system" (IS) and the process which creates such meta cueing, or data about cues, is a critical yet un-modeled component of "situation awareness" (SA). Examples of common information quality meta cueing for quality criteria include custom ring-tones for…

The Organizational Decision Making Climate of Issues Management Programs: A Case Study.

ERIC Educational Resources Information Center

Wills, Sandra

A study examined the decision making climate of organizations that are using issues management and what type of model of issues management is followed--theorists have been attempting to define issues management since it began appearing 20 year ago. Subjects, 112 males and 30 females who were professionals working in the area of issues management…

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.

Networks of conforming or nonconforming individuals tend to reach satisfactory decisions.

PubMed

Ramazi, Pouria; Riehl, James; Cao, Ming

2016-11-15

Binary decisions of agents coupled in networks can often be classified into two types: "coordination," where an agent takes an action if enough neighbors are using that action, as in the spread of social norms, innovations, and viral epidemics, and "anticoordination," where too many neighbors taking a particular action causes an agent to take the opposite action, as in traffic congestion, crowd dispersion, and division of labor. Both of these cases can be modeled using linear-threshold-based dynamics, and a fundamental question is whether the individuals in such networks are likely to reach decisions with which they are satisfied. We show that, in the coordination case, and perhaps more surprisingly, also in the anticoordination case, the agents will indeed always tend to reach satisfactory decisions, that is, the network will almost surely reach an equilibrium state. This holds for every network topology and every distribution of thresholds, for both asynchronous and partially synchronous decision-making updates. These results reveal that irregular network topology, population heterogeneity, and partial synchrony are not sufficient to cause cycles or nonconvergence in linear-threshold dynamics; rather, other factors such as imitation or the coexistence of coordinating and anticoordinating agents must play a role.

On the scene: St Mary's Hospital, Madison, Wisconsin.

PubMed

Baker, Christine; Beglinger, Joan Ellis; Derosa, Jody; Griffin, Carla; Laham, Mary; Leonard, Mary Kay; Vanderkolk, Caprice

2009-01-01

In this article, we discuss Shared Governance as the foundation of our nursing professional practice model. Through the use of case examples and reflections from our management team, we demonstrate how this accountability-based practice model promotes excellence through developing, connecting, and engaging people, clarifying and communicating goals, using data to make decisions, and even shaping our organizational response to a critical incident. We close with a look to our future as our hospital embraces whole-system shared decision making.

Challenging Operations: An Ethical Framework to Assist Humanitarian Aid Workers in their Decision-making Processes.

PubMed

Clarinval, Caroline; Biller-Andorno, Nikola

2014-06-23

This paper aims to raise awareness regarding ethical issues in the context of humanitarian action, and to offer a framework for systematically and effectively addressing such issues. Several cases highlight ethical issues that humanitarian aid workers are confronted with at different levels over the course of their deployments. The first case discusses a situation at a macro-level concerning decisions being made at the headquarters of a humanitarian organization. The second case looks at meso-level issues that need to be solved at a country or regional level. The third case proposes an ethical dilemma at the micro-level of the individual patient-provider relationship. These real-life cases have been selected to illustrate the ethical dimension of conflicts within the context of humanitarian action that might remain unrecognized in everyday practice. In addition, we propose an ethical framework to assist humanitarian aid workers in their decision-making process. The framework draws on the principles and values that guide humanitarian action and public health ethics more generally. Beyond identifying substantive core values, the framework also includes a ten-step process modelled on tools used in the clinical setting that promotes a transparent and clear decision-making process and improves the monitoring and evaluation of aid interventions. Finally, we recommend organizational measures to implement the framework effectively. This paper uses a combination of public health/clinical ethics concepts and practices and applies them to the decision-making challenges encountered in relief operations in the humanitarian aid context.

>From individual choice to group decision-making

NASA Astrophysics Data System (ADS)

Galam, Serge; Zucker, Jean-Daniel

2000-12-01

Some universal features are independent of both the social nature of the individuals making the decision and the nature of the decision itself. On this basis a simple magnet like model is built. Pair interactions are introduced to measure the degree of exchange among individuals while discussing. An external uniform field is included to account for a possible pressure from outside. Individual biases with respect to the issue at stake are also included using local random fields. A unique postulate of minimum conflict is assumed. The model is then solved with emphasis on its psycho-sociological implications. Counter-intuitive results are obtained. At this stage no new physical technicality is involved. Instead the full psycho-sociological implications of the model are drawn. Few cases are then detailed to enlight them. In addition, several numerical experiments based on our model are shown to give both an insight on the dynamics of the model and suggest further research directions.

Discrete Event Simulation for Decision Modeling in Health Care: Lessons from Abdominal Aortic Aneurysm Screening

PubMed Central

Jones, Edmund; Masconi, Katya L.; Sweeting, Michael J.; Thompson, Simon G.; Powell, Janet T.

2018-01-01

Markov models are often used to evaluate the cost-effectiveness of new healthcare interventions but they are sometimes not flexible enough to allow accurate modeling or investigation of alternative scenarios and policies. A Markov model previously demonstrated that a one-off invitation to screening for abdominal aortic aneurysm (AAA) for men aged 65 y in the UK and subsequent follow-up of identified AAAs was likely to be highly cost-effective at thresholds commonly adopted in the UK (£20,000 to £30,000 per quality adjusted life-year). However, new evidence has emerged and the decision problem has evolved to include exploration of the circumstances under which AAA screening may be cost-effective, which the Markov model is not easily able to address. A new model to handle this more complex decision problem was needed, and the case of AAA screening thus provides an illustration of the relative merits of Markov models and discrete event simulation (DES) models. An individual-level DES model was built using the R programming language to reflect possible events and pathways of individuals invited to screening v. those not invited. The model was validated against key events and cost-effectiveness, as observed in a large, randomized trial. Different screening protocol scenarios were investigated to demonstrate the flexibility of the DES. The case of AAA screening highlights the benefits of DES, particularly in the context of screening studies.

A conversation-based process tracing method for use with naturalistic decisions: an evaluation study.

PubMed

Williamson, J; Ranyard, R; Cuthbert, L

2000-05-01

This study is an evaluation of a process tracing method developed for naturalistic decisions, in this case a consumer choice task. The method is based on Huber et al.'s (1997) Active Information Search (AIS) technique, but develops it by providing spoken rather than written answers to respondents' questions, and by including think aloud instructions. The technique is used within a conversation-based situation, rather than the respondent thinking aloud 'into an empty space', as is conventionally the case in think aloud techniques. The method results in a concurrent verbal protocol as respondents make their decisions, and a retrospective report in the form of a post-decision summary. The method was found to be virtually non-reactive in relation to think aloud, although the variable of Preliminary Attribute Elicitation showed some evidence of reactivity. This was a methodological evaluation, and as such the data reported are essentially descriptive. Nevertheless, the data obtained indicate that the method is capable of producing information about decision processes which could have theoretical importance in terms of evaluating models of decision-making.

Business Case Analysis of the Towed Gilder Air Launched System (TGALS)

NASA Technical Reports Server (NTRS)

Webb, Darryl W.; Nguyen, McLinton B.; Seibold, Robert W.; Wong, Frank C.; Budd, Gerald D.

2017-01-01

The Aerospace Corporation developed an integrated Business Case Analysis (BCA) model on behalf of the NASA Armstrong Flight Research Center (AFRC). This model evaluated the potential profitability of the Towed Glider Air Launched System (TGALS) concept, under development at AFRC, identifying potential technical, programmatic, and business decisions that could improve its business viability. The model addressed system performance metrics; development, production and operation cost estimates; market size and product service positioning; pricing alternatives; and market share.

Linking guidelines to Electronic Health Record design for improved chronic disease management.

PubMed

Barretto, Sistine A; Warren, Jim; Goodchild, Andrew; Bird, Linda; Heard, Sam; Stumptner, Markus

2003-01-01

The promise of electronic decision support to promote evidence based practice remains elusive in the context of chronic disease management. We examine the problem of achieving a close relationship of Electronic Health Record (EHR) content to other components of a clinical information system (guidelines, decision support and workflow), particularly linking the decisions made by providers back to the guidelines. We use the openEHR architecture, which allows extension of a core Reference Model via Archetypes to refine the detailed information recording options for specific classes of encounter. We illustrate the use of openEHR for tracking the relationship of a series of clinical encounters to a guideline via a case study of guideline-compliant treatment of hypertension in diabetes. This case study shows the contribution guideline content can have on problem-specific EHR structure and demonstrates the potential for a constructive interaction of electronic decision support and the EHR.

Linking Guidelines to Electronic Health Record Design for Improved Chronic Disease Management

PubMed Central

Barretto, Sistine A.; Warren, Jim; Goodchild, Andrew; Bird, Linda; Heard, Sam; Stumptner, Markus

2003-01-01

The promise of electronic decision support to promote evidence based practice remains elusive in the context of chronic disease management. We examine the problem of achieving a close relationship of Electronic Health Record (EHR) content to other components of a clinical information system (guidelines, decision support and work-flow), particularly linking the decisions made by providers back to the guidelines. We use the openEHR architecture, which allows extension of a core Reference Model via Archetypes to refine the detailed information recording options for specific classes of encounter. We illustrate the use of openEHR for tracking the relationship of a series of clinical encounters to a guideline via a case study of guideline-compliant treatment of hypertension in diabetes. This case study shows the contribution guideline content can have on problem-specific EHR structure and demonstrates the potential for a constructive interaction of electronic decision support and the EHR. PMID:14728135

Building Capacity to Use Earth Observations in Decision Making: A Case Study of NASA's DEVELOP National Program Methods and Best Practices

NASA Astrophysics Data System (ADS)

Childs-Gleason, L. M.; Ross, K. W.; Crepps, G.; Miller, T. N.; Favors, J. E.; Rogers, L.; Allsbrook, K. N.; Bender, M. R.; Ruiz, M. L.

2015-12-01

NASA's DEVELOP National Program fosters an immersive research environment for dual capacity building. Through rapid feasibility Earth science projects, the future workforce and current decision makers are engaged in research projects to build skills and capabilities to use Earth observation in environmental management and policy making. DEVELOP conducts over 80 projects annually, successfully building skills through partnerships with over 150 organizations and providing over 350 opportunities for project participants each year. Filling a void between short-term training courses and long-term research projects, the DEVELOP model has been successful in supporting state, local, federal and international government organizations to adopt methodologies and enhance decision making processes. This presentation will highlight programmatic best practices, feedback from participants and partner organizations, and three sample case studies of successful adoption of methods in the decision making process.

A two-phased fuzzy decision making procedure for IT supplier selection

NASA Astrophysics Data System (ADS)

Shohaimay, Fairuz; Ramli, Nazirah; Mohamed, Siti Rosiah; Mohd, Ainun Hafizah

2013-09-01

In many studies on fuzzy decision making, linguistic terms are usually represented by corresponding fixed triangular or trapezoidal fuzzy numbers. However, the fixed fuzzy numbers used in decision making process may not explain the actual respondents' opinions. Hence, a two-phased fuzzy decision making procedure is proposed. First, triangular fuzzy numbers were built based on respondents' opinions on the appropriate range (0-100) for each seven-scale linguistic terms. Then, the fuzzy numbers were integrated into fuzzy decision making model. The applicability of the proposed method is demonstrated in a case study of supplier selection in Information Technology (IT) department. The results produced via the developed fuzzy numbers were consistent with the results obtained using fixed fuzzy numbers. However, with different set of fuzzy numbers based on respondents, there is a difference in the ranking of suppliers based on criterion X1 (background of supplier). Hopefully the proposed model which incorporates fuzzy numbers based on respondents will provide a more significant meaning towards future decision making.

"Best Case/Worst Case": Qualitative Evaluation of a Novel Communication Tool for Difficult in-the-Moment Surgical Decisions.

PubMed

Kruser, Jacqueline M; Nabozny, Michael J; Steffens, Nicole M; Brasel, Karen J; Campbell, Toby C; Gaines, Martha E; Schwarze, Margaret L

2015-09-01

To evaluate a communication tool called "Best Case/Worst Case" (BC/WC) based on an established conceptual model of shared decision-making. Focus group study. Older adults (four focus groups) and surgeons (two focus groups) using modified questions from the Decision Aid Acceptability Scale and the Decisional Conflict Scale to evaluate and revise the communication tool. Individuals aged 60 and older recruited from senior centers (n = 37) and surgeons from academic and private practices in Wisconsin (n = 17). Qualitative content analysis was used to explore themes and concepts that focus group respondents identified. Seniors and surgeons praised the tool for the unambiguous illustration of multiple treatment options and the clarity gained from presentation of an array of treatment outcomes. Participants noted that the tool provides an opportunity for in-the-moment, preference-based deliberation about options and a platform for further discussion with other clinicians and loved ones. Older adults worried that the format of the tool was not universally accessible for people with different educational backgrounds, and surgeons had concerns that the tool was vulnerable to physicians' subjective biases. The BC/WC tool is a novel decision support intervention that may help facilitate difficult decision-making for older adults and their physicians when considering invasive, acute medical treatments such as surgery. © 2015, Copyright the Authors Journal compilation © 2015, The American Geriatrics Society.

Opinion Dynamics and Decision of Vote in Bipolar Political Systems

NASA Astrophysics Data System (ADS)

Caruso, Filippo; Castorina, Paolo

A model of the opinion dynamics underlying the political decision is proposed. The analysis is restricted to a bipolar scheme with a possible third political area. The interaction among voters is local but the final decision strongly depends on global effects such as the rating of the governments. As in the realistic case, the individual decision making process is determined by the most relevant personal interests and problems. The phenomenological analysis of the national vote in Italy and Germany has been carried out and a prediction of the next Italian vote as a function of the government rating is presented.

Toward a Psychology of Surrogate Decision Making.

PubMed

Tunney, Richard J; Ziegler, Fenja V

2015-11-01

In everyday life, many of the decisions that we make are made on behalf of other people. A growing body of research suggests that we often, but not always, make different decisions on behalf of other people than the other person would choose. This is problematic in the practical case of legally designated surrogate decision makers, who may not meet the substituted judgment standard. Here, we review evidence from studies of surrogate decision making and examine the extent to which surrogate decision making accurately predicts the recipient's wishes, or if it is an incomplete or distorted application of the surrogate's own decision-making processes. We find no existing domain-general model of surrogate decision making. We propose a framework by which surrogate decision making can be assessed and a novel domain-general theory as a unifying explanatory concept for surrogate decisions. © The Author(s) 2015.

Parental decision-making on utilisation of out-of-home respite in children's palliative care: findings of qualitative case study research - a proposed new model.

PubMed

Ling, J; Payne, S; Connaire, K; McCarron, M

2016-01-01

Respite in children's palliative care aims to provide a break for family's from the routine of caring. Parental decision-making regarding the utilisation of out-of-home respite is dependent on many interlinking factors including the child's age, diagnosis, geographical location and the family's capacity to meet their child's care needs. A proposed model for out-of-home respite has been developed based on the findings of qualitative case study research. Utilising multiple, longitudinal, qualitative case study design, the respite needs and experiences of parents caring for a child with a life-limiting condition were explored. Multiple, in-depth interviews were undertaken with the parents identified by a hospital-based children's palliative care team. Data were analysed using thematic analysis. Each individual case consists of a whole study. Cross-case comparison was also conducted. Nine families were recruited and followed for two years. A total of 19 in-depth interviews were conducted with mothers and fathers (one or both) caring for a child with a life-limiting condition in Ireland. Each family reported vastly different needs and experiences of respite from their own unique perspective. Cross-case comparison showed that for all parents utilising respite care, regardless of their child's age and condition, home was the location of choice. Many interlinking factors influencing these decisions included: past experience of in-patient care, and trust and confidence in care providers. Issues were raised regarding the impact of care provision in the home on family life, siblings and the concept of home. Respite is an essential element of children's palliative care. Utilisation of out-of-home respite is heavily dependent on a number of interlinked and intertwined factors. The proposed model of care offers an opportunity to identify how these decisions are made and may ultimately assist in identifying the elements of responsive and family-focused respite that are important to families of children with life-limiting conditions. © 2015 John Wiley & Sons Ltd.

COMPARING THE UTILITY OF MULTIMEDIA MODELS FOR HUMAN AND ECOLOGICAL EXPOSURE ANALYSIS: TWO CASES

EPA Science Inventory

A number of models are available for exposure assessment; however, few are used as tools for both human and ecosystem risks. This discussion will consider two modeling frameworks that have recently been used to support human and ecological decision making. The study will compare ...

A Custody Evaluation Model for Pre-School Children.

ERIC Educational Resources Information Center

Roseby, Vivienne

This document addresses the needs of mental health consultants involved in decision-making in custody disputes. A psycho-ecological model for assessing contexts of development in cases involving preschool children is presented, and the theoretical basis and rationale for the model are discussed. Issues, instruments, and findings of recent…

A state-of-the-art review of transportation systems evaluation techniques relevant to air transportation, volume 1. [urban planning and urban transportation using decision theory

NASA Technical Reports Server (NTRS)

Haefner, L. E.

1975-01-01

Mathematical and philosophical approaches are presented for evaluation and implementation of ground and air transportation systems. Basic decision processes are examined that are used for cost analyses and planning (i.e, statistical decision theory, linear and dynamic programming, optimization, game theory). The effects on the environment and the community that a transportation system may have are discussed and modelled. Algorithmic structures are examined and selected bibliographic annotations are included. Transportation dynamic models were developed. Citizen participation in transportation projects (i.e, in Maryland and Massachusetts) is discussed. The relevance of the modelling and evaluation approaches to air transportation (i.e, airport planning) is examined in a case study in St. Louis, Missouri.

A decision support model for improving a multi-family housing complex based on CO2 emission from electricity consumption.

PubMed

Hong, Taehoon; Koo, Choongwan; Kim, Hyunjoong

2012-12-15

The number of deteriorated multi-family housing complexes in South Korea continues to rise, and consequently their electricity consumption is also increasing. This needs to be addressed as part of the nation's efforts to reduce energy consumption. The objective of this research was to develop a decision support model for determining the need to improve multi-family housing complexes. In this research, 1664 cases located in Seoul were selected for model development. The research team collected the characteristics and electricity energy consumption data of these projects in 2009-2010. The following were carried out in this research: (i) using the Decision Tree, multi-family housing complexes were clustered based on their electricity energy consumption; (ii) using Case-Based Reasoning, similar cases were retrieved from the same cluster; and (iii) using a combination of Multiple Regression Analysis, Artificial Neural Network, and Genetic Algorithm, the prediction performance of the developed model was improved. The results of this research can be used as follows: (i) as basic research data for continuously managing several energy consumption data of multi-family housing complexes; (ii) as advanced research data for predicting energy consumption based on the project characteristics; (iii) as practical research data for selecting the most optimal multi-family housing complex with the most potential in terms of energy savings; and (iv) as consistent and objective criteria for incentives and penalties. Copyright © 2012 Elsevier Ltd. All rights reserved.

Using genetic algorithms to optimise current and future health planning--the example of ambulance locations.

PubMed

Sasaki, Satoshi; Comber, Alexis J; Suzuki, Hiroshi; Brunsdon, Chris

2010-01-28

Ambulance response time is a crucial factor in patient survival. The number of emergency cases (EMS cases) requiring an ambulance is increasing due to changes in population demographics. This is decreasing ambulance response times to the emergency scene. This paper predicts EMS cases for 5-year intervals from 2020, to 2050 by correlating current EMS cases with demographic factors at the level of the census area and predicted population changes. It then applies a modified grouping genetic algorithm to compare current and future optimal locations and numbers of ambulances. Sets of potential locations were evaluated in terms of the (current and predicted) EMS case distances to those locations. Future EMS demands were predicted to increase by 2030 using the model (R2 = 0.71). The optimal locations of ambulances based on future EMS cases were compared with current locations and with optimal locations modelled on current EMS case data. Optimising the location of ambulance stations locations reduced the average response times by 57 seconds. Current and predicted future EMS demand at modelled locations were calculated and compared. The reallocation of ambulances to optimal locations improved response times and could contribute to higher survival rates from life-threatening medical events. Modelling EMS case 'demand' over census areas allows the data to be correlated to population characteristics and optimal 'supply' locations to be identified. Comparing current and future optimal scenarios allows more nuanced planning decisions to be made. This is a generic methodology that could be used to provide evidence in support of public health planning and decision making.

A Behavioural Approach to Understanding Semi-Subsistence Farmers' Technology Adoption Decisions: The Case of Improved Paddy-Prawn System in Indonesia

ERIC Educational Resources Information Center

Sambodo, Leonardo A. A. T.; Nuthall, Peter L.

2010-01-01

Purpose: This study traced the origins of subsistence Farmers' technology adoption attitudes and extracted the critical elements in their decision making systems. Design/Methodology/Approach: The analysis was structured using a model based on the Theory of Planned Behaviour (TPB). The role of a "bargaining process" was particularly…

Air Force Nuclear Enterprise Organization: A Case Study

DTIC Science & Technology

2016-09-15

will improve the performance of the AFNE. Based on analysis of commercial and industrial business models, what organizational structure , or...Business Dictionary 2015). Organizational structures will be developed based on decisions made with regards to design. The core of an...work flows. Based on design parameter decisions, senior leaders will establish an organizational structure that includes the layout of the

Generalisability in economic evaluation studies in healthcare: a review and case studies.

PubMed

Sculpher, M J; Pang, F S; Manca, A; Drummond, M F; Golder, S; Urdahl, H; Davies, L M; Eastwood, A

2004-12-01

To review, and to develop further, the methods used to assess and to increase the generalisability of economic evaluation studies. Electronic databases. Methodological studies relating to economic evaluation in healthcare were searched. This included electronic searches of a range of databases, including PREMEDLINE, MEDLINE, EMBASE and EconLit, and manual searches of key journals. The case studies of a decision analytic model involved highlighting specific features of previously published economic studies related to generalisability and location-related variability. The case-study involving the secondary analysis of cost-effectiveness analyses was based on the secondary analysis of three economic studies using data from randomised trials. The factor most frequently cited as generating variability in economic results between locations was the unit costs associated with particular resources. In the context of studies based on the analysis of patient-level data, regression analysis has been advocated as a means of looking at variability in economic results across locations. These methods have generally accepted that some components of resource use and outcomes are exchangeable across locations. Recent studies have also explored, in cost-effectiveness analysis, the use of tests of heterogeneity similar to those used in clinical evaluation in trials. The decision analytic model has been the main means by which cost-effectiveness has been adapted from trial to non-trial locations. Most models have focused on changes to the cost side of the analysis, but it is clear that the effectiveness side may also need to be adapted between locations. There have been weaknesses in some aspects of the reporting in applied cost-effectiveness studies. These may limit decision-makers' ability to judge the relevance of a study to their specific situations. The case study demonstrated the potential value of multilevel modelling (MLM). Where clustering exists by location (e.g. centre or country), MLM can facilitate correct estimates of the uncertainty in cost-effectiveness results, and also a means of estimating location-specific cost-effectiveness. The review of applied economic studies based on decision analytic models showed that few studies were explicit about their target decision-maker(s)/jurisdictions. The studies in the review generally made more effort to ensure that their cost inputs were specific to their target jurisdiction than their effectiveness parameters. Standard sensitivity analysis was the main way of dealing with uncertainty in the models, although few studies looked explicitly at variability between locations. The modelling case study illustrated how effectiveness and cost data can be made location-specific. In particular, on the effectiveness side, the example showed the separation of location-specific baseline events and pooled estimates of relative treatment effect, where the latter are assumed exchangeable across locations. A large number of factors are mentioned in the literature that might be expected to generate variation in the cost-effectiveness of healthcare interventions across locations. Several papers have demonstrated differences in the volume and cost of resource use between locations, but few studies have looked at variability in outcomes. In applied trial-based cost-effectiveness studies, few studies provide sufficient evidence for decision-makers to establish the relevance or to adjust the results of the study to their location of interest. Very few studies utilised statistical methods formally to assess the variability in results between locations. In applied economic studies based on decision models, most studies either stated their target decision-maker/jurisdiction or provided sufficient information from which this could be inferred. There was a greater tendency to ensure that cost inputs were specific to the target jurisdiction than clinical parameters. Methods to assess generalisability and variability in economic evaluation studies have been discussed extensively in the literature relating to both trial-based and modelling studies. Regression-based methods are likely to offer a systematic approach to quantifying variability in patient-level data. In particular, MLM has the potential to facilitate estimates of cost-effectiveness, which both reflect the variation in costs and outcomes between locations and also enable the consistency of cost-effectiveness estimates between locations to be assessed directly. Decision analytic models will retain an important role in adapting the results of cost-effectiveness studies between locations. Recommendations for further research include: the development of methods of evidence synthesis which model the exchangeability of data across locations and allow for the additional uncertainty in this process; assessment of alternative approaches to specifying multilevel models to the analysis of cost-effectiveness data alongside multilocation randomised trials; identification of a range of appropriate covariates relating to locations (e.g. hospitals) in multilevel models; and further assessment of the role of econometric methods (e.g. selection models) for cost-effectiveness analysis alongside observational datasets, and to increase the generalisability of randomised trials.

Measuring and Modeling Behavioral Decision Dynamics in Collective Evacuation

PubMed Central

Carlson, Jean M.; Alderson, David L.; Stromberg, Sean P.; Bassett, Danielle S.; Craparo, Emily M.; Guiterrez-Villarreal, Francisco; Otani, Thomas

2014-01-01

Identifying and quantifying factors influencing human decision making remains an outstanding challenge, impacting the performance and predictability of social and technological systems. In many cases, system failures are traced to human factors including congestion, overload, miscommunication, and delays. Here we report results of a behavioral network science experiment, targeting decision making in a natural disaster. In a controlled laboratory setting, our results quantify several key factors influencing individual evacuation decision making in a controlled laboratory setting. The experiment includes tensions between broadcast and peer-to-peer information, and contrasts the effects of temporal urgency associated with the imminence of the disaster and the effects of limited shelter capacity for evacuees. Based on empirical measurements of the cumulative rate of evacuations as a function of the instantaneous disaster likelihood, we develop a quantitative model for decision making that captures remarkably well the main features of observed collective behavior across many different scenarios. Moreover, this model captures the sensitivity of individual- and population-level decision behaviors to external pressures, and systematic deviations from the model provide meaningful estimates of variability in the collective response. Identification of robust methods for quantifying human decisions in the face of risk has implications for policy in disasters and other threat scenarios, specifically the development and testing of robust strategies for training and control of evacuations that account for human behavior and network topologies. PMID:24520331

The effect of model uncertainty on cooperation in sensorimotor interactions

PubMed Central

Grau-Moya, J.; Hez, E.; Pezzulo, G.; Braun, D. A.

2013-01-01

Decision-makers have been shown to rely on probabilistic models for perception and action. However, these models can be incorrect or partially wrong in which case the decision-maker has to cope with model uncertainty. Model uncertainty has recently also been shown to be an important determinant of sensorimotor behaviour in humans that can lead to risk-sensitive deviations from Bayes optimal behaviour towards worst-case or best-case outcomes. Here, we investigate the effect of model uncertainty on cooperation in sensorimotor interactions similar to the stag-hunt game, where players develop models about the other player and decide between a pay-off-dominant cooperative solution and a risk-dominant, non-cooperative solution. In simulations, we show that players who allow for optimistic deviations from their opponent model are much more likely to converge to cooperative outcomes. We also implemented this agent model in a virtual reality environment, and let human subjects play against a virtual player. In this game, subjects' pay-offs were experienced as forces opposing their movements. During the experiment, we manipulated the risk sensitivity of the computer player and observed human responses. We found not only that humans adaptively changed their level of cooperation depending on the risk sensitivity of the computer player but also that their initial play exhibited characteristic risk-sensitive biases. Our results suggest that model uncertainty is an important determinant of cooperation in two-player sensorimotor interactions. PMID:23945266

Requiem for the max rule?

PubMed Central

Ma, Wei Ji; Shen, Shan; Dziugaite, Gintare; van den Berg, Ronald

2015-01-01

In tasks such as visual search and change detection, a key question is how observers integrate noisy measurements from multiple locations to make a decision. Decision rules proposed to model this process haven fallen into two categories: Bayes-optimal (ideal observer) rules and ad-hoc rules. Among the latter, the maximum-of-outputs (max) rule has been most prominent. Reviewing recent work and performing new model comparisons across a range of paradigms, we find that in all cases except for one, the optimal rule describes human data as well as or better than every max rule either previously proposed or newly introduced here. This casts doubt on the utility of the max rule for understanding perceptual decision-making. PMID:25584425

From climate model ensembles to climate change impacts and adaptation: A case study of water resource management in the southwest of England

NASA Astrophysics Data System (ADS)

Lopez, Ana; Fung, Fai; New, Mark; Watts, Glenn; Weston, Alan; Wilby, Robert L.

2009-08-01

The majority of climate change impacts and adaptation studies so far have been based on at most a few deterministic realizations of future climate, usually representing different emissions scenarios. Large ensembles of climate models are increasingly available either as ensembles of opportunity or perturbed physics ensembles, providing a wealth of additional data that is potentially useful for improving adaptation strategies to climate change. Because of the novelty of this ensemble information, there is little previous experience of practical applications or of the added value of this information for impacts and adaptation decision making. This paper evaluates the value of perturbed physics ensembles of climate models for understanding and planning public water supply under climate change. We deliberately select water resource models that are already used by water supply companies and regulators on the assumption that uptake of information from large ensembles of climate models will be more likely if it does not involve significant investment in new modeling tools and methods. We illustrate the methods with a case study on the Wimbleball water resource zone in the southwest of England. This zone is sufficiently simple to demonstrate the utility of the approach but with enough complexity to allow a variety of different decisions to be made. Our research shows that the additional information contained in the climate model ensemble provides a better understanding of the possible ranges of future conditions, compared to the use of single-model scenarios. Furthermore, with careful presentation, decision makers will find the results from large ensembles of models more accessible and be able to more easily compare the merits of different management options and the timing of different adaptation. The overhead in additional time and expertise for carrying out the impacts analysis will be justified by the increased quality of the decision-making process. We remark that even though we have focused our study on a water resource system in the United Kingdom, our conclusions about the added value of climate model ensembles in guiding adaptation decisions can be generalized to other sectors and geographical regions.

Simulation modelling as a tool for knowledge mobilisation in health policy settings: a case study protocol.

PubMed

Freebairn, L; Atkinson, J; Kelly, P; McDonnell, G; Rychetnik, L

2016-09-21

Evidence-informed decision-making is essential to ensure that health programs and services are effective and offer value for money; however, barriers to the use of evidence persist. Emerging systems science approaches and advances in technology are providing new methods and tools to facilitate evidence-based decision-making. Simulation modelling offers a unique tool for synthesising and leveraging existing evidence, data and expert local knowledge to examine, in a robust, low risk and low cost way, the likely impact of alternative policy and service provision scenarios. This case study will evaluate participatory simulation modelling to inform the prevention and management of gestational diabetes mellitus (GDM). The risks associated with GDM are well recognised; however, debate remains regarding diagnostic thresholds and whether screening and treatment to reduce maternal glucose levels reduce the associated risks. A diagnosis of GDM may provide a leverage point for multidisciplinary lifestyle modification interventions. This research will apply and evaluate a simulation modelling approach to understand the complex interrelation of factors that drive GDM rates, test options for screening and interventions, and optimise the use of evidence to inform policy and program decision-making. The study design will use mixed methods to achieve the objectives. Policy, clinical practice and research experts will work collaboratively to develop, test and validate a simulation model of GDM in the Australian Capital Territory (ACT). The model will be applied to support evidence-informed policy dialogues with diverse stakeholders for the management of GDM in the ACT. Qualitative methods will be used to evaluate simulation modelling as an evidence synthesis tool to support evidence-based decision-making. Interviews and analysis of workshop recordings will focus on the participants' engagement in the modelling process; perceived value of the participatory process, perceived commitment, influence and confidence of stakeholders in implementing policy and program decisions identified in the modelling process; and the impact of the process in terms of policy and program change. The study will generate empirical evidence on the feasibility and potential value of simulation modelling to support knowledge mobilisation and consensus building in health settings.

A hydrological model for interprovincial water resource planning and management: A case study in the Long Xuyen Quadrangle, Mekong Delta, Vietnam

NASA Astrophysics Data System (ADS)

Hanington, Peter; To, Quang Toan; Van, Pham Dang Tri; Doan, Ngoc Anh Vu; Kiem, Anthony S.

2017-04-01

In this paper we present the results of the development and calibration of a fine-scaled quasi-2D hydrodynamic model (IWRM-LXQ) for the Long Xuyen Quadrangle - an important interprovincial agricultural region in the Vietnamese Mekong Delta. We use the Long Xuyen Quadrangle as a case study to highlight the need for further investment in hydrodynamic modelling at scales relevant to the decisions facing water resource managers and planners in the Vietnamese Mekong Delta. The IWRM-LXQ was calibrated using existing data from a low flood year (2010) and high flood year (2011), including dry season and wet season flows. The model performed well in simulating low flood and high flood events in both dry and wet seasons where good spatial and temporal data exists. However, our study shows that there are data quality issues and key data gaps that need to be addressed before the model can be further refined, validated and then used for decision making. The development of the IWRM-LXQ is timely, as significant investments in land and water resource development and infrastructure are in planning for the Vietnamese Mekong Delta. In order to define the scope of such investments and their feasibility, models such as the IWRM-LXQ are an essential tool to provide objective assessment of investment options and build stakeholder consensus around potentially contentious development decisions.

Effects of Stress on Judgment and Decision Making in Dynamic Tasks

DTIC Science & Technology

1991-06-01

their normal working conditions, (2) to ascertain whether the results from lens model theory and research in static tasks generalize to these...8217 normal work environment. A further generalization from lens model theory is that those precursors (secondary cues) that are more conceptual in...potential microburst cases. Although this sample of cases is admittedly smaller than desirable, many hours of technical work were required to remove

Strategic enterprise resource planning in a health-care system using a multicriteria decision-making model.

PubMed

Lee, Chang Won; Kwak, N K

2011-04-01

This paper deals with strategic enterprise resource planning (ERP) in a health-care system using a multicriteria decision-making (MCDM) model. The model is developed and analyzed on the basis of the data obtained from a leading patient-oriented provider of health-care services in Korea. Goal criteria and priorities are identified and established via the analytic hierarchy process (AHP). Goal programming (GP) is utilized to derive satisfying solutions for designing, evaluating, and implementing an ERP. The model results are evaluated and sensitivity analyses are conducted in an effort to enhance the model applicability. The case study provides management with valuable insights for planning and controlling health-care activities and services.

Anticoagulation therapy advisor: a decision-support system for heparin therapy during ECMO.

PubMed Central

Peverini, R. L.; Sale, M.; Rhine, W. D.; Fagan, L. M.; Lenert, L. A.

1992-01-01

We present a case study describing our development of a mathematical model to control a clinical parameter in a patient--in this case, the degree of anticoagulation during extracorporeal membrane oxygenation (ECMO) support. During ECMO therapy, an anticoagulant agent (heparin) is administered to prevent thrombosis. Under- or over-coagulation can have grave consequences. To improve control of anticoagulation, we developed a pharmacokinetic-pharmacodynamic (PK-PD) model that predicts activated clotting times (ACT) using the NONMEM program. We then integrated this model into a decision-support system, and validated it with an independent data set. The population model had a mean absolute error of prediction for ACT values of 33.5 seconds, with a mean bias in estimation of -14.3 seconds. Individualization of model-parameter estimates using nonlinear regression improved the absolute error prediction to 25.5 seconds, and lowered the mean bias to -3.1 seconds. The PK-PD model is coupled with software for heuristic interpretation of model results to provide a complete environment for the management of anticoagulation. PMID:1482937

Using Performance Analysis for Training in an Organization Implementing ISO-9000 Manufacturing Practices: A Case Study.

ERIC Educational Resources Information Center

Kunneman, Dale E.; Sleezer, Catherine M.

2000-01-01

This case study examines the application of the Performance Analysis for Training (PAT) Model in an organization that was implementing ISO-9000 (International Standards Organization) processes for manufacturing practices. Discusses the interaction of organization characteristics, decision maker characteristics, and analyst characteristics to…

Increasing Effectiveness in Teaching Ethics to Undergraduate Business Students.

ERIC Educational Resources Information Center

Lampe, Marc

1997-01-01

Traditional approaches to teaching business ethics (philosophical analysis, moral quandaries, executive cases) may not be effective in persuading undergraduates of the importance of ethical behavior. Better techniques include values education, ethical decision-making models, analysis of ethical conflicts, and role modeling. (SK)

Modeling Tool for Decision Support during Early Days of an Anthrax Event.

PubMed

Rainisch, Gabriel; Meltzer, Martin I; Shadomy, Sean; Bower, William A; Hupert, Nathaniel

2017-01-01

Health officials lack field-implementable tools for forecasting the effects that a large-scale release of Bacillus anthracis spores would have on public health and hospitals. We created a modeling tool (combining inhalational anthrax caseload projections based on initial case reports, effects of variable postexposure prophylaxis campaigns, and healthcare facility surge capacity requirements) to project hospitalizations and casualties from a newly detected inhalation anthrax event, and we examined the consequences of intervention choices. With only 3 days of case counts, the model can predict final attack sizes for simulated Sverdlovsk-like events (1979 USSR) with sufficient accuracy for decision making and confirms the value of early postexposure prophylaxis initiation. According to a baseline scenario, hospital treatment volume peaks 15 days after exposure, deaths peak earlier (day 5), and recovery peaks later (day 23). This tool gives public health, hospital, and emergency planners scenario-specific information for developing quantitative response plans for this threat.

The BCD of response time analysis in experimental economics.

PubMed

Spiliopoulos, Leonidas; Ortmann, Andreas

2018-01-01

For decisions in the wild, time is of the essence. Available decision time is often cut short through natural or artificial constraints, or is impinged upon by the opportunity cost of time. Experimental economists have only recently begun to conduct experiments with time constraints and to analyze response time (RT) data, in contrast to experimental psychologists. RT analysis has proven valuable for the identification of individual and strategic decision processes including identification of social preferences in the latter case, model comparison/selection, and the investigation of heuristics that combine speed and performance by exploiting environmental regularities. Here we focus on the benefits, challenges, and desiderata of RT analysis in strategic decision making. We argue that unlocking the potential of RT analysis requires the adoption of process-based models instead of outcome-based models, and discuss how RT in the wild can be captured by time-constrained experiments in the lab. We conclude that RT analysis holds considerable potential for experimental economics, deserves greater attention as a methodological tool, and promises important insights on strategic decision making in naturally occurring environments.

Analysis of the decision-making process leading to appendectomy: a grounded theory study.

PubMed

Larsson, Gerry; Weibull, Henrik; Larsson, Bodil Wilde

2004-11-01

The aim was to develop a theoretical understanding of the decision-making process leading to appendectomy. A qualitative interview study was performed in the grounded theory tradition using the constant comparative method to analyze data. The study setting was one county hospital and two local hospitals in Sweden, where 11 surgeons and 15 surgical nurses were interviewed. A model was developed which suggests that surgeons' decision making regarding appendectomy is formed by the interplay between their medical assessment of the patient's condition and a set of contextual characteristics. The latter consist of three interacting factors: (1) organizational conditions, (2) the professional actors' individual characteristics and interaction, and (3) the personal characteristics of the patient and his or her family or relatives. In case the outcome of medical assessment is ambiguous, the risk evaluation and final decision will be influenced by an interaction of the contextual characteristics. It was concluded that, compared to existing, rational models of decision making, the model presented identified potentially important contextual characteristics and an outline on when they come into play.

Incorporating uncertainty of management costs in sensitivity analyses of matrix population models.

PubMed

Salomon, Yacov; McCarthy, Michael A; Taylor, Peter; Wintle, Brendan A

2013-02-01

The importance of accounting for economic costs when making environmental-management decisions subject to resource constraints has been increasingly recognized in recent years. In contrast, uncertainty associated with such costs has often been ignored. We developed a method, on the basis of economic theory, that accounts for the uncertainty in population-management decisions. We considered the case where, rather than taking fixed values, model parameters are random variables that represent the situation when parameters are not precisely known. Hence, the outcome is not precisely known either. Instead of maximizing the expected outcome, we maximized the probability of obtaining an outcome above a threshold of acceptability. We derived explicit analytical expressions for the optimal allocation and its associated probability, as a function of the threshold of acceptability, where the model parameters were distributed according to normal and uniform distributions. To illustrate our approach we revisited a previous study that incorporated cost-efficiency analyses in management decisions that were based on perturbation analyses of matrix population models. Incorporating derivations from this study into our framework, we extended the model to address potential uncertainties. We then applied these results to 2 case studies: management of a Koala (Phascolarctos cinereus) population and conservation of an olive ridley sea turtle (Lepidochelys olivacea) population. For low aspirations, that is, when the threshold of acceptability is relatively low, the optimal strategy was obtained by diversifying the allocation of funds. Conversely, for high aspirations, the budget was directed toward management actions with the highest potential effect on the population. The exact optimal allocation was sensitive to the choice of uncertainty model. Our results highlight the importance of accounting for uncertainty when making decisions and suggest that more effort should be placed on understanding the distributional characteristics of such uncertainty. Our approach provides a tool to improve decision making. © 2013 Society for Conservation Biology.

Human judgment vs. quantitative models for the management of ecological resources.

PubMed

Holden, Matthew H; Ellner, Stephen P

2016-07-01

Despite major advances in quantitative approaches to natural resource management, there has been resistance to using these tools in the actual practice of managing ecological populations. Given a managed system and a set of assumptions, translated into a model, optimization methods can be used to solve for the most cost-effective management actions. However, when the underlying assumptions are not met, such methods can potentially lead to decisions that harm the environment and economy. Managers who develop decisions based on past experience and judgment, without the aid of mathematical models, can potentially learn about the system and develop flexible management strategies. However, these strategies are often based on subjective criteria and equally invalid and often unstated assumptions. Given the drawbacks of both methods, it is unclear whether simple quantitative models improve environmental decision making over expert opinion. In this study, we explore how well students, using their experience and judgment, manage simulated fishery populations in an online computer game and compare their management outcomes to the performance of model-based decisions. We consider harvest decisions generated using four different quantitative models: (1) the model used to produce the simulated population dynamics observed in the game, with the values of all parameters known (as a control), (2) the same model, but with unknown parameter values that must be estimated during the game from observed data, (3) models that are structurally different from those used to simulate the population dynamics, and (4) a model that ignores age structure. Humans on average performed much worse than the models in cases 1-3, but in a small minority of scenarios, models produced worse outcomes than those resulting from students making decisions based on experience and judgment. When the models ignored age structure, they generated poorly performing management decisions, but still outperformed students using experience and judgment 66% of the time. © 2016 by the Ecological Society of America.

The cost and management of different types of clinical mastitis in dairy cows estimated by dynamic programming.

PubMed

Cha, E; Bar, D; Hertl, J A; Tauer, L W; Bennett, G; González, R N; Schukken, Y H; Welcome, F L; Gröhn, Y T

2011-09-01

The objective of this study was to estimate the cost of 3 different types of clinical mastitis (CM) (caused by gram-positive bacteria, gram-negative bacteria, and other organisms) at the individual cow level and thereby identify the economically optimal management decision for each type of mastitis. We made modifications to an existing dynamic optimization and simulation model, studying the effects of various factors (incidence of CM, milk loss, pregnancy rate, and treatment cost) on the cost of different types of CM. The average costs per case (US$) of gram-positive, gram-negative, and other CM were $133.73, $211.03, and $95.31, respectively. This model provided a more informed decision-making process in CM management for optimal economic profitability and determined that 93.1% of gram-positive CM cases, 93.1% of gram-negative CM cases, and 94.6% of other CM cases should be treated. The main contributor to the total cost per case was treatment cost for gram-positive CM (51.5% of the total cost per case), milk loss for gram-negative CM (72.4%), and treatment cost for other CM (49.2%). The model affords versatility as it allows for parameters such as production costs, economic values, and disease frequencies to be altered. Therefore, cost estimates are the direct outcome of the farm-specific parameters entered into the model. Thus, this model can provide farmers economically optimal guidelines specific to their individual cows suffering from different types of CM. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Optimizing patient treatment decisions in an era of rapid technological advances: the case of hepatitis C treatment.

PubMed

Liu, Shan; Brandeau, Margaret L; Goldhaber-Fiebert, Jeremy D

2017-03-01

How long should a patient with a treatable chronic disease wait for more effective treatments before accepting the best available treatment? We develop a framework to guide optimal treatment decisions for a deteriorating chronic disease when treatment technologies are improving over time. We formulate an optimal stopping problem using a discrete-time, finite-horizon Markov decision process. The goal is to maximize a patient's quality-adjusted life expectancy. We derive structural properties of the model and analytically solve a three-period treatment decision problem. We illustrate the model with the example of treatment for chronic hepatitis C virus (HCV). Chronic HCV affects 3-4 million Americans and has been historically difficult to treat, but increasingly effective treatments have been commercialized in the past few years. We show that the optimal treatment decision is more likely to be to accept currently available treatment-despite expectations for future treatment improvement-for patients who have high-risk history, who are older, or who have more comorbidities. Insights from this study can guide HCV treatment decisions for individual patients. More broadly, our model can guide treatment decisions for curable chronic diseases by finding the optimal treatment policy for individual patients in a heterogeneous population.

Optimizing Patient Treatment Decisions in an Era of Rapid Technological Advances: The Case of Hepatitis C Treatment

PubMed Central

Liu, Shan; Goldhaber-Fiebert, Jeremy D.; Brandeau, Margaret L.

2015-01-01

How long should a patient with a treatable chronic disease wait for more effective treatments before accepting the best available treatment? We develop a framework to guide optimal treatment decisions for a deteriorating chronic disease when treatment technologies are improving over time. We formulate an optimal stopping problem using a discrete-time, finite-horizon Markov decision process. The goal is to maximize a patient’s quality-adjusted life expectancy. We derive structural properties of the model and analytically solve a three-period treatment decision problem. We illustrate the model with the example of treatment for chronic hepatitis C virus (HCV). Chronic HCV affects 3–4 million Americans and has been historically difficult to treat, but increasingly effective treatments have been commercialized in the past few years. We show that the optimal treatment decision is more likely to be to accept currently available treatment—despite expectations for future treatment improvement—for patients who have high-risk history, who are older, or who have more comorbidities. Insights from this study can guide HCV treatment decisions for individual patients. More broadly, our model can guide treatment decisions for curable chronic diseases by finding the optimal treatment policy for individual patients in a heterogeneous population. PMID:26188961

Use of statistical and pharmacokinetic-pharmacodynamic modeling and simulation to improve decision-making: A section summary report of the trends and innovations in clinical trial statistics conference.

PubMed

Kimko, Holly; Berry, Seth; O'Kelly, Michael; Mehrotra, Nitin; Hutmacher, Matthew; Sethuraman, Venkat

2017-01-01

The application of modeling and simulation (M&S) methods to improve decision-making was discussed during the Trends & Innovations in Clinical Trial Statistics Conference held in Durham, North Carolina, USA on May 1-4, 2016. Uses of both pharmacometric and statistical M&S were presented during the conference, highlighting the diversity of the methods employed by pharmacometricians and statisticians to address a broad range of quantitative issues in drug development. Five presentations are summarized herein, which cover the development strategy of employing M&S to drive decision-making; European initiatives on best practice in M&S; case studies of pharmacokinetic/pharmacodynamics modeling in regulatory decisions; estimation of exposure-response relationships in the presence of confounding; and the utility of estimating the probability of a correct decision for dose selection when prior information is limited. While M&S has been widely used during the last few decades, it is expected to play an essential role as more quantitative assessments are employed in the decision-making process. By integrating M&S as a tool to compile the totality of evidence collected throughout the drug development program, more informed decisions will be made.

Probabilistic sensitivity analysis incorporating the bootstrap: an example comparing treatments for the eradication of Helicobacter pylori.

PubMed

Pasta, D J; Taylor, J L; Henning, J M

1999-01-01

Decision-analytic models are frequently used to evaluate the relative costs and benefits of alternative therapeutic strategies for health care. Various types of sensitivity analysis are used to evaluate the uncertainty inherent in the models. Although probabilistic sensitivity analysis is more difficult theoretically and computationally, the results can be much more powerful and useful than deterministic sensitivity analysis. The authors show how a Monte Carlo simulation can be implemented using standard software to perform a probabilistic sensitivity analysis incorporating the bootstrap. The method is applied to a decision-analytic model evaluating the cost-effectiveness of Helicobacter pylori eradication. The necessary steps are straightforward and are described in detail. The use of the bootstrap avoids certain difficulties encountered with theoretical distributions. The probabilistic sensitivity analysis provided insights into the decision-analytic model beyond the traditional base-case and deterministic sensitivity analyses and should become the standard method for assessing sensitivity.

Evaluating the cost effectiveness of environmental projects: Case studies in aerospace and defense

NASA Technical Reports Server (NTRS)

Shunk, James F.

1995-01-01

Using the replacement technology of high pressure waterjet decoating systems as an example, a simple methodology is presented for developing a cost effectiveness model. The model uses a four-step process to formulate an economic justification designed for presentation to decision makers as an assessment of the value of the replacement technology over conventional methods. Three case studies from major U.S. and international airlines are used to illustrate the methodology and resulting model. Tax and depreciation impacts are also presented as potential additions to the model.

Epidemiological and Economic Impact of Monovalent and Pentavalent Rotavirus Vaccines in Low and Middle Income Countries: A Cost-effectiveness Modeling Analysis.

PubMed

Paternina-Caicedo, Angel; De la Hoz-Restrepo, Fernando; Alvis-Guzmán, Nelson

2015-07-01

The competing choices of vaccination with either RV1 or RV5, the potential budget impact of vaccines on the EPI with different prices and new evidence make important an updated analysis for health decision makers in each country. The objective of this study is to assess cost-effectiveness of the monovalent and pentavalent rotavirus vaccines and impact on children deaths, inpatient and outpatient visits in 116 low and middle income countries that represent approximately 99% of rotavirus mortality. A decision tree model followed hypothetical cohorts of children from birth up to 5 years of age for each country in 2010. Inputs were gathered from international databases and previous research on incidence and effectiveness of monovalent and pentavalent vaccines. Costs were expressed in 2010 international dollars. Outcomes were reported in terms of cost per disability-adjusted life-year averted, comparing no vaccination with either monovalent or pentavalent mass introduction. Vaccine price was assumed fixed for all world low-income and middle-income countries. Around 292,000 deaths, 3.34 million inpatient cases and 23.09 million outpatient cases would occur with no vaccination. In the base-case scenario, monovalent vaccination would prevent 54.7% of inpatient cases and 45.4% of deaths. Pentavalent vaccination would prevent 51.4% of inpatient cases and 41.1% of deaths. The vaccine was cost-effective in all world countries in the base-case scenario for both vaccines. Cost per disability-adjusted life-year averted in all selected countries was I$372 for monovalent, and I$453 for pentavalent vaccination. Rotavirus vaccine is cost-effective in most analyzed countries. Despite cost-effectiveness analysis is a useful tool for decision making in middle-income countries, for low-income countries health decision makers should also assess the impact of introducing either vaccine on local resources and budget impact analysis of vaccination.

Volume sharing of reservoir water

NASA Astrophysics Data System (ADS)

Dudley, Norman J.

1988-05-01

Previous models optimize short-, intermediate-, and long-run irrigation decision making in a simplified river valley system characterized by highly variable water supplies and demands for a single decision maker controlling both reservoir releases and farm water use. A major problem in relaxing the assumption of one decision maker is communicating the stochastic nature of supplies and demands between reservoir and farm managers. In this paper, an optimizing model is used to develop release rules for reservoir management when all users share equally in releases, and computer simulation is used to generate an historical time sequence of announced releases. These announced releases become a state variable in a farm management model which optimizes farm area-to-irrigate decisions through time. Such modeling envisages the use of growing area climatic data by the reservoir authority to gauge water demand and the transfer of water supply data from reservoir to farm managers via computer data files. Alternative model forms, including allocating water on a priority basis, are discussed briefly. Results show lower mean aggregate farm income and lower variance of aggregate farm income than in the single decision-maker case. This short-run economic efficiency loss coupled with likely long-run economic efficiency losses due to the attenuated nature of property rights indicates the need for quite different ways of integrating reservoir and farm management.

Integrated modelling of stormwater treatment systems uptake.

PubMed

Castonguay, A C; Iftekhar, M S; Urich, C; Bach, P M; Deletic, A

2018-05-24

Nature-based solutions provide a variety of benefits in growing cities, ranging from stormwater treatment to amenity provision such as aesthetics. However, the decision-making process involved in the installation of such green infrastructure is not straightforward, as much uncertainty around the location, size, costs and benefits impedes systematic decision-making. We developed a model to simulate decision rules used by local municipalities to install nature-based stormwater treatment systems, namely constructed wetlands, ponds/basins and raingardens. The model was used to test twenty-four scenarios of policy-making, by combining four asset selection, two location selection and three budget constraint decision rules. Based on the case study of a local municipality in Metropolitan Melbourne, Australia, the modelled uptake of stormwater treatment systems was compared with attributes of real-world systems for the simulation period. Results show that the actual budgeted funding is not reliable to predict systems' uptake and that policy-makers are more likely to plan expenditures based on installation costs. The model was able to replicate the cumulative treatment capacity and the location of systems. As such, it offers a novel approach to investigate the impact of using different decision rules to provide environmental services considering biophysical and economic factors. Copyright © 2018 Elsevier Ltd. All rights reserved.

Integrating Efficiency of Industry Processes and Practices Alongside Technology Effectiveness in Space Transportation Cost Modeling and Analysis

NASA Technical Reports Server (NTRS)

Zapata, Edgar

2012-01-01

This paper presents past and current work in dealing with indirect industry and NASA costs when providing cost estimation or analysis for NASA projects and programs. Indirect costs, when defined as those costs in a project removed from the actual hardware or software hands-on labor; makes up most of the costs of today's complex large scale NASA space/industry projects. This appears to be the case across phases from research into development into production and into the operation of the system. Space transportation is the case of interest here. Modeling and cost estimation as a process rather than a product will be emphasized. Analysis as a series of belief systems in play among decision makers and decision factors will also be emphasized to provide context.

Range Systems Simulation for the NASA Shuttle: Emphasis on Disaster and Prevention Management During Lift-Off

NASA Technical Reports Server (NTRS)

Rabelo, Lisa; Sepulveda, Jose; Moraga, Reinaldo; Compton, Jeppie; Turner, Robert

2005-01-01

This article describes a decision-making system composed of a number of safety and environmental models for the launch phase of a NASA Space Shuttle mission. The components of this distributed simulation environment represent the different systems that must collaborate to establish the Expectation of Casualties (E(sub c)) caused by a failed Space Shuttle launch and subsequent explosion (accidental or instructed) of the spacecraft shortly after liftoff. This decision-making tool employs Space Shuttle reliability models, trajectory models, a blast model, weather dissemination systems, population models, amount and type of toxicants, gas dispersion models, human response functions to toxicants, and a geographical information system. Since one of the important features of this proposed simulation environment is to measure blast, toxic, and debris effects, the clear benefits is that it can help safety managers not only estimate the population at risk, but also to help plan evacuations, make sheltering decisions, establish the resources required to provide aid and comfort, and mitigate damages in case of a disaster.

Decision support system for drinking water management

NASA Astrophysics Data System (ADS)

Janža, M.

2012-04-01

The problems in drinking water management are complex and often solutions must be reached under strict time constrains. This is especially distinct in case of environmental accidents in the catchment areas of the wells that are used for drinking water supply. The beneficial tools that can help decision makers and make program of activities more efficient are decision support systems (DSS). In general they are defined as computer-based support systems that help decision makers utilize data and models to solve unstructured problems. The presented DSS was developed in the frame of INCOME project which is focused on the long-term stable and safe drinking water supply in Ljubljana. The two main water resources Ljubljana polje and Barje alluvial aquifers are characterized by a strong interconnection of surface and groundwater, high vulnerability, high velocities of groundwater flow and pollutant transport. In case of sudden pollution, reactions should be very fast to avoid serious impact to the water supply. In the area high pressures arising from urbanization, industry, traffic, agriculture and old environmental burdens. The aim of the developed DSS is to optimize the activities in cases of emergency water management and to optimize the administrative work regarding the activities that can improve groundwater quality status. The DSS is an interactive computer system that utilizes data base, hydrological modelling, and experts' and stakeholders' knowledge. It consists of three components, tackling the different abovementioned issues in water management. The first one utilizes the work on identification, cleaning up and restoration of illegal dumpsites that are a serious threat to the qualitative status of groundwater. The other two components utilize the predictive capability of the hydrological model and scenario analysis. The user interacts with the system by a graphical interface that guides the user step-by-step to the recommended remedial measures. Consequently, the acquisition of information to support the water management's decisions is simplified and faster, thus contributing to more efficient water management and a safer supply of drinking water.

Challenging Operations: An Ethical Framework to Assist Humanitarian Aid Workers in their Decision-making Processes

PubMed Central

Clarinval, Caroline; Biller-Andorno, Nikola

2014-01-01

Introduction: This paper aims to raise awareness regarding ethical issues in the context of humanitarian action, and to offer a framework for systematically and effectively addressing such issues. Methods: Several cases highlight ethical issues that humanitarian aid workers are confronted with at different levels over the course of their deployments. The first case discusses a situation at a macro-level concerning decisions being made at the headquarters of a humanitarian organization. The second case looks at meso-level issues that need to be solved at a country or regional level. The third case proposes an ethical dilemma at the micro-level of the individual patient-provider relationship. Discussion: These real-life cases have been selected to illustrate the ethical dimension of conflicts within the context of humanitarian action that might remain unrecognized in everyday practice. In addition, we propose an ethical framework to assist humanitarian aid workers in their decision-making process. The framework draws on the principles and values that guide humanitarian action and public health ethics more generally. Beyond identifying substantive core values, the framework also includes a ten-step process modelled on tools used in the clinical setting that promotes a transparent and clear decision-making process and improves the monitoring and evaluation of aid interventions. Finally, we recommend organizational measures to implement the framework effectively. Conclusion: This paper uses a combination of public health/clinical ethics concepts and practices and applies them to the decision-making challenges encountered in relief operations in the humanitarian aid context. PMID:24987575

Reducing uncertainty in value-based pricing using evidence development agreements: the case of continuous intraduodenal infusion of levodopa/carbidopa (Duodopa®) in Sweden.

PubMed

Willis, Michael; Persson, Ulf; Zoellner, York; Gradl, Birgit

2010-01-01

Value-based pricing (VBP), whereby prices are set according to the perceived benefits offered to the consumer at a time when costs and benefits are characterized by considerable uncertainty and are then reviewed ex post, is a much discussed topic in pharmaceutical reimbursement. It is usually combined with coverage with evidence development (CED), a tool in which manufacturers are granted temporary reimbursement but are required to collect and submit additional health economic data at review. Many countries, including the UK, are signalling shifts in this direction. Several countries, including Sweden, have already adopted this approach and offer good insight into the benefits and pitfalls in actual practice. To describe VBP reimbursement decision making using CED in actual practice in Sweden. Decision making by The Dental and Pharmaceutical Benefits Agency (TLV) in Sweden was reviewed using a case study of continuous intraduodenal infusion of levodopa/carbidopa (Duodopa®) in the treatment of advanced Parkinson's disease (PD) with severe motor fluctuations. The manufacturer of Duodopa® applied for reimbursement in late 2003. While the proper economic data were not included in the submission, TLV granted reimbursement until early 2005 to provide time for the manufacturer to submit a formal economic evaluation. The re-submission with economic data was considered inadequate to judge cost effectiveness, so TLV granted an additional extension of reimbursement until August 2007, at which time conclusive data were expected. The manufacturer initiated a 3-year, prospective health economic study and a formal economic model. Data from a pre-planned interim analysis of the data were loaded into the model and the cost-effectiveness ratio was the basis of the next re-submission. TLV concluded that the data were suitable for making a definite decision and that the drug was not cost effective, deciding to discontinue reimbursement for any new patients (current patients were unaffected). The manufacturer continued to collect data and to improve the economic model and re-submitted in 2008. New data and the improved model resulted in reduced uncertainty and a lower cost-effectiveness ratio in the range of Swedish kronor (SEK)430,000 per QALY gained in the base-case analysis, ranging up to SEK900,000 in the most conservative sensitivity analysis, resulting in reimbursement being granted. The case of Duodopa® provides excellent insight into VBP reimbursement decision making in combination with CED and ex post review in actual practice. Publicly available decisions document the rigorous, time-consuming process (four iterations were required before a final decision could be reached). The data generated as part of the risk-sharing agreement proved correct the initial decision to grant limited coverage despite lack of economic data. Access was provided to 100 patients while evidence was generated. Economic appraisal differs from clinical assessment, and decision makers benefit from analysis of naturalistic, actual practice data. Despite reviewing the initial trial-based, 'piggy-back' economic analysis, TLV was uncertain of the cost effectiveness in actual practice and deferred a final decision until observational data from the DAPHNE study became available. Second, acceptance of economic modelling and use of temporary reimbursement conditional on additional evidence development provide a mechanism for risk sharing between TLV and manufacturers, which enabled patient access to a drug with proven clinical benefit while necessary evidence to support claims of cost effectiveness could be generated.

How do strategic decisions and operative practices affect operating room productivity?

PubMed

Peltokorpi, Antti

2011-12-01

Surgical operating rooms are cost-intensive parts of health service production. Managing operating units efficiently is essential when hospitals and healthcare systems aim to maximize health outcomes with limited resources. Previous research about operating room management has focused on studying the effect of management practices and decisions on efficiency by utilizing mainly modeling approach or before-after analysis in single hospital case. The purpose of this research is to analyze the synergic effect of strategic decisions and operative management practices on operating room productivity and to use a multiple case study method enabling statistical hypothesis testing with empirical data. 11 hypotheses that propose connections between the use of strategic and operative practices and productivity were tested in a multi-hospital study that included 26 units. The results indicate that operative practices, such as personnel management, case scheduling and performance measurement, affect productivity more remarkably than do strategic decisions that relate to, e.g., units' size, scope or academic status. Units with different strategic positions should apply different operative practices: Focused hospital units benefit most from sophisticated case scheduling and parallel processing whereas central and ambulatory units should apply flexible working hours, incentives and multi-skilled personnel. Operating units should be more active in applying management practices which are adequate for their strategic orientation.

Renewable generation technology choice and policies in a competitive electricity supply industry

NASA Astrophysics Data System (ADS)

Sarkar, Ashok

Renewable energy generation technologies have lower externality costs but higher private costs than fossil fuel-based generation. As a result, the choice of renewables in the future generation mix could be affected by the industry's future market-oriented structure because market objectives based on private value judgments may conflict with social policy objectives toward better environmental quality. This research assesses how renewable energy generation choices would be affected in a restructured electricity generation market. A multi-period linear programming-based model (Resource Planning Model) is used to characterize today's electricity supply market in the United States. The model simulates long-range (2000-2020) generation capacity planning and operation decisions under alternative market paradigms. Price-sensitive demand is used to simulate customer preferences in the market. Dynamically changing costs for renewables and a two-step load duration curve are used. A Reference Case represents the benchmark for a socially-optimal diffusion of renewables and a basis for comparing outcomes under alternative market structures. It internalizes externality costs associated with emissions of sulfur dioxide (SOsb2), nitrous oxides (NOsbx), and carbon dioxide (COsb2). A Competitive Case represents a market with many generation suppliers and decision-making based on private costs. Finally, a Market Power Case models the extreme case of market power: monopoly. The results suggest that the share of renewables would decrease (and emissions would increase) considerably in both the Competitive and the Market Power Cases with respect to the Reference Case. The reduction is greater in the Market Power Case due to pricing decisions under existing supply capability. The research evaluates the following environmental policy options that could overcome market failures in achieving an appropriate level of renewable generation: COsb2 emissions tax, SOsb2 emissions cap, renewable portfolio standards (RPS), and enhanced research and development (R&D). RPS would best ensure an appropriate share of renewables, whereas SOsb2 emissions caps would not support a shift to renewables in an era of inexpensive natural gas. The effectiveness of the policies are dependent on the market structure. If market power exists, the analyses indicate that generally higher levels of intervention would be necessary to achieve a shift to renewables.

Models of the First-Term Reenlistment Decision.

DTIC Science & Technology

1980-09-01

cases in each cell . bIncludes 41 cases indicated as E2 in survey. elncludes 18 cases indicated as E6 in survey. creases. For example, among E-4...1876) (4078) oThe numbers in parentheses show the number of cases in each cell . Clearly, the extent to which these correlations reflect causal...101) NOTE: Numbers in parentheses show the number of cases in each cell . aAmount computed from October 1975 pay tables based on the individual’s

Land-use evaluation for sustainable construction in a protected area: A case of Sara mountain national park.

PubMed

Ristić, Vladica; Maksin, Marija; Nenković-Riznić, Marina; Basarić, Jelena

2018-01-15

The process of making decisions on sustainable development and construction begins in spatial and urban planning when defining the suitability of using land for sustainable construction in a protected area (PA) and its immediate and regional surroundings. The aim of this research is to propose and assess a model for evaluating land-use suitability for sustainable construction in a PA and its surroundings. The methodological approach of Multi-Criteria Decision Analysis was used in the formation of this model and adapted for the research; it was combined with the adapted Analytical hierarchy process and the Delphi process, and supported by a geographical information system (GIS) within the framework of ESRI ArcGIS software - Spatial analyst. The model is applied to the case study of Sara mountain National Park in Kosovo. The result of the model is a "map of integrated assessment of land-use suitability for sustainable construction in a PA for the natural factor". Copyright © 2017 Elsevier Ltd. All rights reserved.

PLANNING MODELS FOR URBAN WATER SUPPLY EXPANSION. VOLUME 1. PLANNING FOR THE EXPANSION OF REGIONAL WATER SUPPLY SYSTEMS

EPA Science Inventory

A three-volume report was developed relative to the modelling of investment strategies for regional water supply planning. Volume 1 is the study of capacity expansion over time. Models to aid decision making for the deterministic case are presented, and a planning process under u...

Technosocial Predictive Analytics in Support of Naturalistic Decision Making

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

Sanfilippo, Antonio P.; Cowell, Andrew J.; Malone, Elizabeth L.

2009-06-23

A main challenge we face in fostering sustainable growth is to anticipate outcomes through predictive and proactive across domains as diverse as energy, security, the environment, health and finance in order to maximize opportunities, influence outcomes and counter adversities. The goal of this paper is to present new methods for anticipatory analytical thinking which address this challenge through the development of a multi-perspective approach to predictive modeling as a core to a creative decision making process. This approach is uniquely multidisciplinary in that it strives to create decision advantage through the integration of human and physical models, and leverages knowledgemore » management and visual analytics to support creative thinking by facilitating the achievement of interoperable knowledge inputs and enhancing the user’s cognitive access. We describe a prototype system which implements this approach and exemplify its functionality with reference to a use case in which predictive modeling is paired with analytic gaming to support collaborative decision-making in the domain of agricultural land management.« less

Decision Support Model for Mosque Renovation and Rehabilitation (Case Study: Ten Mosques in Jakarta Barat, Indonesia)

NASA Astrophysics Data System (ADS)

Utama, D. N.; Triana, Y. S.; Iqbal, M. M.; Iksal, M.; Fikri, I.; Dharmawan, T.

2018-03-01

Mosque, for Muslim, is not only a place for daily worshipping, however as a center of culture as well. It is an important and valuable building to be well managed. For a responsible department or institution (such as Religion or Plan Department in Indonesia), to practically manage a lot of mosques is not simple task to handle. The challenge is in relation to data number and characteristic problems tackled. Specifically for renovating and rehabilitating the damaged mosques, a decision to determine the first damaged mosque priority to be renovated and rehabilitated is problematic. Through two types of optimization method, simulated-annealing and hill-climbing, a decision support model for mosque renovation and rehabilitation was systematically constructed. The method fuzzy-logic was also operated to establish the priority of eleven selected parameters. The constructed model is able to simulate an efficiency comparison between two optimization methods used and suggest the most objective decision coming from 196 generated alternatives.

Thinking like an expert: surgical decision making as a cyclical process of being aware.

PubMed

Cristancho, Sayra M; Apramian, Tavis; Vanstone, Meredith; Lingard, Lorelei; Ott, Michael; Forbes, Thomas; Novick, Richard

2016-01-01

Education researchers are studying the practices of high-stake professionals as they learn how to better train for flexibility under uncertainty. This study explores the "Reconciliation Cycle" as the core element of an intraoperative decision-making model of how experienced surgeons assess and respond to challenges. We analyzed 32 semistructured interviews using constructivist grounded theory to develop a model of intraoperative decision making. Using constant comparison analysis, we built on this model with 9 follow-up interviews about the most challenging cases described in our dataset. The Reconciliation Cycle constituted an iterative process of "gaining" and "transforming information." The cyclical nature of surgeons' decision making suggested that transforming information requires a higher degree of awareness, not yet accounted by current conceptualizations of situation awareness. This study advances the notion of situation awareness in surgery. This characterization will support further investigations on how expert and nonexpert surgeons implement strategies to cope with unexpected events. Copyright © 2016 Elsevier Inc. All rights reserved.

On determining firing delay time of transitions for Petri net based signaling pathways by introducing stochastic decision rules.

PubMed

Miwa, Yoshimasa; Li, Chen; Ge, Qi-Wei; Matsuno, Hiroshi; Miyano, Satoru

2010-01-01

Parameter determination is important in modeling and simulating biological pathways including signaling pathways. Parameters are determined according to biological facts obtained from biological experiments and scientific publications. However, such reliable data describing detailed reactions are not reported in most cases. This prompted us to develop a general methodology of determining the parameters of a model in the case of that no information of the underlying biological facts is provided. In this study, we use the Petri net approach for modeling signaling pathways, and propose a method to determine firing delay times of transitions for Petri net models of signaling pathways by introducing stochastic decision rules. Petri net technology provides a powerful approach to modeling and simulating various concurrent systems, and recently have been widely accepted as a description method for biological pathways. Our method enables to determine the range of firing delay time which realizes smooth token flows in the Petri net model of a signaling pathway. The availability of this method has been confirmed by the results of an application to the interleukin-1 induced signaling pathway.

On determining firing delay time of transitions for petri net based signaling pathways by introducing stochastic decision rules.

PubMed

Miwa, Yoshimasa; Li, Chen; Ge, Qi-Wei; Matsuno, Hiroshi; Miyano, Satoru

2011-01-01

Parameter determination is important in modeling and simulating biological pathways including signaling pathways. Parameters are determined according to biological facts obtained from biological experiments and scientific publications. However, such reliable data describing detailed reactions are not reported in most cases. This prompted us to develop a general methodology of determining the parameters of a model in the case of that no information of the underlying biological facts is provided. In this study, we use the Petri net approach for modeling signaling pathways, and propose a method to determine firing delay times of transitions for Petri net models of signaling pathways by introducing stochastic decision rules. Petri net technology provides a powerful approach to modeling and simulating various concurrent systems, and recently have been widely accepted as a description method for biological pathways. Our method enables to determine the range of firing delay time which realizes smooth token flows in the Petri net model of a signaling pathway. The availability of this method has been confirmed by the results of an application to the interleukin-1 induced signaling pathway.

Multiple methods for multiple futures: Integrating qualitative scenario planning and quantitative simulation modeling for natural resource decision making

USGS Publications Warehouse

Symstad, Amy J.; Fisichelli, Nicholas A.; Miller, Brian W.; Rowland, Erika; Schuurman, Gregor W.

2017-01-01

Scenario planning helps managers incorporate climate change into their natural resource decision making through a structured “what-if” process of identifying key uncertainties and potential impacts and responses. Although qualitative scenarios, in which ecosystem responses to climate change are derived via expert opinion, often suffice for managers to begin addressing climate change in their planning, this approach may face limits in resolving the responses of complex systems to altered climate conditions. In addition, this approach may fall short of the scientific credibility managers often require to take actions that differ from current practice. Quantitative simulation modeling of ecosystem response to climate conditions and management actions can provide this credibility, but its utility is limited unless the modeling addresses the most impactful and management-relevant uncertainties and incorporates realistic management actions. We use a case study to compare and contrast management implications derived from qualitative scenario narratives and from scenarios supported by quantitative simulations. We then describe an analytical framework that refines the case study’s integrated approach in order to improve applicability of results to management decisions. The case study illustrates the value of an integrated approach for identifying counterintuitive system dynamics, refining understanding of complex relationships, clarifying the magnitude and timing of changes, identifying and checking the validity of assumptions about resource responses to climate, and refining management directions. Our proposed analytical framework retains qualitative scenario planning as a core element because its participatory approach builds understanding for both managers and scientists, lays the groundwork to focus quantitative simulations on key system dynamics, and clarifies the challenges that subsequent decision making must address.

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.

A fuzzy MCDM model with objective and subjective weights for evaluating service quality in hotel industries

NASA Astrophysics Data System (ADS)

Zoraghi, Nima; Amiri, Maghsoud; Talebi, Golnaz; Zowghi, Mahdi

2013-12-01

This paper presents a fuzzy multi-criteria decision-making (FMCDM) model by integrating both subjective and objective weights for ranking and evaluating the service quality in hotels. The objective method selects weights of criteria through mathematical calculation, while the subjective method uses judgments of decision makers. In this paper, we use a combination of weights obtained by both approaches in evaluating service quality in hotel industries. A real case study that considered ranking five hotels is illustrated. Examples are shown to indicate capabilities of the proposed method.

Effluent trading in river systems through stochastic decision-making process: a case study.

PubMed

Zolfagharipoor, Mohammad Amin; Ahmadi, Azadeh

2017-09-01

The objective of this paper is to provide an efficient framework for effluent trading in river systems. The proposed framework consists of two pessimistic and optimistic decision-making models to increase the executability of river water quality trading programs. The models used for this purpose are (1) stochastic fallback bargaining (SFB) to reach an agreement among wastewater dischargers and (2) stochastic multi-criteria decision-making (SMCDM) to determine the optimal treatment strategy. The Monte-Carlo simulation method is used to incorporate the uncertainty into analysis. This uncertainty arises from stochastic nature and the errors in the calculation of wastewater treatment costs. The results of river water quality simulation model are used as the inputs of models. The proposed models are used in a case study on the Zarjoub River in northern Iran to determine the best solution for the pollution load allocation. The best treatment alternatives selected by each model are imported, as the initial pollution discharge permits, into an optimization model developed for trading of pollution discharge permits among pollutant sources. The results show that the SFB-based water pollution trading approach reduces the costs by US$ 14,834 while providing a relative consensus among pollutant sources. Meanwhile, the SMCDM-based water pollution trading approach reduces the costs by US$ 218,852, but it is less acceptable by pollutant sources. Therefore, it appears that giving due attention to stability, or in other words acceptability of pollution trading programs for all pollutant sources, is an essential element of their success.

Next generation data systems and knowledge products to support agricultural producers and science-based policy decision making.

PubMed

Capalbo, Susan M; Antle, John M; Seavert, Clark

2017-07-01

Research on next generation agricultural systems models shows that the most important current limitation is data, both for on-farm decision support and for research investment and policy decision making. One of the greatest data challenges is to obtain reliable data on farm management decision making, both for current conditions and under scenarios of changed bio-physical and socio-economic conditions. This paper presents a framework for the use of farm-level and landscape-scale models and data to provide analysis that could be used in NextGen knowledge products, such as mobile applications or personal computer data analysis and visualization software. We describe two analytical tools - AgBiz Logic and TOA-MD - that demonstrate the current capability of farmlevel and landscape-scale models. The use of these tools is explored with a case study of an oilseed crop, Camelina sativa , which could be used to produce jet aviation fuel. We conclude with a discussion of innovations needed to facilitate the use of farm and policy-level models to generate data and analysis for improved knowledge products.

Design and application of a CA-BDI model to determine farmers' land-use behavior.

PubMed

Liang, Xiaoying; Chen, Hai; Wang, Yanni; Song, Shixiong

2016-01-01

The belief-desire-intention (BDI) model has been widely used to construct reasoning systems for complex tasks in dynamic environments. We have designed a capabilities and abilities (CA)-BDI farmer decision-making model, which is an extension of the BDI architecture and includes internal representations for farmer household Capabilities and Abilities. This model is used to explore farmer learning mechanisms and to simulate the bounded rational decisions made by farmer households. Our case study focuses on the Gaoqu Commune of Mizhi County, Shaanxi Province, China, where scallion is one of the main cash crops. After comparing the differences between actual land-use changes from 2007 to 2009 and the simulation results, we analyze the validity of the model and discuss the potential and limitations of the farmer land-use decision-making model under three scenarios. Based on the design and implementation of the model, the following conclusions can be drawn: (1) the CA-BDI framework is an appropriate model for exploring learning mechanisms and simulating bounded rational decisions; and (2) local governments should encourage scallion planting by assisting scallion farmer cooperatives and farmers to understand the market risk, standardize the rules of their cooperation, and supervise the contracts made between scallion cooperatives and farmers.

Diagnosing Expertise: Human Capital, Decision Making, and Performance among Physicians

PubMed Central

Currie, Janet; MacLeod, W. Bentley

2017-01-01

Expert performance is often evaluated assuming that good experts have good outcomes. We examine expertise in medicine and develop a model that allows for two dimensions of physician performance: decision making and procedural skill. Better procedural skill increases the use of intensive procedures for everyone, while better decision making results in a reallocation of procedures from fewer low-risk to high-risk cases. We show that poor diagnosticians can be identified using administrative data and that improving decision making improves birth outcomes by reducing C-section rates at the bottom of the risk distribution and increasing them at the top of the distribution. PMID:29276336

Contribution of Geographic Information Systems and location models to planning of wastewater systems.

PubMed

Leitão, J P; Matos, J S; Gonçalves, A B; Matos, J L

2005-01-01

This paper presents the contributions of Geographic Information Systems (GIS) and location models towards planning regional wastewater systems (sewers and wastewater treatment plants) serving small agglomerations, i.e. agglomerations with less than 2,000 inhabitants. The main goal was to develop a decision support tool for tracing and locating regional wastewater systems. The main results of the model are expressed in terms of number, capacity and location of Wastewater Treatment Plants (WWTP) and the length of main sewers. The decision process concerning the location and capacity of wastewater systems has a number of parameters that can be optimized. These parameters include the total sewer length and number, capacity and location of WWTP. The optimization of parameters should lead to the minimization of construction and operation costs of the integrated system. Location models have been considered as tools for decision support, mainly when a geo-referenced database can be used. In these cases, the GIS may represent an important role for the analysis of data and results especially in the preliminary stage of planning and design. After selecting the spatial location model and the heuristics, two greedy algorithms were implemented in Visual Basic for Applications on the ArcGIS software environment. To illustrate the application of these algorithms a case study was developed, in a rural area located in the central part of Portugal.

Stochastic Technology Choice Model for Consequential Life Cycle Assessment.

PubMed

Kätelhön, Arne; Bardow, André; Suh, Sangwon

2016-12-06

Discussions on Consequential Life Cycle Assessment (CLCA) have relied largely on partial or general equilibrium models. Such models are useful for integrating market effects into CLCA, but also have well-recognized limitations such as the poor granularity of the sectoral definition and the assumption of perfect oversight by all economic agents. Building on the Rectangular-Choice-of-Technology (RCOT) model, this study proposes a new modeling approach for CLCA, the Technology Choice Model (TCM). In this approach, the RCOT model is adapted for its use in CLCA and extended to incorporate parameter uncertainties and suboptimal decisions due to market imperfections and information asymmetry in a stochastic setting. In a case study on rice production, we demonstrate that the proposed approach allows modeling of complex production technology mixes and their expected environmental outcomes under uncertainty, at a high level of detail. Incorporating the effect of production constraints, uncertainty, and suboptimal decisions by economic agents significantly affects technology mixes and associated greenhouse gas (GHG) emissions of the system under study. The case study also shows the model's ability to determine both the average and marginal environmental impacts of a product in response to changes in the quantity of final demand.

Using exposure prediction tools to link exposure and dosimetry for risk based decisions: a case study with phthalates

EPA Science Inventory

The Population Life-course Exposure to Health Effects Modeling (PLETHEM) platform being developed provides a tool that links results from emerging toxicity testing tools to exposure estimates for humans as defined by the USEPA. A reverse dosimetry case study using phthalates was ...

Climate Induced Spillover and Implications for U.S. Security.

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

Tidwell, Vincent C.; Naugle, Asmeret Bier; Backus, George A.

Developing nations incur a greater risk to climate change than the developed world due to poorly managed human/natural resources, unreliable infrastructure and brittle governing/economic institutions. These vulnerabilities often give rise to a climate induced “domino effect” of reduced natural resource production-leading to economic hardship, social unrest, and humanitarian crises. Integral to this cascading set of events is increased human migration, leading to the “spillover” of impacts to adjoining areas with even broader impact on global markets and security. Given the complexity of factors influencing human migration and the resultant spill-over effect, quantitative tools are needed to aid policy analysis. Towardmore » this need, a series of migration models were developed along with a system dynamics model of the spillover effect. The migration decision models were structured according to two interacting paths, one that captured long-term “chronic” impacts related to protracted deteriorating quality of life and a second focused on short-term “acute” impacts of disaster and/or conflict. Chronic migration dynamics were modeled for two different cases; one that looked only at emigration but at a national level for the entire world; and a second that looked at both emigration and immigration but focused on a single nation. Model parameterization for each of the migration models was accomplished through regression analysis using decadal data spanning the period 1960-2010. A similar approach was taken with acute migration dynamics except regression analysis utilized annual data sets limited to a shorter time horizon (2001-2013). The system dynamics spillover model was organized around two broad modules, one simulating the decision dynamics of migration and a second module that treats the changing environmental conditions that influence the migration decision. The environmental module informs the migration decision, endogenously simulating interactions/changes in the economy, labor, population, conflict, water, and food. A regional model focused on Mali in western Africa was used as a test case to demonstrate the efficacy of the model.« less

Making the case for evidence-based design in healthcare: a descriptive case study of organizational decision making.

PubMed

Shoemaker, Lorie K; Kazley, Abby Swanson; White, Andrea

2010-01-01

The aim of this study was to describe the organizational decision-making process used in the selection of evidence-based design (EBD) concepts, the criteria used to make these decisions, and the extent to which leadership style may have influenced the decision-making process. Five research questions were formulated to frame the direction of this study, including: (1) How did healthcare leaders learn of innovations in design? (2) How did healthcare leaders make decisions in the selection of healthcare design concepts? (3) What criteria did healthcare leaders use in the decision-making process? (4) How did healthcare leaders consider input from the staff in design decisions? and (5) To what extent did the leadership style of administrators affect the outcomes of the decision-making process? Current issues affecting healthcare in the community led the principal investigator's organization to undertake an ambitious facilities expansion project. As part of its planning process, the organization learned of EBD principles that seemingly had a positive impact on patient care and safety and staff working conditions. Although promising, a paucity of empirical research addressed the cost/benefit of incorporating many EBD concepts into one hospital setting, and there was no research that articulated the organizational decision-making process used by healthcare administrators when considering the use of EBD in expansion projects. A mixed-method, descriptive, qualitative, single-case study and quantitative design were used to address the five research questions. The Systems Research Organizing Model provided the theoretical framework. A variety of data collection methods was used, including interviews of key respondents, the review of documentary evidence, and the Multifactor Leadership Questionnaire. A participatory process was used throughout the design decision phases, involving staff at all levels of the organization. The Internet and architects facilitated learning about EBD. Financial considerations were a factor in decision making. The prevalence of the transformational leadership style among the organization's administrators exceeded the U.S. mean.

Requiem for the max rule?

PubMed

Ma, Wei Ji; Shen, Shan; Dziugaite, Gintare; van den Berg, Ronald

2015-11-01

In tasks such as visual search and change detection, a key question is how observers integrate noisy measurements from multiple locations to make a decision. Decision rules proposed to model this process have fallen into two categories: Bayes-optimal (ideal observer) rules and ad-hoc rules. Among the latter, the maximum-of-outputs (max) rule has been the most prominent. Reviewing recent work and performing new model comparisons across a range of paradigms, we find that in all cases except for one, the optimal rule describes human data as well as or better than every max rule either previously proposed or newly introduced here. This casts doubt on the utility of the max rule for understanding perceptual decision-making. Copyright © 2015 Elsevier Ltd. All rights reserved.

Buffelgrass-Integrated modeling of an invasive plant

USGS Publications Warehouse

Holcombe, Tracy R.

2011-01-01

Buffelgrass (Pennisetum ciliare) poses a problem in the deserts of the United States, growing in dense stands and introducing a wildfire risk in an ecosystem not adapted to fire. The Invasive Species Science Branch of the Fort Collins Science Center has worked with many partners to develop a decision support model and a data management system to address the problem. The decision support model evaluates potential strategies for resource use and allocation. The data management system is a portal where users can submit, view, and download buffelgrass data. These tools provide a case study showcasing how the FORT is working to address the urgent issue of invasive species in the United States.

Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization.

PubMed

Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu

2016-08-15

Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision. Copyright © 2016 Elsevier B.V. All rights reserved.

A framework for sensitivity analysis of decision trees.

PubMed

Kamiński, Bogumił; Jakubczyk, Michał; Szufel, Przemysław

2018-01-01

In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.

Shapelet analysis of pupil dilation for modeling visuo-cognitive behavior in screening mammography

NASA Astrophysics Data System (ADS)

Alamudun, Folami; Yoon, Hong-Jun; Hammond, Tracy; Hudson, Kathy; Morin-Ducote, Garnetta; Tourassi, Georgia

2016-03-01

Our objective is to improve understanding of visuo-cognitive behavior in screening mammography under clinically equivalent experimental conditions. To this end, we examined pupillometric data, acquired using a head-mounted eye-tracking device, from 10 image readers (three breast-imaging radiologists and seven Radiology residents), and their corresponding diagnostic decisions for 100 screening mammograms. The corpus of mammograms comprised cases of varied pathology and breast parenchymal density. We investigated the relationship between pupillometric fluctuations, experienced by an image reader during mammographic screening, indicative of changes in mental workload, the pathological characteristics of a mammographic case, and the image readers' diagnostic decision and overall task performance. To answer these questions, we extract features from pupillometric data, and additionally applied time series shapelet analysis to extract discriminative patterns in changes in pupil dilation. Our results show that pupillometric measures are adequate predictors of mammographic case pathology, and image readers' diagnostic decision and performance with an average accuracy of 80%.

Development of prognostic model for predicting survival after retrograde placement of ureteral stent in advanced gastrointestinal cancer patients and its evaluation by decision curve analysis.

PubMed

Kawano, Shingo; Komai, Yoshinobu; Ishioka, Junichiro; Sakai, Yasuyuki; Fuse, Nozomu; Ito, Masaaki; Kihara, Kazunori; Saito, Norio

2016-10-01

The aim of this study was to determine risk factors for survival after retrograde placement of ureteral stents and develop a prognostic model for advanced gastrointestinal tract (GIT: esophagus, stomach, colon and rectum) cancer patients. We examined the clinical records of 122 patients who underwent retrograde placement of a ureteral stent against malignant extrinsic ureteral obstruction. A prediction model for survival after stenting was developed. We compared its clinical usefulness with our previous model based on the results from nephrostomy cases by decision curve analysis. Median follow-up period was 201 days (8-1490) and 97 deaths occurred. The 1-year survival rate in this cohort was 29%. Based on multivariate analysis, primary site of colon origin, absence of retroperitoneal lymph node metastasis and serum albumin >3g/dL were significantly associated with a prolonged survival time. To develop a prognostic model, we divided the patients into 3 risk groups of favorable: 0-1 factors (N.=53), intermediate: 2 risk factors (N.=54), and poor: 3 risk factors (N.=15). There were significant differences in the survival profiles of these 3 risk groups (P<0.0001). Decision curve analyses revealed that the current model has a superior net benefit than our previous model for most of the examined probabilities. We have developed a novel prognostic model for GIT cancer patients who were treated with retrograde placement of a ureteral stent. The current model should help urologists and medical oncologists to predict survival in cases of malignant extrinsic ureteral obstruction.

An interdisciplinary framework for participatory modeling design and evaluation—What makes models effective participatory decision tools?

NASA Astrophysics Data System (ADS)

Falconi, Stefanie M.; Palmer, Richard N.

2017-02-01

Increased requirements for public involvement in water resources management (WRM) over the past century have stimulated the development of more collaborative decision-making methods. Participatory modeling (PM) uses computer models to inform and engage stakeholders in the planning process in order to influence collaborative decisions in WRM. Past evaluations of participatory models focused on process and final outcomes, yet, were hindered by diversity of purpose and inconsistent documentation. This paper presents a two-stage framework for evaluating PM based on mechanisms for improving model effectiveness as participatory tools. The five dimensions characterize the "who, when, how, and why" of each participatory effort (stage 1). Models are evaluated as "boundary objects," a concept used to describe tools that bridge understanding and translate different bodies of knowledge to improve credibility, salience, and legitimacy (stage 2). This evaluation framework is applied to five existing case studies from the literature. Though the goals of participation can be diverse, the novel contribution of the two-stage proposed framework is the flexibility it has to evaluate a wide range of cases that differ in scope, modeling approach, and participatory context. Also, the evaluation criteria provide a structured vocabulary based on clear mechanisms that extend beyond previous process-based and outcome-based evaluations. Effective models are those that take advantage of mechanisms that facilitate dialogue and resolution and improve the accessibility and applicability of technical knowledge. Furthermore, the framework can help build more complete records and systematic documentation of evidence to help standardize the field of PM.

Developing quality indicators and auditing protocols from formal guideline models: knowledge representation and transformations.

PubMed

Advani, Aneel; Goldstein, Mary; Shahar, Yuval; Musen, Mark A

2003-01-01

Automated quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe a model representation and algorithm for deriving structured quality indicators and auditing protocols from formalized specifications of guidelines used in decision support systems. We apply the model and algorithm to the assessment of physician concordance with a guideline knowledge model for hypertension used in a decision-support system. The properties of our solution include the ability to derive automatically context-specific and case-mix-adjusted quality indicators that can model global or local levels of detail about the guideline parameterized by defining the reliability of each indicator or element of the guideline.

Determining if an older adult can make and execute decisions to live safely at home: a capacity assessment and intervention model

PubMed Central

Skelton, Felicia; Kunik, Mark E.; Regev, Tziona; Naik, Aanand D.

2009-01-01

Determining an older adult’s capacity to live safely and independently in the community presents a serious and complicated challenge to the health care system. Evaluating one’s ability to make and execute decisions regarding safe and independent living incorporates clinical assessments, bioethical considerations, and often legal declarations of capacity. Capacity assessments usually result in life changes for patients and their families, including a caregiver managing some everyday tasks, placement outside of the home, and even legal guardianship. The process of determining capacity and recommending intervention is often inefficient and highly variable in most cases. Physicians are rarely trained to conduct capacity assessments and assessment methods are heterogeneous. An interdisciplinary team of clinicians developed the capacity assessment and intervention (CAI) model at a community outpatient geriatrics clinic to address these critical gaps. This report follows one patient through the entire CAI model, describing processes for a typical case. It then examines two additional case reports that highlight common challenges in capacity assessment. The CAI model uses assessment methods common to geriatrics clinical practice and conducts assessments and interventions in a standardized fashion. Reliance on common, validated measures increases generalizability of the model across geriatrics practice settings and patient populations. PMID:19481271

SketchBio: a scientist's 3D interface for molecular modeling and animation.

PubMed

Waldon, Shawn M; Thompson, Peter M; Hahn, Patrick J; Taylor, Russell M

2014-10-30

Because of the difficulties involved in learning and using 3D modeling and rendering software, many scientists hire programmers or animators to create models and animations. This both slows the discovery process and provides opportunities for miscommunication. Working with multiple collaborators, a tool was developed (based on a set of design goals) to enable them to directly construct models and animations. SketchBio is presented, a tool that incorporates state-of-the-art bimanual interaction and drop shadows to enable rapid construction of molecular structures and animations. It includes three novel features: crystal-by-example, pose-mode physics, and spring-based layout that accelerate operations common in the formation of molecular models. Design decisions and their consequences are presented, including cases where iterative design was required to produce effective approaches. The design decisions, novel features, and inclusion of state-of-the-art techniques enabled SketchBio to meet all of its design goals. These features and decisions can be incorporated into existing and new tools to improve their effectiveness.

A nonlinear bi-level programming approach for product portfolio management.

PubMed

Ma, Shuang

2016-01-01

Product portfolio management (PPM) is a critical decision-making for companies across various industries in today's competitive environment. Traditional studies on PPM problem have been motivated toward engineering feasibilities and marketing which relatively pay less attention to other competitors' actions and the competitive relations, especially in mathematical optimization domain. The key challenge lies in that how to construct a mathematical optimization model to describe this Stackelberg game-based leader-follower PPM problem and the competitive relations between them. The primary work of this paper is the representation of a decision framework and the optimization model to leverage the PPM problem of leader and follower. A nonlinear, integer bi-level programming model is developed based on the decision framework. Furthermore, a bi-level nested genetic algorithm is put forward to solve this nonlinear bi-level programming model for leader-follower PPM problem. A case study of notebook computer product portfolio optimization is reported. Results and analyses reveal that the leader-follower bi-level optimization model is robust and can empower product portfolio optimization.

Evaluating uncertainty in environmental life-cycle assessment. A case study comparing two insulation options for a Dutch one-family dwelling.

PubMed

Huijbregts, Mark A J; Gilijamse, Wim; Ragas, Ad M J; Reijnders, Lucas

2003-06-01

The evaluation of uncertainty is relatively new in environmental life-cycle assessment (LCA). It provides useful information to assess the reliability of LCA-based decisions and to guide future research toward reducing uncertainty. Most uncertainty studies in LCA quantify only one type of uncertainty, i.e., uncertainty due to input data (parameter uncertainty). However, LCA outcomes can also be uncertain due to normative choices (scenario uncertainty) and the mathematical models involved (model uncertainty). The present paper outlines a new methodology that quantifies parameter, scenario, and model uncertainty simultaneously in environmental life-cycle assessment. The procedure is illustrated in a case study that compares two insulation options for a Dutch one-family dwelling. Parameter uncertainty was quantified by means of Monte Carlo simulation. Scenario and model uncertainty were quantified by resampling different decision scenarios and model formulations, respectively. Although scenario and model uncertainty were not quantified comprehensively, the results indicate that both types of uncertainty influence the case study outcomes. This stresses the importance of quantifying parameter, scenario, and model uncertainty simultaneously. The two insulation options studied were found to have significantly different impact scores for global warming, stratospheric ozone depletion, and eutrophication. The thickest insulation option has the lowest impact on global warming and eutrophication, and the highest impact on stratospheric ozone depletion.

A Conceptual Analytics Model for an Outcome-Driven Quality Management Framework as Part of Professional Healthcare Education.

PubMed

Hervatis, Vasilis; Loe, Alan; Barman, Linda; O'Donoghue, John; Zary, Nabil

2015-10-06

Preparing the future health care professional workforce in a changing world is a significant undertaking. Educators and other decision makers look to evidence-based knowledge to improve quality of education. Analytics, the use of data to generate insights and support decisions, have been applied successfully across numerous application domains. Health care professional education is one area where great potential is yet to be realized. Previous research of Academic and Learning analytics has mainly focused on technical issues. The focus of this study relates to its practical implementation in the setting of health care education. The aim of this study is to create a conceptual model for a deeper understanding of the synthesizing process, and transforming data into information to support educators' decision making. A deductive case study approach was applied to develop the conceptual model. The analytics loop works both in theory and in practice. The conceptual model encompasses the underlying data, the quality indicators, and decision support for educators. The model illustrates how a theory can be applied to a traditional data-driven analytics approach, and alongside the context- or need-driven analytics approach.

A Conceptual Analytics Model for an Outcome-Driven Quality Management Framework as Part of Professional Healthcare Education

PubMed Central

Loe, Alan; Barman, Linda; O'Donoghue, John; Zary, Nabil

2015-01-01

Background Preparing the future health care professional workforce in a changing world is a significant undertaking. Educators and other decision makers look to evidence-based knowledge to improve quality of education. Analytics, the use of data to generate insights and support decisions, have been applied successfully across numerous application domains. Health care professional education is one area where great potential is yet to be realized. Previous research of Academic and Learning analytics has mainly focused on technical issues. The focus of this study relates to its practical implementation in the setting of health care education. Objective The aim of this study is to create a conceptual model for a deeper understanding of the synthesizing process, and transforming data into information to support educators’ decision making. Methods A deductive case study approach was applied to develop the conceptual model. Results The analytics loop works both in theory and in practice. The conceptual model encompasses the underlying data, the quality indicators, and decision support for educators. Conclusions The model illustrates how a theory can be applied to a traditional data-driven analytics approach, and alongside the context- or need-driven analytics approach. PMID:27731840

Tracing Growth of Teachers' Classroom Interactions with Representations of Functions in the Connected Classroom

NASA Astrophysics Data System (ADS)

Morton, Brian Lee

The purpose of this study is to create an empirically based theoretic model of change of the use and treatment of representations of functions with the use of Connected Classroom Technology (CCT) using data previously collected for the Classroom Connectivity in Promoting Mathematics and Science Achievement (CCMS) project. Qualitative analysis of videotapes of three algebra teachers' instruction focused on different categories thought to influence teaching representations with technology: representations, discourse, technology, and decisions. Models for rating teachers low, medium, or high for each of these categories were created using a priori codes and grounded methodology. A cross case analysis was conducted after the completion of the case studies by comparing and contrasting the three cases. Data revealed that teachers' decisions shifted to incorporate the difference in student ideas/representations made visible by the CCT into their instruction and ultimately altered their orientation to mathematics teaching. The shift in orientation seemed to lead to the teachers' growth with regards to representations, discourse, and technology.

Factors leading to the involvement of Forensic Advisors in the Belgian criminal justice system.

PubMed

Bitzer, Sonja

2018-04-01

Forensic Advisors at the National Institute for Criminalistics and Criminology in Brussels act as advising body to the magistrate regarding analytical possibilities and the usefulness of trace analysis in a case. Initially, their function was devised to assist in complex murder cases with unknown offender. However, in a previous study, the increasing diversity of the cases they are requested for has been observed (Bitzer et al., in press). In order to deepen our understanding of the decision steps in the criminal investigation process, the decision to involve a Forensic Advisor and the factors leading to their involvement were evaluated. The study focused on homicide, robbery and burglary cases with and without requests for a Forensic Advisor between January 2014 and June 2016. The factors were categorised into five knowledge dimensions: strategic, immediate, physical, criminal and utility. Decision tree modelling was carried out in order to identify the factors influencing the request for a Forensic Advisor in the case. The decision to request a Forensic Advisor differs between different types of offences. It also depends on the complexity of the case in terms of the number of traces and objects collected at the crime scene, and the availability of witness reports. Indeed, Forensic Advisors take the role of trace analysis coordinator by providing an overview of all available traces, objects, analyses and results. According to the principal implication factors and the performed case study, the contribution of Forensic Advisors consists mainly in summarising all information and advise on potential additional analyses. This might be explained by a loss of overview of the information and the possibilities regarding trace analysis by the magistrate responsible of the case. Copyright © 2018 Elsevier B.V. All rights reserved.

Environmental impact assessment of transportation projects: An analysis using an integrated GIS, remote sensing, and spatial modeling approach

NASA Astrophysics Data System (ADS)

El-Gafy, Mohamed Anwar

Transportation projects will have impact on the environment. The general environmental pollution and damage caused by roads is closely associated with the level of economic activity. Although Environmental Impact Assessments (EIAs) are dependent on geo-spatial information in order to make an assessment, there are no rules per se how to conduct an environmental assessment. Also, the particular objective of each assessment is dictated case-by-case, based on what information and analyses are required. The conventional way of Environmental Impact Assessment (EIA) study is a time consuming process because it has large number of dependent and independent variables which have to be taken into account, which also have different consequences. With the emergence of satellite remote sensing technology and Geographic Information Systems (GIS), this research presents a new framework for the analysis phase of the Environmental Impact Assessment (EIA) for transportation projects based on the integration between remote sensing technology, geographic information systems, and spatial modeling. By integrating the merits of the map overlay method and the matrix method, the framework analyzes comprehensively the environmental vulnerability around the road and its impact on the environment. This framework is expected to: (1) improve the quality of the decision making process, (2) be applied both to urban and inter-urban projects, regardless of transport mode, and (3) present the data and make the appropriate analysis to support the decision of the decision-makers and allow them to present these data to the public hearings in a simple manner. Case studies, transportation projects in the State of Florida, were analyzed to illustrate the use of the decision support framework and demonstrate its capabilities. This cohesive and integrated system will facilitate rational decisions through cost effective coordination of environmental information and data management that can be tailored to specific projects. The framework would facilitate collecting, organizing, analyzing, archiving, and coordinating the information and data necessary to support technical and policy transportation decisions.

Carbon Cycle Science in Support of Decision-Making

NASA Astrophysics Data System (ADS)

Brown, M. E.; West, T. O.; McGlynn, E.; Gurwick, N. P.; Duren, R. M.; Ocko, I.; Paustian, K.

2016-12-01

There has been an extensive amount of basic and applied research conducted on biogeochemical cycles, land cover change, watershed to earth system modeling, climate change, and energy efficiency. Concurrently, there continues to be interest in how to best reduce net carbon emissions, including maintaining or augmenting global carbon stocks and decreasing fossil fuel emissions. Decisions surrounding reductions in net emissions should be grounded in, and informed by, existing scientific knowledge and analyses in order to be most effective. The translation of scientific research to decision-making is rarely direct, and often requires coordination of objectives or intermediate research steps. For example, complex model output may need to be simplified to provide mean estimates for given activities; biogeochemical models used for climate change prediction may need to be altered to estimate net carbon flux associated with particular activities; or scientific analyses may need to aggregate and analyze data in a different manner to address specific questions. In the aforementioned cases, expertise and capabilities of researchers and decision-makers are both needed, and early coordination and communication is most effective. Initial analysis of existing science and current decision-making needs indicate that (a) knowledge that is co-produced by scientists and decision-makers has a higher probability of being usable for decision making, (b) scientific work in the past decade to integrate activity data into models has resulted in more usable information for decision makers, (c) attribution and accounting of carbon cycle fluxes is key to using carbon cycle science for decision-making, and (d) stronger, long-term links among research on climate and management of carbon-related sectors (e.g., energy, land use, industry, and buildings) are needed to adequately address current issues.

Modelling a flows in supply chain with analytical models: Case of a chemical industry

NASA Astrophysics Data System (ADS)

Benhida, Khalid; Azougagh, Yassine; Elfezazi, Said

2016-02-01

This study is interested on the modelling of the logistics flows in a supply chain composed on a production sites and a logistics platform. The contribution of this research is to develop an analytical model (integrated linear programming model), based on a case study of a real company operating in the phosphate field, considering a various constraints in this supply chain to resolve the planning problems for a better decision-making. The objectives of this model is to determine and define the optimal quantities of different products to route, to and from the various entities in the supply chain studied.

Modeling of shallot supply decisions: the case of Indonesia

NASA Astrophysics Data System (ADS)

Prabawati, N. F.; Pujawan, I. N.; Widodo, E.

2018-04-01

To optimize supply chain role, the players of supply chain need to integrate its function. One of the general problems in supply chain was the unbalanced quantity of sales and quantity of supply. This paper focused on modelling a simple method to manage the gap between the demand and the supply. The gap might cause an overstock or a loss. This paper propose a buffer quantity in order to handle the gap by using import decision. The case study was about shallot supply - demand in Indonesia. In this study we model the supply decisions of shallot in Indonesia. While the demand was quite stable over time, the supply was heavily affected by the yield from the farms. The shortage could result in the government importing shallot from other countries. Hence, the government also needed to have a proper buffering mechanism in order to ensure the supply was sufficient and the price was quite stable. The initial model of this research was built by stochastic parameters and the extended model to gain pricing mechanism was built by Shapley value principal with modification. The primary variables were supply quantity, demand quantity, buffer and purchased quantity (stock needed), actual consumption, and price for three players. The validation proved that the result of price at each player presented a significant difference. Therefore, the model could be applied to decide the stock quantity needed and to keep the price stable at each player especially at the end player which would influence the market price.

Estimating the Expected Value of Sample Information Using the Probabilistic Sensitivity Analysis Sample

PubMed Central

Oakley, Jeremy E.; Brennan, Alan; Breeze, Penny

2015-01-01

Health economic decision-analytic models are used to estimate the expected net benefits of competing decision options. The true values of the input parameters of such models are rarely known with certainty, and it is often useful to quantify the value to the decision maker of reducing uncertainty through collecting new data. In the context of a particular decision problem, the value of a proposed research design can be quantified by its expected value of sample information (EVSI). EVSI is commonly estimated via a 2-level Monte Carlo procedure in which plausible data sets are generated in an outer loop, and then, conditional on these, the parameters of the decision model are updated via Bayes rule and sampled in an inner loop. At each iteration of the inner loop, the decision model is evaluated. This is computationally demanding and may be difficult if the posterior distribution of the model parameters conditional on sampled data is hard to sample from. We describe a fast nonparametric regression-based method for estimating per-patient EVSI that requires only the probabilistic sensitivity analysis sample (i.e., the set of samples drawn from the joint distribution of the parameters and the corresponding net benefits). The method avoids the need to sample from the posterior distributions of the parameters and avoids the need to rerun the model. The only requirement is that sample data sets can be generated. The method is applicable with a model of any complexity and with any specification of model parameter distribution. We demonstrate in a case study the superior efficiency of the regression method over the 2-level Monte Carlo method. PMID:25810269

Work-Based Learning: A Practical Approach for Learning to Work and Working to Learn. A Case Study on Decision-Makers' Professional Development in Iran

ERIC Educational Resources Information Center

Arani, Mohammad Reza Sarkar; Alagamandan, Jafar; Tourani, Heidar

2004-01-01

The work-based learning model of human resource development has captured a great deal of attention and has gained increasing importance in higher education in recent years. Work-based learning is a powerful phenomenon that attempts to help policy-makers, managers and curriculum developers improve the quality of the decision and organizational…

Collective Labor Supply: A Single-Equation Model and Some Evidence from French Data

ERIC Educational Resources Information Center

Donni, Olivier; Moreau, Nicolas

2007-01-01

In Chiappori's (1988) collective model of labor supply, hours of work are supposed flexible. In many countries, however, male labor supply does not vary much. In that case, the husband's labor supply is no longer informative about the household decision process and individual preferences. To identify structural components of the model, additional…

An ontology-driven, case-based clinical decision support model for removable partial denture design

NASA Astrophysics Data System (ADS)

Chen, Qingxiao; Wu, Ji; Li, Shusen; Lyu, Peijun; Wang, Yong; Li, Miao

2016-06-01

We present the initial work toward developing a clinical decision support model for specific design of removable partial dentures (RPDs) in dentistry. We developed an ontological paradigm to represent knowledge of a patient’s oral conditions and denture component parts. During the case-based reasoning process, a cosine similarity algorithm was applied to calculate similarity values between input patients and standard ontology cases. A group of designs from the most similar cases were output as the final results. To evaluate this model, the output designs of RPDs for 104 randomly selected patients were compared with those selected by professionals. An area under the curve of the receiver operating characteristic (AUC-ROC) was created by plotting true-positive rates against the false-positive rate at various threshold settings. The precision at position 5 of the retrieved cases was 0.67 and at the top of the curve it was 0.96, both of which are very high. The mean average of precision (MAP) was 0.61 and the normalized discounted cumulative gain (NDCG) was 0.74 both of which confirmed the efficient performance of our model. All the metrics demonstrated the efficiency of our model. This methodology merits further research development to match clinical applications for designing RPDs. This paper is organized as follows. After the introduction and description of the basis for the paper, the evaluation and results are presented in Section 2. Section 3 provides a discussion of the methodology and results. Section 4 describes the details of the ontology, similarity algorithm, and application.

An ontology-driven, case-based clinical decision support model for removable partial denture design.

PubMed

Chen, Qingxiao; Wu, Ji; Li, Shusen; Lyu, Peijun; Wang, Yong; Li, Miao

2016-06-14

We present the initial work toward developing a clinical decision support model for specific design of removable partial dentures (RPDs) in dentistry. We developed an ontological paradigm to represent knowledge of a patient's oral conditions and denture component parts. During the case-based reasoning process, a cosine similarity algorithm was applied to calculate similarity values between input patients and standard ontology cases. A group of designs from the most similar cases were output as the final results. To evaluate this model, the output designs of RPDs for 104 randomly selected patients were compared with those selected by professionals. An area under the curve of the receiver operating characteristic (AUC-ROC) was created by plotting true-positive rates against the false-positive rate at various threshold settings. The precision at position 5 of the retrieved cases was 0.67 and at the top of the curve it was 0.96, both of which are very high. The mean average of precision (MAP) was 0.61 and the normalized discounted cumulative gain (NDCG) was 0.74 both of which confirmed the efficient performance of our model. All the metrics demonstrated the efficiency of our model. This methodology merits further research development to match clinical applications for designing RPDs. This paper is organized as follows. After the introduction and description of the basis for the paper, the evaluation and results are presented in Section 2. Section 3 provides a discussion of the methodology and results. Section 4 describes the details of the ontology, similarity algorithm, and application.

Museum Accessibility: Combining Audience Research and Staff Training

ERIC Educational Resources Information Center

Levent, Nina; Reich, Christine

2013-01-01

This article discusses an audience-informed professional development model that combines audience research focus groups and staff training that includes interaction and direct feedback from visitors, in this case, visitors with low vision. There are two critical components to this model: one is that museums' programming decisions are informed by…

Predicting agricultural impacts of large-scale drought: 2012 and the case for better modeling

USDA-ARS?s Scientific Manuscript database

We present an example of a simulation-based forecast for the 2012 U.S. maize growing season produced as part of a high-resolution, multi-scale, predictive mechanistic modeling study designed for decision support, risk management, and counterfactual analysis. The simulations undertaken for this analy...

Combining Bayesian Networks and Agent Based Modeling to develop a decision-support model in Vietnam

NASA Astrophysics Data System (ADS)

Nong, Bao Anh; Ertsen, Maurits; Schoups, Gerrit

2016-04-01

Complexity and uncertainty in natural resources management have been focus themes in recent years. Within these debates, with the aim to define an approach feasible for water management practice, we are developing an integrated conceptual modeling framework for simulating decision-making processes of citizens, in our case in the Day river area, Vietnam. The model combines Bayesian Networks (BNs) and Agent-Based Modeling (ABM). BNs are able to combine both qualitative data from consultants / experts / stakeholders, and quantitative data from observations on different phenomena or outcomes from other models. Further strengths of BNs are that the relationship between variables in the system is presented in a graphical interface, and that components of uncertainty are explicitly related to their probabilistic dependencies. A disadvantage is that BNs cannot easily identify the feedback of agents in the system once changes appear. Hence, ABM was adopted to represent the reaction among stakeholders under changes. The modeling framework is developed as an attempt to gain better understanding about citizen's behavior and factors influencing their decisions in order to reduce uncertainty in the implementation of water management policy.

Multi-objective decision-making model based on CBM for an aircraft fleet

NASA Astrophysics Data System (ADS)

Luo, Bin; Lin, Lin

2018-04-01

Modern production management patterns, in which multi-unit (e.g., a fleet of aircrafts) are managed in a holistic manner, have brought new challenges for multi-unit maintenance decision making. To schedule a good maintenance plan, not only does the individual machine maintenance have to be considered, but also the maintenance of the other individuals have to be taken into account. Since most condition-based maintenance researches for aircraft focused on solely reducing maintenance cost or maximizing the availability of single aircraft, as well as considering that seldom researches concentrated on both the two objectives: minimizing cost and maximizing the availability of a fleet (total number of available aircraft in fleet), a multi-objective decision-making model based on condition-based maintenance concentrated both on the above two objectives is established. Furthermore, in consideration of the decision maker may prefer providing the final optimal result in the form of discrete intervals instead of a set of points (non-dominated solutions) in real decision-making problem, a novel multi-objective optimization method based on support vector regression is proposed to solve the above multi-objective decision-making model. Finally, a case study regarding a fleet is conducted, with the results proving that the approach efficiently generates outcomes that meet the schedule requirements.

Robustness for slope stability modelling under deep uncertainty

NASA Astrophysics Data System (ADS)

Almeida, Susana; Holcombe, Liz; Pianosi, Francesca; Wagener, Thorsten

2015-04-01

Landslides can have large negative societal and economic impacts, such as loss of life and damage to infrastructure. However, the ability of slope stability assessment to guide management is limited by high levels of uncertainty in model predictions. Many of these uncertainties cannot be easily quantified, such as those linked to climate change and other future socio-economic conditions, restricting the usefulness of traditional decision analysis tools. Deep uncertainty can be managed more effectively by developing robust, but not necessarily optimal, policies that are expected to perform adequately under a wide range of future conditions. Robust strategies are particularly valuable when the consequences of taking a wrong decision are high as is often the case of when managing natural hazard risks such as landslides. In our work a physically based numerical model of hydrologically induced slope instability (the Combined Hydrology and Stability Model - CHASM) is applied together with robust decision making to evaluate the most important uncertainties (storm events, groundwater conditions, surface cover, slope geometry, material strata and geotechnical properties) affecting slope stability. Specifically, impacts of climate change on long-term slope stability are incorporated, accounting for the deep uncertainty in future climate projections. Our findings highlight the potential of robust decision making to aid decision support for landslide hazard reduction and risk management under conditions of deep uncertainty.

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.

Decision-relevant evaluation of climate models: A case study of chill hours in California

NASA Astrophysics Data System (ADS)

Jagannathan, K. A.; Jones, A. D.; Kerr, A. C.

2017-12-01

The past decade has seen a proliferation of different climate datasets with over 60 climate models currently in use. Comparative evaluation and validation of models can assist practitioners chose the most appropriate models for adaptation planning. However, such assessments are usually conducted for `climate metrics' such as seasonal temperature, while sectoral decisions are often based on `decision-relevant outcome metrics' such as growing degree days or chill hours. Since climate models predict different metrics with varying skill, the goal of this research is to conduct a bottom-up evaluation of model skill for `outcome-based' metrics. Using chill hours (number of hours in winter months where temperature is lesser than 45 deg F) in Fresno, CA as a case, we assess how well different GCMs predict the historical mean and slope of chill hours, and whether and to what extent projections differ based on model selection. We then compare our results with other climate-based evaluations of the region, to identify similarities and differences. For the model skill evaluation, historically observed chill hours were compared with simulations from 27 GCMs (and multiple ensembles). Model skill scores were generated based on a statistical hypothesis test of the comparative assessment. Future projections from RCP 8.5 runs were evaluated, and a simple bias correction was also conducted. Our analysis indicates that model skill in predicting chill hour slope is dependent on its skill in predicting mean chill hours, which results from the non-linear nature of the chill metric. However, there was no clear relationship between the models that performed well for the chill hour metric and those that performed well in other temperature-based evaluations (such winter minimum temperature or diurnal temperature range). Further, contrary to conclusions from other studies, we also found that the multi-model mean or large ensemble mean results may not always be most appropriate for this outcome metric. Our assessment sheds light on key differences between global versus local skill, and broad versus specific skill of climate models, highlighting that decision-relevant model evaluation may be crucial for providing practitioners with the best available climate information for their specific needs.

Decision analysis of shoreline protection under climate change uncertainty

NASA Astrophysics Data System (ADS)

Chao, Philip T.; Hobbs, Benjamin F.

1997-04-01

If global warming occurs, it could significantly affect water resource distribution and availability. Yet it is unclear whether the prospect of such change is relevant to water resources management decisions being made today. We model a shoreline protection decision problem with a stochastic dynamic program (SDP) to determine whether consideration of the possibility of climate change would alter the decision. Three questions are addressed with the SDP: (l) How important is climate change compared to other uncertainties?, (2) What is the economic loss if climate change uncertainty is ignored?, and (3) How does belief in climate change affect the timing of the decision? In the case study, sensitivity analysis shows that uncertainty in real discount rates has a stronger effect upon the decision than belief in climate change. Nevertheless, a strong belief in climate change makes the shoreline protection project less attractive and often alters the decision to build it.

Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotely-sensed data.

PubMed

Rotela, Camilo H; Spinsanti, Lorena I; Lamfri, Mario A; Contigiani, Marta S; Almirón, Walter R; Scavuzzo, Carlos M

2011-11-01

In response to the first human outbreak (January May 2005) of Saint Louis encephalitis (SLE) virus in Córdoba province, Argentina, we developed an environmental SLE virus risk map for the capital, i.e. Córdoba city. The aim was to provide a map capable of detecting macro-environmental factors associated with the spatial distribution of SLE cases, based on remotely sensed data and a geographical information system. Vegetation, soil brightness, humidity status, distances to water-bodies and areas covered by vegetation were assessed based on pre-outbreak images provided by the Landsat 5TM satellite. A strong inverse relationship between the number of humans infected by SLEV and distance to high-vigor vegetation was noted. A statistical non-hierarchic decision tree model was constructed, based on environmental variables representing the areas surrounding patient residences. From this point of view, 18% of the city could be classified as being at high risk for SLEV infection, while 34% carried a low risk, or none at all. Taking the whole 2005 epidemic into account, 80% of the cases came from areas classified by the model as medium-high or high risk. Almost 46% of the cases were registered in high-risk areas, while there were no cases (0%) in areas affirmed as risk free.

Prediction of Disease Case Severity Level To Determine INA CBGs Rate

NASA Astrophysics Data System (ADS)

Puspitorini, Sukma; Kusumadewi, Sri; Rosita, Linda

2017-03-01

Indonesian Case-Based Groups (INA CBGs) is case-mix payment system using software grouper application. INA CBGs consisting of four digits code where the last digits indicating the severity level of disease cases. Severity level influence by secondary diagnosis (complications and co-morbidity) related to resource intensity level. It is medical resources used to treat a hospitalized patient. Objectives of this research is developing decision support system to predict severity level of disease cases and illustrate INA CBGs rate by using data mining decision tree classification model. Primary diagnosis (DU), first secondary diagnosis (DS 1), and second secondary diagnosis (DS 2) are attributes that used as input of severity level. The training process using C4.5 algorithm and the rules will represent in the IF-THEN form. Credibility of the system analyzed through testing process and confusion matrix present the results. Outcome of this research shows that first secondary diagnosis influence significant to form severity level predicting rules from new disease cases and INA CBGs rate illustration.

Lexical decision with pseudohomophones and reading in the semantic variant of primary progressive aphasia: A double dissociation.

PubMed

Boukadi, Mariem; Potvin, Karel; Macoir, Joël; Jr Laforce, Robert; Poulin, Stéphane; Brambati, Simona M; Wilson, Maximiliano A

2016-06-01

The co-occurrence of semantic impairment and surface dyslexia in the semantic variant of primary progressive aphasia (svPPA) has often been taken as supporting evidence for the central role of semantics in visual word processing. According to connectionist models, semantic access is needed to accurately read irregular words. They also postulate that reliance on semantics is necessary to perform the lexical decision task under certain circumstances (for example, when the stimulus list comprises pseudohomophones). In the present study, we report two svPPA cases: M.F. who presented with surface dyslexia but performed accurately on the lexical decision task with pseudohomophones, and R.L. who showed no surface dyslexia but performed below the normal range on the lexical decision task with pseudohomophones. This double dissociation between reading and lexical decision with pseudohomophones is in line with the dual-route cascaded (DRC) model of reading. According to this model, impairments in visual word processing in svPPA are not necessarily associated with the semantic deficits characterizing this disease. Our findings also call into question the central role given to semantics in visual word processing within the connectionist account. Copyright © 2016 Elsevier Ltd. All rights reserved.

Exploratory Case Study: On School Counselors' Perceived Influence on Low Socioeconomic Students' College Enrollment

ERIC Educational Resources Information Center

Deslonde, Vernell L.

2017-01-01

The purpose of this qualitative exploratory case study was to examine the high school counselors' perception of their ability to influence low socioeconomic students' postsecondary enrollment decisions in seven Title I high schools in southern California. Perna and Thomas' Student Success model and the Delivery System of the American School…

Women's Rights Project--Sports Packet.

ERIC Educational Resources Information Center

American Civil Liberties Union, New York, NY. Women's Rights Project.

This document represents a model legal case for defending a girl who wants to play soccer on her high school's all male soccer team. Contained are descriptions of the parties involved, the statement of claim, the causes of action, the laws that action is taken under, and former court decisions that might have bearing on the case. Also included are…

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

PubMed

Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

2016-07-07

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography

NASA Astrophysics Data System (ADS)

Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

2016-07-01

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

Automatic staging of bladder cancer on CT urography

NASA Astrophysics Data System (ADS)

Garapati, Sankeerth S.; Hadjiiski, Lubomir M.; Cha, Kenny H.; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.; Weizer, Alon; Alva, Ajjai; Paramagul, Chintana; Wei, Jun; Zhou, Chuan

2016-03-01

Correct staging of bladder cancer is crucial for the decision of neoadjuvant chemotherapy treatment and minimizing the risk of under- or over-treatment. Subjectivity and variability of clinicians in utilizing available diagnostic information may lead to inaccuracy in staging bladder cancer. An objective decision support system that merges the information in a predictive model based on statistical outcomes of previous cases and machine learning may assist clinicians in making more accurate and consistent staging assessments. In this study, we developed a preliminary method to stage bladder cancer. With IRB approval, 42 bladder cancer cases with CTU scans were collected from patient files. The cases were classified into two classes based on pathological stage T2, which is the decision threshold for neoadjuvant chemotherapy treatment (i.e. for stage >=T2) clinically. There were 21 cancers below stage T2 and 21 cancers at stage T2 or above. All 42 lesions were automatically segmented using our auto-initialized cascaded level sets (AI-CALS) method. Morphological features were extracted, which were selected and merged by linear discriminant analysis (LDA) classifier. A leave-one-case-out resampling scheme was used to train and test the classifier using the 42 lesions. The classification accuracy was quantified using the area under the ROC curve (Az). The average training Az was 0.97 and the test Az was 0.85. The classifier consistently selected the lesion volume, a gray level feature and a contrast feature. This predictive model shows promise for assisting in assessing the bladder cancer stage.

Tackling Complex Emergency Response Solutions Evaluation Problems in Sustainable Development by Fuzzy Group Decision Making Approaches with Considering Decision Hesitancy and Prioritization among Assessing Criteria.

PubMed

Qi, Xiao-Wen; Zhang, Jun-Ling; Zhao, Shu-Ping; Liang, Chang-Yong

2017-10-02

In order to be prepared against potential balance-breaking risks affecting economic development, more and more countries have recognized emergency response solutions evaluation (ERSE) as an indispensable activity in their governance of sustainable development. Traditional multiple criteria group decision making (MCGDM) approaches to ERSE have been facing simultaneous challenging characteristics of decision hesitancy and prioritization relations among assessing criteria, due to the complexity in practical ERSE problems. Therefore, aiming at the special type of ERSE problems that hold the two characteristics, we investigate effective MCGDM approaches by hiring interval-valued dual hesitant fuzzy set (IVDHFS) to comprehensively depict decision hesitancy. To exploit decision information embedded in prioritization relations among criteria, we firstly define an fuzzy entropy measure for IVDHFS so that its derivative decision models can avoid potential information distortion in models based on classic IVDHFS distance measures with subjective supplementing mechanism; further, based on defined entropy measure, we develop two fundamental prioritized operators for IVDHFS by extending Yager's prioritized operators. Furthermore, on the strength of above methods, we construct two hesitant fuzzy MCGDM approaches to tackle complex scenarios with or without known weights for decision makers, respectively. Finally, case studies have been conducted to show effectiveness and practicality of our proposed approaches.

Tackling Complex Emergency Response Solutions Evaluation Problems in Sustainable Development by Fuzzy Group Decision Making Approaches with Considering Decision Hesitancy and Prioritization among Assessing Criteria

PubMed Central

Qi, Xiao-Wen; Zhang, Jun-Ling; Zhao, Shu-Ping; Liang, Chang-Yong

2017-01-01

In order to be prepared against potential balance-breaking risks affecting economic development, more and more countries have recognized emergency response solutions evaluation (ERSE) as an indispensable activity in their governance of sustainable development. Traditional multiple criteria group decision making (MCGDM) approaches to ERSE have been facing simultaneous challenging characteristics of decision hesitancy and prioritization relations among assessing criteria, due to the complexity in practical ERSE problems. Therefore, aiming at the special type of ERSE problems that hold the two characteristics, we investigate effective MCGDM approaches by hiring interval-valued dual hesitant fuzzy set (IVDHFS) to comprehensively depict decision hesitancy. To exploit decision information embedded in prioritization relations among criteria, we firstly define an fuzzy entropy measure for IVDHFS so that its derivative decision models can avoid potential information distortion in models based on classic IVDHFS distance measures with subjective supplementing mechanism; further, based on defined entropy measure, we develop two fundamental prioritized operators for IVDHFS by extending Yager’s prioritized operators. Furthermore, on the strength of above methods, we construct two hesitant fuzzy MCGDM approaches to tackle complex scenarios with or without known weights for decision makers, respectively. Finally, case studies have been conducted to show effectiveness and practicality of our proposed approaches. PMID:28974045

Developing integrated clinical reasoning competencies in dental students using scaffolded case-based learning - empirical evidence.

PubMed

Postma, T C; White, J G

2016-08-01

This study provides empirical evidence of the development of integrated clinical reasoning in the discipline-based School of Dentistry, University of Pretoria, South Africa. Students were exposed to case-based learning in comprehensive patient care (CPC) in the preclinical year of study, scaffolded by means of the four-component instructional design model for complex learning. Progress test scores of third- to fifth-year dental students, who received case-based teaching and learning in the third year (2009-2011), were compared to the scores of preceding fourth- and fifth-year cohorts. These fourth- and fifth-year cohorts received content-based teaching concurrently with their clinical training in CPC. The progress test consisted of a complex case study and 32 MCQs on tracer conditions. Students had to gather the necessary information and had to make diagnostic and treatment-planning decisions. Preclinical students who participated in the case-based teaching and learning achieved similar scores compared to final-year students who received lecture-based teaching and learning. Final-year students who participated in the case-based learning made three more correct clinical decisions per student, compared to those who received content-based teaching. Students struggled more with treatment-planning than with diagnostic decisions. The scaffolded case-based learning appears to contribute to accurate clinical decisions when compared to lecture-based teaching. It is suggested that the development of integrated reasoning competencies starts as early as possible in a dental curriculum, perhaps even in the preclinical year of study. Treatment-planning should receive particular attention. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Statewide transportation planning for healthy communities

DOT National Transportation Integrated Search

2014-04-01

This white paper presents insights and a flexible model for State Departments of Transportation (DOTs) that choose to integrate public health considerations into their transportation planning and decision-making. It draws from five case studies of in...

Assessment of credit risk based on fuzzy relations

NASA Astrophysics Data System (ADS)

Tsabadze, Teimuraz

2017-06-01

The purpose of this paper is to develop a new approach for an assessment of the credit risk to corporate borrowers. There are different models for borrowers' risk assessment. These models are divided into two groups: statistical and theoretical. When assessing the credit risk for corporate borrowers, statistical model is unacceptable due to the lack of sufficiently large history of defaults. At the same time, we cannot use some theoretical models due to the lack of stock exchange. In those cases, when studying a particular borrower given that statistical base does not exist, the decision-making process is always of expert nature. The paper describes a new approach that may be used in group decision-making. An example of the application of the proposed approach is given.

Evidence-based dentistry: a model for clinical practice.

PubMed

Faggion, Clóvis M; Tu, Yu-Kang

2007-06-01

Making decisions in dentistry should be based on the best evidence available. The objective of this study was to demonstrate a practical procedure and model that clinicians can use to apply the results of well-conducted studies to patient care by critically appraising the evidence with checklists and letter grade scales. To demonstrate application of this model for critically appraising the quality of research evidence, a hypothetical case involving an adult male with chronic periodontitis is used as an example. To determine the best clinical approach for this patient, a four-step, evidence-based model is demonstrated, consisting of the following: definition of a research question using the PICO format, search and selection of relevant literature, critical appraisal of identified research reports using checklists, and the application of evidence. In this model, the quality of research evidence was assessed quantitatively based on different levels of quality that are assigned letter grades of A, B, and C by evaluating the studies against the QUOROM (Quality of Reporting Meta-Analyses) and CONSORT (Consolidated Standards of Reporting Trials) checklists in a tabular format. For this hypothetical periodontics case, application of the model identified the best available evidence for clinical decision making, i.e., one randomized controlled trial and one systematic review of randomized controlled trials. Both studies showed similar answers for the research question. The use of a letter grade scale allowed an objective analysis of the quality of evidence. A checklist-driven model that assesses and applies evidence to dental practice may substantially improve dentists' decision making skill.

Identifying the decision to be supported: a review of papers from environmental modelling and software

USGS Publications Warehouse

Sojda, Richard S.; Chen, Serena H.; El Sawah, Sondoss; Guillaume, Joseph H.A.; Jakeman, A.J.; Lautenbach, Sven; McIntosh, Brian S.; Rizzoli, A.E.; Seppelt, Ralf; Struss, Peter; Voinov, Alexey; Volk, Martin

2012-01-01

Two of the basic tenets of decision support system efforts are to help identify and structure the decisions to be supported, and to then provide analysis in how those decisions might be best made. One example from wetland management would be that wildlife biologists must decide when to draw down water levels to optimise aquatic invertebrates as food for breeding ducks. Once such a decision is identified, a system or tool to help them make that decision in the face of current and projected climate conditions could be developed. We examined a random sample of 100 papers published from 2001-2011 in Environmental Modelling and Software that used the phrase “decision support system” or “decision support tool”, and which are characteristic of different sectors. In our review, 41% of the systems and tools related to the water resources sector, 34% were related to agriculture, and 22% to the conservation of fish, wildlife, and protected area management. Only 60% of the papers were deemed to be reporting on DSS. This was based on the papers reviewed not having directly identified a specific decision to be supported. We also report on the techniques that were used to identify the decisions, such as formal survey, focus group, expert opinion, or sole judgment of the author(s). The primary underlying modelling system, e.g., expert system, agent based model, Bayesian belief network, geographical information system (GIS), and the like was categorised next. Finally, since decision support typically should target some aspect of unstructured decisions, we subjectively determined to what degree this was the case. In only 23% of the papers reviewed, did the system appear to tackle unstructured decisions. This knowledge should be useful in helping workers in the field develop more effective systems and tools, especially by being exposed to the approaches in different, but related, disciplines. We propose that a standard blueprint for reporting on DSS be developed for consideration by journal editors to aid them in filtering papers that use the term, “decision support”.

A user-friendly mathematical modelling web interface to assist local decision making in the fight against drug-resistant tuberculosis.

PubMed

Ragonnet, Romain; Trauer, James M; Denholm, Justin T; Marais, Ben J; McBryde, Emma S

2017-05-30

Multidrug-resistant and rifampicin-resistant tuberculosis (MDR/RR-TB) represent an important challenge for global tuberculosis (TB) control. The high rates of MDR/RR-TB observed among re-treatment cases can arise from diverse pathways: de novo amplification during initial treatment, inappropriate treatment of undiagnosed MDR/RR-TB, relapse despite appropriate treatment, or reinfection with MDR/RR-TB. Mathematical modelling allows quantification of the contribution made by these pathways in different settings. This information provides valuable insights for TB policy-makers, allowing better contextualised solutions. However, mathematical modelling outputs need to consider local data and be easily accessible to decision makers in order to improve their usefulness. We present a user-friendly web-based modelling interface, which can be used by people without technical knowledge. Users can input their own parameter values and produce estimates for their specific setting. This innovative tool provides easy access to mathematical modelling outputs that are highly relevant to national TB control programs. In future, the same approach could be applied to a variety of modelling applications, enhancing local decision making.

AQUATOOL, a generalized decision-support system for water-resources planning and operational management

NASA Astrophysics Data System (ADS)

Andreu, J.; Capilla, J.; Sanchís, E.

1996-04-01

This paper describes a generic decision-support system (DSS) which was originally designed for the planning stage of dicision-making associated with complex river basins. Subsequently, it was expanded to incorporate modules relating to the operational stage of decision-making. Computer-assisted design modules allow any complex water-resource system to be represented in graphical form, giving access to geographically referenced databases and knowledge bases. The modelling capability includes basin simulation and optimization modules, an aquifer flow modelling module and two modules for risk assessment. The Segura and Tagus river basins have been used as case studies in the development and validation phases. The value of this DSS is demonstrated by the fact that both River Basin Agencies currently use a version for the efficient management of their water resources.

A decision model for selecting sustainable drinking water supply and greywater reuse systems for developing communities with a case study in Cimahi, Indonesia.

PubMed

Henriques, Justin J; Louis, Garrick E

2011-01-01

Capacity Factor Analysis is a decision support system for selection of appropriate technologies for municipal sanitation services in developing communities. Developing communities are those that lack the capability to provide adequate access to one or more essential services, such as water and sanitation, to their residents. This research developed two elements of Capacity Factor Analysis: a capacity factor based classification for technologies using requirements analysis, and a matching policy for choosing technology options. First, requirements analysis is used to develop a ranking for drinking water supply and greywater reuse technologies. Second, using the Capacity Factor Analysis approach, a matching policy is developed to guide decision makers in selecting the appropriate drinking water supply or greywater reuse technology option for their community. Finally, a scenario-based informal hypothesis test is developed to assist in qualitative model validation through case study. Capacity Factor Analysis is then applied in Cimahi Indonesia as a form of validation. The completed Capacity Factor Analysis model will allow developing communities to select drinking water supply and greywater reuse systems that are safe, affordable, able to be built and managed by the community using local resources, and are amenable to expansion as the community's management capacity increases. Copyright © 2010 Elsevier Ltd. All rights reserved.

A Chaotic Ordered Hierarchies Consistency Analysis Performance Evaluation Model

NASA Astrophysics Data System (ADS)

Yeh, Wei-Chang

2013-02-01

The Hierarchies Consistency Analysis (HCA) is proposed by Guh in-cooperated along with some case study on a Resort to reinforce the weakness of Analytical Hierarchy Process (AHP). Although the results obtained enabled aid for the Decision Maker to make more reasonable and rational verdicts, the HCA itself is flawed. In this paper, our objective is to indicate the problems of HCA, and then propose a revised method called chaotic ordered HCA (COH in short) which can avoid problems. Since the COH is based upon Guh's method, the Decision Maker establishes decisions in a way similar to that of the original method.

Rapid Accurate Identification of Tuberculous Meningitis Among South African Children Using a Novel Clinical Decision Tool.

PubMed

Goenka, Anu; Jeena, Prakash M; Mlisana, Koleka; Solomon, Tom; Spicer, Kevin; Stephenson, Rebecca; Verma, Arpana; Dhada, Barnesh; Griffiths, Michael J

2018-03-01

Early diagnosis of tuberculous meningitis (TBM) is crucial to achieve optimum outcomes. There is no effective rapid diagnostic test for use in children. We aimed to develop a clinical decision tool to facilitate the early diagnosis of childhood TBM. Retrospective case-control study was performed across 7 hospitals in KwaZulu-Natal, South Africa (2010-2014). We identified the variables most predictive of microbiologically confirmed TBM in children (3 months to 15 years) by univariate analysis. These variables were modelled into a clinical decision tool and performance tested on an independent sample group. Of 865 children with suspected TBM, 3% (25) were identified with microbiologically confirmed TBM. Clinical information was retrieved for 22 microbiologically confirmed cases of TBM and compared with 66 controls matched for age, ethnicity, sex and geographical origin. The 9 most predictive variables among the confirmed cases were used to develop a clinical decision tool (CHILD TB LP): altered Consciousness; caregiver HIV infected; Illness length >7 days; Lethargy; focal neurologic Deficit; failure to Thrive; Blood/serum sodium <132 mmol/L; CSF >10 Lymphocytes ×10/L; CSF Protein >0.65 g/L. This tool successfully classified an independent sample of 7 cases and 21 controls with a sensitivity of 100% and specificity of 90%. The CHILD TB LP decision tool accurately classified microbiologically confirmed TBM. We propose that CHILD TB LP is prospectively evaluated as a novel rapid diagnostic tool for use in the initial evaluation of children with suspected neurologic infection presenting to hospitals in similar settings.

Merging spatially variant physical process models under an optimized systems dynamics framework.

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

Cain, William O.; Lowry, Thomas Stephen; Pierce, Suzanne A.

The complexity of water resource issues, its interconnectedness to other systems, and the involvement of competing stakeholders often overwhelm decision-makers and inhibit the creation of clear management strategies. While a range of modeling tools and procedures exist to address these problems, they tend to be case specific and generally emphasize either a quantitative and overly analytic approach or present a qualitative dialogue-based approach lacking the ability to fully explore consequences of different policy decisions. The integration of these two approaches is needed to drive toward final decisions and engender effective outcomes. Given these limitations, the Computer Assisted Dispute Resolution systemmore » (CADRe) was developed to aid in stakeholder inclusive resource planning. This modeling and negotiation system uniquely addresses resource concerns by developing a spatially varying system dynamics model as well as innovative global optimization search techniques to maximize outcomes from participatory dialogues. Ultimately, the core system architecture of CADRe also serves as the cornerstone upon which key scientific innovation and challenges can be addressed.« less

Developing Quality Indicators and Auditing Protocols from Formal Guideline Models: Knowledge Representation and Transformations

PubMed Central

Advani, Aneel; Goldstein, Mary; Shahar, Yuval; Musen, Mark A.

2003-01-01

Automated quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe a model representation and algorithm for deriving structured quality indicators and auditing protocols from formalized specifications of guidelines used in decision support systems. We apply the model and algorithm to the assessment of physician concordance with a guideline knowledge model for hypertension used in a decision-support system. The properties of our solution include the ability to derive automatically (1) context-specific and (2) case-mix-adjusted quality indicators that (3) can model global or local levels of detail about the guideline (4) parameterized by defining the reliability of each indicator or element of the guideline. PMID:14728124

Diagnostic Machine Learning Models for Acute Abdominal Pain: Towards an e-Learning Tool for Medical Students.

PubMed

Khumrin, Piyapong; Ryan, Anna; Judd, Terry; Verspoor, Karin

2017-01-01

Computer-aided learning systems (e-learning systems) can help medical students gain more experience with diagnostic reasoning and decision making. Within this context, providing feedback that matches students' needs (i.e. personalised feedback) is both critical and challenging. In this paper, we describe the development of a machine learning model to support medical students' diagnostic decisions. Machine learning models were trained on 208 clinical cases presenting with abdominal pain, to predict five diagnoses. We assessed which of these models are likely to be most effective for use in an e-learning tool that allows students to interact with a virtual patient. The broader goal is to utilise these models to generate personalised feedback based on the specific patient information requested by students and their active diagnostic hypotheses.

[Case finding in early prevention networks - a heuristic for ambulatory care settings].

PubMed

Barth, Michael; Belzer, Florian

2016-06-01

One goal of early prevention is the support of families with small children up to three years who are exposed to psychosocial risks. The identification of these cases is often complex and not well-directed, especially in the ambulatory care setting. Development of a model of a feasible and empirical based strategy for case finding in ambulatory care. Based on the risk factors of postpartal depression, lack of maternal responsiveness, parental stress with regulation disorders and poverty a lexicographic and non-compensatory heuristic model with simple decision rules, will be constructed and empirically tested. Therefore the original data set from an evaluation of the pediatric documentary form on psychosocial issues of families with small children in well-child visits will be used and reanalyzed. The first diagnostic step in the non-compensatory and hierarchical classification process is the assessment of postpartal depression followed by maternal responsiveness, parental stress and poverty. The classification model identifies 89.0 % cases from the original study. Compared to the original study the decision process becomes clearer and more concise. The evidence-based and data-driven model exemplifies a strategy for the assessment of psychosocial risk factors in ambulatory care settings. It is based on four evidence-based risk factors and offers a quick and reliable classification. A further advantage of this model is that after a risk factor is identified the diagnostic procedure will be stopped and the counselling process can commence. For further validation of the model studies, in well suited early prevention networks are needed.

The Gist of Juries: Testing a Model of Damage Award Decision Making

PubMed Central

Reyna, Valerie F.; Hans, Valerie P.; Corbin, Jonathan C.; Yeh, Ryan; Lin, Kelvin; Royer, Caisa

2017-01-01

Despite the importance of damage awards, juries are often at sea about the amounts that should be awarded, with widely differing awards for cases that seem comparable. We tested a new model of damage award decision making by systematically varying the size, context, and meaningfulness of numerical comparisons or anchors. As a result, we were able to elicit large differences in award amounts that replicated for 2 different cases. Although even arbitrary dollar amounts (unrelated to the cases) influenced the size of award judgments, the most consistent effects of numerical anchors were achieved when the amounts were meaningful in the sense that they conveyed the gist of numbers as small or large. Consistent with the model, the ordinal gist of the severity of plaintiff’s damages and defendant’s liability predicted damage awards, controlling for other factors such as motivation for the award-judgment task and perceived economic damages. Contrary to traditional dual-process approaches, numeracy and cognitive style (e.g., need for cognition and cognitive reflection) were not significant predictors of these numerical judgments, but they were associated with lower levels of variability once the gist of the judgments was taken into account. Implications for theory and policy are discussed. PMID:29075092

Fuzzy Naive Bayesian model for medical diagnostic decision support.

PubMed

Wagholikar, Kavishwar B; Vijayraghavan, Sundararajan; Deshpande, Ashok W

2009-01-01

This work relates to the development of computational algorithms to provide decision support to physicians. The authors propose a Fuzzy Naive Bayesian (FNB) model for medical diagnosis, which extends the Fuzzy Bayesian approach proposed by Okuda. A physician's interview based method is described to define a orthogonal fuzzy symptom information system, required to apply the model. For the purpose of elaboration and elicitation of characteristics, the algorithm is applied to a simple simulated dataset, and compared with conventional Naive Bayes (NB) approach. As a preliminary evaluation of FNB in real world scenario, the comparison is repeated on a real fuzzy dataset of 81 patients diagnosed with infectious diseases. The case study on simulated dataset elucidates that FNB can be optimal over NB for diagnosing patients with imprecise-fuzzy information, on account of the following characteristics - 1) it can model the information that, values of some attributes are semantically closer than values of other attributes, and 2) it offers a mechanism to temper exaggerations in patient information. Although the algorithm requires precise training data, its utility for fuzzy training data is argued for. This is supported by the case study on infectious disease dataset, which indicates optimality of FNB over NB for the infectious disease domain. Further case studies on large datasets are required to establish utility of FNB.

Novel flood risk assessment framework for rapid decision making

NASA Astrophysics Data System (ADS)

Valyrakis, Manousos; Koursari, Eftychia; Solley, Mark

2016-04-01

The impacts of catastrophic flooding, have significantly increased over the last few decades. This is due to primarily the increased urbanisation in ever-expanding mega-cities as well as due to the intensification both in magnitude and frequency of extreme hydrologic events. Herein a novel conceptual framework is presented that incorporates the use of real-time information to inform and update low dimensionality hydraulic models, to allow for rapid decision making towards preventing loss of life and safeguarding critical infrastructure. In particular, a case study from the recent UK floods in the area of Whitesands (Dumfries), is presented to demonstrate the utility of this approach. It is demonstrated that effectively combining a wealth of readily available qualitative information (such as crowdsourced visual documentation or using live data from sensing techniques), with existing quantitative data, can help appropriately update hydraulic models and reduce modelling uncertainties in future flood risk assessments. This approach is even more useful in cases where hydraulic models are limited, do not exist or were not needed before unpredicted dynamic modifications to the river system took place (for example in the case of reduced or eliminated hydraulic capacity due to blockages). The low computational cost and rapid assessment this framework offers, render it promising for innovating in flood management.

Clinician time used for decision making: a best case workflow study using cardiovascular risk assessments and Ask Mayo Expert algorithmic care process models.

PubMed

North, Frederick; Fox, Samuel; Chaudhry, Rajeev

2016-07-20

Risk calculation is increasingly used in lipid management, congestive heart failure, and atrial fibrillation. The risk scores are then used for decisions about statin use, anticoagulation, and implantable defibrillator use. Calculating risks for patients and making decisions based on these risks is often done at the point of care and is an additional time burden for clinicians that can be decreased by automating the tasks and using clinical decision-making support. Using Morae Recorder software, we timed 30 healthcare providers tasked with calculating the overall risk of cardiovascular events, sudden death in heart failure, and thrombotic event risk in atrial fibrillation. Risk calculators used were the American College of Cardiology Atherosclerotic Cardiovascular Disease risk calculator (AHA-ASCVD risk), Seattle Heart Failure Model (SHFM risk), and CHA2DS2VASc. We also timed the 30 providers using Ask Mayo Expert care process models for lipid management, heart failure management, and atrial fibrillation management based on the calculated risk scores. We used the Mayo Clinic primary care panel to estimate time for calculating an entire panel risk. Mean provider times to complete the CHA2DS2VASc, AHA-ASCVD risk, and SHFM were 36, 45, and 171 s respectively. For decision making about atrial fibrillation, lipids, and heart failure, the mean times (including risk calculations) were 85, 110, and 347 s respectively. Even under best case circumstances, providers take a significant amount of time to complete risk assessments. For a complete panel of patients this can lead to hours of time required to make decisions about prescribing statins, use of anticoagulation, and medications for heart failure. Informatics solutions are needed to capture data in the medical record and serve up automatically calculated risk assessments to physicians and other providers at the point of care.

Uncertainty Analysis of Coupled Socioeconomic-Cropping Models: Building Confidence in Climate Change Decision-Support Tools for Local Stakeholders

NASA Astrophysics Data System (ADS)

Malard, J. J.; Rojas, M.; Adamowski, J. F.; Gálvez, J.; Tuy, H. A.; Melgar-Quiñonez, H.

2015-12-01

While cropping models represent the biophysical aspects of agricultural systems, system dynamics modelling offers the possibility of representing the socioeconomic (including social and cultural) aspects of these systems. The two types of models can then be coupled in order to include the socioeconomic dimensions of climate change adaptation in the predictions of cropping models.We develop a dynamically coupled socioeconomic-biophysical model of agricultural production and its repercussions on food security in two case studies from Guatemala (a market-based, intensive agricultural system and a low-input, subsistence crop-based system). Through the specification of the climate inputs to the cropping model, the impacts of climate change on the entire system can be analysed, and the participatory nature of the system dynamics model-building process, in which stakeholders from NGOs to local governmental extension workers were included, helps ensure local trust in and use of the model.However, the analysis of climate variability's impacts on agroecosystems includes uncertainty, especially in the case of joint physical-socioeconomic modelling, and the explicit representation of this uncertainty in the participatory development of the models is important to ensure appropriate use of the models by the end users. In addition, standard model calibration, validation, and uncertainty interval estimation techniques used for physically-based models are impractical in the case of socioeconomic modelling. We present a methodology for the calibration and uncertainty analysis of coupled biophysical (cropping) and system dynamics (socioeconomic) agricultural models, using survey data and expert input to calibrate and evaluate the uncertainty of the system dynamics as well as of the overall coupled model. This approach offers an important tool for local decision makers to evaluate the potential impacts of climate change and their feedbacks through the associated socioeconomic system.

UQ for Decision Making: How (at least five) Kinds of Probability Might Come Into Play

NASA Astrophysics Data System (ADS)

Smith, L. A.

2013-12-01

In 1959 IJ Good published the discussion "Kinds of Probability" in Science. Good identified (at least) five kinds. The need for (at least) a sixth kind of probability when quantifying uncertainty in the context of climate science is discussed. This discussion brings out the differences in weather-like forecasting tasks and climate-links tasks, with a focus on the effective use both of science and of modelling in support of decision making. Good also introduced the idea of a "Dynamic probability" a probability one expects to change without any additional empirical evidence; the probabilities assigned by a chess playing program when it is only half thorough its analysis being an example. This case is contrasted with the case of "Mature probabilities" where a forecast algorithm (or model) has converged on its asymptotic probabilities and the question hinges in whether or not those probabilities are expected to change significantly before the event in question occurs, even in the absence of new empirical evidence. If so, then how might one report and deploy such immature probabilities in scientific-support of decision-making rationally? Mature Probability is suggested as a useful sixth kind, although Good would doubtlessly argue that we can get by with just one, effective communication with decision makers may be enhanced by speaking as if the others existed. This again highlights the distinction between weather-like contexts and climate-like contexts. In the former context one has access to a relevant climatology (a relevant, arguably informative distribution prior to any model simulations), in the latter context that information is not available although one can fall back on the scientific basis upon which the model itself rests, and estimate the probability that the model output is in fact misinformative. This subjective "probability of a big surprise" is one way to communicate the probability of model-based information holding in practice, the probability that the information the model-based probability is conditioned on holds. It is argued that no model-based climate-like probability forecast is complete without a quantitative estimate of its own irrelevance, and that the clear identification of model-based probability forecasts as mature or immature, are critical elements for maintaining the credibility of science-based decision support, and can shape uncertainty quantification more widely.

Simply criminal: predicting burglars' occupancy decisions with a simple heuristic.

PubMed

Snook, Brent; Dhami, Mandeep K; Kavanagh, Jennifer M

2011-08-01

Rational choice theories of criminal decision making assume that offenders weight and integrate multiple cues when making decisions (i.e., are compensatory). We tested this assumption by comparing how well a compensatory strategy called Franklin's Rule captured burglars' decision policies regarding residence occupancy compared to a non-compensatory strategy (i.e., Matching Heuristic). Forty burglars each decided on the occupancy of 20 randomly selected photographs of residences (for which actual occupancy was known when the photo was taken). Participants also provided open-ended reports on the cues that influenced their decisions in each case, and then rated the importance of eight cues (e.g., deadbolt visible) over all decisions. Burglars predicted occupancy beyond chance levels. The Matching Heuristic was a significantly better predictor of burglars' decisions than Franklin's Rule, and cue use in the Matching Heuristic better corresponded to the cue ecological validities in the environment than cue use in Franklin's Rule. The most important cue in burglars' models was also the most ecologically valid or predictive of actual occupancy (i.e., vehicle present). The majority of burglars correctly identified the most important cue in their models, and the open-ended technique showed greater correspondence between self-reported and captured cue use than the rating over decision technique. Our findings support a limited rationality perspective to understanding criminal decision making, and have implications for crime prevention.

The Paradox of Power in Leadership in Early Childhood Education

ERIC Educational Resources Information Center

Ho, Dora

2012-01-01

Western frameworks for school improvement, including the stakeholder model and the model of decentralized leadership, have recently been promoted as solutions for school improvement. Using early childhood education in Hong Kong as an illustrative case, this article focuses on the power and authority of leadership in school decision making. The…

The Italian Middle School in a Deregulation Era: Modernity through Path-Dependency and Global Models

ERIC Educational Resources Information Center

Mincu, Monica E.

2015-01-01

In the current context of intensified moves towards educational deregulation, the configuration of the Italian middle school and its relationship to education governance is an interesting case. Historically, it represents a unique example of the successful "decision-making" model of the welfarist era. Despite some internal constraints,…

Participatory Systems Modeling to Explore Sustainable Solutions: Triple Value Simulation Modeling Cases Tackle Nutrient and Watershed Management from a Socio-Ecological Systems (SES) perspective

EPA Science Inventory

Decision makers often need assistance in understanding dynamic interactions and linkages among economic, environmental and social systems in coastal watersheds. They also need scientific input to better evaluate potential costs and benefits of alternative policy interventions. Th...

Involving youth in program decision-making: how common and what might it do for youth?

PubMed

Akiva, Thomas; Cortina, Kai S; Smith, Charles

2014-11-01

The strategy of sharing program decision-making with youth in youth programs, a specific form of youth-adult partnership, is widely recommended in practitioner literature; however, empirical study is relatively limited. We investigated the prevalence and correlates of youth program decision-making practices (e.g., asking youth to help decide what activities are offered), using single-level and multilevel methods with a cross-sectional dataset of 979 youth attending 63 multipurpose after-school programs (average age of youth = 11.4, 53 % female). The prevalence of such practices was relatively high, particularly for forms that involved low power sharing such as involving youth in selecting the activities a program offers. Hierarchical linear modeling revealed positive associations between youth program decision-making practices and youth motivation to attend programs. We also found positive correlations between decision-making practices and youth problem-solving efficacy, expression efficacy, and empathy. Significant interactions with age suggest that correlations with problem solving and empathy are more pronounced for older youth. Overall, the findings suggest that involving youth in program decision-making is a promising strategy for promoting youth motivation and skill building, and in some cases this is particularly the case for older (high school-age) youth.

A Neuropsychological Approach to Understanding Risk-Taking for Potential Gains and Losses

PubMed Central

Levin, Irwin P.; Xue, Gui; Weller, Joshua A.; Reimann, Martin; Lauriola, Marco; Bechara, Antoine

2012-01-01

Affective neuroscience has helped guide research and theory development in judgment and decision-making by revealing the role of emotional processes in choice behavior, especially when risk is involved. Evidence is emerging that qualitatively and quantitatively different processes may be involved in risky decision-making for gains and losses. We start by reviewing behavioral work by Kahneman and Tversky (1979) and others, which shows that risk-taking differs for potential gains and potential losses. We then turn to the literature in decision neuroscience to support the gain versus loss distinction. Relying in part on data from a new task that separates risky decision-making for gains and losses, we test a neural model that assigns unique mechanisms for risky decision-making involving potential losses. Included are studies using patients with lesions to brain areas specified as important in the model and studies with healthy individuals whose brains are scanned to reveal activation in these and other areas during risky decision-making. In some cases, there is evidence that gains and losses are processed in different regions of the brain, while in other cases the same region appears to process risk in a different manner for gains and losses. At a more general level, we provide strong support for the notion that decisions involving risk-taking for gains and decisions involving risk-taking for losses represent different psychological processes. At a deeper level, we present mounting evidence that different neural structures play different roles in guiding risky choices in these different domains. Some structures are differentially activated by risky gains and risky losses while others respond uniquely in one domain or the other. Taken together, these studies support a clear functional dissociation between risk-taking for gains and risk-taking for losses, and further dissociation at the neural level. PMID:22347161

Individual differences in emotion processing: how similar are diffusion model parameters across tasks?

PubMed

Mueller, Christina J; White, Corey N; Kuchinke, Lars

2017-11-27

The goal of this study was to replicate findings of diffusion model parameters capturing emotion effects in a lexical decision task and investigating whether these findings extend to other tasks of implicit emotion processing. Additionally, we were interested in the stability of diffusion model parameters across emotional stimuli and tasks for individual subjects. Responses to words in a lexical decision task were compared with responses to faces in a gender categorization task for stimuli of the emotion categories: happy, neutral and fear. Main effects of emotion as well as stability of emerging response style patterns as evident in diffusion model parameters across these tasks were analyzed. Based on earlier findings, drift rates were assumed to be more similar in response to stimuli of the same emotion category compared to stimuli of a different emotion category. Results showed that emotion effects of the tasks differed with a processing advantage for happy followed by neutral and fear-related words in the lexical decision task and a processing advantage for neutral followed by happy and fearful faces in the gender categorization task. Both emotion effects were captured in estimated drift rate parameters-and in case of the lexical decision task also in the non-decision time parameters. A principal component analysis showed that contrary to our hypothesis drift rates were more similar within a specific task context than within a specific emotion category. Individual response patterns of subjects across tasks were evident in significant correlations regarding diffusion model parameters including response styles, non-decision times and information accumulation.

Integrating multimodal transport into cellulosic biofuel supply chain design under feedstock seasonality with a case study based on California.

PubMed

Xie, Fei; Huang, Yongxi; Eksioglu, Sandra

2014-01-01

A multistage, mixed integer programing model was developed that fully integrates multimodal transport into the cellulosic biofuel supply chain design under feedstock seasonality. Three transport modes are considered: truck, single railcar, and unit train. The goal is to minimize the total cost for infrastructure, feedstock harvesting, biofuel production, and transportation. Strategic decisions including the locations and capacities of transshipment hubs, biorefineries, and terminals and tactical decisions on system operations are optimized in an integrated manner. When the model was implemented to a case study of cellulosic ethanol production in California, it was found that trucks are convenient for short-haul deliveries while rails are more effective for long-haul transportation. Taking the advantage of these benefits, the multimodal transport provides more cost effective solutions than the single-mode transport (truck). Copyright © 2013 Elsevier Ltd. All rights reserved.

The values underlying team decision-making in work rehabilitation for musculoskeletal disorders.

PubMed

Loisel, Patrick; Falardeau, Marlène; Baril, Raymond; José-Durand, Marie; Langley, Ann; Sauvé, Sandrine; Gervais, Julie

2005-05-20

This paper presents the results of a qualitative study on the values underlying the decision-making process of an interdisciplinary team working in a work rehabilitation facility of a Québec teaching hospital. In order to document the values underlying the decision-making process, a single case observational study was conducted. Interdisciplinary team weekly discussions on ongoing cases of 22 workers absent from work due to musculoskeletal disorders were videotaped. All discourses were transcribed and analyzed following an inductive and iterative approach. The values identified were validated by feedback from team members. Ten common decision values emerged from the data: (1) team unity and credibility, (2) collaboration with stakeholders, (3) worker's internal motivation, (4) worker's adherence to the program, (5) worker's reactivation, (6) single message, (7) reassurance, (8) graded intervention, (9) pain management and (10) return to work as a therapy. The analysis of these values led to the design of a model describing interrelations between them. This study throws light on some mechanisms underlying the decisions made by the team and determining its action. This improves understanding of the actions taken by an interdisciplinary team in work rehabilitation and may facilitate knowledge transfer in the training of other teams.

The doctor-patient relationship as a toolkit for uncertain clinical decisions.

PubMed

Diamond-Brown, Lauren

2016-06-01

Medical uncertainty is a well-recognized problem in healthcare, yet how doctors make decisions in the face of uncertainty remains to be understood. This article draws on interdisciplinary literature on uncertainty and physician decision-making to examine a specific physician response to uncertainty: using the doctor-patient relationship as a toolkit. Additionally, I ask what happens to this process when the doctor-patient relationship becomes fragmented. I answer these questions by examining obstetrician-gynecologists' narratives regarding how they make decisions when faced with uncertainty in childbirth. Between 2013 and 2014, I performed 21 semi-structured interviews with obstetricians in the United States. Obstetricians were selected to maximize variation in relevant physician, hospital, and practice characteristics. I began with grounded theory and moved to analytical coding of themes in relation to relevant literature. My analysis renders it evident that some physicians use the doctor-patient relationship as a toolkit for dealing with uncertainty. I analyze how this process varies for physicians in different models of care by comparing doctors' experiences in models with continuous versus fragmented doctor-patient relationships. My key findings are that obstetricians in both models appealed to the ideal of patient-centered decision-making to cope with uncertain decisions, but in practice physicians in fragmented care faced a number of challenges to using the doctor-patient relationship as a toolkit for decision-making. These challenges led to additional uncertainties and in some cases to poor outcomes for doctors and/or patients; they also raised concerns about the reproduction of inequality. Thus organization of care delivery mitigates the efficacy of doctors' use of the doctor-patient relationship toolkit for uncertain decisions. These findings have implications for theorizing about decision-making under conditions of medical uncertainty, for understanding how the doctor-patient relationship and model of care affect physician decision-making, and for forming policy on the optimal structure of medical work. Copyright © 2016 Elsevier Ltd. All rights reserved.

Examining Preservice Teachers' Decision Behaviors and Individual Differences in Three Online Case-Based Approaches

ERIC Educational Resources Information Center

Cevik, Yasemin Demiraslan; Andre, Thomas

2013-01-01

This study compared the impact of three types of case-based methods (case-based reasoning, worked example, and faded worked example) on preservice teachers' (n = 71) interaction with decision tasks and whether decision related measures (task difficulty, mental effort, decision making performance) were associated with the differences in student…

Recent advances in applying decision science to managing national forests

USGS Publications Warehouse

Marcot, Bruce G.; Thompson, Matthew P.; Runge, Michael C.; Thompson, Frank R.; McNulty, Steven; Cleaves, David; Tomosy, Monica; Fisher, Larry A.; Andrew, Bliss

2012-01-01

Management of federal public forests to meet sustainability goals and multiple use regulations is an immense challenge. To succeed, we suggest use of formal decision science procedures and tools in the context of structured decision making (SDM). SDM entails four stages: problem structuring (framing the problem and defining objectives and evaluation criteria), problem analysis (defining alternatives, evaluating likely consequences, identifying key uncertainties, and analyzing tradeoffs), decision point (identifying the preferred alternative), and implementation and monitoring the preferred alternative with adaptive management feedbacks. We list a wide array of models, techniques, and tools available for each stage, and provide three case studies of their selected use in National Forest land management and project plans. Successful use of SDM involves participation by decision-makers, analysts, scientists, and stakeholders. We suggest specific areas for training and instituting SDM to foster transparency, rigor, clarity, and inclusiveness in formal decision processes regarding management of national forests.

Integrating strategic and tactical decisions in livestock supply chain using bi-level programming, case study: Iran poultry supply chain.

PubMed

Teimoury, Ebrahim; Jabbarzadeh, Armin; Babaei, Mohammadhosein

2017-01-01

Inventory management has frequently been targeted by researchers as one of the most pivotal problems in supply chain management. With the expansion of research studies on inventory management in supply chains, perishable inventory has been introduced and its fundamental differences from non-perishable inventory have been emphasized. This article presents livestock as a type of inventory that has been less studied in the literature. Differences between different inventory types, affect various levels of strategic, tactical and operational decision-making. In most articles, different levels of decision-making are discussed independently and sequentially. In this paper, not only is the livestock inventory introduced, but also a model has been developed to integrate decisions across different levels of decision-making using bi-level programming. Computational results indicate that the proposed bi-level approach is more efficient than the sequential decision-making approach.

Integrating strategic and tactical decisions in livestock supply chain using bi-level programming, case study: Iran poultry supply chain

PubMed Central

Jabbarzadeh, Armin; Babaei, Mohammadhosein

2017-01-01

Inventory management has frequently been targeted by researchers as one of the most pivotal problems in supply chain management. With the expansion of research studies on inventory management in supply chains, perishable inventory has been introduced and its fundamental differences from non-perishable inventory have been emphasized. This article presents livestock as a type of inventory that has been less studied in the literature. Differences between different inventory types, affect various levels of strategic, tactical and operational decision-making. In most articles, different levels of decision-making are discussed independently and sequentially. In this paper, not only is the livestock inventory introduced, but also a model has been developed to integrate decisions across different levels of decision-making using bi-level programming. Computational results indicate that the proposed bi-level approach is more efficient than the sequential decision-making approach. PMID:28982180

A nurse-led model at public academic hospitals maintains high adherence to colorectal cancer surveillance guidelines.

PubMed

Symonds, Erin L; Simpson, Kalindra; Coats, Michelle; Chaplin, Angela; Saxty, Karen; Sandford, Jayne; Young Am, Graeme P; Cock, Charles; Fraser, Robert; Bampton, Peter A

2018-06-18

To examine the compliance of colorectal cancer surveillance decisions for individuals at greater risk with current evidence-based guidelines and to determine whether compliance differs between surveillance models. Prospective auditing of compliance of surveillance decisions with evidence-based guidelines (NHMRC) in two decision-making models: nurse coordinator-led decision making in public academic hospitals and physician-led decision making in private non-academic hospitals. Selected South Australian hospitals participating in the Southern Co-operative Program for the Prevention of Colorectal Cancer (SCOOP). Proportions of recall recommendations that matched NHMRC guideline recommendations (March-May 2015); numbers of surveillance colonoscopies undertaken more than 6 months ahead of schedule (January-December 2015); proportions of significant neoplasia findings during the 15 years of SCOOP operation (2000-2015). For the nurse-led/public academic hospital model, the recall interval recommendation following 398 of 410 colonoscopies (97%) with findings covered by NHMRC guidelines corresponded to the guideline recommendations; for the physician-led/private non-academic hospital model, this applied to 257 of 310 colonoscopies (83%) (P < 0.001). During 2015, 27% of colonoscopies in public academic hospitals (mean, 27 months; SD, 13 months) and 20% of those in private non-academic hospitals (mean, 23 months; SD, 12 months) were performed more than 6 months earlier than scheduled, in most cases because of patient-related factors (symptoms, faecal occult blood test results). The ratio of the numbers of high risk adenomas to cancers increased from 6.6:1 during 2001-2005 to 16:1 during 2011-2015. The nurse-led/public academic hospital model for decisions about colorectal cancer surveillance intervals achieves a high degree of compliance with guideline recommendations, which should relieve burdening of colonoscopy resources.

Resource allocation for mitigating regional air pollution–related mortality: A summertime case study for five cities in the United States

PubMed Central

Liao, Kuo-Jen; Hou, Xiangting; Strickland, Matthew J.

2016-01-01

ABSTRACT An important issue of regional air quality management is to allocate air quality management funds to maximize environmental and human health benefits. In this study, we use an innovative approach to tackle this air quality management issue. We develop an innovative resource allocation model that allows identification of air pollutant emission control strategies that maximize mortality avoidances subject to a resource constraint. We first present the development of the resource allocation model and then a case study to show how the model can be used to identify resource allocation strategies that maximize mortality avoidances for top five Metropolitan Statistical Areas (MSAs) (i.e., New York, Los Angeles, Chicago, Dallas-Fort Worth, and Philadelphia) in the continental United States collectively. Given budget constraints in the U.S. Environmental Protection Agency’s (EPA) Clean Air Act assessment, the results of the case study suggest that controls of sulfur dioxide (SO2) and primary carbon (PC) emissions from EPA Regions 2, 3, 5, 6, and 9 would have significant health benefits for the five selected cities collectively. Around 30,800 air pollution–related mortalities could be avoided during the selected 2-week summertime episode for the five cities collectively if the budget could be allocated based on the results of the resource allocation model. Although only five U.S. cities during a 2-week episode are considered in the case study, the resource allocation model can be used by decision-makers to plan air pollution mitigation strategies to achieve the most significant health benefits for other seasons and more cities over a region or the continental U.S.Implications: Effective allocations of air quality management resources are challenging and complicated, and it is desired to have a tool that can help decision-makers better allocate the funds to maximize health benefits of air pollution mitigation. An innovative resource allocation model developed in this study can help decision-makers identify the best resource allocation strategies for multiple cities collectively. The results of a case study suggest that controls of primary carbon and sulfur dioxides emissions would achieve the most significant health benefits for five selected cities collectively. PMID:27441782

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

Investigation of effective decision criteria for multiobjective optimization in IMRT.

PubMed

Holdsworth, Clay; Stewart, Robert D; Kim, Minsun; Liao, Jay; Phillips, Mark H

2011-06-01

To investigate how using different sets of decision criteria impacts the quality of intensity modulated radiation therapy (IMRT) plans obtained by multiobjective optimization. A multiobjective optimization evolutionary algorithm (MOEA) was used to produce sets of IMRT plans. The MOEA consisted of two interacting algorithms: (i) a deterministic inverse planning optimization of beamlet intensities that minimizes a weighted sum of quadratic penalty objectives to generate IMRT plans and (ii) an evolutionary algorithm that selects the superior IMRT plans using decision criteria and uses those plans to determine the new weights and penalty objectives of each new plan. Plans resulting from the deterministic algorithm were evaluated by the evolutionary algorithm using a set of decision criteria for both targets and organs at risk (OARs). Decision criteria used included variation in the target dose distribution, mean dose, maximum dose, generalized equivalent uniform dose (gEUD), an equivalent uniform dose (EUD(alpha,beta) formula derived from the linear-quadratic survival model, and points on dose volume histograms (DVHs). In order to quantatively compare results from trials using different decision criteria, a neutral set of comparison metrics was used. For each set of decision criteria investigated, IMRT plans were calculated for four different cases: two simple prostate cases, one complex prostate Case, and one complex head and neck Case. When smaller numbers of decision criteria, more descriptive decision criteria, or less anti-correlated decision criteria were used to characterize plan quality during multiobjective optimization, dose to OARs and target dose variation were reduced in the final population of plans. Mean OAR dose and gEUD (a = 4) decision criteria were comparable. Using maximum dose decision criteria for OARs near targets resulted in inferior populations that focused solely on low target variance at the expense of high OAR dose. Target dose range, (D(max) - D(min)), decision criteria were found to be most effective for keeping targets uniform. Using target gEUD decision criteria resulted in much lower OAR doses but much higher target dose variation. EUD(alpha,beta) based decision criteria focused on a region of plan space that was a compromise between target and OAR objectives. None of these target decision criteria dominated plans using other criteria, but only focused on approaching a different area of the Pareto front. The choice of decision criteria implemented in the MOEA had a significant impact on the region explored and the rate of convergence toward the Pareto front. When more decision criteria, anticorrelated decision criteria, or decision criteria with insufficient information were implemented, inferior populations are resulted. When more informative decision criteria were used, such as gEUD, EUD(alpha,beta), target dose range, and mean dose, MOEA optimizations focused on approaching different regions of the Pareto front, but did not dominate each other. Using simple OAR decision criteria and target EUD(alpha,beta) decision criteria demonstrated the potential to generate IMRT plans that significantly reduce dose to OARs while achieving the same or better tumor control when clinical requirements on target dose variance can be met or relaxed.

A decision-analytic approach to predict state regulation of hydraulic fracturing.

PubMed

Linkov, Igor; Trump, Benjamin; Jin, David; Mazurczak, Marcin; Schreurs, Miranda

2014-01-01

The development of horizontal drilling and hydraulic fracturing methods has dramatically increased the potential for the extraction of previously unrecoverable natural gas. Nonetheless, the potential risks and hazards associated with such technologies are not without controversy and are compounded by frequently changing information and an uncertain landscape of international politics and laws. Where each nation has its own energy policies and laws, predicting how a state with natural gas reserves that require hydraulic fracturing will regulate the industry is of paramount importance for potential developers and extractors. We present a method for predicting hydraulic fracturing decisions using multiple-criteria decision analysis. The case study evaluates the decisions of five hypothetical countries with differing political, social, environmental, and economic priorities, choosing among four policy alternatives: open hydraulic fracturing, limited hydraulic fracturing, completely banned hydraulic fracturing, and a cap and trade program. The result is a model that identifies the preferred policy alternative for each archetypal country and demonstrates the sensitivity the decision to particular metrics. Armed with such information, observers can predict each country's likely decisions related to natural gas exploration as more data become available or political situations change. Decision analysis provides a method to manage uncertainty and address forecasting concerns where rich and objective data may be lacking. For the case of hydraulic fracturing, the various political pressures and extreme uncertainty regarding the technology's risks and benefits serve as a prime platform to demonstrate how decision analysis can be used to predict future behaviors.

Parental intimate partner homicide and its consequences for children: protocol for a population-based study.

PubMed

Alisic, Eva; Groot, Arend; Snetselaar, Hanneke; Stroeken, Tielke; van de Putte, Elise

2015-07-29

The loss of a parent due to intimate partner homicide has a major impact on children. Professionals involved have to make far-reaching decisions regarding placement, guardianship, mental health care and contact with the perpetrating parent, without an evidence base to guide these decisions. We introduce a study protocol to a) systematically describe the demographics, circumstances, mental health and wellbeing of children bereaved by intimate partner homicide and b) build a predictive model of factors associated with children's mental health and wellbeing after intimate partner homicide. This study focuses on children bereaved by parental intimate partner homicide in the Netherlands over a period of 20 years (1993 - 2012). It involves an incidence study to identify all Dutch intimate partner homicide cases between 1993 and 2012 by which children have been bereaved; systematic case reviews to describe the demographics, circumstances and care trajectories of these children; and a mixed-methods study to assess mental health, wellbeing, and experiences regarding decisions made and care provided. Clinical experience and initial research suggest that the children involved often need long-term intensive mental health and case management. The costs of these services are extensive and the stakes are high. This study lays the foundation for an international dataset and evidence-informed decision making.

A Fuzzy Robust Optimization Model for Waste Allocation Planning Under Uncertainty

PubMed Central

Xu, Ye; Huang, Guohe; Xu, Ling

2014-01-01

Abstract In this study, a fuzzy robust optimization (FRO) model was developed for supporting municipal solid waste management under uncertainty. The Development Zone of the City of Dalian, China, was used as a study case for demonstration. Comparing with traditional fuzzy models, the FRO model made improvement by considering the minimization of the weighted summation among the expected objective values, the differences between two extreme possible objective values, and the penalty of the constraints violation as the objective function, instead of relying purely on the minimization of expected value. Such an improvement leads to enhanced system reliability and the model becomes especially useful when multiple types of uncertainties and complexities are involved in the management system. Through a case study, the applicability of the FRO model was successfully demonstrated. Solutions under three future planning scenarios were provided by the FRO model, including (1) priority on economic development, (2) priority on environmental protection, and (3) balanced consideration for both. The balanced scenario solution was recommended for decision makers, since it respected both system economy and reliability. The model proved valuable in providing a comprehensive profile about the studied system and helping decision makers gain an in-depth insight into system complexity and select cost-effective management strategies. PMID:25317037

A Fuzzy Robust Optimization Model for Waste Allocation Planning Under Uncertainty.

PubMed

Xu, Ye; Huang, Guohe; Xu, Ling

2014-10-01

In this study, a fuzzy robust optimization (FRO) model was developed for supporting municipal solid waste management under uncertainty. The Development Zone of the City of Dalian, China, was used as a study case for demonstration. Comparing with traditional fuzzy models, the FRO model made improvement by considering the minimization of the weighted summation among the expected objective values, the differences between two extreme possible objective values, and the penalty of the constraints violation as the objective function, instead of relying purely on the minimization of expected value. Such an improvement leads to enhanced system reliability and the model becomes especially useful when multiple types of uncertainties and complexities are involved in the management system. Through a case study, the applicability of the FRO model was successfully demonstrated. Solutions under three future planning scenarios were provided by the FRO model, including (1) priority on economic development, (2) priority on environmental protection, and (3) balanced consideration for both. The balanced scenario solution was recommended for decision makers, since it respected both system economy and reliability. The model proved valuable in providing a comprehensive profile about the studied system and helping decision makers gain an in-depth insight into system complexity and select cost-effective management strategies.

Offshore safety case approach and formal safety assessment of ships.

PubMed

Wang, J

2002-01-01

Tragic marine and offshore accidents have caused serious consequences including loss of lives, loss of property, and damage of the environment. A proactive, risk-based "goal setting" regime is introduced to the marine and offshore industries to increase the level of safety. To maximize marine and offshore safety, risks need to be modeled and safety-based decisions need to be made in a logical and confident way. Risk modeling and decision-making tools need to be developed and applied in a practical environment. This paper describes both the offshore safety case approach and formal safety assessment of ships in detail with particular reference to the design aspects. The current practices and the latest development in safety assessment in both the marine and offshore industries are described. The relationship between the offshore safety case approach and formal ship safety assessment is described and discussed. Three examples are used to demonstrate both the offshore safety case approach and formal ship safety assessment. The study of risk criteria in marine and offshore safety assessment is carried out. The recommendations on further work required are given. This paper gives safety engineers in the marine and offshore industries an overview of the offshore safety case approach and formal ship safety assessment. The significance of moving toward a risk-based "goal setting" regime is given.

Domain Modeling for Adaptive Training and Education in Support of the US Army Learning Model-Research Outline

DTIC Science & Technology

2015-06-01

Definitions are provided for this section to distinguish between adaptive training and education elements and also to highlight their relationships ...illustrate this point Franke (2011) asserts that through the use of case study examples, instruction can provide the pedagogical foundation for decision...a prime example of an adaptive training and education system: a learner or trainee model, an instructional or pedagogical model, a domain model

Benefits and challenges of a QSP approach through case study: Evaluation of a hypothetical GLP-1/GIP dual agonist therapy.

PubMed

Rieger, Theodore R; Musante, Cynthia J

2016-10-30

Quantitative Systems Pharmacology (QSP) is an emerging science with increasing application to pharmaceutical research and development paradigms. Through case study we provide an overview of the benefits and challenges of applying QSP approaches to inform program decisions in the early stages of drug discovery and development. Specifically, we describe the use of a type 2 diabetes systems model to inform a No-Go decision prior to lead development for a potential GLP-1/GIP dual agonist program, enabling prioritization of exploratory programs with higher probability of clinical success. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

A farm-level precision land management framework based on integer programming

PubMed Central

Li, Qi; Hu, Guiping; Jubery, Talukder Zaki; Ganapathysubramanian, Baskar

2017-01-01

Farmland management involves several planning and decision making tasks including seed selection and irrigation management. A farm-level precision farmland management model based on mixed integer linear programming is proposed in this study. Optimal decisions are designed for pre-season planning of crops and irrigation water allocation. The model captures the effect of size and shape of decision scale as well as special irrigation patterns. The authors illustrate the model with a case study on a farm in the state of California in the U.S. and show the model can capture the impact of precision farm management on profitability. The results show that threefold increase of annual net profit for farmers could be achieved by carefully choosing irrigation and seed selection. Although farmers could increase profits by applying precision management to seed or irrigation alone, profit increase is more significant if farmers apply precision management on seed and irrigation simultaneously. The proposed model can also serve as a risk analysis tool for farmers facing seasonal irrigation water limits as well as a quantitative tool to explore the impact of precision agriculture. PMID:28346499

The effect of fight cost structure on fighting behaviour involving simultaneous decisions and variable investment levels.

PubMed

Broom, Mark; Johanis, Michal; Rychtář, Jan

2018-01-01

In the "producer-scrounger" model, a producer discovers a resource and is in turn discovered by a second individual, the scrounger, who attempts to steal it. This resource can be food or a territory, and in some situations, potentially divisible. In a previous paper we considered a producer and scrounger competing for an indivisible resource, where each individual could choose the level of energy that they would invest in the contest. The higher the investment, the higher the probability of success, but also the higher the costs incurred in the contest. In that paper decisions were sequential with the scrounger choosing their strategy before the producer. In this paper we consider a version of the game where decisions are made simultaneously. For the same cost functions as before, we analyse this case in detail, and then make comparisons between the two cases. Finally we discuss some real examples with potentially variable and asymmetric energetic investments, including intraspecific contests amongst spiders and amongst parasitoid wasps. In the case of the spiders, detailed estimates of energetic expenditure are available which demonstrate the asymmetric values assumed in our models. For the wasps the value of the resource can affect the probabilities of success of the defender and attacker, and differential energetic investment can be inferred. In general for real populations energy usage varies markedly depending upon crucial parameters extrinsic to the individual such as resource value and intrinsic ones such as age, and is thus an important factor to consider when modelling.

Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features.

PubMed

Hor, Soheil; Moradi, Mehdi

2016-12-01

Incomplete and inconsistent datasets often pose difficulties in multimodal studies. We introduce the concept of scandent decision trees to tackle these difficulties. Scandent trees are decision trees that optimally mimic the partitioning of the data determined by another decision tree, and crucially, use only a subset of the feature set. We show how scandent trees can be used to enhance the performance of decision forests trained on a small number of multimodal samples when we have access to larger datasets with vastly incomplete feature sets. Additionally, we introduce the concept of tree-based feature transforms in the decision forest paradigm. When combined with scandent trees, the tree-based feature transforms enable us to train a classifier on a rich multimodal dataset, and use it to classify samples with only a subset of features of the training data. Using this methodology, we build a model trained on MRI and PET images of the ADNI dataset, and then test it on cases with only MRI data. We show that this is significantly more effective in staging of cognitive impairments compared to a similar decision forest model trained and tested on MRI only, or one that uses other kinds of feature transform applied to the MRI data. Copyright © 2016. Published by Elsevier B.V.

An improved hybrid multi-criteria/multidimensional model for strategic industrial location selection: Casablanca industrial zones as a case study.

PubMed

Boutkhoum, Omar; Hanine, Mohamed; Agouti, Tarik; Tikniouine, Abdessadek

2015-01-01

In this paper, we examine the issue of strategic industrial location selection in uncertain decision making environments for implanting new industrial corporation. In fact, the industrial location issue is typically considered as a crucial factor in business research field which is related to many calculations about natural resources, distributors, suppliers, customers, and most other things. Based on the integration of environmental, economic and social decisive elements of sustainable development, this paper presents a hybrid decision making model combining fuzzy multi-criteria analysis with analytical capabilities that OLAP systems can provide for successful and optimal industrial location selection. The proposed model mainly consists in three stages. In the first stage, a decision-making committee has been established to identify the evaluation criteria impacting the location selection process. In the second stage, we develop fuzzy AHP software based on the extent analysis method to assign the importance weights to the selected criteria, which allows us to model the linguistic vagueness, ambiguity, and incomplete knowledge. In the last stage, OLAP analysis integrated with multi-criteria analysis employs these weighted criteria as inputs to evaluate, rank and select the strategic industrial location for implanting new business corporation in the region of Casablanca, Morocco. Finally, a sensitivity analysis is performed to evaluate the impact of criteria weights and the preferences given by decision makers on the final rankings of strategic industrial locations.

Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences.

PubMed

Constantinou, Anthony Costa; Yet, Barbaros; Fenton, Norman; Neil, Martin; Marsh, William

2016-01-01

Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision. The method is based on the underlying principle of Value of Information to enhance decision analysis in interventional and counterfactual Bayesian networks. The method is applied to two real-world Bayesian network models (previously developed for decision support in forensic medical sciences) to examine the average gain in terms of both Value of Information (average relative gain ranging from 11.45% and 59.91%) and decision making (potential amendments in decision making ranging from 0% to 86.8%). We have shown how the method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from the trouble of seeking information about the particular set of risk factors. Further, we have also extended the assessment of this implication to the counterfactual case and demonstrated how answers about interventional actions are expected to change when some unknown factors become known, and how useful this becomes in forensic medical science. Copyright © 2015 Elsevier B.V. All rights reserved.

The Case for Absolute Ligand Discrimination: Modeling Information Processing and Decision by Immune T Cells

NASA Astrophysics Data System (ADS)

François, Paul; Altan-Bonnet, Grégoire

2016-03-01

Some cells have to take decision based on the quality of surroundings ligands, almost irrespective of their quantity, a problem we name "absolute discrimination". An example of absolute discrimination is recognition of not-self by immune T Cells. We show how the problem of absolute discrimination can be solved by a process called "adaptive sorting". We review several implementations of adaptive sorting, as well as its generic properties such as antagonism. We show how kinetic proofreading with negative feedback implement an approximate version of adaptive sorting in the immune context. Finally, we revisit the decision problem at the cell population level, showing how phenotypic variability and feedbacks between population and single cells are crucial for proper decision.

The New AVA Statement of Professional Ethics in Volunteer Administration.

ERIC Educational Resources Information Center

Seel, Keith

1996-01-01

Core ethical values of the Association for Volunteer Administration are citizenship and philanthropy, respect, responsibility, caring, justice and fairness, and trustworthiness. An ethical decision-making model shows how to apply these standards to actual cases. (SK)

Using Models to Inform Policy: Insights from Modeling the Complexities of Global Polio Eradication

NASA Astrophysics Data System (ADS)

Thompson, Kimberly M.

Drawing on over 20 years of experience modeling risks in complex systems, this talk will challenge SBP participants to develop models that provide timely and useful answers to critical policy questions when decision makers need them. The talk will include reflections on the opportunities and challenges associated with developing integrated models for complex problems and communicating their results effectively. Dr. Thompson will focus the talk largely on collaborative modeling related to global polio eradication and the application of system dynamics tools. After successful global eradication of wild polioviruses, live polioviruses will still present risks that could potentially lead to paralytic polio cases. This talk will present the insights of efforts to use integrated dynamic, probabilistic risk, decision, and economic models to address critical policy questions related to managing global polio risks. Using a dynamic disease transmission model combined with probabilistic model inputs that characterize uncertainty for a stratified world to account for variability, we find that global health leaders will face some difficult choices, but that they can take actions that will manage the risks effectively. The talk will emphasize the need for true collaboration between modelers and subject matter experts, and the importance of working with decision makers as partners to ensure the development of useful models that actually get used.

A GIS-based generic real-time risk assessment framework and decision tools for chemical spills in the river basin.

PubMed

Jiang, Jiping; Wang, Peng; Lung, Wu-seng; Guo, Liang; Li, Mei

2012-08-15

This paper presents a generic framework and decision tools of real-time risk assessment on Emergency Environmental Decision Support System for response to chemical spills in river basin. The generic "4-step-3-model" framework is able to delineate the warning area and the impact on vulnerable receptors considering four types of hazards referring to functional area, societal impact, and human health and ecology system. Decision tools including the stand-alone system and software components were implemented on GIS platform. A detailed case study on the Songhua River nitrobenzene spill illustrated the goodness of the framework and tool Spill first responders and decision makers of catchment management will benefit from the rich, visual and dynamic hazard information output from the software. Copyright © 2012 Elsevier B.V. All rights reserved.

Zero-sum politics, the Herbert thesis, and the Ryan White CARE Act: lessons learned from the local side of AIDS.

PubMed

Slack, J

2001-01-01

This study examines the dynamics of grass-roots decision-making processes involved in the implementation of the Ryan White CARE Act. Providing social services to persons with HIV/AIDS, the CARE act requires participation of all relevant groups, including representatives of the HIV/AIDS and gay communities. Decision-making behavior is explored by applying a political (zero-sum) model and a bureaucratic (the Herbert Thesis) model. Using qualitative research techniques, the Kern County (California) Consortium is used as a case study. Findings shed light on the decision-making behavior of social service organizations characterized by intense advocacy and structured on the basis of volunteerism and non-hierarchical relationships. Findings affirm bureaucratic behavior predicted by the Herbert Thesis and also discern factors which seem to trigger more conflictual zero-sum behavior.

Service Level Decision-making in Rural Physiotherapy: Development of Conceptual Models.

PubMed

Adams, Robyn; Jones, Anne; Lefmann, Sophie; Sheppard, Lorraine

2016-06-01

Understanding decision-making about health service provision is increasingly important in an environment of increasing demand and constrained resources. Multiple factors are likely to influence decisions about which services will be provided, yet workforce is the most noted factor in the rural physiotherapy literature. This paper draws together results obtained from exploration of service level decision-making (SLDM) to propose 'conceptual' models of rural physiotherapy SLDM. A prioritized qualitative approach enabled exploration of participant perspectives about rural physiotherapy decision-making. Stakeholder perspectives were obtained through surveys and in-depth interviews. Interviews were transcribed verbatim and reviewed by participants. Participant confidentiality was maintained by coding both participants and sites. A system theory-case study heuristic provided a framework for exploration across sites within the investigation area: a large area of one Australian state with a mix of regional, rural and remote communities. Thirty-nine surveys were received from participants in 11 communities. Nineteen in-depth interviews were conducted with physiotherapists and key decision-makers. Results reveal the complexity of factors influencing rural physiotherapy service provision and the value of a systems approach when exploring decision-making about rural physiotherapy service provision. Six key features were identified that formed the rural physiotherapy SLDM system: capacity and capability; contextual influences; layered decision-making; access issues; value and beliefs; and tensions and conflict. Rural physiotherapy SLDM is not a one-dimensional process but results from the complex interaction of clusters of systems issues. Decision-making about physiotherapy service provision is influenced by both internal and external factors. Similarities in influencing factors and the iterative nature of decision-making emerged, which enabled linking physiotherapy SLDM with clinical decision-making and placing both within the broader healthcare context. The conceptual models provide a way of thinking about decisions informing rural physiotherapy service provision. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

An application of prospect theory to a SHM-based decision problem

NASA Astrophysics Data System (ADS)

Bolognani, Denise; Verzobio, Andrea; Tonelli, Daniel; Cappello, Carlo; Glisic, Branko; Zonta, Daniele

2017-04-01

Decision making investigates choices that have uncertain consequences and that cannot be completely predicted. Rational behavior may be described by the so-called expected utility theory (EUT), whose aim is to help choosing among several solutions to maximize the expectation of the consequences. However, Kahneman and Tversky developed an alternative model, called prospect theory (PT), showing that the basic axioms of EUT are violated in several instances. In respect of EUT, PT takes into account irrational behaviors and heuristic biases. It suggests an alternative approach, in which probabilities are replaced by decision weights, which are strictly related to the decision maker's preferences and may change for different individuals. In particular, people underestimate the utility of uncertain scenarios compared to outcomes obtained with certainty, and show inconsistent preferences when the same choice is presented in different forms. The goal of this paper is precisely to analyze a real case study involving a decision problem regarding the Streicker Bridge, a pedestrian bridge on Princeton University campus. By modelling the manager of the bridge with the EUT first, and with PT later, we want to verify the differences between the two approaches and to investigate how the two models are sensitive to unpacking probabilities, which represent a common cognitive bias in irrational behaviors.

[Application of the Balance of Care model in decision-making regarding the best care for patients with dementia].

PubMed

Risco, Ester; Zabalegui, Adelaida; Miguel, Susana; Farré, Marta; Alvira, Carme; Cabrera, Esther

To describe the implementation of the Balance of Care model in decision-making regarding the best care for patients with dementia in Spain. The Balance of Care model was used, which consists of (1) describing the profile of the typical cases of people with dementia and their caregivers, (2) identifying the most suitable care setting for each of the cases (home-care or long-term care institution), (3) designing specific care plans for each case, and (4) evaluating the cost of the proposed care plans. A total of 1,641 people with dementia and their caregivers from eight European countries were used in the case design. The evaluation of cases was conducted by 20 experts in different medical fields of dementia. In Spain, the results indicated that initially the most suitable placement to take care of people with dementia was the home, however in cases with higher dependency in activities of daily living, the long-term care setting was the best option. For the best care plan, the following resources were chosen: professional help to perform basic activities; day center; multidisciplinary home care team; financial support; community nurse; and social worker. The Balance of Care method allows us to assess the most appropriate place of care for people with dementia systematically, objectively and with a multidisciplinary team. Other cost-effective interventions should be integrated in patients with dementia care in order to improve home care. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Decision support system for emergency management of oil spill accidents in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Liubartseva, Svitlana; Coppini, Giovanni; Pinardi, Nadia; De Dominicis, Michela; Lecci, Rita; Turrisi, Giuseppe; Cretì, Sergio; Martinelli, Sara; Agostini, Paola; Marra, Palmalisa; Palermo, Francesco

2016-08-01

This paper presents an innovative web-based decision support system to facilitate emergency management in the case of oil spill accidents, called WITOIL (Where Is The Oil). The system can be applied to create a forecast of oil spill events, evaluate uncertainty of the predictions, and calculate hazards based on historical meteo-oceanographic datasets. To compute the oil transport and transformation, WITOIL uses the MEDSLIK-II oil spill model forced by operational meteo-oceanographic services. Results of the modeling are visualized through Google Maps. A special application for Android is designed to provide mobile access for competent authorities, technical and scientific institutions, and citizens.

Towards the ecotourism: a decision support model for the assessment of sustainability of mountain huts in the Alps.

PubMed

Stubelj Ars, Mojca; Bohanec, Marko

2010-12-01

This paper studies mountain hut infrastructure in the Alps as an important element of ecotourism in the Alpine region. To improve the decision-making process regarding the implementation of future infrastructure and improvement of existing infrastructure in the vulnerable natural environment of mountain ecosystems, a new decision support model has been developed. The methodology is based on qualitative multi-attribute modelling supported by the DEXi software. The integrated rule-based model is hierarchical and consists of two submodels that cover the infrastructure of the mountain huts and that of the huts' surroundings. The final goal for the designed tool is to help minimize the ecological footprint of tourists in environmentally sensitive and undeveloped mountain areas and contribute to mountain ecotourism. The model has been tested in the case study of four mountain huts in Triglav National Park in Slovenia. Study findings provide a new empirical approach to evaluating existing mountain infrastructure and predicting improvements for the future. The assessment results are of particular interest for decision makers in protected areas, such as Alpine national parks managers and administrators. In a way, this model proposes an approach to the management assessment of mountain huts with the main aim of increasing the quality of life of mountain environment visitors as well as the satisfaction of tourists who may eventually become ecotourists. Copyright © 2010 Elsevier Ltd. All rights reserved.

Mapping marine habitat suitability and uncertainty of Bayesian networks: a case study using Pacific benthic macrofauna

Treesearch

Andrea Havron; Chris Goldfinger; Sarah Henkel; Bruce G. Marcot; Chris Romsos; Lisa Gilbane

2017-01-01

Resource managers increasingly use habitat suitability map products to inform risk management and policy decisions. Modeling habitat suitability of data-poor species over large areas requires careful attention to assumptions and limitations. Resulting habitat suitability maps can harbor uncertainties from data collection and modeling processes; yet these limitations...

A Model for Institutional Policy Analysis: The Case of Student Financial Aid. AIR Forum 1981 Paper.

ERIC Educational Resources Information Center

Fenske, Robert H.; Parker, John D.

The development of an operational model that would enable a college institutional research unit to improve administrative decision-making by expanding its data base to include new activities not widely recognized throughout the institution is considered. Attention is directed to institutional research as a function within an institution,…

Hybrid algorithms for fuzzy reverse supply chain network design.

PubMed

Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.

Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

PubMed Central

Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057

Data Sufficiency Assessment and Pumping Test Design for Groundwater Prediction Using Decision Theory and Genetic Algorithms

NASA Astrophysics Data System (ADS)

McPhee, J.; William, Y. W.

2005-12-01

This work presents a methodology for pumping test design based on the reliability requirements of a groundwater model. Reliability requirements take into consideration the application of the model results in groundwater management, expressed in this case as a multiobjective management model. The pumping test design is formulated as a mixed-integer nonlinear programming (MINLP) problem and solved using a combination of genetic algorithm (GA) and gradient-based optimization. Bayesian decision theory provides a formal framework for assessing the influence of parameter uncertainty over the reliability of the proposed pumping test. The proposed methodology is useful for selecting a robust design that will outperform all other candidate designs under most potential 'true' states of the system

Cost-Effectiveness of Procedures for Treatment of Ostium Secundum Atrial Septal Defects Occlusion Comparing Conventional Surgery and Septal Percutaneous Implant

PubMed Central

da Costa, Márcia Gisele Santos; Santos, Marisa da Silva; Sarti, Flávia Mori; Senna, Kátia Marie Simões e.; Tura, Bernardo Rangel; Goulart, Marcelo Correia

2014-01-01

Objectives The study performs a cost-effectiveness analysis of procedures for atrial septal defects occlusion, comparing conventional surgery to septal percutaneous implant. Methods A model of analytical decision was structured with symmetric branches to estimate cost-effectiveness ratio between the procedures. The decision tree model was based on evidences gathered through meta-analysis of literature, and validated by a panel of specialists. The lower number of surgical procedures performed for atrial septal defects occlusion at each branch was considered as the effectiveness outcome. Direct medical costs and probabilities for each event were inserted in the model using data available from Brazilian public sector database system and information extracted from the literature review, using micro-costing technique. Sensitivity analysis included price variations of percutaneous implant. Results The results obtained from the decision model demonstrated that the percutaneous implant was more cost effective in cost-effectiveness analysis at a cost of US$8,936.34 with a reduction in the probability of surgery occurrence in 93% of the cases. Probability of atrial septal communication occlusion and cost of the implant are the determinant factors of cost-effectiveness ratio. Conclusions The proposal of a decision model seeks to fill a void in the academic literature. The decision model proposed includes the outcomes that present major impact in relation to the overall costs of the procedure. The atrial septal defects occlusion using percutaneous implant reduces the physical and psychological distress to the patients in relation to the conventional surgery, which represent intangible costs in the context of economic evaluation. PMID:25302806

Cost-effectiveness of procedures for treatment of ostium secundum atrial septal defects occlusion comparing conventional surgery and septal percutaneous implant.

PubMed

da Costa, Márcia Gisele Santos; Santos, Marisa da Silva; Sarti, Flávia Mori; Simões e Senna, Kátia Marie; Tura, Bernardo Rangel; Correia, Marcelo Goulart; Goulart, Marcelo Correia

2014-01-01

The study performs a cost-effectiveness analysis of procedures for atrial septal defects occlusion, comparing conventional surgery to septal percutaneous implant. A model of analytical decision was structured with symmetric branches to estimate cost-effectiveness ratio between the procedures. The decision tree model was based on evidences gathered through meta-analysis of literature, and validated by a panel of specialists. The lower number of surgical procedures performed for atrial septal defects occlusion at each branch was considered as the effectiveness outcome. Direct medical costs and probabilities for each event were inserted in the model using data available from Brazilian public sector database system and information extracted from the literature review, using micro-costing technique. Sensitivity analysis included price variations of percutaneous implant. The results obtained from the decision model demonstrated that the percutaneous implant was more cost effective in cost-effectiveness analysis at a cost of US$8,936.34 with a reduction in the probability of surgery occurrence in 93% of the cases. Probability of atrial septal communication occlusion and cost of the implant are the determinant factors of cost-effectiveness ratio. The proposal of a decision model seeks to fill a void in the academic literature. The decision model proposed includes the outcomes that present major impact in relation to the overall costs of the procedure. The atrial septal defects occlusion using percutaneous implant reduces the physical and psychological distress to the patients in relation to the conventional surgery, which represent intangible costs in the context of economic evaluation.

Exploring model based engineering for large telescopes: getting started with descriptive models

NASA Astrophysics Data System (ADS)

Karban, R.; Zamparelli, M.; Bauvir, B.; Koehler, B.; Noethe, L.; Balestra, A.

2008-07-01

Large telescopes pose a continuous challenge to systems engineering due to their complexity in terms of requirements, operational modes, long duty lifetime, interfaces and number of components. A multitude of decisions must be taken throughout the life cycle of a new system, and a prime means of coping with complexity and uncertainty is using models as one decision aid. The potential of descriptive models based on the OMG Systems Modeling Language (OMG SysMLTM) is examined in different areas: building a comprehensive model serves as the basis for subsequent activities of soliciting and review for requirements, analysis and design alike. Furthermore a model is an effective communication instrument against misinterpretation pitfalls which are typical of cross disciplinary activities when using natural language only or free-format diagrams. Modeling the essential characteristics of the system, like interfaces, system structure and its behavior, are important system level issues which are addressed. Also shown is how to use a model as an analysis tool to describe the relationships among disturbances, opto-mechanical effects and control decisions and to refine the control use cases. Considerations on the scalability of the model structure and organization, its impact on the development process, the relation to document-centric structures, style and usage guidelines and the required tool chain are presented.

Life cycle assessment based environmental impact estimation model for pre-stressed concrete beam bridge in the early design phase

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

Kim, Kyong Ju, E-mail: kjkim@cau.ac.kr; Yun, Won Gun, E-mail: ogun78@naver.com; Cho, Namho, E-mail: nhc51@cau.ac.kr

The late rise in global concern for environmental issues such as global warming and air pollution is accentuating the need for environmental assessments in the construction industry. Promptly evaluating the environmental loads of the various design alternatives during the early stages of a construction project and adopting the most environmentally sustainable candidate is therefore of large importance. Yet, research on the early evaluation of a construction project's environmental load in order to aid the decision making process is hitherto lacking. In light of this dilemma, this study proposes a model for estimating the environmental load by employing only the mostmore » basic information accessible during the early design phases of a project for the pre-stressed concrete (PSC) beam bridge, the most common bridge structure. Firstly, a life cycle assessment (LCA) was conducted on the data from 99 bridges by integrating the bills of quantities (BOQ) with a life cycle inventory (LCI) database. The processed data was then utilized to construct a case based reasoning (CBR) model for estimating the environmental load. The accuracy of the estimation model was then validated using five test cases; the model's mean absolute error rates (MAER) for the total environmental load was calculated as 7.09%. Such test results were shown to be superior compared to those obtained from a multiple-regression based model and a slab area base-unit analysis model. Henceforth application of this model during the early stages of a project is expected to highly complement environmentally friendly designs and construction by facilitating the swift evaluation of the environmental load from multiple standpoints. - Highlights: • This study is to develop the model of assessing the environmental impacts on LCA. • Bills of quantity from completed designs of PSC Beam were linked with the LCI DB. • Previous cases were used to estimate the environmental load of new case by CBR model. • CBR model produces more accurate estimations (7.09%) than other conventional models. • This study supports decision making process in the early stage of a new construction case.« less

A case study exploring the ethical and policy dimensions of allocating acute care resources to a dying patient.

PubMed

Cooke, Mary; Hurley, Ciarán

2008-05-01

We aimed to identify policy, process and ethical issues related to allocation of National Health Service resources when patients with end-of-life illness are referred to acute care services. Sharing healthcare decisions denotes a different partnership between professionals and patients when patients are empowered to define their needs. Implementation of a transition from professional to patient decision-making appears to be dependent upon its interpretation by personnel delivering care using the local trust policy. The outcome of this is a reformation of responsibility for budget allocation, choice of acute care provider and selecting services, currently in the realm of primary care; be it the general practitioner, community practitioners, or the patient. We used a 'lens' approach to case study analysis in which the lens is constructed of a model of policy analysis and four principles of biomedical ethics. A patient's decision to decline care proposed by an Accident and Emergency department nurse and the nurse's response to that decision expose a policy that restricts the use of ambulance transport and with that, flexibility in responses to patients' decisions. End-of-life care partnership decisions require sensitivity and flexibility from all healthcare practitioners. We found that policy-based systems currently used to deliver care across the primary care - hospital care border are far from seamless and can lead to foreseeable problems. Health professionals responsible for the care of a patient at the end of life should consider the holistic outcomes of resource allocation decisions for patients. Government and health professional agenda suggest that patients should be given a greater element of control over their healthcare than has historically been the case. When patients take responsibility for their decisions, healthcare personnel should recognize that this signals a shift in the nature of the professional-patient relationship to one of partnership.

Variation in GP decisions on antihypertensive treatment in oldest-old and frail individuals across 29 countries.

PubMed

Streit, Sven; Verschoor, Marjolein; Rodondi, Nicolas; Bonfim, Daiana; Burman, Robert A; Collins, Claire; Biljana, Gerasimovska Kitanovska; Gintere, Sandra; Gómez Bravo, Raquel; Hoffmann, Kathryn; Iftode, Claudia; Johansen, Kasper L; Kerse, Ngaire; Koskela, Tuomas H; Peštić, Sanda Kreitmayer; Kurpas, Donata; Mallen, Christian D; Maisoneuve, Hubert; Merlo, Christoph; Mueller, Yolanda; Muth, Christiane; Šter, Marija Petek; Petrazzuoli, Ferdinando; Rosemann, Thomas; Sattler, Martin; Švadlenková, Zuzana; Tatsioni, Athina; Thulesius, Hans; Tkachenko, Victoria; Torzsa, Peter; Tsopra, Rosy; Canan, Tuz; Viegas, Rita P A; Vinker, Shlomo; de Waal, Margot W M; Zeller, Andreas; Gussekloo, Jacobijn; Poortvliet, Rosalinde K E

2017-04-20

In oldest-old patients (>80), few trials showed efficacy of treating hypertension and they included mostly the healthiest elderly. The resulting lack of knowledge has led to inconsistent guidelines, mainly based on systolic blood pressure (SBP), cardiovascular disease (CVD) but not on frailty despite the high prevalence in oldest-old. This may lead to variation how General Practitioners (GPs) treat hypertension. Our aim was to investigate treatment variation of GPs in oldest-olds across countries and to identify the role of frailty in that decision. Using a survey, we compared treatment decisions in cases of oldest-old varying in SBP, CVD, and frailty. GPs were asked if they would start antihypertensive treatment in each case. In 2016, we invited GPs in Europe, Brazil, Israel, and New Zealand. We compared the percentage of cases that would be treated per countries. A logistic mixed-effects model was used to derive odds ratio (OR) for frailty with 95% confidence intervals (CI), adjusted for SBP, CVD, and GP characteristics (sex, location and prevalence of oldest-old per GP office, and years of experience). The mixed-effects model was used to account for the multiple assessments per GP. The 29 countries yielded 2543 participating GPs: 52% were female, 51% located in a city, 71% reported a high prevalence of oldest-old in their offices, 38% and had >20 years of experience. Across countries, considerable variation was found in the decision to start antihypertensive treatment in the oldest-old ranging from 34 to 88%. In 24/29 (83%) countries, frailty was associated with GPs' decision not to start treatment even after adjustment for SBP, CVD, and GP characteristics (OR 0.53, 95%CI 0.48-0.59; ORs per country 0.11-1.78). Across countries, we found considerable variation in starting antihypertensive medication in oldest-old. The frail oldest-old had an odds ratio of 0.53 of receiving antihypertensive treatment. Future hypertension trials should also include frail patients to acquire evidence on the efficacy of antihypertensive treatment in oldest-old patients with frailty, with the aim to get evidence-based data for clinical decision-making.

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

Mundaca, Luis; Neij, Lena; Worrell, Ernst

The growing complexities of energy systems, environmental problems and technology markets are driving and testing most energy-economy models to their limits. To further advance bottom-up models from a multidisciplinary energy efficiency policy evaluation perspective, we review and critically analyse bottom-up energy-economy models and corresponding evaluation studies on energy efficiency policies to induce technological change. We use the household sector as a case study. Our analysis focuses on decision frameworks for technology choice, type of evaluation being carried out, treatment of market and behavioural failures, evaluated policy instruments, and key determinants used to mimic policy instruments. Although the review confirms criticismmore » related to energy-economy models (e.g. unrealistic representation of decision-making by consumers when choosing technologies), they provide valuable guidance for policy evaluation related to energy efficiency. Different areas to further advance models remain open, particularly related to modelling issues, techno-economic and environmental aspects, behavioural determinants, and policy considerations.« less

Evaluation of strategies for nature-based solutions to drought: a decision support model at the national scale

NASA Astrophysics Data System (ADS)

Simpson, Mike; Ives, Matthew; Hall, Jim

2016-04-01

There is an increasing body of evidence in support of the use of nature based solutions as a strategy to mitigate drought. Restored or constructed wetlands, grasslands and in some cases forests have been used with success in numerous case studies. Such solutions remain underused in the UK, where they are not considered as part of long-term plans for supply by water companies. An important step is the translation of knowledge on the benefits of nature based solutions at the upland/catchment scale into a model of the impact of these solutions on national water resource planning in terms of financial costs, carbon benefits and robustness to drought. Our project, 'A National Scale Model of Green Infrastructure for Water Resources', addresses this issue through development of a model that can show the costs and benefits associated with a broad roll-out of nature based solutions for water supply. We have developed generalised models of both the hydrological effects of various classes and implementations of nature-based approaches and their economic impacts in terms of construction costs, running costs, time to maturity, land use and carbon benefits. Our next step will be to compare this work with our recent evaluation of conventional water infrastructure, allowing a case to be made in financial terms and in terms of security of water supply. By demonstrating the benefits of nature based solutions under multiple possible climate and population scenarios we aim to demonstrate the potential value of using nature based solutions as a component of future long-term water resource plans. Strategies for decision making regarding the selection of nature based and conventional approaches, developed through discussion with government and industry, will be applied to the final model. Our focus is on keeping our work relevant to the requirements of decision-makers involved in conventional water planning. We propose to present the outcomes of our model for the evaluation of nature-based solutions at catchment scale and ongoing results of our national-scale model.

Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach.

PubMed

Bennett, Casey C; Hauser, Kris

2013-01-01

In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This framework serves two potential functions: (1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and (2) the basis for clinical artificial intelligence - an AI that can "think like a doctor". This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans as actions are performed and new observations are obtained. This framework was evaluated using real patient data from an electronic health record. The results demonstrate the feasibility of this approach; such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare. The cost per unit of outcome change (CPUC) was $189 vs. $497 for AI vs. TAU (where lower is considered optimal) - while at the same time the AI approach could obtain a 30-35% increase in patient outcomes. Tweaking certain AI model parameters could further enhance this advantage, obtaining approximately 50% more improvement (outcome change) for roughly half the costs. Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine. Copyright © 2012 Elsevier B.V. All rights reserved.

Integrated strategic and tactical biomass-biofuel supply chain optimization.

PubMed

Lin, Tao; Rodríguez, Luis F; Shastri, Yogendra N; Hansen, Alan C; Ting, K C

2014-03-01

To ensure effective biomass feedstock provision for large-scale biofuel production, an integrated biomass supply chain optimization model was developed to minimize annual biomass-ethanol production costs by optimizing both strategic and tactical planning decisions simultaneously. The mixed integer linear programming model optimizes the activities range from biomass harvesting, packing, in-field transportation, stacking, transportation, preprocessing, and storage, to ethanol production and distribution. The numbers, locations, and capacities of facilities as well as biomass and ethanol distribution patterns are key strategic decisions; while biomass production, delivery, and operating schedules and inventory monitoring are key tactical decisions. The model was implemented to study Miscanthus-ethanol supply chain in Illinois. The base case results showed unit Miscanthus-ethanol production costs were $0.72L(-1) of ethanol. Biorefinery related costs accounts for 62% of the total costs, followed by biomass procurement costs. Sensitivity analysis showed that a 50% reduction in biomass yield would increase unit production costs by 11%. Copyright © 2014 Elsevier Ltd. All rights reserved.

An inventory-theory-based interval-parameter two-stage stochastic programming model for water resources management

NASA Astrophysics Data System (ADS)

Suo, M. Q.; Li, Y. P.; Huang, G. H.

2011-09-01

In this study, an inventory-theory-based interval-parameter two-stage stochastic programming (IB-ITSP) model is proposed through integrating inventory theory into an interval-parameter two-stage stochastic optimization framework. This method can not only address system uncertainties with complex presentation but also reflect transferring batch (the transferring quantity at once) and period (the corresponding cycle time) in decision making problems. A case of water allocation problems in water resources management planning is studied to demonstrate the applicability of this method. Under different flow levels, different transferring measures are generated by this method when the promised water cannot be met. Moreover, interval solutions associated with different transferring costs also have been provided. They can be used for generating decision alternatives and thus help water resources managers to identify desired policies. Compared with the ITSP method, the IB-ITSP model can provide a positive measure for solving water shortage problems and afford useful information for decision makers under uncertainty.

Probabilistic approach to decision making under uncertainty during volcanic crises. Retrospective analysis of the 2011 eruption of El Hierro, in the Canary Islands

NASA Astrophysics Data System (ADS)

Sobradelo, Rosa; Martí, Joan; Kilburn, Christopher; López, Carmen

2014-05-01

Understanding the potential evolution of a volcanic crisis is crucial to improving the design of effective mitigation strategies. This is especially the case for volcanoes close to densely-populated regions, where inappropriate decisions may trigger widespread loss of life, economic disruption and public distress. An outstanding goal for improving the management of volcanic crises, therefore, is to develop objective, real-time methodologies for evaluating how an emergency will develop and how scientists communicate with decision makers. Here we present a new model BADEMO (Bayesian Decision Model) that applies a general and flexible, probabilistic approach to managing volcanic crises. The model combines the hazard and risk factors that decision makers need for a holistic analysis of a volcanic crisis. These factors include eruption scenarios and their probabilities of occurrence, the vulnerability of populations and their activities, and the costs of false alarms and failed forecasts. The model can be implemented before an emergency, to identify actions for reducing the vulnerability of a district; during an emergency, to identify the optimum mitigating actions and how these may change as new information is obtained; and after an emergency, to assess the effectiveness of a mitigating response and, from the results, to improve strategies before another crisis occurs. As illustrated by a retrospective analysis of the 2011 eruption of El Hierro, in the Canary Islands, BADEMO provides the basis for quantifying the uncertainty associated with each recommended action as an emergency evolves, and serves as a mechanism for improving communications between scientists and decision makers.

Consumer and case manager perspectives of service empowerment: relationship to mental health recovery.

PubMed

Crane-Ross, Dushka; Lutz, Wilma J; Roth, Dee

2006-04-01

This study examines the relationship between service empowerment and recovery. Service empowerment is defined as the extent to which consumers participate in service decisions and the level of reciprocity and respect within the relationship with their case managers. Assessments were made from two perspectives: consumers and their case managers. Structural equation models were developed to examine the direct and indirect effects of service empowerment on four recovery outcomes: Quality of Life, Level of Functioning, Consumer-Reported Symptomatology, and Case Manager-Reported Symptomatology. Consumers' perceptions of service empowerment were the most powerful predictor of recovery outcomes across the four models. Consumers' and case managers' perceptions were related but the magnitude of the relationship was small, indicating that considerable differences exist between their perceptions of service empowerment.

The management of patients with T1 adenocarcinoma of the low rectum: a decision analysis.

PubMed

Johnston, Calvin F; Tomlinson, George; Temple, Larissa K; Baxter, Nancy N

2013-04-01

Decision making for patients with T1 adenocarcinoma of the low rectum, when treatment options are limited to a transanal local excision or abdominoperineal resection, is challenging. The aim of this study was to develop a contemporary decision analysis to assist patients and clinicians in balancing the goals of maximizing life expectancy and quality of life in this situation. We constructed a Markov-type microsimulation in open-source software. Recurrence rates and quality-of-life parameters were elicited by systematic literature reviews. Sensitivity analyses were performed on key model parameters. Our base case for analysis was a 65-year-old man with low-lying T1N0 rectal cancer. We determined the sensitivity of our model for sex, age up to 80, and T stage. The main outcome measured was quality-adjusted life-years. In the base case, selecting transanal local excision over abdominoperineal resection resulted in a loss of 0.53 years of life expectancy but a gain of 0.97 quality-adjusted life-years. One-way sensitivity analysis demonstrated a health state utility value threshold for permanent colostomy of 0.93. This value ranged from 0.88 to 1.0 based on tumor recurrence risk. There were no other model sensitivities. Some model parameter estimates were based on weak data. In our model, transanal local excision was found to be the preferable approach for most patients. An abdominoperineal resection has a 3.5% longer life expectancy, but this advantage is lost when the quality-of-life reduction reported by stoma patients is weighed in. The minority group in whom abdominoperineal resection is preferred are those who are unwilling to sacrifice 7% of their life expectancy to avoid a permanent stoma. This is estimated to be approximately 25% of all patients. The threshold increases to 12% of life expectancy in high-risk tumors. No other factors are found to be relevant to the decision.

Cost-effectiveness of orthoptic screening in kindergarten: a decision-analytic model.

PubMed

König, H H; Barry, J C; Leidl, R; Zrenner, E

2000-06-01

The purpose of this study was to analyze the cost-effectiveness of orthoptic screening for amblyopia in kindergarten. A decision-analytic model was used. In this model all kindergarten children in Germany aged 3 years were examined by an orthoptist. Children with positive screening results were referred to an ophthalmologist for diagnosis. The number of newly diagnosed cases of amblyopia, amblyogenic non-obvious strabismus and amblyogenic refractive errors was used as the measure of effectiveness. Direct costs were measured form a third-party payer perspective. Data for model parameters were obtained from the literature and from own measurements in kindergartens. A base analysis was performed using median parameter values. The influence of uncertain parameters was tested in sensitivity analyses. According to the base analysis, the cost of one orthoptic screening test was 7.87 euro. One ophthalmologic examination cost 36.40 euro. The total cost of the screening program in all kindergartens was 3.1 million euro. A total of 4,261 new cases would be detected. The cost-effectiveness ratio was 727 euro per case detected. Sensitivity analysis showed considerable influence of the prevalence rate of target conditions and of the specificity of the orthopic examination on the cost-effectiveness ratio. This analysis provides information which is useful for discussion about the implementation of orthoptic screening and for planning a field study.

Decision Support Tools for Clinical Diagnosis of Disease in Cows with Suspected Bovine Spongiform Encephalopathy

PubMed Central

Saegerman, C.; Speybroeck, N.; Roels, S.; Vanopdenbosch, E.; Thiry, E.; Berkvens, D.

2004-01-01

Reporting of clinically suspected cattle is currently the most common method for detecting cases of bovine spongiform encephalopathy (BSE). Improvement of clinical diagnosis and decision-making remains crucial. A comparison of clinical patterns, consisting of 25 signs, was made between all 30 BSE cases, confirmed in Belgium before October 2002, and 272 suspected cases that were subsequently determined to be histologically, immunohistochemically, and scrapie-associated-fiber negative. Seasonality in reporting suspected cases was observed, with more cases being reported during wintertime when animals were kept indoors. The median duration of illness was 30 days. The 10 most relevant signs of BSE were kicking in the milking parlor, hypersensitivity to touch and/or sound, head shyness, panic-stricken response, reluctance to enter in the milking parlor, abnormal ear movement or carriage, increased alertness behavior, reduced milk yield, teeth grinding, and temperament change. Ataxia did not appear to be a specific sign of BSE. A classification and regression tree was constructed by using the following four features: age of the animal, year of birth, number of relevant BSE signs noted, and number of clinical signs, typical for listeriosis, noted. The model had a sensitivity of 100% and a specificity of 85%. This approach allows the use of an interactive decision-support tool, based entirely on odds ratios, a statistic independent of disease prevalence. PMID:14715749

Ecologically rational choice and the structure of the environment.

PubMed

Pleskac, Timothy J; Hertwig, Ralph

2014-10-01

In life, risk is reward and vice versa. Unfortunately, the big rewards people desire are relatively unlikely to occur. This relationship between risk and reward or probabilities and payoffs seems obvious to the financial community and to laypeople alike. Yet theories of decision making have largely ignored it. We conducted an ecological analysis of life's gambles, ranging from the domains of roulette and life insurance to scientific publications and artificial insemination. Across all domains, payoffs and probabilities proved intimately tied, with payoff magnitudes signaling their probabilities. In some cases, the constraints of the market result in these two core elements of choice being related via a power function; in other cases, other factors such as social norms appear to produce the inverse relationship between risks and rewards. We offer evidence that decision makers exploit this relationship in the form of a heuristic--the risk-reward heuristic--to infer the probability of a payoff during decisions under uncertainty. We demonstrate how the heuristic can help explain observed ambiguity aversion. We further show how this ecological relationship can inform other aspects of decision making, particularly the approach of using monetary lotteries to study choice under risk and uncertainty. Taken together, these findings suggest that theories of decision making need to model not only the decision process but also the environment to which the process is adapted.

Expectation Violation in Political Decision Making: A Psychological Case Study.

PubMed

Öllinger, Michael; Meissner, Karin; von Müller, Albrecht; Collado Seidel, Carlos

2017-01-01

Since the early Gestaltists there has been a strong interest in the question of how problem solvers get stuck in a mental impasse. A key idea is that the repeated activation of a successful strategy from the past results in a mental set ('Einstellung') which determines and constrains the option space to solve a problem. We propose that this phenomenon, which mostly was tested by fairly restricted experiments in the lab, could also be applied to more complex problem constellations and naturalistic decision making. We aim at scrutinizing and reconstructing how a mental set determines the misinterpretation of facts in the field of political decision making and leads in consequence to wrong expectations and an ill-defined problem representation. We will exemplify this psychological mechanism considering a historical example, namely the unexpected stabilization of the Franco regime at the end of World War II and its survival thereafter. A specific focus will be drawn to the significant observation that erroneous expectations were taken as the basis for decisions. This is congruent with the notion that in case of discrepancy between preconceived notions and new information, the former prevails over the new findings. Based on these findings, we suggest a theoretical model for expectation violation in political decision making and develop novel approaches for cognitive empirical research on the mechanisms of expectation violation and its maintenance in political decision making processes.

Expectation Violation in Political Decision Making: A Psychological Case Study

PubMed Central

Öllinger, Michael; Meissner, Karin; von Müller, Albrecht; Collado Seidel, Carlos

2017-01-01

Since the early Gestaltists there has been a strong interest in the question of how problem solvers get stuck in a mental impasse. A key idea is that the repeated activation of a successful strategy from the past results in a mental set (‘Einstellung’) which determines and constrains the option space to solve a problem. We propose that this phenomenon, which mostly was tested by fairly restricted experiments in the lab, could also be applied to more complex problem constellations and naturalistic decision making. We aim at scrutinizing and reconstructing how a mental set determines the misinterpretation of facts in the field of political decision making and leads in consequence to wrong expectations and an ill-defined problem representation. We will exemplify this psychological mechanism considering a historical example, namely the unexpected stabilization of the Franco regime at the end of World War II and its survival thereafter. A specific focus will be drawn to the significant observation that erroneous expectations were taken as the basis for decisions. This is congruent with the notion that in case of discrepancy between preconceived notions and new information, the former prevails over the new findings. Based on these findings, we suggest a theoretical model for expectation violation in political decision making and develop novel approaches for cognitive empirical research on the mechanisms of expectation violation and its maintenance in political decision making processes. PMID:29085316

A Computational Model of Reasoning from the Clinical Literature

PubMed Central

Rennels, Glenn D.

1986-01-01

This paper explores the premise that a formalized representation of empirical studies can play a central role in computer-based decision support. The specific motivations underlying this research include the following propositions: 1. Reasoning from experimental evidence contained in the clinical literature is central to the decisions physicians make in patient care. 2. A computational model, based upon a declarative representation for published reports of clinical studies, can drive a computer program that selectively tailors knowledge of the clinical literature as it is applied to a particular case. 3. The development of such a computational model is an important first step toward filling a void in computer-based decision support systems. Furthermore, the model may help us better understand the general principles of reasoning from experimental evidence both in medicine and other domains. Roundsman is a developmental computer system which draws upon structured representations of the clinical literature in order to critique plans for the management of primary breast cancer. Roundsman is able to produce patient-specific analyses of breast cancer management options based on the 24 clinical studies currently encoded in its knowledge base. The Roundsman system is a first step in exploring how the computer can help to bring a critical analysis of the relevant literature to the physician, structured around a particular patient and treatment decision.

A dynamic dual process model of risky decision making.

PubMed

Diederich, Adele; Trueblood, Jennifer S

2018-03-01

Many phenomena in judgment and decision making are often attributed to the interaction of 2 systems of reasoning. Although these so-called dual process theories can explain many types of behavior, they are rarely formalized as mathematical or computational models. Rather, dual process models are typically verbal theories, which are difficult to conclusively evaluate or test. In the cases in which formal (i.e., mathematical) dual process models have been proposed, they have not been quantitatively fit to experimental data and are often silent when it comes to the timing of the 2 systems. In the current article, we present a dynamic dual process model framework of risky decision making that provides an account of the timing and interaction of the 2 systems and can explain both choice and response-time data. We outline several predictions of the model, including how changes in the timing of the 2 systems as well as time pressure can influence behavior. The framework also allows us to explore different assumptions about how preferences are constructed by the 2 systems as well as the dynamic interaction of the 2 systems. In particular, we examine 3 different possible functional forms of the 2 systems and 2 possible ways the systems can interact (simultaneously or serially). We compare these dual process models with 2 single process models using risky decision making data from Guo, Trueblood, and Diederich (2017). Using this data, we find that 1 of the dual process models significantly outperforms the other models in accounting for both choices and response times. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Integrating Geomorphic and Social Dynamics in the Analysis of Anthropogenic Landforms: Examining Landscape Evolution of Terrain Modified by Agricultural Terracing

NASA Astrophysics Data System (ADS)

Glaubius, J.; Maerker, M.

2016-12-01

Anthropogenic landforms, such as mines and agricultural terraces, are impacted by both geomorphic and social processes at varying intensities through time. In the case of agricultural terraces, decisions regarding terrace maintenance are intertwined with land use, such as when terraced fields are abandoned. Furthermore, terrace maintenance and land use decisions, either jointly or separately, may be in response to geomorphic processes, as well as geomorphic feedbacks. Previous studies of these complex geomorphic systems considered agricultural terraces as static features or analyzed only the geomorphic response to landowner decisions. Such research is appropriate for short-term or binary landscape scenarios (e.g. the impact of maintained vs. abandoned terraces), but the complexities inherent in these socio-natural systems requires an approach that includes both social and geomorphic processes. This project analyzes feedbacks and emergent properties in terraced systems by implementing a coupled landscape evolution model (LEM) and agent-based model (ABM) using the Landlab and Mesa modeling libraries. In the ABM portion of the model, agricultural terraces are conceptualized using a life-cycle stages schema and implemented using Markov Decision Processes to simulate the changing geomorphic impact of terracing based on human decisions. This paper examines the applicability of this approach by comparing results from a LEM-only model against the coupled LEM-ABM model for a terraced region. Model results are compared by quantify and spatial patterning of sediment transport. This approach fully captures long-term landscape evolution of terraced terrain that is otherwise lost when the life-cycle of terraces is not considered. The coupled LEM-ABM approach balances both environmental and social processes so that the socio-natural feedbacks in such anthropogenic systems can be disentangled.

Reaching beyond the review of research evidence: a qualitative study of decision making during the development of clinical practice guidelines for disease prevention in healthcare.

PubMed

Richter Sundberg, Linda; Garvare, Rickard; Nyström, Monica Elisabeth

2017-05-11

The judgment and decision making process during guideline development is central for producing high-quality clinical practice guidelines, but the topic is relatively underexplored in the guideline research literature. We have studied the development process of national guidelines with a disease-prevention scope produced by the National board of Health and Welfare (NBHW) in Sweden. The NBHW formal guideline development model states that guideline recommendations should be based on five decision-criteria: research evidence; curative/preventive effect size, severity of the condition; cost-effectiveness; and ethical considerations. A group of health profession representatives (i.e. a prioritization group) was assigned the task of ranking condition-intervention pairs for guideline recommendations, taking into consideration the multiple decision criteria. The aim of this study was to investigate the decision making process during the two-year development of national guidelines for methods of preventing disease. A qualitative inductive longitudinal case study approach was used to investigate the decision making process. Questionnaires, non-participant observations of nine two-day group meetings, and documents provided data for the analysis. Conventional and summative qualitative content analysis was used to analyse data. The guideline development model was modified ad-hoc as the group encountered three main types of dilemmas: high quality evidence vs. low adoptability of recommendation; insufficient evidence vs. high urgency to act; and incoherence in assessment and prioritization within and between four different lifestyle areas. The formal guideline development model guided the decision-criteria used, but three new or revised criteria were added by the group: 'clinical knowledge and experience', 'potential guideline consequences' and 'needs of vulnerable groups'. The frequency of the use of various criteria in discussions varied over time. Gender, professional status, and interpersonal skills were perceived to affect individuals' relative influence on group discussions. The study shows that guideline development groups make compromises between rigour and pragmatism. The formal guideline development model incorporated multiple aspects, but offered few details on how the different criteria should be handled. The guideline development model devoted little attention to the role of the decision-model and group-related factors. Guideline development models could benefit from clarifying the role of the group-related factors and non-research evidence, such as clinical experience and ethical considerations, in decision-processes during guideline development.

Whistleblowing and boundary violations: exposing a colleague in the forensic milieu.

PubMed

Peternelj-Taylor, Cindy

2003-09-01

The purpose of this article is to examine the phenomenon of whistleblowing as it relates to a reconstructed case study of an erotic boundary violation that emerged from a clinical situation in forensic psychiatric nursing practice. The unique features of this case are illustrated with the help of a model for decision making. Although the ramifications of exposing a colleague are many, it is argued that, in this particular case, it was morally and ethically the right thing to do.

Real Options in Defense R and D: A Decision Tree Analysis Approach for Options to Defer, Abandon, and Expand

DTIC Science & Technology

2016-12-01

chosen rather than complex ones , and responds to the criticism of the DTA approach. Chapter IV provides three separate case studies in defense R&D...defense R&D projects. To this end, the first section describes the case study method and the advantages of using simple models over more complex ones ...the analysis lacked empirical data and relied on subjective data, the analysis successfully combined the DTA approach with the case study method and

Model-Checking with Edge-Valued Decision Diagrams

NASA Technical Reports Server (NTRS)

Roux, Pierre; Siminiceanu, Radu I.

2010-01-01

We describe an algebra of Edge-Valued Decision Diagrams (EVMDDs) to encode arithmetic functions and its implementation in a model checking library along with state-of-the-art algorithms for building the transition relation and the state space of discrete state systems. We provide efficient algorithms for manipulating EVMDDs and give upper bounds of the theoretical time complexity of these algorithms for all basic arithmetic and relational operators. We also demonstrate that the time complexity of the generic recursive algorithm for applying a binary operator on EVMDDs is no worse than that of Multi-Terminal Decision Diagrams. We have implemented a new symbolic model checker with the intention to represent in one formalism the best techniques available at the moment across a spectrum of existing tools: EVMDDs for encoding arithmetic expressions, identity-reduced MDDs for representing the transition relation, and the saturation algorithm for reachability analysis. We compare our new symbolic model checking EVMDD library with the widely used CUDD package and show that, in many cases, our tool is several orders of magnitude faster than CUDD.

The emergence of coherence over the course of decision making.

PubMed

Simon, D; Pham, L B; Le, Q A; Holyoak, K J

2001-09-01

Previous research has indicated that decision making is accompanied by an increase in the coherence of assessments of the factors related to the decision alternatives. In the present study, the authors investigated whether this coherence shift is obtained before people commit to a decision, and whether it is obtained in the course of a number of other processing tasks. College students were presented with a complex legal case involving multiple conflicting arguments. Participants rated agreement with the individual arguments in isolation before seeing the case and after processing it under various initial sets, including playing the role of a judge assigned to decide the case. Coherence shifts were observed when participants were instructed to delay making the decision (Experiment 1), to memorize the case (Experiment 2), and to comprehend the case (Experiment 3). The findings support the hypothesis that a coherence-generating mechanism operates in a variety of processing tasks, including decision making.

A comparative assessment of tools for ecosystem services quantification and valuation

USGS Publications Warehouse

Bagstad, Kenneth J.; Semmens, Darius; Waage, Sissel; Winthrop, Robert

2013-01-01

To enter widespread use, ecosystem service assessments need to be quantifiable, replicable, credible, flexible, and affordable. With recent growth in the field of ecosystem services, a variety of decision-support tools has emerged to support more systematic ecosystem services assessment. Despite the growing complexity of the tool landscape, thorough reviews of tools for identifying, assessing, modeling and in some cases monetarily valuing ecosystem services have generally been lacking. In this study, we describe 17 ecosystem services tools and rate their performance against eight evaluative criteria that gauge their readiness for widespread application in public- and private-sector decision making. We describe each of the tools′ intended uses, services modeled, analytical approaches, data requirements, and outputs, as well time requirements to run seven tools in a first comparative concurrent application of multiple tools to a common location – the San Pedro River watershed in southeast Arizona, USA, and northern Sonora, Mexico. Based on this work, we offer conclusions about these tools′ current ‘readiness’ for widespread application within both public- and private-sector decision making processes. Finally, we describe potential pathways forward to reduce the resource requirements for running ecosystem services models, which are essential to facilitate their more widespread use in environmental decision making.

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.

Patterns of innovation in weapons acquisition decisions: the case of the long-range cruise missile

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

Ockenden, S.E.

1987-01-01

This study was conducted to determine why organizations can appear innovative on some occasions, and non-innovative on others. The particular focus of the study concerns two comparable organizations - the US Air Force and the US Navy - which responded differently to the opportunity for weapons innovation presented by a promising technology, the long-range cruise missile. Explanations for weapons innovation and acquisition decisions are traditionally found in four approaches: arms-race models; rational actor models; bureaucratic and cybernetics models; and idiographic case studies. None of these approaches is sufficient to offer general, stable, and consistent predictions about weapons innovation. Research outsidemore » of political science offers some insights. This literature was reviewed to develop a four-fold matrix describing patterns of behavior when a given organization confronts an opportunity to innovate at a given time. Because of significant differences between the two organizations in terms of goal consensus, significant differences in behavior were found. The air Force strongly resisted the cruise missile, while the Navy incrementally adopted it. While the entire matrix could not be tested, conclusions could be drawn.« less

Investigating Miranda waiver decisions: An examination of the rational consequences.

PubMed

Blackwood, Hayley L; Rogers, Richard; Steadham, Jennifer A; Fiduccia, Chelsea E

2015-01-01

Millions of custodial suspects waive their Miranda rights each year without the benefit of legal counsel. Miranda understanding, appreciation, and reasoning abilities are essential to courts' acceptance of Miranda waivers (Grisso, 2003; Rogers & Shuman, 2005). The question posed to forensic psychologists and psychiatrists in the disputed Miranda waivers is whether a particular waiver decision was knowing, intelligent, and voluntary. Despite the remarkable development of Miranda research in recent decades, studies have generally focused on understanding and appreciation of Miranda rights, but with comparatively minimal emphasis on Miranda reasoning and attendant waiver decisions. Research on defendants' decisional capacities constitutes a critical step in further developing theoretical and clinical models for Miranda waiver decisions. The current study evaluated Miranda waiver decisions for 80 pretrial defendants from two Oklahoma jails to study systematically how rational decision abilities affect defendants' personal waiver decisions. In stark contrast to what was expected, many defendants were able to identify a rational decisional process in their own legal cases, yet cast such reasoning aside and chose a completely contradictory Miranda waiver decision. Published by Elsevier Ltd.

Prioritizing Congenital Syphilis Control in South China: A Decision Analytic Model to Inform Policy Implementation

PubMed Central

Tan, Nicholas X.; Rydzak, Chara; Yang, Li-Gang; Vickerman, Peter; Yang, Bin; Peeling, Rosanna W.; Hawkes, Sarah; Chen, Xiang-Sheng; Tucker, Joseph D.

2013-01-01

Background Syphilis is a major public health problem in many regions of China, with increases in congenital syphilis (CS) cases causing concern. The Chinese Ministry of Health recently announced a comprehensive 10-y national syphilis control plan focusing on averting CS. The decision analytic model presented here quantifies the impact of the planned strategies to determine whether they are likely to meet the goals laid out in the control plan. Methods and Findings Our model incorporated data on age-stratified fertility, female adult syphilis cases, and empirical syphilis transmission rates to estimate the number of CS cases associated with prenatal syphilis infection on a yearly basis. Guangdong Province was the focus of this analysis because of the availability of high-quality demographic and public health data. Each model outcome was simulated 1,000 times to incorporate uncertainty in model inputs. The model was validated using data from a CS intervention program among 477,656 women in China. Sensitivity analyses were performed to identify which variables are likely to be most influential in achieving Chinese and international policy goals. Increasing prenatal screening coverage was the single most effective strategy for reducing CS cases. An incremental increase in prenatal screening from the base case of 57% coverage to 95% coverage was associated with 106 (95% CI: 101, 111) CS cases averted per 100,000 live births (58% decrease). The policy strategies laid out in the national plan led to an outcome that fell short of the target, while a four-pronged comprehensive syphilis control strategy consisting of increased prenatal screening coverage, increased treatment completion, earlier prenatal screening, and improved syphilis test characteristics was associated with 157 (95% CI: 154, 160) CS cases averted per 100,000 live births (85% decrease). Conclusions The Chinese national plan provides a strong foundation for syphilis control, but more comprehensive measures that include earlier and more extensive screening are necessary for reaching policy goals. Please see later in the article for the Editors' Summary PMID:23349624

Equilibrium approach towards water resource management and pollution control in coal chemical industrial park.

PubMed

Xu, Jiuping; Hou, Shuhua; Xie, Heping; Lv, Chengwei; Yao, Liming

2018-08-01

In this study, an integrated water and waste load allocation model is proposed to assist decision makers in better understanding the trade-offs between economic growth, resource utilization, and environmental protection of coal chemical industries which characteristically have high water consumption and pollution. In the decision framework, decision makers in a same park, each of whom have different goals and preferences, work together to seek a collective benefit. Similar to a Stackelberg-Nash game, the proposed approach illuminates the decision making interrelationships and involves in the conflict coordination between the park authority and the individual coal chemical company stockholders. In the proposed method, to response to climate change and other uncertainties, a risk assessment tool, Conditional Value-at-Risk (CVaR) and uncertainties through reflecting parameters and coefficients using probability and fuzzy set theory are integrated in the modeling process. Then a case study from Yuheng coal chemical park is presented to demonstrate the practicality and efficiency of the optimization model. To reasonable search the potential consequences of different responses to water and waste load allocation strategies, a number of scenario results considering environmental uncertainty and decision maker' attitudes are examined to explore the tradeoffs between economic development and environmental protection and decision makers' objectives. The results are helpful for decision/police makers to adjust current strategies adapting for current changes. Based on the scenario analyses and discussion, some propositions and operational policies are given and sensitive adaptation strategies are presented to support the efficient, balanced and sustainable development of coal chemical industrial parks. Copyright © 2018 Elsevier Ltd. All rights reserved.

A judgment and decision-making model for plant behavior.

PubMed

Karban, Richard; Orrock, John L

2018-06-12

Recently plant biologists have documented that plants, like animals, engage in many activities that can be considered as behaviors, although plant biologists currently lack a conceptual framework to understand these processes. Borrowing the well-established framework developed by psychologists, we propose that plant behaviors can be constructively modeled by identifying four distinct components: 1) a cue or stimulus that provides information, 2) a judgment whereby the plant perceives and processes this informative cue, 3) a decision whereby the plant chooses among several options based on their relative costs and benefits, and 4) action. Judgment for plants can be determined empirically by monitoring signaling associated with electrical, calcium, or hormonal fluxes. Decision-making can be evaluated empirically by monitoring gene expression or differential allocation of resources. We provide examples of the utility of this judgment and decision-making framework by considering cases in which plants either successfully or unsuccessfully induced resistance against attacking herbivores. Separating judgment from decision-making suggests new analytical paradigms (i.e., Bayesian methods for judgment and economic utility models for decision-making). Following this framework, we propose an experimental approach to plant behavior that explicitly manipulates the stimuli provided to plants, uses plants that vary in sensory abilities, and examines how environmental context affects plant responses. The concepts and approaches that follow from the judgment and decision-making framework can shape how we study and understand plant-herbivore interactions, biological invasions, plant responses to climate change, and the susceptibility of plants to evolutionary traps. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

[Breast cancer and pregnancy: decision making and the point of view of the mother].

PubMed

Eisinger, François; Noizet, Agnès

2002-09-01

For the treatment of breast cancer, modifications of decision making related to pregnancy could be assessed through three questions. Why a decision had been chosen? In that case, the hypothesis is that decisions are based on the expected utility. The theory assumes weighting and computation of complete possibilities with their associated probabilities and values. However values exhibits a wide inter-individual variation range. Therefore the predictability of choice based on this model is indeed very low. Furthermore it is likely that the willingness of pregnancy after breast cancer contains besides classic constituents of appeals of motherhood, a specific meaning of recovery both of health and femininity. The second question: who is in charge of the decision? And under the paradigm of autonomy, women' decision is, merely by itself, the right decision. The last question is how? For some situations for which foreseeing is quiet complex, the value of the process in itself is increased and could help the end-oriented or self-determined decision. Casuistic analysis could therefore improve women' decisions. The issue is not only about decision but also related to patient-physician relationship, about an issue that is not only a biomedical problem.

Accelerating policy decisions to adopt haemophilus influenzae type B vaccine: a global, multivariable analysis.

PubMed

Shearer, Jessica C; Stack, Meghan L; Richmond, Marcie R; Bear, Allyson P; Hajjeh, Rana A; Bishai, David M

2010-03-16

Adoption of new and underutilized vaccines by national immunization programs is an essential step towards reducing child mortality. Policy decisions to adopt new vaccines in high mortality countries often lag behind decisions in high-income countries. Using the case of Haemophilus influenzae type b (Hib) vaccine, this paper endeavors to explain these delays through the analysis of country-level economic, epidemiological, programmatic and policy-related factors, as well as the role of the Global Alliance for Vaccines and Immunisation (GAVI Alliance). Data for 147 countries from 1990 to 2007 were analyzed in accelerated failure time models to identify factors that are associated with the time to decision to adopt Hib vaccine. In multivariable models that control for Gross National Income, region, and burden of Hib disease, the receipt of GAVI support speeded the time to decision by a factor of 0.37 (95% CI 0.18-0.76), or 63%. The presence of two or more neighboring country adopters accelerated decisions to adopt by a factor of 0.50 (95% CI 0.33-0.75). For each 1% increase in vaccine price, decisions to adopt are delayed by a factor of 1.02 (95% CI 1.00-1.04). Global recommendations and local studies were not associated with time to decision. This study substantiates previous findings related to vaccine price and presents new evidence to suggest that GAVI eligibility is associated with accelerated decisions to adopt Hib vaccine. The influence of neighboring country decisions was also highly significant, suggesting that approaches to support the adoption of new vaccines should consider supply- and demand-side factors.

The madness of crowds phenomenon in the collective decision-making by the cells, the cell's metacognition and cancer

NASA Astrophysics Data System (ADS)

Saakian, David B.

2018-02-01

Recently it has been found that the collective decision-making in the group is efficient only when the confidences (a version of metacognition) of the members are similar, and it has been assumed that the metacognition (self-reference) in general is crucial for the human cooperation. Our goal is to map the decision making by the cells to decision making by humans, looking the analog of metacognition in the cells, accurately calculate the collective sensing of chemical gradients by the cells, and apply our results to cancer. We formulated the model for the chemeosensing by the cells with different diameters, solved it accurately and found that the collective chemosensing is very similar to the collective decision making by humans. We found that the collective sensing of the ligand concentration can be worse than for the most sensitive cell. We introduced the metacognition of the cells, and verify that the metacognition is impaired for the cancer case. We assume as a hypothesis that the impaired cell metacognition in case of cancer does not allow normal multi-cellularity, and cancer can arise when the "two heads are better than one" principle fails, and there is a "madness of crowds" phenomenon instead.

Communication with patients during the prenatal testing procedure: an explorative qualitative study.

PubMed

van Zwieten, Myra; Willems, Dick; Knegt, Lia; Leschot, Nico

2006-10-01

While generally two phases of prenatal genetic counseling are distinguished, i.e. pre- and post-test counseling, we revealed a third form of communication during the testing procedure. The content of this intermediate communication was explored. A secondary analysis was performed on data obtained in another observational study, which was focussed on how indefinite testing results are clarified. Thirteen testing trajectories in which communication with parents took place during the testing procedure were further analysed. In the majority of cases the content of intermediate communication was similar to the content of pre-test counseling. In four cases the content was different, because the communication involved the parents in decision-making about a testing result, which was still being processed. Communication in (prenatal) genetic testing is not always restricted to separate phases, but can be an ongoing process occurring parallel to, and sometimes even intertwined with, the testing process. The advocated model of shared decision-making might work better once it is determined if the decision concerns the area wherein the provider is the expert, or the patient. Further research into the process of continuing decision-making could clarify how providers' and patients' responsibilities regarding the diagnostic process are distributed. Meanwhile, the possible occurrence of continuous decision-making should be mentioned in (prenatal) genetic counseling.

Uncertainty analysis for effluent trading planning using a Bayesian estimation-based simulation-optimization modeling approach.

PubMed

Zhang, J L; Li, Y P; Huang, G H; Baetz, B W; Liu, J

2017-06-01

In this study, a Bayesian estimation-based simulation-optimization modeling approach (BESMA) is developed for identifying effluent trading strategies. BESMA incorporates nutrient fate modeling with soil and water assessment tool (SWAT), Bayesian estimation, and probabilistic-possibilistic interval programming with fuzzy random coefficients (PPI-FRC) within a general framework. Based on the water quality protocols provided by SWAT, posterior distributions of parameters can be analyzed through Bayesian estimation; stochastic characteristic of nutrient loading can be investigated which provides the inputs for the decision making. PPI-FRC can address multiple uncertainties in the form of intervals with fuzzy random boundaries and the associated system risk through incorporating the concept of possibility and necessity measures. The possibility and necessity measures are suitable for optimistic and pessimistic decision making, respectively. BESMA is applied to a real case of effluent trading planning in the Xiangxihe watershed, China. A number of decision alternatives can be obtained under different trading ratios and treatment rates. The results can not only facilitate identification of optimal effluent-trading schemes, but also gain insight into the effects of trading ratio and treatment rate on decision making. The results also reveal that decision maker's preference towards risk would affect decision alternatives on trading scheme as well as system benefit. Compared with the conventional optimization methods, it is proved that BESMA is advantageous in (i) dealing with multiple uncertainties associated with randomness and fuzziness in effluent-trading planning within a multi-source, multi-reach and multi-period context; (ii) reflecting uncertainties existing in nutrient transport behaviors to improve the accuracy in water quality prediction; and (iii) supporting pessimistic and optimistic decision making for effluent trading as well as promoting diversity of decision alternatives. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Group Decision Framework with Intuitionistic Preference Relations and Its Application to Low Carbon Supplier Selection.

PubMed

Tong, Xiayu; Wang, Zhou-Jing

2016-09-19

This article develops a group decision framework with intuitionistic preference relations. An approach is first devised to rectify an inconsistent intuitionistic preference relation to derive an additive consistent one. A new aggregation operator, the so-called induced intuitionistic ordered weighted averaging (IIOWA) operator, is proposed to aggregate individual intuitionistic fuzzy judgments. By using the mean absolute deviation between the original and rectified intuitionistic preference relations as an order inducing variable, the rectified consistent intuitionistic preference relations are aggregated into a collective preference relation. This treatment is presumably able to assign different weights to different decision-makers' judgments based on the quality of their inputs (in terms of consistency of their original judgments). A solution procedure is then developed for tackling group decision problems with intuitionistic preference relations. A low carbon supplier selection case study is developed to illustrate how to apply the proposed decision model in practice.

A Group Decision Framework with Intuitionistic Preference Relations and Its Application to Low Carbon Supplier Selection

PubMed Central

Tong, Xiayu; Wang, Zhou-Jing

2016-01-01

This article develops a group decision framework with intuitionistic preference relations. An approach is first devised to rectify an inconsistent intuitionistic preference relation to derive an additive consistent one. A new aggregation operator, the so-called induced intuitionistic ordered weighted averaging (IIOWA) operator, is proposed to aggregate individual intuitionistic fuzzy judgments. By using the mean absolute deviation between the original and rectified intuitionistic preference relations as an order inducing variable, the rectified consistent intuitionistic preference relations are aggregated into a collective preference relation. This treatment is presumably able to assign different weights to different decision-makers’ judgments based on the quality of their inputs (in terms of consistency of their original judgments). A solution procedure is then developed for tackling group decision problems with intuitionistic preference relations. A low carbon supplier selection case study is developed to illustrate how to apply the proposed decision model in practice. PMID:27657097

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.

Robust CO2 Injection: Application of Bayesian-Information-Gap Decision Theory

NASA Astrophysics Data System (ADS)

Grasinger, M.; O'Malley, D.; Vesselinov, V. V.; Karra, S.

2015-12-01

Carbon capture and sequestration has the potential to reduce greenhouse gasemissions. However, care must be taken when choosing a site for CO2 seques-tration to ensure that the CO2 remains sequestered for many years, and thatthe environment is not harmed in any way. Making a rational decision be-tween potential sites for sequestration is not without its challenges because, asin the case of many environmental and subsurface problems, there is a lot ofuncertainty that exists. A method for making decisions under various typesand severities of uncertainty, Bayesian-Information-Gap Decision Theory (BIGDT), is presented. BIG DT was coupled with a numerical model for CO2 wellinjection and the resulting framework was then applied to a problem of selectingbetween two potential sites for CO2 sequestration. The results of the analysisare presented, followed by a discussion of the decision process.

Incorporating uncertainty in watershed management decision-making: A mercury TMDL case study

USGS Publications Warehouse

Labiosa, W.; Leckie, J.; Shachter, R.; Freyberg, D.; Rytuba, J.; ,

2005-01-01

Water quality impairment due to high mercury fish tissue concentrations and high mercury aqueous concentrations is a widespread problem in several sub-watersheds that are major sources of mercury to the San Francisco Bay. Several mercury Total Maximum Daily Load regulations are currently being developed to address this problem. Decisions about control strategies are being made despite very large uncertainties about current mercury loading behavior, relationships between total mercury loading and methyl mercury formation, and relationships between potential controls and mercury fish tissue levels. To deal with the issues of very large uncertainties, data limitations, knowledge gaps, and very limited State agency resources, this work proposes a decision analytical alternative for mercury TMDL decision support. The proposed probabilistic decision model is Bayesian in nature and is fully compatible with a "learning while doing" adaptive management approach. Strategy evaluation, sensitivity analysis, and information collection prioritization are examples of analyses that can be performed using this approach.

A System Dynamics Model for Integrated Decision Making ...

EPA Pesticide Factsheets

EPA’s Sustainable and Healthy Communities Research Program (SHC) is conducting transdisciplinary research to inform and empower decision-makers. EPA tools and approaches are being developed to enable communities to effectively weigh and integrate human health, socioeconomic, environmental, and ecological factors into their decisions to promote community sustainability. To help achieve this goal, EPA researchers have developed systems approaches to account for the linkages among resources, assets, and outcomes managed by a community. System dynamics (SD) is a member of the family of systems approaches and provides a framework for dynamic modeling that can assist with assessing and understanding complex issues across multiple dimensions. To test the utility of such tools when applied to a real-world situation, the EPA has developed a prototype SD model for community sustainability using the proposed Durham-Orange Light Rail Project (D-O LRP) as a case study.The EPA D-O LRP SD modeling team chose the proposed D-O LRP to demonstrate that an integrated modeling approach could represent the multitude of related cross-sectoral decisions that would be made and the cascading impacts that could result from a light rail transit system connecting Durham and Chapel Hill, NC. In keeping with the SHC vision described above, the proposal for the light rail is a starting point solution for the more intractable problems of population growth, unsustainable land use, environmenta

A Conceptual Modeling Approach for OLAP Personalization

NASA Astrophysics Data System (ADS)

Garrigós, Irene; Pardillo, Jesús; Mazón, Jose-Norberto; Trujillo, Juan

Data warehouses rely on multidimensional models in order to provide decision makers with appropriate structures to intuitively analyze data with OLAP technologies. However, data warehouses may be potentially large and multidimensional structures become increasingly complex to be understood at a glance. Even if a departmental data warehouse (also known as data mart) is used, these structures would be also too complex. As a consequence, acquiring the required information is more costly than expected and decision makers using OLAP tools may get frustrated. In this context, current approaches for data warehouse design are focused on deriving a unique OLAP schema for all analysts from their previously stated information requirements, which is not enough to lighten the complexity of the decision making process. To overcome this drawback, we argue for personalizing multidimensional models for OLAP technologies according to the continuously changing user characteristics, context, requirements and behaviour. In this paper, we present a novel approach to personalizing OLAP systems at the conceptual level based on the underlying multidimensional model of the data warehouse, a user model and a set of personalization rules. The great advantage of our approach is that a personalized OLAP schema is provided for each decision maker contributing to better satisfy their specific analysis needs. Finally, we show the applicability of our approach through a sample scenario based on our CASE tool for data warehouse development.

A Bayesian model averaging method for the derivation of reservoir operating rules

NASA Astrophysics Data System (ADS)

Zhang, Jingwen; Liu, Pan; Wang, Hao; Lei, Xiaohui; Zhou, Yanlai

2015-09-01

Because the intrinsic dynamics among optimal decision making, inflow processes and reservoir characteristics are complex, functional forms of reservoir operating rules are always determined subjectively. As a result, the uncertainty of selecting form and/or model involved in reservoir operating rules must be analyzed and evaluated. In this study, we analyze the uncertainty of reservoir operating rules using the Bayesian model averaging (BMA) model. Three popular operating rules, namely piecewise linear regression, surface fitting and a least-squares support vector machine, are established based on the optimal deterministic reservoir operation. These individual models provide three-member decisions for the BMA combination, enabling the 90% release interval to be estimated by the Markov Chain Monte Carlo simulation. A case study of China's the Baise reservoir shows that: (1) the optimal deterministic reservoir operation, superior to any reservoir operating rules, is used as the samples to derive the rules; (2) the least-squares support vector machine model is more effective than both piecewise linear regression and surface fitting; (3) BMA outperforms any individual model of operating rules based on the optimal trajectories. It is revealed that the proposed model can reduce the uncertainty of operating rules, which is of great potential benefit in evaluating the confidence interval of decisions.

Expert knowledge maps for knowledge management: a case study in Traditional Chinese Medicine research.

PubMed

Cui, Meng; Yang, Shuo; Yu, Tong; Yang, Ce; Gao, Yonghong; Zhu, Haiyan

2013-10-01

To design a model to capture information on the state and trends of knowledge creation, at both an individual and an organizational level, in order to enhance knowledge management. We designed a graph-theoretic knowledge model, the expert knowledge map (EKM), based on literature-based annotation. A case study in the domain of Traditional Chinese Medicine research was used to illustrate the usefulness of the model. The EKM successfully captured various aspects of knowledge and enhanced knowledge management within the case-study organization through the provision of knowledge graphs, expert graphs, and expert-knowledge biography. Our model could help to reveal the hot topics, trends, and products of the research done by an organization. It can potentially be used to facilitate knowledge learning, sharing and decision-making among researchers, academicians, students, and administrators of organizations.

Using structured decision making to manage disease risk for Montana wildlife

USGS Publications Warehouse

Mitchell, Michael S.; Gude, Justin A.; Anderson, Neil J.; Ramsey, Jennifer M.; Thompson, Michael J.; Sullivan, Mark G.; Edwards, Victoria L.; Gower, Claire N.; Cochrane, Jean Fitts; Irwin, Elise R.; Walshe, Terry

2013-01-01

We used structured decision-making to develop a 2-part framework to assist managers in the proactive management of disease outbreaks in Montana, USA. The first part of the framework is a model to estimate the probability of disease outbreak given field observations available to managers. The second part of the framework is decision analysis that evaluates likely outcomes of management alternatives based on the estimated probability of disease outbreak, and applies managers' values for different objectives to indicate a preferred management strategy. We used pneumonia in bighorn sheep (Ovis canadensis) as a case study for our approach, applying it to 2 populations in Montana that differed in their likelihood of a pneumonia outbreak. The framework provided credible predictions of both probability of disease outbreaks, as well as biological and monetary consequences of management actions. The structured decision-making approach to this problem was valuable for defining the challenges of disease management in a decentralized agency where decisions are generally made at the local level in cooperation with stakeholders. Our approach provides local managers with the ability to tailor management planning for disease outbreaks to local conditions. Further work is needed to refine our disease risk models and decision analysis, including robust prediction of disease outbreaks and improved assessment of management alternatives.

Determining the Best Treatment for Coronal Angular Deformity of the Knee Joint in Growing Children: A Decision Analysis

PubMed Central

Sung, Ki Hyuk; Chung, Chin Youb; Lee, Kyoung Min; Lee, Seung Yeol; Choi, In Ho; Cho, Tae-Joon; Yoo, Won Joon; Park, Moon Seok

2014-01-01

This study aimed to determine the best treatment modality for coronal angular deformity of the knee joint in growing children using decision analysis. A decision tree was created to evaluate 3 treatment modalities for coronal angular deformity in growing children: temporary hemiepiphysiodesis using staples, percutaneous screws, or a tension band plate. A decision analysis model was constructed containing the final outcome score, probability of metal failure, and incomplete correction of deformity. The final outcome was defined as health-related quality of life and was used as a utility in the decision tree. The probabilities associated with each case were obtained by literature review, and health-related quality of life was evaluated by a questionnaire completed by 25 pediatric orthopedic experts. Our decision analysis model favored temporary hemiepiphysiodesis using a tension band plate over temporary hemiepiphysiodesis using percutaneous screws or stapling, with utilities of 0.969, 0.957, and 0.962, respectively. One-way sensitivity analysis showed that hemiepiphysiodesis using a tension band plate was better than temporary hemiepiphysiodesis using percutaneous screws, when the overall complication rate of hemiepiphysiodesis using a tension band plate was lower than 15.7%. Two-way sensitivity analysis showed that hemiepiphysiodesis using a tension band plate was more beneficial than temporary hemiepiphysiodesis using percutaneous screws. PMID:25276801

Robust Distribution Network Reconfiguration

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

Lee, Changhyeok; Liu, Cong; Mehrotra, Sanjay

2015-03-01

We propose a two-stage robust optimization model for the distribution network reconfiguration problem with load uncertainty. The first-stage decision is to configure the radial distribution network and the second-stage decision is to find the optimal a/c power flow of the reconfigured network for given demand realization. We solve the two-stage robust model by using a column-and-constraint generation algorithm, where the master problem and subproblem are formulated as mixed-integer second-order cone programs. Computational results for 16, 33, 70, and 94-bus test cases are reported. We find that the configuration from the robust model does not compromise much the power loss undermore » the nominal load scenario compared to the configuration from the deterministic model, yet it provides the reliability of the distribution system for all scenarios in the uncertainty set.« less

Multi-objective game-theory models for conflict analysis in reservoir watershed management.

PubMed

Lee, Chih-Sheng

2012-05-01

This study focuses on the development of a multi-objective game-theory model (MOGM) for balancing economic and environmental concerns in reservoir watershed management and for assistance in decision. Game theory is used as an alternative tool for analyzing strategic interaction between economic development (land use and development) and environmental protection (water-quality protection and eutrophication control). Geographic information system is used to concisely illustrate and calculate the areas of various land use types. The MOGM methodology is illustrated in a case study of multi-objective watershed management in the Tseng-Wen reservoir, Taiwan. The innovation and advantages of MOGM can be seen in the results, which balance economic and environmental concerns in watershed management and which can be interpreted easily by decision makers. For comparison, the decision-making process using conventional multi-objective method to produce many alternatives was found to be more difficult. Copyright © 2012 Elsevier Ltd. All rights reserved.

Development of an integrated medical supply information system

NASA Astrophysics Data System (ADS)

Xu, Eric; Wermus, Marek; Blythe Bauman, Deborah

2011-08-01

The integrated medical supply inventory control system introduced in this study is a hybrid system that is shaped by the nature of medical supply, usage and storage capacity limitations of health care facilities. The system links demand, service provided at the clinic, health care service provider's information, inventory storage data and decision support tools into an integrated information system. ABC analysis method, economic order quantity model, two-bin method and safety stock concept are applied as decision support models to tackle inventory management issues at health care facilities. In the decision support module, each medical item and storage location has been scrutinised to determine the best-fit inventory control policy. The pilot case study demonstrates that the integrated medical supply information system holds several advantages for inventory managers, since it entails benefits of deploying enterprise information systems to manage medical supply and better patient services.

Extraction of decision rules via imprecise probabilities

NASA Astrophysics Data System (ADS)

Abellán, Joaquín; López, Griselda; Garach, Laura; Castellano, Javier G.

2017-05-01

Data analysis techniques can be applied to discover important relations among features. This is the main objective of the Information Root Node Variation (IRNV) technique, a new method to extract knowledge from data via decision trees. The decision trees used by the original method were built using classic split criteria. The performance of new split criteria based on imprecise probabilities and uncertainty measures, called credal split criteria, differs significantly from the performance obtained using the classic criteria. This paper extends the IRNV method using two credal split criteria: one based on a mathematical parametric model, and other one based on a non-parametric model. The performance of the method is analyzed using a case study of traffic accident data to identify patterns related to the severity of an accident. We found that a larger number of rules is generated, significantly supplementing the information obtained using the classic split criteria.

Making assessments while taking repeated risks: a pattern of multiple response pathways.

PubMed

Pleskac, Timothy J; Wershbale, Avishai

2014-02-01

Beyond simply a decision process, repeated risky decisions also require a number of cognitive processes including learning, search and exploration, and attention. In this article, we examine how multiple response pathways develop over repeated risky decisions. Using the Balloon Analogue Risk Task (BART) as a case study, we show that 2 different response pathways emerge over the course of the task. The assessment pathway is a slower, more controlled pathway where participants deliberate over taking a risk. The 2nd pathway is a faster, more automatic process where no deliberation occurs. Results imply the slower assessment pathway is taken as choice conflict increases and that the faster automatic response is a learned response. Based on these results, we modify an existing formal cognitive model of decision making during the BART to account for these dual response pathways. The slower more deliberative response process is modeled with a sequential sampling process where evidence is accumulated to a threshold, while the other response is given automatically. We show that adolescents with conduct disorder and substance use disorder symptoms not only evaluate risks differently during the BART but also differ in the rate at which they develop the more automatic response. More broadly, our results suggest cognitive models of judgment decision making need to transition from treating observed decisions as the result of a single response pathway to the result of multiple response pathways that change and develop over time.

Linking science, public values, and decision-making: Case study development for public deliberations about climate change resilience

NASA Astrophysics Data System (ADS)

Weller, N.; Bennett, I.; Bernstein, M.; Farooque, M.; Lloyd, J.; Lowenthal, C.; Sittenfeld, D.

2016-12-01

Actionable science seeks to align scientific inquiry with decision-making priorities to overcome rifts between scientific knowledge and the needs of decision makers. Combining actionable science with explorations of public values and priorities creates useful support for decision makers facing uncertainty, tradeoffs, and limited resources. As part of a broader project to create public forums about climate change resilience, we convened workshops with decision makers, resilience experts, and community stakeholders to discuss climate change resilience. Our goals were 1) to create case studies of resilience strategies for use in public deliberations at science museums across 8 U.S. cities; and 2) to build relationships with decision makers and stakeholders interested in these public deliberations. Prior to workshops, we created summaries of resilience strategies using academic literature, government assessments, municipal resilience plans, and conversations with workshop participants. Workshops began with example deliberation activities followed by semi-structured discussions of resilience strategies centered on 4 questions: 1) What are the key decisions to be made regarding each strategy? 2) What stakeholders and perspectives are relevant to each strategy? 3) What available data are relevant to each strategy? 4) What visualizations or other resources are useful for communicating things about each strategy? Workshops yielded actionable dialogue regarding issues of justice, feasibility, and the socio-ecological-technical systems impacted by climate change hazards and resilience strategies. For example, discussions of drought revealed systemic and individual-level challenges and opportunities; discussions of sea level rise included ways to account for the cultural significance of many coastal communities. The workshops provide a model for identifying decision-making priorities and tradeoffs and building partnerships among stakeholders, scientists, and decision makers.

Cost-effectiveness analysis of initial treatment strategies for mild-to-moderate Clostridium difficile infection in hospitalized patients.

PubMed

Ford, Diana C; Schroeder, Mary C; Ince, Dilek; Ernst, Erika J

2018-06-14

The cost-effectiveness of initial treatment strategies for mild-to-moderate Clostridium difficile infection (CDI) in hospitalized patients was evaluated. Decision-analytic models were constructed to compare initial treatment with metronidazole, vancomycin, and fidaxomicin. The primary model included 1 recurrence, and the secondary model included up to 3 recurrences. Model variables were extracted from published literature with costs based on a healthcare system perspective. The primary outcome was the incremental cost-effective ratio (ICER) between initial treatment strategies. In the primary model, the overall percentage of patients cured was 94.23%, 95.19%, and 96.53% with metronidazole, vancomycin, and fidaxomicin, respectively. Expected costs per case were $1,553.01, $1,306.62, and $5,095.70, respectively. In both models, vancomycin was more effective and less costly than metronidazole, resulting in negative ICERs. The ICERs for fidaxomicin compared with those for metronidazole and vancomycin in the primary model were $1,540.23 and $2,828.69 per 1% gain in cure, respectively. Using these models, a hospital currently treating initial episodes of mild-to-moderate CDI with metronidazole could expect to save $246.39-$388.37 per case treated by using vancomycin for initial therapy. A decision-analytic model revealed vancomycin to be cost-effective, compared with metronidazole, for treatment of initial episodes of mild-to-moderate CDI in adult inpatients. From the hospital perspective, initial treatment with vancomycin resulted in a higher probability of cure and a lower probability of colectomy, recurrence, persistent recurrence, and cost per case treated, compared with metronidazole. Use of fidaxomicin was associated with an increased probability of cure compared with metronidazole and vancomycin, but at a substantially increased cost. Copyright © 2018 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Model For Marketing Strategy Decision Based On Multicriteria Decicion Making: A Case Study In Batik Madura Industry

NASA Astrophysics Data System (ADS)

Anna, I. D.; Cahyadi, I.; Yakin, A.

2018-01-01

Selection of marketing strategy is a prominent competitive advantage for small and medium enterprises business development. The selection process is is a multiple criteria decision-making problem, which includes evaluation of various attributes or criteria in a process of strategy formulation. The objective of this paper is to develop a model for the selection of a marketing strategy in Batik Madura industry. The current study proposes an integrated approach based on analytic network process (ANP) and technique for order preference by similarity to ideal solution (TOPSIS) to determine the best strategy for Batik Madura marketing problems. Based on the results of group decision-making technique, this study selected fourteen criteria, including consistency, cost, trend following, customer loyalty, business volume, uniqueness manpower, customer numbers, promotion, branding, bussiness network, outlet location, credibility and the inovation as Batik Madura marketing strategy evaluation criteria. A survey questionnaire developed from literature review was distributed to a sample frame of Batik Madura SMEs in Pamekasan. In the decision procedure step, expert evaluators were asked to establish the decision matrix by comparing the marketing strategy alternatives under each of the individual criteria. Then, considerations obtained from ANP and TOPSIS methods were applied to build the specific criteria constraints and range of the launch strategy in the model. The model in this study demonstrates that, under current business situation, Straight-focus marketing strategy is the best marketing strategy for Batik Madura SMEs in Pamekasan.

Modeling a Nursing Guideline with Standard Terminology and Unified Modeling Language for a Nursing Decision Support System: A Case Study.

PubMed

Choi, Jeeyae; Jansen, Kay; Coenen, Amy

In recent years, Decision Support Systems (DSSs) have been developed and used to achieve "meaningful use". One approach to developing DSSs is to translate clinical guidelines into a computer-interpretable format. However, there is no specific guideline modeling approach to translate nursing guidelines to computer-interpretable guidelines. This results in limited use of DSSs in nursing. Unified modeling language (UML) is a software writing language known to accurately represent the end-users' perspective, due to its expressive characteristics. Furthermore, standard terminology enabled DSSs have been shown to smoothly integrate into existing health information systems. In order to facilitate development of nursing DSSs, the UML was used to represent a guideline for medication management for older adults encode with the International Classification for Nursing Practice (ICNP®). The UML was found to be a useful and sufficient tool to model a nursing guideline for a DSS.

Modeling a Nursing Guideline with Standard Terminology and Unified Modeling Language for a Nursing Decision Support System: A Case Study

PubMed Central

Choi, Jeeyae; Jansen, Kay; Coenen, Amy

2015-01-01

In recent years, Decision Support Systems (DSSs) have been developed and used to achieve “meaningful use”. One approach to developing DSSs is to translate clinical guidelines into a computer-interpretable format. However, there is no specific guideline modeling approach to translate nursing guidelines to computer-interpretable guidelines. This results in limited use of DSSs in nursing. Unified modeling language (UML) is a software writing language known to accurately represent the end-users’ perspective, due to its expressive characteristics. Furthermore, standard terminology enabled DSSs have been shown to smoothly integrate into existing health information systems. In order to facilitate development of nursing DSSs, the UML was used to represent a guideline for medication management for older adults encode with the International Classification for Nursing Practice (ICNP®). The UML was found to be a useful and sufficient tool to model a nursing guideline for a DSS. PMID:26958174

Thinking Together: Modeling Clinical Decision-Support as a Sociotechnical System

PubMed Central

Hussain, Mustafa I.; Reynolds, Tera L.; Mousavi, Fatemeh E.; Chen, Yunan; Zheng, Kai

2017-01-01

Computerized clinical decision-support systems are members of larger sociotechnical systems, composed of human and automated actors, who send, receive, and manipulate artifacts. Sociotechnical consideration is rare in the literature. This makes it difficult to comparatively evaluate the success of CDS implementations, and it may also indicate that sociotechnical context receives inadequate consideration in practice. To facilitate sociotechnical consideration, we developed the Thinking Together model, a flexible diagrammatical means of representing CDS systems as sociotechnical systems. To develop this model, we examined the literature with the lens of Distributed Cognition (DCog) theory. We then present two case studies of vastly different CDSSs, one almost fully automated and the other with minimal automation, to illustrate the flexibility of the Thinking Together model. We show that this model, informed by DCog and the CDS literature, are capable of supporting both research, by enabling comparative evaluation, and practice, by facilitating explicit sociotechnical planning and communication. PMID:29854164

Lessons learned in detailed clinical modeling at Intermountain Healthcare

PubMed Central

Oniki, Thomas A; Coyle, Joseph F; Parker, Craig G; Huff, Stanley M

2014-01-01

Background and objective Intermountain Healthcare has a long history of using coded terminology and detailed clinical models (DCMs) to govern storage of clinical data to facilitate decision support and semantic interoperability. The latest iteration of DCMs at Intermountain is called the clinical element model (CEM). We describe the lessons learned from our CEM efforts with regard to subjective decisions a modeler frequently needs to make in creating a CEM. We present insights and guidelines, but also describe situations in which use cases conflict with the guidelines. We propose strategies that can help reconcile the conflicts. The hope is that these lessons will be helpful to others who are developing and maintaining DCMs in order to promote sharing and interoperability. Methods We have used the Clinical Element Modeling Language (CEML) to author approximately 5000 CEMs. Results Based on our experience, we have formulated guidelines to lead our modelers through the subjective decisions they need to make when authoring models. Reported here are guidelines regarding precoordination/postcoordination, dividing content between the model and the terminology, modeling logical attributes, and creating iso-semantic models. We place our lessons in context, exploring the potential benefits of an implementation layer, an iso-semantic modeling framework, and ontologic technologies. Conclusions We assert that detailed clinical models can advance interoperability and sharing, and that our guidelines, an implementation layer, and an iso-semantic framework will support our progress toward that goal. PMID:24993546

Gender Difference or Indifference? Detective Decision Making in Sexual Assault Cases

ERIC Educational Resources Information Center

Alderden, Megan A.; Ullman, Sarah E.

2012-01-01

Prior research examining sexual assault case decision making has failed to account for the demographic characteristics of the criminal justice practitioners charged with making case decisions. Inclusion of such information is important because it provides researchers with a greater understanding of how criminal justice practitioners' own gender,…

Smart Grid as Multi-layer Interacting System for Complex Decision Makings

NASA Astrophysics Data System (ADS)

Bompard, Ettore; Han, Bei; Masera, Marcelo; Pons, Enrico

This chapter presents an approach to the analysis of Smart Grids based on a multi-layer representation of their technical, cyber, social and decision-making aspects, as well as the related environmental constraints. In the Smart Grid paradigm, self-interested active customers (prosumers), system operators and market players interact among themselves making use of an extensive cyber infrastructure. In addition, policy decision makers define regulations, incentives and constraints to drive the behavior of the competing operators and prosumers, with the objective of ensuring the global desired performance (e.g. system stability, fair prices). For these reasons, the policy decision making is more complicated than in traditional power systems, and needs proper modeling and simulation tools for assessing "in vitro" and ex-ante the possible impacts of the decisions assumed. In this chapter, we consider the smart grids as multi-layered interacting complex systems. The intricacy of the framework, characterized by several interacting layers, cannot be captured by closed-form mathematical models. Therefore, a new approach using Multi Agent Simulation is described. With case studies we provide some indications about how to develop agent-based simulation tools presenting some preliminary examples.

Uncertainty in sample estimates and the implicit loss function for soil information.

NASA Astrophysics Data System (ADS)

Lark, Murray

2015-04-01

One significant challenge in the communication of uncertain information is how to enable the sponsors of sampling exercises to make a rational choice of sample size. One way to do this is to compute the value of additional information given the loss function for errors. The loss function expresses the costs that result from decisions made using erroneous information. In certain circumstances, such as remediation of contaminated land prior to development, loss functions can be computed and used to guide rational decision making on the amount of resource to spend on sampling to collect soil information. In many circumstances the loss function cannot be obtained prior to decision making. This may be the case when multiple decisions may be based on the soil information and the costs of errors are hard to predict. The implicit loss function is proposed as a tool to aid decision making in these circumstances. Conditional on a logistical model which expresses costs of soil sampling as a function of effort, and statistical information from which the error of estimates can be modelled as a function of effort, the implicit loss function is the loss function which makes a particular decision on effort rational. In this presentation the loss function is defined and computed for a number of arbitrary decisions on sampling effort for a hypothetical soil monitoring problem. This is based on a logistical model of sampling cost parameterized from a recent geochemical survey of soil in Donegal, Ireland and on statistical parameters estimated with the aid of a process model for change in soil organic carbon. It is shown how the implicit loss function might provide a basis for reflection on a particular choice of sample size by comparing it with the values attributed to soil properties and functions. Scope for further research to develop and apply the implicit loss function to help decision making by policy makers and regulators is then discussed.

A Practical Application of Value of Information and Prospective Payback of Research to Prioritize Evaluative Research.

PubMed

Andronis, Lazaros; Billingham, Lucinda J; Bryan, Stirling; James, Nicholas D; Barton, Pelham M

2016-04-01

Efforts to ensure that funded research represents "value for money" have led to increasing calls for the use of analytic methods in research prioritization. A number of analytic approaches have been proposed to assist research funding decisions, the most prominent of which are value of information (VOI) and prospective payback of research (PPoR). Despite the increasing interest in the topic, there are insufficient VOI and PPoR applications on the same case study to contrast their methods and compare their outcomes. We undertook VOI and PPoR analyses to determine the value of conducting 2 proposed research programs. The application served as a vehicle for identifying differences and similarities between the methods, provided insight into the assumptions and practical requirements of undertaking prospective analyses for research prioritization, and highlighted areas for future research. VOI and PPoR were applied to case studies representing proposals for clinical trials in advanced non-small-cell lung cancer and prostate cancer. Decision models were built to synthesize the evidence available prior to the funding decision. VOI (expected value of perfect and sample information) and PPoR (PATHS model) analyses were undertaken using the developed models. VOI and PPoR results agreed in direction, suggesting that the proposed trials would be cost-effective investments. However, results differed in magnitude, largely due to the way each method conceptualizes the possible outcomes of further research and the implementation of research results in practice. Compared with VOI, PPoR is less complex but requires more assumptions. Although the approaches are not free from limitations, they can provide useful input for research funding decisions. © The Author(s) 2015.

Toward better public health reporting using existing off the shelf approaches: A comparison of alternative cancer detection approaches using plaintext medical data and non-dictionary based feature selection.

PubMed

Kasthurirathne, Suranga N; Dixon, Brian E; Gichoya, Judy; Xu, Huiping; Xia, Yuni; Mamlin, Burke; Grannis, Shaun J

2016-04-01

Increased adoption of electronic health records has resulted in increased availability of free text clinical data for secondary use. A variety of approaches to obtain actionable information from unstructured free text data exist. These approaches are resource intensive, inherently complex and rely on structured clinical data and dictionary-based approaches. We sought to evaluate the potential to obtain actionable information from free text pathology reports using routinely available tools and approaches that do not depend on dictionary-based approaches. We obtained pathology reports from a large health information exchange and evaluated the capacity to detect cancer cases from these reports using 3 non-dictionary feature selection approaches, 4 feature subset sizes, and 5 clinical decision models: simple logistic regression, naïve bayes, k-nearest neighbor, random forest, and J48 decision tree. The performance of each decision model was evaluated using sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. Decision models parameterized using automated, informed, and manual feature selection approaches yielded similar results. Furthermore, non-dictionary classification approaches identified cancer cases present in free text reports with evaluation measures approaching and exceeding 80-90% for most metrics. Our methods are feasible and practical approaches for extracting substantial information value from free text medical data, and the results suggest that these methods can perform on par, if not better, than existing dictionary-based approaches. Given that public health agencies are often under-resourced and lack the technical capacity for more complex methodologies, these results represent potentially significant value to the public health field. Copyright © 2016 Elsevier Inc. All rights reserved.

A study on building data warehouse of hospital information system.

PubMed

Li, Ping; Wu, Tao; Chen, Mu; Zhou, Bin; Xu, Wei-guo

2011-08-01

Existing hospital information systems with simple statistical functions cannot meet current management needs. It is well known that hospital resources are distributed with private property rights among hospitals, such as in the case of the regional coordination of medical services. In this study, to integrate and make full use of medical data effectively, we propose a data warehouse modeling method for the hospital information system. The method can also be employed for a distributed-hospital medical service system. To ensure that hospital information supports the diverse needs of health care, the framework of the hospital information system has three layers: datacenter layer, system-function layer, and user-interface layer. This paper discusses the role of a data warehouse management system in handling hospital information from the establishment of the data theme to the design of a data model to the establishment of a data warehouse. Online analytical processing tools assist user-friendly multidimensional analysis from a number of different angles to extract the required data and information. Use of the data warehouse improves online analytical processing and mitigates deficiencies in the decision support system. The hospital information system based on a data warehouse effectively employs statistical analysis and data mining technology to handle massive quantities of historical data, and summarizes from clinical and hospital information for decision making. This paper proposes the use of a data warehouse for a hospital information system, specifically a data warehouse for the theme of hospital information to determine latitude, modeling and so on. The processing of patient information is given as an example that demonstrates the usefulness of this method in the case of hospital information management. Data warehouse technology is an evolving technology, and more and more decision support information extracted by data mining and with decision-making technology is required for further research.

Estimation of Survival Probabilities for Use in Cost-effectiveness Analyses: A Comparison of a Multi-state Modeling Survival Analysis Approach with Partitioned Survival and Markov Decision-Analytic Modeling

PubMed Central

Williams, Claire; Lewsey, James D.; Mackay, Daniel F.; Briggs, Andrew H.

2016-01-01

Modeling of clinical-effectiveness in a cost-effectiveness analysis typically involves some form of partitioned survival or Markov decision-analytic modeling. The health states progression-free, progression and death and the transitions between them are frequently of interest. With partitioned survival, progression is not modeled directly as a state; instead, time in that state is derived from the difference in area between the overall survival and the progression-free survival curves. With Markov decision-analytic modeling, a priori assumptions are often made with regard to the transitions rather than using the individual patient data directly to model them. This article compares a multi-state modeling survival regression approach to these two common methods. As a case study, we use a trial comparing rituximab in combination with fludarabine and cyclophosphamide v. fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia. We calculated mean Life Years and QALYs that involved extrapolation of survival outcomes in the trial. We adapted an existing multi-state modeling approach to incorporate parametric distributions for transition hazards, to allow extrapolation. The comparison showed that, due to the different assumptions used in the different approaches, a discrepancy in results was evident. The partitioned survival and Markov decision-analytic modeling deemed the treatment cost-effective with ICERs of just over £16,000 and £13,000, respectively. However, the results with the multi-state modeling were less conclusive, with an ICER of just over £29,000. This work has illustrated that it is imperative to check whether assumptions are realistic, as different model choices can influence clinical and cost-effectiveness results. PMID:27698003

Estimation of Survival Probabilities for Use in Cost-effectiveness Analyses: A Comparison of a Multi-state Modeling Survival Analysis Approach with Partitioned Survival and Markov Decision-Analytic Modeling.

PubMed

Williams, Claire; Lewsey, James D; Mackay, Daniel F; Briggs, Andrew H

2017-05-01

Modeling of clinical-effectiveness in a cost-effectiveness analysis typically involves some form of partitioned survival or Markov decision-analytic modeling. The health states progression-free, progression and death and the transitions between them are frequently of interest. With partitioned survival, progression is not modeled directly as a state; instead, time in that state is derived from the difference in area between the overall survival and the progression-free survival curves. With Markov decision-analytic modeling, a priori assumptions are often made with regard to the transitions rather than using the individual patient data directly to model them. This article compares a multi-state modeling survival regression approach to these two common methods. As a case study, we use a trial comparing rituximab in combination with fludarabine and cyclophosphamide v. fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia. We calculated mean Life Years and QALYs that involved extrapolation of survival outcomes in the trial. We adapted an existing multi-state modeling approach to incorporate parametric distributions for transition hazards, to allow extrapolation. The comparison showed that, due to the different assumptions used in the different approaches, a discrepancy in results was evident. The partitioned survival and Markov decision-analytic modeling deemed the treatment cost-effective with ICERs of just over £16,000 and £13,000, respectively. However, the results with the multi-state modeling were less conclusive, with an ICER of just over £29,000. This work has illustrated that it is imperative to check whether assumptions are realistic, as different model choices can influence clinical and cost-effectiveness results.

Implementing health information exchange for public health reporting: a comparison of decision and risk management of three regional health information organizations in New York state

PubMed Central

Phillips, Andrew B; Wilson, Rosalind V; Kaushal, Rainu; Merrill, Jacqueline A

2014-01-01

Health information exchange (HIE) is a significant component of healthcare transformation strategies at both the state and national levels. HIE is expected to improve care coordination, and advance public health, but implementation is massively complex and involves significant risk. In New York, three regional health information organizations (RHIOs) implemented an HIE use case for public health reporting by demonstrating capability to deliver accurate responses to electronic queries via a set of services called the Universal Public Health Node. We investigated process and outcomes of the implementation with a comparative case study. Qualitative analysis was structured around a decision and risk matrix. Although each RHIO had a unique operational model, two common factors influenced risk management and implementation success: leadership capable of agile decision-making and commitment to a strong organizational vision. While all three RHIOs achieved certification for the public health reporting, only one has elected to deploy a production version. PMID:23975626

Implementing health information exchange for public health reporting: a comparison of decision and risk management of three regional health information organizations in New York state.

PubMed

Phillips, Andrew B; Wilson, Rosalind V; Kaushal, Rainu; Merrill, Jacqueline A

2014-02-01

Health information exchange (HIE) is a significant component of healthcare transformation strategies at both the state and national levels. HIE is expected to improve care coordination, and advance public health, but implementation is massively complex and involves significant risk. In New York, three regional health information organizations (RHIOs) implemented an HIE use case for public health reporting by demonstrating capability to deliver accurate responses to electronic queries via a set of services called the Universal Public Health Node. We investigated process and outcomes of the implementation with a comparative case study. Qualitative analysis was structured around a decision and risk matrix. Although each RHIO had a unique operational model, two common factors influenced risk management and implementation success: leadership capable of agile decision-making and commitment to a strong organizational vision. While all three RHIOs achieved certification for the public health reporting, only one has elected to deploy a production version.

Bayesian-information-gap decision theory with an application to CO 2 sequestration

DOE PAGES

O'Malley, D.; Vesselinov, V. V.

2015-09-04

Decisions related to subsurface engineering problems such as groundwater management, fossil fuel production, and geologic carbon sequestration are frequently challenging because of an overabundance of uncertainties (related to conceptualizations, parameters, observations, etc.). Because of the importance of these problems to agriculture, energy, and the climate (respectively), good decisions that are scientifically defensible must be made despite the uncertainties. We describe a general approach to making decisions for challenging problems such as these in the presence of severe uncertainties that combines probabilistic and non-probabilistic methods. The approach uses Bayesian sampling to assess parametric uncertainty and Information-Gap Decision Theory (IGDT) to addressmore » model inadequacy. The combined approach also resolves an issue that frequently arises when applying Bayesian methods to real-world engineering problems related to the enumeration of possible outcomes. In the case of zero non-probabilistic uncertainty, the method reduces to a Bayesian method. Lastly, to illustrate the approach, we apply it to a site-selection decision for geologic CO 2 sequestration.« less

Canadian mock juror attitudes and decisions in domestic violence cases involving asian and white interracial and intraracial couples.

PubMed

Maeder, Evelyn M; Mossière, Annik; Cheung, Liann

2013-03-01

This study manipulated the race of the defendant and the victim (White/White, White/Asian, Asian/Asian, and Asian/White) in a domestic violence case to examine the potential prejudicial impact of race on juror decision making. A total of 181 undergraduate students read a trial transcript involving an allegation of spousal abuse in which defendant and victim race were manipulated using photographs. They then provided a verdict and confidence rating, a sentence, and responsibility attributions, and completed various scales measuring attitudes toward wife abuse and women. Findings revealed that female jurors were harsher toward the defendant than were male jurors. When controlling for attitudes toward Asians, jurors found the defendant guilty more often in cases involving interracial couples, as compared to same-race couples. Path analyses revealed various factors and attitudes involved in domestic violence trial outcomes. Findings contribute to the scarce literature on legal proceedings involving Asians, particularly in domestic violence cases. Outcomes also provide a model for relevant factors and characteristics of jurors in domestic violence cases. Roadblocks inherent in jury research are also discussed.

Placement decisions and disparities among aboriginal groups: an application of the decision making ecology through multi-level analysis.

PubMed

Fluke, John D; Chabot, Martin; Fallon, Barbara; MacLaurin, Bruce; Blackstock, Cindy

2010-01-01

This paper examined the relative influence of clinical and organizational characteristics on the decision to place a child in out-of-home care at the conclusion of a child maltreatment investigation. It tested the hypothesis that extraneous factors, specifically, organizational characteristics, impact the decision to place a child in out-of-home care. A secondary aim was to identify possible decision making influences related to disparities in placement decisions tied to Aboriginal children. Research suggests that the Aboriginal status of the child and structural risk factors affecting the family, such as poverty and poor housing, substantially account for this overrepresentation. The decision to place a child in out-of-home care was examined using data from the Canadian Incidence Study of Reported Child Abuse and Neglect. This child welfare dataset collected information about the results of nearly 5,000 child maltreatment investigations as well as a description of the characteristics of the workers and organization responsible for conducting those investigations. Multi-level statistical models were developed using MPlus software, which can accommodate dichotomous outcome variables, which are more reflective of decision making in child welfare. MPlus allows the specific case of the logistic link function for binary outcome variables under maximum likelihood estimation. Final models revealed the importance of the number of Aboriginal reports to an organization as a key second level predictor of the placement decision. It is the only second level factor that remains in the final model. This finding was very stable when tested over several different levels of proportionate caseload representation ranging from greater than 50% to 20% of the caseload. Disparities among Aboriginal children in child welfare decision making were identified at the agency level. The study provides additional evidence supporting the possibility that one source of overrepresentation of Aboriginal children in the Canadian foster care system is a lack of appropriate resources at the agency or community level. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

Resilience thinking and a decision-analytic approach to conservation: strange bedfellows or essential partners?

USGS Publications Warehouse

Johnson, Fred A.; Williams, Byron K.; Nichols, James D.

2013-01-01

There has been some tendency to view decision science and resilience theory as opposing approaches, or at least as contending perspectives, for natural resource management. Resilience proponents have been especially critical of optimization in decision science, at least for those cases where it is focused on the aggressive pursuit of efficiency. In general, optimization of resource systems is held to reduce spatial, temporal, or organizational heterogeneity that would otherwise limit efficiency, leading to homogenization of a system and making it less able to cope with unexpected changes or disturbances. For their part, decision analysts have been critical of resilience proponents for not providing much practical advice to decision makers. We believe a key source of tension between resilience thinking and application of decision science is the pursuit of efficiency in the latter (i.e., choosing the “best” management action or strategy option to maximize productivity of one or few resource components), vs. a desire in the former to keep options open (i.e., maintaining and enhancing diversity). It seems obvious, however, that with managed natural systems, there must be a principle by which to guide decision making, which at a minimumallows for a comparison of projected outcomes associated with decision alternatives. This is true even if the primary concern of decision making is the preservation of system resilience. We describe how a careful framing of conservation problems, especially in terms of management objectives and predictive models, can help reduce the purported tension between resiliencethinking and decision analysis. In particular, objective setting in conservation problems needs to be more attuned to the dynamics of ecological systems and to the possibility of deep uncertainties that underlie the risk of unintended, if not irreversible, outcomes. Resilience thinking also leads to the suggestion that model development should focus more on process rather than pattern, on multiple scales of influence, and on phenomena that can create alternative stability regimes. Although we acknowledge the inherent difficulties in modeling ecological processes, we stress that formulation of useful models need not depend on a thorough mechanistic understanding or precise parameterization, assuming that uncertainty is acknowledged and treated in a systematic manner.

Theoretical aspects and modelling of cellular decision making, cell killing and information-processing in photodynamic therapy of cancer.

PubMed

Gkigkitzis, Ioannis

2013-01-01

The aim of this report is to provide a mathematical model of the mechanism for making binary fate decisions about cell death or survival, during and after Photodynamic Therapy (PDT) treatment, and to supply the logical design for this decision mechanism as an application of rate distortion theory to the biochemical processing of information by the physical system of a cell. Based on system biology models of the molecular interactions involved in the PDT processes previously established, and regarding a cellular decision-making system as a noisy communication channel, we use rate distortion theory to design a time dependent Blahut-Arimoto algorithm where the input is a stimulus vector composed of the time dependent concentrations of three PDT related cell death signaling molecules and the output is a cell fate decision. The molecular concentrations are determined by a group of rate equations. The basic steps are: initialize the probability of the cell fate decision, compute the conditional probability distribution that minimizes the mutual information between input and output, compute the cell probability of cell fate decision that minimizes the mutual information and repeat the last two steps until the probabilities converge. Advance to the next discrete time point and repeat the process. Based on the model from communication theory described in this work, and assuming that the activation of the death signal processing occurs when any of the molecular stimulants increases higher than a predefined threshold (50% of the maximum concentrations), for 1800s of treatment, the cell undergoes necrosis within the first 30 minutes with probability range 90.0%-99.99% and in the case of repair/survival, it goes through apoptosis within 3-4 hours with probability range 90.00%-99.00%. Although, there is no experimental validation of the model at this moment, it reproduces some patterns of survival ratios of predicted experimental data. Analytical modeling based on cell death signaling molecules has been shown to be an independent and useful tool for prediction of cell surviving response to PDT. The model can be adjusted to provide important insights for cellular response to other treatments such as hyperthermia, and diseases such as neurodegeneration.

Inductive reasoning 2.0.

PubMed

Hayes, Brett K; Heit, Evan

2018-05-01

Inductive reasoning entails using existing knowledge to make predictions about novel cases. The first part of this review summarizes key inductive phenomena and critically evaluates theories of induction. We highlight recent theoretical advances, with a special emphasis on the structured statistical approach, the importance of sampling assumptions in Bayesian models, and connectionist modeling. A number of new research directions in this field are identified including comparisons of inductive and deductive reasoning, the identification of common core processes in induction and memory tasks and induction involving category uncertainty. The implications of induction research for areas as diverse as complex decision-making and fear generalization are discussed. This article is categorized under: Psychology > Reasoning and Decision Making Psychology > Learning. © 2017 Wiley Periodicals, Inc.

Development of a model-based flood emergency management system in Yujiang River Basin, South China

NASA Astrophysics Data System (ADS)

Zeng, Yong; Cai, Yanpeng; Jia, Peng; Mao, Jiansu

2014-06-01

Flooding is the most frequent disaster in China. It affects people's lives and properties, causing considerable economic loss. Flood forecast and operation of reservoirs are important in flood emergency management. Although great progress has been achieved in flood forecast and reservoir operation through using computer, network technology, and geographic information system technology in China, the prediction accuracy of models are not satisfactory due to the unavailability of real-time monitoring data. Also, real-time flood control scenario analysis is not effective in many regions and can seldom provide online decision support function. In this research, a decision support system for real-time flood forecasting in Yujiang River Basin, South China (DSS-YRB) is introduced in this paper. This system is based on hydrological and hydraulic mathematical models. The conceptual framework and detailed components of the proposed DSS-YRB is illustrated, which employs real-time rainfall data conversion, model-driven hydrologic forecasting, model calibration, data assimilation methods, and reservoir operational scenario analysis. Multi-tiered architecture offers great flexibility, portability, reusability, and reliability. The applied case study results show the development and application of a decision support system for real-time flood forecasting and operation is beneficial for flood control.

Agent-based modeling of deforestation in southern Yucatán, Mexico, and reforestation in the Midwest United States

PubMed Central

Manson, Steven M.; Evans, Tom

2007-01-01

We combine mixed-methods research with integrated agent-based modeling to understand land change and economic decision making in the United States and Mexico. This work demonstrates how sustainability science benefits from combining integrated agent-based modeling (which blends methods from the social, ecological, and information sciences) and mixed-methods research (which interleaves multiple approaches ranging from qualitative field research to quantitative laboratory experiments and interpretation of remotely sensed imagery). We test assumptions of utility-maximizing behavior in household-level landscape management in south-central Indiana, linking parcel data, land cover derived from aerial photography, and findings from laboratory experiments. We examine the role of uncertainty and limited information, preferences, differential demographic attributes, and past experience and future time horizons. We also use evolutionary programming to represent bounded rationality in agriculturalist households in the southern Yucatán of Mexico. This approach captures realistic rule of thumb strategies while identifying social and environmental factors in a manner similar to econometric models. These case studies highlight the role of computational models of decision making in land-change contexts and advance our understanding of decision making in general. PMID:18093928

The solution of target assignment problem in command and control decision-making behaviour simulation

NASA Astrophysics Data System (ADS)

Li, Ni; Huai, Wenqing; Wang, Shaodan

2017-08-01

C2 (command and control) has been understood to be a critical military component to meet an increasing demand for rapid information gathering and real-time decision-making in a dynamically changing battlefield environment. In this article, to improve a C2 behaviour model's reusability and interoperability, a behaviour modelling framework was proposed to specify a C2 model's internal modules and a set of interoperability interfaces based on the C-BML (coalition battle management language). WTA (weapon target assignment) is a typical C2 autonomous decision-making behaviour modelling problem. Different from most WTA problem descriptions, here sensors were considered to be available resources of detection and the relationship constraints between weapons and sensors were also taken into account, which brought it much closer to actual application. A modified differential evolution (MDE) algorithm was developed to solve this high-dimension optimisation problem and obtained an optimal assignment plan with high efficiency. In case study, we built a simulation system to validate the proposed C2 modelling framework and interoperability interface specification. Also, a new optimisation solution was used to solve the WTA problem efficiently and successfully.

Development of a decision model for the techno-economic assessment of municipal solid waste utilization pathways.

PubMed

Khan, Md Mohib-Ul-Haque; Jain, Siddharth; Vaezi, Mahdi; Kumar, Amit

2016-02-01

Economic competitiveness is one of the key factors in making decisions towards the development of waste conversion facilities and devising a sustainable waste management strategy. The goal of this study is to develop a framework, as well as to develop and demonstrate a comprehensive techno-economic model to help county and municipal decision makers in establishing waste conversion facilities. The user-friendly data-intensive model, called the FUNdamental ENgineering PrinciplEs-based ModeL for Estimation of Cost of Energy and Fuels from MSW (FUNNEL-Cost-MSW), compares nine different waste management scenarios, including landfilling and composting, in terms of economic parameters such as gate fees and return on investment. In addition, a geographic information system (GIS) model was developed to determine suitable locations for waste conversion facilities and landfill sites based on integration of environmental, social, and economic factors. Finally, a case study on Parkland County and its surrounding counties in the province of Alberta, Canada, was conducted and a sensitivity analysis was performed to assess the influence of the key technical and economic parameters on the calculated results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Assessing clinical reasoning (ASCLIRE): Instrument development and validation.

PubMed

Kunina-Habenicht, Olga; Hautz, Wolf E; Knigge, Michel; Spies, Claudia; Ahlers, Olaf

2015-12-01

Clinical reasoning is an essential competency in medical education. This study aimed at developing and validating a test to assess diagnostic accuracy, collected information, and diagnostic decision time in clinical reasoning. A norm-referenced computer-based test for the assessment of clinical reasoning (ASCLIRE) was developed, integrating the entire clinical decision process. In a cross-sectional study participants were asked to choose as many diagnostic measures as they deemed necessary to diagnose the underlying disease of six different cases with acute or sub-acute dyspnea and provide a diagnosis. 283 students and 20 content experts participated. In addition to diagnostic accuracy, respective decision time and number of used relevant diagnostic measures were documented as distinct performance indicators. The empirical structure of the test was investigated using a structural equation modeling approach. Experts showed higher accuracy rates and lower decision times than students. In a cross-sectional comparison, the diagnostic accuracy of students improved with the year of study. Wrong diagnoses provided by our sample were comparable to wrong diagnoses in practice. We found an excellent fit for a model with three latent factors-diagnostic accuracy, decision time, and choice of relevant diagnostic information-with diagnostic accuracy showing no significant correlation with decision time. ASCLIRE considers decision time as an important performance indicator beneath diagnostic accuracy and provides evidence that clinical reasoning is a complex ability comprising diagnostic accuracy, decision time, and choice of relevant diagnostic information as three partly correlated but still distinct aspects.

Multicenter external validation of two malignancy risk prediction models in patients undergoing 18F-FDG-PET for solitary pulmonary nodule evaluation.

PubMed

Perandini, Simone; Soardi, G A; Larici, A R; Del Ciello, A; Rizzardi, G; Solazzo, A; Mancino, L; Zeraj, F; Bernhart, M; Signorini, M; Motton, M; Montemezzi, S

2017-05-01

To achieve multicentre external validation of the Herder and Bayesian Inference Malignancy Calculator (BIMC) models. Two hundred and fifty-nine solitary pulmonary nodules (SPNs) collected from four major hospitals which underwent 18-FDG-PET characterization were included in this multicentre retrospective study. The Herder model was tested on all available lesions (group A). A subgroup of 180 SPNs (group B) was used to provide unbiased comparison between the Herder and BIMC models. Receiver operating characteristic (ROC) area under the curve (AUC) analysis was performed to assess diagnostic accuracy. Decision analysis was performed by adopting the risk threshold stated in British Thoracic Society (BTS) guidelines. Unbiased comparison performed In Group B showed a ROC AUC for the Herder model of 0.807 (95 % CI 0.742-0.862) and for the BIMC model of 0.822 (95 % CI 0.758-0.875). Both the Herder and the BIMC models were proven to accurately predict the risk of malignancy when tested on a large multicentre external case series. The BIMC model seems advantageous on the basis of a more favourable decision analysis. • The Herder model showed a ROC AUC of 0.807 on 180 SPNs. • The BIMC model showed a ROC AUC of 0.822 on 180 SPNs. • Decision analysis is more favourable to the BIMC model.

Corporate Information Management: A Case Study

DTIC Science & Technology

1991-03-01

SUBJECT TERMS ( FIELD GROUP SUB-GROUP ICorporate Information Management (CIM), Case study, Strategic level decision making, Department Of Defense. 19...ABSTRACT ( This thesis documents in a case format the events, environment and decisions in the genesis and evolution of the Department of Defense’s...case format the events, environment and decisions in the genesis and evolution of the Department of Defense’s Corporate Information Management

Beyond access: a case study on the intersection between accessibility, sustainability, and universal design.

PubMed

Gossett, Andrea; Mirza, Mansha; Barnds, Ann Kathleen; Feidt, Daisy

2009-11-01

A growing emphasis has been placed on providing equal opportunities for all people, particularly people with disabilities, to support participation. Barriers to participation are represented in part by physical space restrictions. This article explores the decision-making process during the construction of a new office building housing a disability-rights organization. The building project featured in this study was developed on the principles of universal design, maximal accessibility, and sustainability to support access and participation. A qualitative case study approach was used involving collection of data through in-depth interviews with key decision-makers; non-participant observations at design meetings; and on-site tours. Qualitative thematic analysis along with the development of a classification system was used to understand specific building elements and the relevant decision processes from which they resulted. Recording and analyzing the design process revealed several key issues including grassroots involvement of stakeholders; interaction between universal design and sustainable design; addressing diversity through flexibility and universality; and segregationist accessibility versus universal design. This case study revealed complex interactions between accessibility, universal design, and sustainability. Two visual models were proposed to understand and analyze these complexities.

Shapley value-based multi-objective data envelopment analysis application for assessing academic efficiency of university departments

NASA Astrophysics Data System (ADS)

Abing, Stephen Lloyd N.; Barton, Mercie Grace L.; Dumdum, Michael Gerard M.; Bongo, Miriam F.; Ocampo, Lanndon A.

2018-02-01

This paper adopts a modified approach of data envelopment analysis (DEA) to measure the academic efficiency of university departments. In real-world case studies, conventional DEA models often identify too many decision-making units (DMUs) as efficient. This occurs when the number of DMUs under evaluation is not large enough compared to the total number of decision variables. To overcome this limitation and reduce the number of decision variables, multi-objective data envelopment analysis (MODEA) approach previously presented in the literature is applied. The MODEA approach applies Shapley value as a cooperative game to determine the appropriate weights and efficiency score of each category of inputs. To illustrate the performance of the adopted approach, a case study is conducted in a university in the Philippines. The input variables are academic staff, non-academic staff, classrooms, laboratories, research grants, and department expenditures, while the output variables are the number of graduates and publications. The results of the case study revealed that all DMUs are inefficient. DMUs with efficiency scores close to the ideal efficiency score may be emulated by other DMUs with least efficiency scores.

A Framework to Improve Surgeon Communication in High-Stakes Surgical Decisions: Best Case/Worst Case.

PubMed

Taylor, Lauren J; Nabozny, Michael J; Steffens, Nicole M; Tucholka, Jennifer L; Brasel, Karen J; Johnson, Sara K; Zelenski, Amy; Rathouz, Paul J; Zhao, Qianqian; Kwekkeboom, Kristine L; Campbell, Toby C; Schwarze, Margaret L

2017-06-01

Although many older adults prefer to avoid burdensome interventions with limited ability to preserve their functional status, aggressive treatments, including surgery, are common near the end of life. Shared decision making is critical to achieve value-concordant treatment decisions and minimize unwanted care. However, communication in the acute inpatient setting is challenging. To evaluate the proof of concept of an intervention to teach surgeons to use the Best Case/Worst Case framework as a strategy to change surgeon communication and promote shared decision making during high-stakes surgical decisions. Our prospective pre-post study was conducted from June 2014 to August 2015, and data were analyzed using a mixed methods approach. The data were drawn from decision-making conversations between 32 older inpatients with an acute nonemergent surgical problem, 30 family members, and 25 surgeons at 1 tertiary care hospital in Madison, Wisconsin. A 2-hour training session to teach each study-enrolled surgeon to use the Best Case/Worst Case communication framework. We scored conversation transcripts using OPTION 5, an observer measure of shared decision making, and used qualitative content analysis to characterize patterns in conversation structure, description of outcomes, and deliberation over treatment alternatives. The study participants were patients aged 68 to 95 years (n = 32), 44% of whom had 5 or more comorbid conditions; family members of patients (n = 30); and surgeons (n = 17). The median OPTION 5 score improved from 41 preintervention (interquartile range, 26-66) to 74 after Best Case/Worst Case training (interquartile range, 60-81). Before training, surgeons described the patient's problem in conjunction with an operative solution, directed deliberation over options, listed discrete procedural risks, and did not integrate preferences into a treatment recommendation. After training, surgeons using Best Case/Worst Case clearly presented a choice between treatments, described a range of postoperative trajectories including functional decline, and involved patients and families in deliberation. Using the Best Case/Worst Case framework changed surgeon communication by shifting the focus of decision-making conversations from an isolated surgical problem to a discussion about treatment alternatives and outcomes. This intervention can help surgeons structure challenging conversations to promote shared decision making in the acute setting.

Designing a rapid response program to support evidence-informed decision-making in the Americas region: using the best available evidence and case studies.

PubMed

Haby, Michelle M; Chapman, Evelina; Clark, Rachel; Barreto, Jorge; Reveiz, Ludovic; Lavis, John N

2016-08-18

The objective of this work was to inform the design of a rapid response program to support evidence-informed decision-making in health policy and practice for the Americas region. Specifically, we focus on the following: (1) What are the best methodological approaches for rapid reviews of the research evidence? (2) What other strategies are needed to facilitate evidence-informed decision-making in health policy and practice? and (3) How best to operationalize a rapid response program? The evidence used to inform the design of a rapid response program included (i) two rapid reviews of methodological approaches for rapid reviews of the research evidence and strategies to facilitate evidence-informed decision-making, (ii) supplementary literature in relation to the "shortcuts" that could be considered to reduce the time needed to complete rapid reviews, (iii) four case studies, and (iv) supplementary literature to identify additional operational issues for the design of the program. There is no agreed definition of rapid reviews in the literature and no agreed methodology for conducting them. Better reporting of rapid review methods is needed. The literature found in relation to shortcuts will be helpful in choosing shortcuts that maximize timeliness while minimizing the impact on quality. Evidence for other strategies that can be used concurrently to facilitate the uptake of research evidence, including evidence drawn from rapid reviews, is presented. Operational issues that need to be considered in designing a rapid response program include the implications of a "user-pays" model, the importance of recruiting staff with the right mix of skills and qualifications, and ensuring that the impact of the model on research use in decision-making is formally evaluated. When designing a new rapid response program, greater attention needs to be given to specifying the rapid review methods and reporting these in sufficient detail to allow a quality assessment. It will also be important to engage in other strategies to facilitate the uptake of the rapid reviews and to evaluate the chosen model in order to make refinements and add to the evidence base for evidence-informed decision-making.

Using an ecosystem service decision support tool to support ridge to reef management: An example of sediment reduction in west Maui, Hawaii

NASA Astrophysics Data System (ADS)

Falinski, K. A.; Oleson, K.; Htun, H.; Kappel, C.; Lecky, J.; Rowe, C.; Selkoe, K.; White, C.

2016-12-01

Faced with anthropogenic stressors and declining coral reef states, managers concerned with restoration and resilience of coral reefs are increasingly recognizing the need to take a ridge-to-reef, ecosystem-based approach. An ecosystem services framing can help managers move towards these goals, helping to illustrate trade-offs and opportunities of management actions in terms of their impacts on society. We describe a research program building a spatial ecosystem services-based decision-support tool, and being applied to guide ridge-to-reef management in a NOAA priority site in West Maui. We use multiple modeling methods to link biophysical processes to ecosystem services and their spatial flows and social values in an integrating platform. Modeled services include water availability, sediment retention, nutrient retention and carbon sequestration on land. A coral reef ecosystem service model is under development to capture the linkages between terrestrial and coastal ecosystem services. Valuation studies are underway to quantify the implications for human well-being. The tool integrates techniques from decision science to facilitate decision making. We use the sediment retention model to illustrate the types of analyses the tool can support. The case study explores the tradeoffs between road rehabilitation costs and sediment export avoided. We couple the sediment and cost models with trade-off analysis to identify optimal distributed solutions that are most cost-effective in reducing erosion, and then use those models to estimate sediment exposure to coral reefs. We find that cooperation between land owners reveals opportunities for maximizing the benefits of fixing roads and minimizes costs. This research forms the building blocks of an ecosystem service decision support tool that we intend to continue to test and apply in other Pacific Island settings.

A leader-follower-interactive method for regional water resources management with considering multiple water demands and eco-environmental constraints

NASA Astrophysics Data System (ADS)

Chen, Yizhong; Lu, Hongwei; Li, Jing; Ren, Lixia; He, Li

2017-05-01

This study presents the mathematical formulation and implementations of a synergistic optimization framework based on an understanding of water availability and reliability together with the characteristics of multiple water demands. This framework simultaneously integrates a set of leader-followers-interactive objectives established by different decision makers during the synergistic optimization. The upper-level model (leader's one) determines the optimal pollutants discharge to satisfy the environmental target. The lower-level model (follower's one) accepts the dispatch requirement from the upper-level one and dominates the optimal water-allocation strategy to maximize economic benefits representing the regional authority. The complicated bi-level model significantly improves upon the conventional programming methods through the mutual influence and restriction between the upper- and lower-level decision processes, particularly when limited water resources are available for multiple completing users. To solve the problem, a bi-level interactive solution algorithm based on satisfactory degree is introduced into the decision-making process for measuring to what extent the constraints are met and the objective reaches its optima. The capabilities of the proposed model are illustrated through a real-world case study of water resources management system in the district of Fengtai located in Beijing, China. Feasible decisions in association with water resources allocation, wastewater emission and pollutants discharge would be sequentially generated for balancing the objectives subject to the given water-related constraints, which can enable Stakeholders to grasp the inherent conflicts and trade-offs between the environmental and economic interests. The performance of the developed bi-level model is enhanced by comparing with single-level models. Moreover, in consideration of the uncertainty in water demand and availability, sensitivity analysis and policy analysis are employed for identifying their impacts on the final decisions and improving the practical applications.

TRIPLE-VALUE SIMULATION MODELING CASES TACKLE NUTRIENT AND WATERSHED MANAGEMENT FROM A SOCIAL-ECOLOGICAL SYSTEMS PERSPECTIVE

EPA Science Inventory

Decision makers often need assistance in understanding dynamic interactions and linkages among economic, environmental and social systems in coastal watersheds. They also need scientific input to better evaluate potential costs and benefits of alternative policy interventions. EP...

Applying analysis tools in planning for operations : case study #4 -- application of microsimulation in combination with travel demand models

DOT National Transportation Integrated Search

2009-09-01

More and more, transportation system operators are seeing the benefits of strengthening links between planning and operations. A critical element in improving transportation decision-making and the effectiveness of transportation systems related to o...

Pro Se Court: A Simulation Game

ERIC Educational Resources Information Center

Gallagher, Arlene F.; Hartstein, Elliott

1973-01-01

The complexities of courtroom procedure and rule of evidence often dissuade the classroom teacher from using the mock trial strategy. This model has been designed for role playing and for focusing on the judicial decision-making process: deliberation on the issues of a case. (Author/JB)

Ethical Dilemmas in Office Practice: Physician Response and Rationale

PubMed Central

Secundy, Marian Gray

1985-01-01

A survey of black and white family physicians in the District of Columbia is described. The survey provides insight into decision-making processes and the ability to recognize ethical dilemmas in medical practice. Comments were elicited to hypothetical case vignettes typical of ethical conflict in office practice. Findings note physician ability to recognize ethical dilemmas in day-to-day aspects of medical practice. Methods of decision making and rationale for decisions made, however, appear to be inconsistent, nonuniversal, and individualistic without evidence of specific models or criteria. No significant differences were noted between black and white physicians. The need in physician training for clarification and development of criteria is evident. PMID:4078929

A multi-criteria decision aid methodology to design electric vehicles public charging networks

NASA Astrophysics Data System (ADS)

Raposo, João; Rodrigues, Ana; Silva, Carlos; Dentinho, Tomaz

2015-05-01

This article presents a new multi-criteria decision aid methodology, dynamic-PROMETHEE, here used to design electric vehicle charging networks. In applying this methodology to a Portuguese city, results suggest that it is effective in designing electric vehicle charging networks, generating time and policy based scenarios, considering offer and demand and the city's urban structure. Dynamic-PROMETHE adds to the already known PROMETHEE's characteristics other useful features, such as decision memory over time, versatility and adaptability. The case study, used here to present the dynamic-PROMETHEE, served as inspiration and base to create this new methodology. It can be used to model different problems and scenarios that may present similar requirement characteristics.

Metacognitive Control of Categorial Neurobehavioral Decision Systems

PubMed Central

Foxall, Gordon R.

2016-01-01

The competing neuro-behavioral decision systems (CNDS) model proposes that the degree to which an individual discounts the future is a function of the relative hyperactivity of an impulsive system based on the limbic and paralimbic brain regions and the relative hypoactivity of an executive system based in prefrontal cortex (PFC). The model depicts the relationship between these categorial systems in terms of the antipodal neurophysiological, behavioral, and decision (cognitive) functions that engender normal and addictive responding. However, a case may be made for construing several components of the impulsive and executive systems depicted in the model as categories (elements) of additional systems that are concerned with the metacognitive control of behavior. Hence, this paper proposes a category-based structure for understanding the effects on behavior of CNDS, which includes not only the impulsive and executive systems of the basic model but a superordinate level of reflective or rational decision-making. Following recent developments in the modeling of cognitive control which contrasts Type 1 (rapid, autonomous, parallel) processing with Type 2 (slower, computationally demanding, sequential) processing, the proposed model incorporates an arena in which the potentially conflicting imperatives of impulsive and executive systems are examined and from which a more appropriate behavioral response than impulsive choice emerges. This configuration suggests a forum in which the interaction of picoeconomic interests, which provide a cognitive dimension for CNDS, can be conceptualized. This proposition is examined in light of the resolution of conflict by means of bundling. PMID:26925004

Correlated Observations, the Law of Small Numbers and Bank Runs

PubMed Central

2016-01-01

Empirical descriptions and studies suggest that generally depositors observe a sample of previous decisions before deciding if to keep their funds deposited or to withdraw them. These observed decisions may exhibit different degrees of correlation across depositors. In our model depositors decide sequentially and are assumed to follow the law of small numbers in the sense that they believe that a bank run is underway if the number of observed withdrawals in their sample is large. Theoretically, with highly correlated samples and infinite depositors runs occur with certainty, while with random samples it needs not be the case, as for many parameter settings the likelihood of bank runs is zero. We investigate the intermediate cases and find that i) decreasing the correlation and ii) increasing the sample size reduces the likelihood of bank runs, ceteris paribus. Interestingly, the multiplicity of equilibria, a feature of the canonical Diamond-Dybvig model that we use also, disappears almost completely in our setup. Our results have relevant policy implications. PMID:27035435

Correlated Observations, the Law of Small Numbers and Bank Runs.

PubMed

Horváth, Gergely; Kiss, Hubert János

2016-01-01

Empirical descriptions and studies suggest that generally depositors observe a sample of previous decisions before deciding if to keep their funds deposited or to withdraw them. These observed decisions may exhibit different degrees of correlation across depositors. In our model depositors decide sequentially and are assumed to follow the law of small numbers in the sense that they believe that a bank run is underway if the number of observed withdrawals in their sample is large. Theoretically, with highly correlated samples and infinite depositors runs occur with certainty, while with random samples it needs not be the case, as for many parameter settings the likelihood of bank runs is zero. We investigate the intermediate cases and find that i) decreasing the correlation and ii) increasing the sample size reduces the likelihood of bank runs, ceteris paribus. Interestingly, the multiplicity of equilibria, a feature of the canonical Diamond-Dybvig model that we use also, disappears almost completely in our setup. Our results have relevant policy implications.

Participative management in health care services.

PubMed

Muller, M

1995-03-01

The need and demand for the highest-quality management of all health care delivery activities requires a participative management approach. The purpose with this article is to explore the process of participative management, to generate and describe a model for such management, focusing mainly on the process of participative management, and to formulate guidelines for operationalization of the procedure. An exploratory, descriptive and theory-generating research design is pursued. After a brief literature review, inductive reasoning is mainly employed to identify and define central concepts, followed by the formulation of a few applicable statements and guidelines. Participative management is viewed as a process of that constitutes the elements of dynamic interactive decision-making and problem-solving, shared governance, empowerment, organisational transformation, and dynamic communication within the health care organisation. The scientific method of assessment, planning, implementation and evaluation is utilised throughout the process of participative management. A continuum of interactive decision-making and problem-solving is described, the different role-players involved, as well as the levels of interactive decision-making and problem-solving. The most appropriate decision-making strategy should be employed in pro-active and reactive decision-making. Applicable principles and assumptions in each element of participative management is described. It is recommended that this proposed model for participative management be refined by means of a literature control, interactive dialogue with experts and a model case description or participative management, to ensure the trustworthiness of this research.

Liver transplantation for Wilson's disease in pediatric patients: decision making and timing.

PubMed

Narumi, S; Umehara, M; Toyoki, Y; Ishido, K; Kudo, D; Kimura, N; Kobayashi, T; Sugai, M; Hakamada, K

2012-03-01

Transplantation for Wilson's disease occupies 1/3 of the cases for metabolic diseases in Japan. At the end of 2009, 109 transplantations had been performed including three deceased donor cases in the Japanese registry. We herein discuss problems of transplantation for Wilson's disease as well as its indication, timing, and social care. We retrospectively reviewed four fulminant cases and two chronic cases who underwent living donor liver transplantation. There were two boys and two girls. Four adolescents of average age 11.3 years underwent living donor liver transplantation. Duration from onset to transplantation ranged from 10 to 23 days. Average Model for End-stage Liver Disease (MELD) score was 27.8 (range=24-31). All patients were administrated chelates prior to transplantation. MELD, New Wilson's index, Japanese scoring for liver transplantation, and liver atrophy were useful tools for transplantation decision making; however, none of them was an independent decisive tool. Clinical courses after transplantation were almost uneventful. One girl, however, developed an acute rejection episode due to noncompliance at 3 years after transplantation. All patients currently survive without a graft loss. No disease recurrence had been noted even using living related donors. Two adults evaluated for liver transplantation were listed for deceased donor liver transplantation. Both candidates developed cirrhosis despite long-term medical treatment. There were no appropriate living donors for them. There are many problems in transplantation for Wilson's disease. The indications for liver transplantation should be considered individually using some decision-making tools. The safety of the living donor should be paid the most attention. Copyright © 2012 Elsevier Inc. All rights reserved.

Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts

NASA Astrophysics Data System (ADS)

Tommasi, Desiree; Stock, Charles A.; Hobday, Alistair J.; Methot, Rick; Kaplan, Isaac C.; Eveson, J. Paige; Holsman, Kirstin; Miller, Timothy J.; Gaichas, Sarah; Gehlen, Marion; Pershing, Andrew; Vecchi, Gabriel A.; Msadek, Rym; Delworth, Tom; Eakin, C. Mark; Haltuch, Melissa A.; Séférian, Roland; Spillman, Claire M.; Hartog, Jason R.; Siedlecki, Samantha; Samhouri, Jameal F.; Muhling, Barbara; Asch, Rebecca G.; Pinsky, Malin L.; Saba, Vincent S.; Kapnick, Sarah B.; Gaitan, Carlos F.; Rykaczewski, Ryan R.; Alexander, Michael A.; Xue, Yan; Pegion, Kathleen V.; Lynch, Patrick; Payne, Mark R.; Kristiansen, Trond; Lehodey, Patrick; Werner, Francisco E.

2017-03-01

Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months to decades, before highlighting a range of pioneering case studies using climate predictions to inform LMR decisions. The success of these case studies suggests that many additional applications are possible. Progress, however, is limited by observational and modeling challenges. Priority developments include strengthening of the mechanistic linkages between climate and marine resource responses, development of LMR models able to explicitly represent such responses, integration of climate driven LMR dynamics in the multi-driver context within which marine resources exist, and improved prediction of ecosystem-relevant variables at the fine regional scales at which most marine resource decisions are made. While there are fundamental limits to predictability, continued advances in these areas have considerable potential to make LMR managers and industry decision more resilient to climate variability and help sustain valuable resources. Concerted dialog between scientists, LMR managers and industry is essential to realizing this potential.

Watershed sediment measurement and sediment transport modeling techniques: Case study to quantify the impact of converting cropland to forested stream buffers on soil loss and water quality at the watershed scale

USDA-ARS?s Scientific Manuscript database

Watershed models such as the Soil and Water Assessment Tool (SWAT) have been widely used to simulate watershed hydrologic processes and the effect of management, such as agroforestry, on soil and water resources. In order to use model outputs for tasks ranging from aiding policy decision making to r...

Linking Science and Management in an Interactive Geospatial, Mutli-Criterion, Structured Decision Support Framework: Use Case Studies of the "Future Forests Geo-visualization and Decision Support Tool

NASA Astrophysics Data System (ADS)

Pontius, J.; Duncan, J.

2017-12-01

Land managers are often faced with balancing management activities to accomplish a diversity of management objectives, in systems faced with many stress agents. Advances in ecosystem modeling provide a rich source of information to inform management. Coupled with advances in decision support techniques and computing capabilities, interactive tools are now accessible for a broad audience of stakeholders. Here we present one such tool designed to capture information on how climate change may impact forested ecosystems, and how that impact varies spatially across the landscape. This tool integrates empirical models of current and future forest structure and function in a structured decision framework that allows users to customize weights for multiple management objectives and visualize suitability outcomes across the landscape. Combined with climate projections, the resulting products allow stakeholders to compare the relative success of various management objectives on a pixel by pixel basis and identify locations where management outcomes are most likely to be met. Here we demonstrate this approach with the integration of several of the preliminary models developed to map species distributions, sugar maple health, forest fragmentation risk and hemlock vulnerability to hemlock woolly adelgid under current and future climate scenarios. We compare three use case studies with objective weightings designed to: 1) Identify key parcels for sugarbush conservation and management, 2) Target state lands that may serve as hemlock refugia from hemlock woolly adelgid induced mortality, and 3) Examine how climate change may alter the success of managing for both sugarbush and hemlock across privately owned lands. This tool highlights the value of flexible models that can be easily run with customized weightings in a dynamic, integrated assessment that allows users to hone in on their potentially complex management objectives, and to visualize and prioritize locations across the landscape. It also demonstrates the importance of including climate considerations for long-term management. This merging of scientific knowledge with the diversity of stakeholder needs is an important step towards using science to inform management and policy decisions.

Dynamic decision making for dam-break emergency management - Part 1: Theoretical framework

NASA Astrophysics Data System (ADS)

Peng, M.; Zhang, L. M.

2013-02-01

An evacuation decision for dam breaks is a very serious issue. A late decision may lead to loss of lives and properties, but a very early evacuation will incur unnecessary expenses. This paper presents a risk-based framework of dynamic decision making for dam-break emergency management (DYDEM). The dam-break emergency management in both time scale and space scale is introduced first to define the dynamic decision problem. The probability of dam failure is taken as a stochastic process and estimated using a time-series analysis method. The flood consequences are taken as functions of warning time and evaluated with a human risk analysis model (HURAM) based on Bayesian networks. A decision criterion is suggested to decide whether to evacuate the population at risk (PAR) or to delay the decision. The optimum time for evacuating the PAR is obtained by minimizing the expected total loss, which integrates the time-related probabilities and flood consequences. When a delayed decision is chosen, the decision making can be updated with available new information. A specific dam-break case study is presented in a companion paper to illustrate the application of this framework to complex dam-breaching problems.

Valuation of design adaptability in aerospace systems

NASA Astrophysics Data System (ADS)

Fernandez Martin, Ismael

As more information is brought into early stages of the design, more pressure is put on engineers to produce a reliable, high quality, and financially sustainable product. Unfortunately, requirements established at the beginning of a new project by customers, and the environment that surrounds them, continue to change in some unpredictable ways. The risk of designing a system that may become obsolete during early stages of production is currently tackled by the use of robust design simulation, a method that allows to simultaneously explore a plethora of design alternatives and requirements with the intention of accounting for uncertain factors in the future. Whereas this design technique has proven to be quite an improvement in design methods, under certain conditions, it fails to account for the change of uncertainty over time and the intrinsic value embedded in the system when certain design features are activated. This thesis introduces the concepts of adaptability and real options to manage risk foreseen in the face of uncertainty at early design stages. The method described herein allows decision-makers to foresee the financial impact of their decisions at the design level, as well as the final exposure to risk. In this thesis, cash flow models, traditionally used to obtain the forecast of a project's value over the years, were replaced with surrogate models that are capable of showing fluctuations on value every few days. This allowed a better implementation of real options valuation, optimization, and strategy selection. Through the option analysis model, an optimization exercise allows the user to obtain the best implementation strategy in the face of uncertainty as well as the overall value of the design feature. Here implementation strategy refers to the decision to include a new design feature in the system, after the design has been finalized, but before the end of its production life. The ability to do this in a cost efficient manner after the system design is under production is referred to as adaptability. This thesis contains two relevant examples regarding the decision of introducing new technologies. First, the case study of Southwest Airlines, and the decision it took to retrofit blended winglets technology in its already delivered Boeing 737-700, is introduced as a validation exercise and for calibration purposes. Such case also demonstrates that the method is applicable to a real life example with simple technologies. The second example analyzes the decision of introducing new technologies into the design of the new jet engine to power the next generation of narrow body aircraft. The development of such aircraft, set to replace the Boeing 737 and Airbus 320 models, is currently at conceptual levels. In this case, the manufacturer evaluates whether technologies should be included in the design, left out, or offered as an option to retrofit in the future. This case demonstrates the benefits of each of these actions and the monetary value of offering retrofitting options as upgrades to the airlines when the value of the technology fluctuates considerably between profitable and not profitable. The purpose of this case is to demonstrate the applicability of the method to the preliminary design phases of complex systems while accounting for uncertainty of external factors over time.

[Ethical case discussions in the intensive care unit : from testing to routine].

PubMed

Meyer-Zehnder, B; Barandun Schäfer, U; Albisser Schleger, H; Reiter-Theil, S; Pargger, H

2014-06-01

The daily work of many healthcare professionals has become more complex and demanding in recent years. Apart from purely medical issues, ethical questions and problems arise quite often. Managing these problems requires ethical knowledge. Questions about the usefulness of a therapy and treatment occur especially at the end of life. So-called medical futility, a useless futile therapy, is often perceived by nurses and physicians in intensive care units who themselves often develop symptoms of depression or burnout. The clinical ethical model METAP (acronym from module, ethics, therapy decision, allocation and process) provides methods and criteria that allow the clinical team to treat and solve ethical issues according to a solution-oriented approach. The ethical decision-making of this model addresses these issues according to a series of sequential stages in the form of a so-called escalation model. When it is not possible to tackle and solve an ethical problem or dilemma in one stage, one moves to the next. The implementation of this approach in everyday practice requires the commitment of all team members in addition to certain basic conditions. In a surgical intensive care unit a fixed date in the schedule is reserved for ethical case discussions (level 3 of the escalation model). At this level a team member who has been specified according to a quarterly plan is responsible for the organization and performance of the discussion. All protocols of the 44 ethical case discussions in 41 patients between January 2011 and July 2012 were collected and summarized. A short questionnaire to all participants recorded their assessment of the benefits for the patient and the team as well as their perception of personal stress reduction. Also queried was the impact of this method on the collaboration between nurses and physicians and the ethical competence. Ethical case discussions among the care team took place regularly (44 case discussions between January 2011 and June 2012). The duration of these discussions ranged from 30 to 60 min. On average 6.2 persons took part, including 2.7 nurses and 3.2 physicians. Of the 41 patients (16 female, 25 male) for whom a discussion was carried out, 23 died during the continued hospital stay. The respondents (response rate 52 %) assessed the benefit for patients and team as high (slightly higher benefit for physicians than nurses) and 55 % of physicians and 71 % of nurses perceived a reduction in the burden of decision-making in difficult cases due to the case discussions. All physicians and 66 % of the nurses reported an improvement in the cooperation between the professional groups and 80 % of the nurses and more than half of the physicians noticed an increase in their own ethical competence. A methodically structured ethical decision-making process can and should be integrated into the clinical routine. This process requires a fixed place in everyday practice and the defined responsibility for the actual organization and performance. Support by medical and nursing management personnel is also essential for the implementation. The regular occurrence of ethical case discussions among the care team relieves the participants and improves collaboration between nurses and physicians.

Team Leadership and Cancer End-of-Life Decision Making.

PubMed

Waldfogel, Julie M; Battle, Dena J; Rosen, Michael; Knight, Louise; Saiki, Catherine B; Nesbit, Suzanne A; Cooper, Rhonda S; Browner, Ilene S; Hoofring, Laura H; Billing, Lynn S; Dy, Sydney M

2016-11-01

End-of-life decision making in cancer can be a complicated process. Patients and families encounter multiple providers throughout their cancer care. When the efforts of these providers are not well coordinated in teams, opportunities for high-quality, longitudinal goals of care discussions can be missed. This article reviews the case of a 55-year-old man with lung cancer, illustrating the barriers and missed opportunities for end-of-life decision making in his care through the lens of team leadership, a key principle in the science of teams. The challenges demonstrated in this case reflect the importance of the four functions of team leadership: information search and structuring, information use in problem solving, managing personnel resources, and managing material resources. Engaging in shared leadership of these four functions can help care providers improve their interactions with patients and families concerning end-of-life care decision making. This shared leadership can also produce a cohesive care plan that benefits from the expertise of the range of available providers while reflecting patient needs and preferences. Clinicians and researchers should consider the roles of team leadership functions and shared leadership in improving patient care when developing and studying models of cancer care delivery.

Joint location, inventory, and preservation decisions for non-instantaneous deterioration items under delay in payments

NASA Astrophysics Data System (ADS)

Tsao, Yu-Chung

2016-02-01

This study models a joint location, inventory and preservation decision-making problem for non-instantaneous deteriorating items under delay in payments. An outside supplier provides a credit period to the wholesaler which has a distribution system with distribution centres (DCs). The non-instantaneous deteriorating means no deterioration occurs in the earlier stage, which is very useful for items such as fresh food and fruits. This paper also considers that the deteriorating rate will decrease and the reservation cost will increase as the preservation effort increases. Therefore, how much preservation effort should be made is a crucial decision. The objective of this paper is to determine the optimal locations and number of DCs, the optimal replenishment cycle time at DCs, and the optimal preservation effort simultaneously such that the total network profit is maximised. The problem is formulated as piecewise nonlinear functions and has three different cases. Algorithms based on piecewise nonlinear optimisation are provided to solve the joint location and inventory problem for all cases. Computational analysis illustrates the solution procedures and the impacts of the related parameters on decisions and profits. The results of this study can serve as references for business managers or administrators.

Brazilian LTER: ecosystem and biodiversity information in support of decision-making.

PubMed

Barbosa, F A R; Scarano, F R; Sabará, M G; Esteves, F A

2004-01-01

Brazil officially joined the International Long Term Ecological Research (ILTER) network in January 2000, when nine research sites were created and funded by the Brazilian Council for Science and Technology (CNPq). Two-years later some positive signs already emerge of the scientific, social and political achievements of the Brazilian LTER program. We discuss examples of how ecosystem and biodiversity information gathered within a long-term research approach are currently subsidizing decision-making as regards biodiversity conservation and watershed management at local and regional scales. Success in this respect has often been related to satisfactory communication between scientists, private companies, government and local citizens. Environmental education programs in the LTER sites are playing an important role in social and political integration. Most examples of integration of ecological research to decision-making in Brazil derive from case studies at local or regional scale. Despite the predominance of a bottom-up integrative pathway (from case studies to models; from local to national scale), some top-down initiatives are also in order, such as the construction of a model to estimate the inpact of different macroeconomic policies and growth trajectories on land use. We believe science and society in Brazil will benefit of the coexistence of bottom-up and top-down integrative approaches.

Out of sight, out of mind: the presence of forensic evidence counts more than its absence.

PubMed

Eerland, Anita; Post, Lysanne S; Rassin, Eric; Bouwmeester, Samantha; Zwaan, Rolf A

2012-05-01

Recent evidence suggests that decision makers in criminal procedures are susceptible to biases. We previously found support for the presence of a feature positive effect (FPE, i.e., people attach more meaning to present than to absent information) in legal-decision making. In this study, we tried to uncover the mechanisms behind the FPE. Taking a cue from the literature on situation models in language comprehension, we investigated whether a FPE manifests itself in the memorization and use of forensic evidence. Students read a case file about a fistfight as well as additional evidence. The forensic evidence was manipulated such that a FPE on guilt estimation and conviction rate could be assessed. While subjects read additional forensic evidence, their eye movements were recorded to explore the presence of FPE in online processing. Afterwards, subjects were asked to decide on the suspect's guilt. They had to recall all information they remembered from the case file and indicate which parts of information they considered relevant to this decision. The results provided evidence for the occurrence of FPE in memorization and use of information and can be explained by the theoretical construct of situation models. Copyright © 2012 Elsevier B.V. All rights reserved.

A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology

PubMed Central

Henriques, David; González, Patricia; Doallo, Ramón; Saez-Rodriguez, Julio; Banga, Julio R.

2017-01-01

Background We consider a general class of global optimization problems dealing with nonlinear dynamic models. Although this class is relevant to many areas of science and engineering, here we are interested in applying this framework to the reverse engineering problem in computational systems biology, which yields very large mixed-integer dynamic optimization (MIDO) problems. In particular, we consider the framework of logic-based ordinary differential equations (ODEs). Methods We present saCeSS2, a parallel method for the solution of this class of problems. This method is based on an parallel cooperative scatter search metaheuristic, with new mechanisms of self-adaptation and specific extensions to handle large mixed-integer problems. We have paid special attention to the avoidance of convergence stagnation using adaptive cooperation strategies tailored to this class of problems. Results We illustrate its performance with a set of three very challenging case studies from the domain of dynamic modelling of cell signaling. The simpler case study considers a synthetic signaling pathway and has 84 continuous and 34 binary decision variables. A second case study considers the dynamic modeling of signaling in liver cancer using high-throughput data, and has 135 continuous and 109 binaries decision variables. The third case study is an extremely difficult problem related with breast cancer, involving 690 continuous and 138 binary decision variables. We report computational results obtained in different infrastructures, including a local cluster, a large supercomputer and a public cloud platform. Interestingly, the results show how the cooperation of individual parallel searches modifies the systemic properties of the sequential algorithm, achieving superlinear speedups compared to an individual search (e.g. speedups of 15 with 10 cores), and significantly improving (above a 60%) the performance with respect to a non-cooperative parallel scheme. The scalability of the method is also good (tests were performed using up to 300 cores). Conclusions These results demonstrate that saCeSS2 can be used to successfully reverse engineer large dynamic models of complex biological pathways. Further, these results open up new possibilities for other MIDO-based large-scale applications in the life sciences such as metabolic engineering, synthetic biology, drug scheduling. PMID:28813442

A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology.

PubMed

Penas, David R; Henriques, David; González, Patricia; Doallo, Ramón; Saez-Rodriguez, Julio; Banga, Julio R

2017-01-01

We consider a general class of global optimization problems dealing with nonlinear dynamic models. Although this class is relevant to many areas of science and engineering, here we are interested in applying this framework to the reverse engineering problem in computational systems biology, which yields very large mixed-integer dynamic optimization (MIDO) problems. In particular, we consider the framework of logic-based ordinary differential equations (ODEs). We present saCeSS2, a parallel method for the solution of this class of problems. This method is based on an parallel cooperative scatter search metaheuristic, with new mechanisms of self-adaptation and specific extensions to handle large mixed-integer problems. We have paid special attention to the avoidance of convergence stagnation using adaptive cooperation strategies tailored to this class of problems. We illustrate its performance with a set of three very challenging case studies from the domain of dynamic modelling of cell signaling. The simpler case study considers a synthetic signaling pathway and has 84 continuous and 34 binary decision variables. A second case study considers the dynamic modeling of signaling in liver cancer using high-throughput data, and has 135 continuous and 109 binaries decision variables. The third case study is an extremely difficult problem related with breast cancer, involving 690 continuous and 138 binary decision variables. We report computational results obtained in different infrastructures, including a local cluster, a large supercomputer and a public cloud platform. Interestingly, the results show how the cooperation of individual parallel searches modifies the systemic properties of the sequential algorithm, achieving superlinear speedups compared to an individual search (e.g. speedups of 15 with 10 cores), and significantly improving (above a 60%) the performance with respect to a non-cooperative parallel scheme. The scalability of the method is also good (tests were performed using up to 300 cores). These results demonstrate that saCeSS2 can be used to successfully reverse engineer large dynamic models of complex biological pathways. Further, these results open up new possibilities for other MIDO-based large-scale applications in the life sciences such as metabolic engineering, synthetic biology, drug scheduling.

Why we should use animals to study economic decision making - a perspective.

PubMed

Kalenscher, Tobias; van Wingerden, Marijn

2011-01-01

Despite the rich tradition in psychology and biology, animals as research subjects have never gained a similar acceptance in microeconomics research. With this article, we counter this trend of negligence and try to convey the message that animal models are an indispensible complement to the literature on human economic decision making. This perspective review departs from a description of the similarities in economic and evolutionary theories of human and animal decision making, with particular emphasis on the optimality aspect that both classes of theories have in common. In a second part, we outline that actual, empirically observed decisions often do not conform to the normative ideals of economic and ecological models, and that many of the behavioral violations found in humans can also be found in animals. In a third part, we make a case that the sense or nonsense of the behavioral violations of optimality principles in humans can best be understood from an evolutionary perspective, thus requiring animal research. Finally, we conclude with a critical discussion of the parallels and inherent differences in human and animal research.

Why We Should Use Animals to Study Economic Decision Making – A Perspective

PubMed Central

Kalenscher, Tobias; van Wingerden, Marijn

2011-01-01

Despite the rich tradition in psychology and biology, animals as research subjects have never gained a similar acceptance in microeconomics research. With this article, we counter this trend of negligence and try to convey the message that animal models are an indispensible complement to the literature on human economic decision making. This perspective review departs from a description of the similarities in economic and evolutionary theories of human and animal decision making, with particular emphasis on the optimality aspect that both classes of theories have in common. In a second part, we outline that actual, empirically observed decisions often do not conform to the normative ideals of economic and ecological models, and that many of the behavioral violations found in humans can also be found in animals. In a third part, we make a case that the sense or nonsense of the behavioral violations of optimality principles in humans can best be understood from an evolutionary perspective, thus requiring animal research. Finally, we conclude with a critical discussion of the parallels and inherent differences in human and animal research. PMID:21731558

Cost-effectiveness on a local level: whether and when to adopt a new technology.

PubMed

Woertman, Willem H; Van De Wetering, Gijs; Adang, Eddy M M

2014-04-01

Cost-effectiveness analysis has become a widely accepted tool for decision making in health care. The standard textbook cost-effectiveness analysis focuses on whether to make the switch from an old or common practice technology to an innovative technology, and in doing so, it takes a global perspective. In this article, we are interested in a local perspective, and we look at the questions of whether and when the switch from old to new should be made. A new approach to cost-effectiveness from a local (e.g., a hospital) perspective, by means of a mathematical model for cost-effectiveness that explicitly incorporates time, is proposed. A decision rule is derived for establishing whether a new technology should be adopted, as well as a general rule for establishing when it pays to postpone adoption by 1 more period, and a set of decision rules that can be used to determine the optimal timing of adoption. Finally, a simple example is presented to illustrate our model and how it leads to optimal decision making in a number of cases.

Eradicating the grey squirrel Sciurus carolinensis from urban areas: an innovative decision-making approach based on lessons learnt in Italy.

PubMed

La Morgia, Valentina; Paoloni, Daniele; Genovesi, Piero

2017-02-01

Eradication of invasive alien species supports the recovery of native biodiversity. A new European Union Regulation introduces obligations to eradicate the most harmful invasive species. However, eradications of charismatic mammals may encounter strong opposition. Considering the case study of the eastern grey squirrel (Sciurus carolinensis Gmelin, 1788) in central Italy, we developed a structured decision-making technique based on a Bayesian decision network model and explicitly considering the plurality of environmental values of invasive species management to reduce potential social conflicts. The model identified priority areas for management activities. These areas corresponded to the core of the grey squirrel range, but they also included peripheral zones, where rapid eradication is fundamental to prevent the spread of squirrels. However, when the model was expanded to integrate the attitude of citizens towards the project, the intervention strategy slightly changed. In some areas, the citizens' support was limited, and this resulted in a reduced overall utility of intervention. The suggested approach extends the scientific basis for management decisions, evaluated in terms of technical efficiency, feasibility and social impact. Here, the Bayesian decision network model analysed the potential technical and social consequences of management actions, and it responded to the need for transparency in the decision-making process, but it can easily be extended to consider further issues that are common in many mammal eradication programmes. Owing to its flexibility and comprehensiveness, it provides an innovative example of how to plan rapid eradication or control activities, as required by the new EU Regulation. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.