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Sample records for multi-attribute decision model

  1. Supporting Nurses’ Decisions with a Multi-Attribute Model for Patient Health Evaluation

    PubMed Central

    Rajkovič, Uroš; Dinevski, Dejan; Šušteršič, Olga; Prijatelj, Vesna; Rajkovič, Vladislav

    2012-01-01

    Nurses are required to make many important decisions, for instance on determining the level of the nursing problem, setting nursing diagnoses and interventions. The model presented in this paper is a tool for better and easier decision making is such situations. Multi-attribute modeling of patients’ basic living activities is used for evaluation and explanation of their health status. It offers also visualization and quantification of the data which facilitate decision making in the framework of the process work method. The model can be viewed as an active check-list as it helps us reduce the possibility of “overlooking the queen on the chess board”. The model was critically evaluated in practice. PMID:24199115

  2. Evaluating patients' health using a hierarchical multi-attribute decision model.

    PubMed

    Sustersic, O; Rajkovic, U; Dinevski, D; Jereb, E; Rajkovic, Vladislav

    2009-01-01

    Evaluation of a patient's health status is an essential part of the healthcare process. For this purpose, Henderson's model of basic living activities (BLA) is often proposed as a set of criteria to be used in nursing. Despite its clarity and theoretical background, the model is only partially used in clinical practice. In this paper, we present the methodology for a hierarchical multi-attribute decision model to increase the practical efficiency of the BLA model. The result is a computerized model for the evaluation of a patient's health status. This model was tested in clinical practice by 17 nurses in two health centres in Slovenia and a strengths, weaknesses, opportunities and threats (SWOT) analysis was carried out. The strengths included providing a holistic understanding of the nature and level of the nursing problems, enriching the documentation and reducing the possibility of overlooking something important. As a part of electronic documentation, this computerized model supports systematic patient data gathering and evaluation. PMID:19930874

  3. An improvement of multi-attribute decision model of grey target with interval number

    NASA Astrophysics Data System (ADS)

    Hu, Ming-li

    2013-10-01

    In view of the limits of existing decision model of grey target with interval number, a new formula for normalizing decision matrix is given based on range transformation. At the same time, on the basis of the principle of TOPSIS, a new decision model of grey target is set up considering not only the distance from positive bulls eye but also from negative one. An example is given to show the application of the method, and the results are compared with other methods. The results verify the validity and practicability of the method.

  4. Multi-attribute decision analysis for the protection of groundwater resources

    NASA Astrophysics Data System (ADS)

    Shih, C. S.; Ingram, J. W.

    1981-05-01

    Decision analysis is a powerful management tool for situations involving a complex set of alternatives for a particular set of objectives. Decision analysis, coupled with multi-attribute utility assessments, is shown to be a viable problem solving method for a complex water-resource management problem. The City of San Antonio is faced with the difficult decision of how to protect its sole water source, the Edwards aquifer, from the threat of pollution resulting from urban sprawl over the aquifer recharge zone. This decision problem has been structured as a decision analysis model in an effort to provide local decision-makers with a highly objective and easily-documented means of deciding among alternative management policies. Multi-attribute utility functions were used as the measure of effectiveness for the various alternatives. The necessary judgmental information was gathered from a group of local water-resource decision-makers through a series of cyclic opinion surveys. Sensitivity analysis was conducted to illustrate the degree to which the solution of the problem was dependent upon the identified uncertain events.

  5. Detecting Hotspot Information Using Multi-Attribute Based Topic Model.

    PubMed

    Wang, Jing; Li, Li; Tan, Feng; Zhu, Ying; Feng, Weisi

    2015-01-01

    Microblogging as a kind of social network has become more and more important in our daily lives. Enormous amounts of information are produced and shared on a daily basis. Detecting hot topics in the mountains of information can help people get to the essential information more quickly. However, due to short and sparse features, a large number of meaningless tweets and other characteristics of microblogs, traditional topic detection methods are often ineffective in detecting hot topics. In this paper, we propose a new topic model named multi-attribute latent dirichlet allocation (MA-LDA), in which the time and hashtag attributes of microblogs are incorporated into LDA model. By introducing time attribute, MA-LDA model can decide whether a word should appear in hot topics or not. Meanwhile, compared with the traditional LDA model, applying hashtag attribute in MA-LDA model gives the core words an artificially high ranking in results meaning the expressiveness of outcomes can be improved. Empirical evaluations on real data sets demonstrate that our method is able to detect hot topics more accurately and efficiently compared with several baselines. Our method provides strong evidence of the importance of the temporal factor in extracting hot topics. PMID:26496635

  6. Detecting Hotspot Information Using Multi-Attribute Based Topic Model

    PubMed Central

    Wang, Jing; Li, Li; Tan, Feng; Zhu, Ying; Feng, Weisi

    2015-01-01

    Microblogging as a kind of social network has become more and more important in our daily lives. Enormous amounts of information are produced and shared on a daily basis. Detecting hot topics in the mountains of information can help people get to the essential information more quickly. However, due to short and sparse features, a large number of meaningless tweets and other characteristics of microblogs, traditional topic detection methods are often ineffective in detecting hot topics. In this paper, we propose a new topic model named multi-attribute latent dirichlet allocation (MA-LDA), in which the time and hashtag attributes of microblogs are incorporated into LDA model. By introducing time attribute, MA-LDA model can decide whether a word should appear in hot topics or not. Meanwhile, compared with the traditional LDA model, applying hashtag attribute in MA-LDA model gives the core words an artificially high ranking in results meaning the expressiveness of outcomes can be improved. Empirical evaluations on real data sets demonstrate that our method is able to detect hot topics more accurately and efficiently compared with several baselines. Our method provides strong evidence of the importance of the temporal factor in extracting hot topics. PMID:26496635

  7. Application of Choquet integral in solving multi-attribute decision making problems

    NASA Astrophysics Data System (ADS)

    Krishnan, Anath Rau; Mat Kasim, Maznah; Engku Abu Bakar, Engku Muhammad Nazri

    2011-10-01

    Aggregation is one of the imperative phases in solving a multi-attribute decision making (MADM) problems where the performance values of each choice will be composed into single score. Based on these final scores, the decision maker (DM) will make the decision by selecting, ranking or sorting the choices. The major issue in aggregation phase is most of the DMs are being ignorant or insensitive to the aspect of interaction between evaluation criteria while aggregating the performance values. Therefore, this paper intended to offer an appraisal on definition and several properties of aggregation operator, types of aggregation operator, and finally suggests Choquet integral operator and its associated fuzzy measure as the most appropriate tool in solving MADM problems. The suggested operator considers the interaction among evaluation criteria during aggregation. A simple numerical example is presented to emphasize the advantage of Choquet integral. However, the complexity of applying Choquet integral is discussed as well to provide some indications for future study.

  8. Multi-Attribute Decision Theory methodology for pollution control measure analysis

    SciTech Connect

    Barrera Roldan, A.S.; Corona Juarez, A.; Hardie, R.W.; Thayer, G.R.

    1992-12-31

    A methodology based in Multi-Attribute Decision Theory was developed to prioritize air pollution control measures and strategies (a set of measures) for Mexico City Metropolitan Area (MCMA). We have developed a framework that takes into account economic, technical feasibility, environmental, social, political, and institutional factors to evaluate pollution mitigation measures and strategies utilizing a decision analysis process. In a series of meetings with a panel of experts in air pollution from different offices of the mexican government we have developed General and Specific criteria for a decision analysis tree. With these tools the measures or strategies can be graded and a figure of merit can be assigned to each of them, so they can be ranked. Two pollution mitigation measures were analyzed to test the methodology, the results are presented. This methodology was developed specifically for Mexico City, though the experience gained in this work can be used to develop similar methodologies for other metropolitan areas throughout the world.

  9. Multi-Attribute Decision Theory methodology for pollution control measure analysis

    SciTech Connect

    Barrera Roldan, A.S.; Corona Juarez, A. ); Hardie, R.W.; Thayer, G.R. )

    1992-01-01

    A methodology based in Multi-Attribute Decision Theory was developed to prioritize air pollution control measures and strategies (a set of measures) for Mexico City Metropolitan Area (MCMA). We have developed a framework that takes into account economic, technical feasibility, environmental, social, political, and institutional factors to evaluate pollution mitigation measures and strategies utilizing a decision analysis process. In a series of meetings with a panel of experts in air pollution from different offices of the mexican government we have developed General and Specific criteria for a decision analysis tree. With these tools the measures or strategies can be graded and a figure of merit can be assigned to each of them, so they can be ranked. Two pollution mitigation measures were analyzed to test the methodology, the results are presented. This methodology was developed specifically for Mexico City, though the experience gained in this work can be used to develop similar methodologies for other metropolitan areas throughout the world.

  10. Decision support for personalized cloud service selection through multi-attribute trustworthiness evaluation.

    PubMed

    Ding, Shuai; Xia, Cheng-Yi; Xia, Chen-Yi; Zhou, Kai-Le; Yang, Shan-Lin; Shang, Jennifer S

    2014-01-01

    Facing a customer market with rising demands for cloud service dependability and security, trustworthiness evaluation techniques are becoming essential to cloud service selection. But these methods are out of the reach to most customers as they require considerable expertise. Additionally, since the cloud service evaluation is often a costly and time-consuming process, it is not practical to measure trustworthy attributes of all candidates for each customer. Many existing models cannot easily deal with cloud services which have very few historical records. In this paper, we propose a novel service selection approach in which the missing value prediction and the multi-attribute trustworthiness evaluation are commonly taken into account. By simply collecting limited historical records, the current approach is able to support the personalized trustworthy service selection. The experimental results also show that our approach performs much better than other competing ones with respect to the customer preference and expectation in trustworthiness assessment. PMID:24972237

  11. Decision Support for Personalized Cloud Service Selection through Multi-Attribute Trustworthiness Evaluation

    PubMed Central

    Ding, Shuai; Xia, Chen-Yi; Zhou, Kai-Le; Yang, Shan-Lin; Shang, Jennifer S.

    2014-01-01

    Facing a customer market with rising demands for cloud service dependability and security, trustworthiness evaluation techniques are becoming essential to cloud service selection. But these methods are out of the reach to most customers as they require considerable expertise. Additionally, since the cloud service evaluation is often a costly and time-consuming process, it is not practical to measure trustworthy attributes of all candidates for each customer. Many existing models cannot easily deal with cloud services which have very few historical records. In this paper, we propose a novel service selection approach in which the missing value prediction and the multi-attribute trustworthiness evaluation are commonly taken into account. By simply collecting limited historical records, the current approach is able to support the personalized trustworthy service selection. The experimental results also show that our approach performs much better than other competing ones with respect to the customer preference and expectation in trustworthiness assessment. PMID:24972237

  12. A Generalized Measurement Model to Quantify Health: The Multi-Attribute Preference Response Model

    PubMed Central

    Krabbe, Paul F. M.

    2013-01-01

    After 40 years of deriving metric values for health status or health-related quality of life, the effective quantification of subjective health outcomes is still a challenge. Here, two of the best measurement tools, the discrete choice and the Rasch model, are combined to create a new model for deriving health values. First, existing techniques to value health states are briefly discussed followed by a reflection on the recent revival of interest in patients’ experience with regard to their possible role in health measurement. Subsequently, three basic principles for valid health measurement are reviewed, namely unidimensionality, interval level, and invariance. In the main section, the basic operation of measurement is then discussed in the framework of probabilistic discrete choice analysis (random utility model) and the psychometric Rasch model. It is then shown how combining the main features of these two models yields an integrated measurement model, called the multi-attribute preference response (MAPR) model, which is introduced here. This new model transforms subjective individual rank data into a metric scale using responses from patients who have experienced certain health states. Its measurement mechanism largely prevents biases such as adaptation and coping. Several extensions of the MAPR model are presented. The MAPR model can be applied to a wide range of research problems. If extended with the self-selection of relevant health domains for the individual patient, this model will be more valid than existing valuation techniques. PMID:24278141

  13. Comprehensive evaluation of water resources security in the Yellow River basin based on a Fuzzy Multi-Attribute Decision Analysis Approach

    NASA Astrophysics Data System (ADS)

    Liu, K. K.; Li, C. H.; Cai, Y. P.; Xu, M.; Xia, X. H.

    2014-01-01

    In this paper, a Fuzzy Multi-Attribute Decision Analysis Approach (FMADAA) was adopted in water resources security evaluation for the nine provinces in the Yellow River basin in 2006. A numerical approximation system and a modified left-right scoring approach were adopted to cope with the uncertainties in the acquired information. Four multi-attribute decision making methods were implemented in the evaluation model for impact evaluation, including simple weighted addition (SWA), weighted product (WP), cooperative game theory (CGT) and technique for order preference by similarity to ideal solution (TOPSIS) which could be used for helping rank the water resources security in those nine provinces as well as the criteria alternatives. Moreover, several aggregation methods including average ranking procedure, borda and copeland methods were used to integrate the ranking results. The ranking results showed that the water resources security of the entire basin is in critical, insecurity and absolute insecurity state, especially in Shanxi, Inner Mongolia and Ningxia provinces in which water resources were lower than the average quantity in China. Hence, future planning of the Yellow River basin should mainly focus on the improvement of water eco-environment status in the provinces above.

  14. Comprehensive evaluation of water resources security in the Yellow River basin based on a fuzzy multi-attribute decision analysis approach

    NASA Astrophysics Data System (ADS)

    Liu, K. K.; Li, C. H.; Cai, Y. P.; Xu, M.; Xia, X. H.

    2014-05-01

    In this paper, a fuzzy multi-attribute decision analysis approach (FMADAA) was developed for supporting the evaluation of water resources security in nine provinces within the Yellow River basin. A numerical approximation system and a modified left-right scoring approach were adopted to cope with the uncertainties in the acquired information. Also, four conventional multi-attribute decision analysis (MADA) methods were implemented in the evaluation model for impact evaluation, including simple weighted addition (SWA), weighted product (WP), cooperative game theory (CGT) and technique for order preference by similarity to ideal solution (TOPSIS). Moreover, several aggregation methods including average ranking procedure, Borda and Copeland methods were used to integrate the ranking results, helping rank the water resources security in those nine provinces as well as improving reliability of evaluation results. The ranking results showed that the water resources security of the entire basin was in critical condition, including the insecurity and absolute insecurity states, especially in Shanxi, Inner Mongolia and Ningxia provinces in which water resources were lower than the average quantity in China. Hence, the improvement of water eco-environment statuses in the above-mentioned provinces should be prioritized in the future planning of the Yellow River basin.

  15. A multi-attribute decision analysis for decommissioning offshore oil and gas platforms.

    PubMed

    Henrion, Max; Bernstein, Brock; Swamy, Surya

    2015-10-01

    The 27 oil and gas platforms off the coast of southern California are reaching the end of their economic lives. Because their decommissioning involves large costs and potential environmental impacts, this became an issue of public controversy. As part of a larger policy analysis conducted for the State of California, we implemented a decision analysis as a software tool (PLATFORM) to clarify and evaluate decision strategies against a comprehensive set of objectives. Key options selected for in-depth analysis are complete platform removal and partial removal to 85 feet below the water line, with the remaining structure converted in place to an artificial reef to preserve the rich ecosystems supported by the platform's support structure. PLATFORM was instrumental in structuring and performing key analyses of the impacts of each option (e.g., on costs, fishery production, air emissions) and dramatically improved the team's productivity. Sensitivity analysis found that disagreement about preferences, especially about the relative importance of strict compliance with lease agreements, has much greater effects on the preferred option than does uncertainty about specific outcomes, such as decommissioning costs. It found a near-consensus of stakeholders in support of partial removal and "rigs-to-reefs" program. The project's results played a role in the decision to pass legislation enabling an expanded California "rigs-to-reefs" program that includes a mechanism for sharing cost savings between operators and the state. PMID:26415010

  16. Propulsion Airframe Aeroacoustics Technology Evaluation and Selection Using a Multi-Attribute Decision Making Process and Non-Deterministic Design

    NASA Technical Reports Server (NTRS)

    Burg, Cecile M.; Hill, Geoffrey A.; Brown, Sherilyn A.; Geiselhart, Karl A.

    2004-01-01

    The Systems Analysis Branch at NASA Langley Research Center has investigated revolutionary Propulsion Airframe Aeroacoustics (PAA) technologies and configurations for a Blended-Wing-Body (BWB) type aircraft as part of its research for NASA s Quiet Aircraft Technology (QAT) Project. Within the context of the long-term NASA goal of reducing the perceived aircraft noise level by a factor of 4 relative to 1997 state of the art, major configuration changes in the propulsion airframe integration system were explored with noise as a primary design consideration. An initial down-select and assessment of candidate PAA technologies for the BWB was performed using a Multi-Attribute Decision Making (MADM) process consisting of organized brainstorming and decision-making tools. The assessments focused on what effect the PAA technologies had on both the overall noise level of the BWB and what effect they had on other major design considerations such as weight, performance and cost. A probabilistic systems analysis of the PAA configurations that presented the best noise reductions with the least negative impact on the system was then performed. Detailed results from the MADM study and the probabilistic systems analysis will be published in the near future.

  17. Multi-attribute mate choice decisions and uncertainty in the decision process: a generalized sequential search strategy.

    PubMed

    Wiegmann, Daniel D; Weinersmith, Kelly L; Seubert, Steven M

    2010-04-01

    The behavior of females in search of a mate determines the likelihood that high quality males are encountered and adaptive search strategies rely on the effective use of available information on the quality of prospective mates. The sequential search strategy was formulated, like most models of search behavior, on the assumption that females obtain perfect information on the quality of encountered males. In this paper, we modify the strategy to allow for uncertainty of male quality and we determine how the magnitude of this uncertainty and the ability of females to inspect multiple male attributes to reduce uncertainty influence mate choice decisions. In general, searchers are sensitive to search costs and higher costs lower acceptance criteria under all versions of the model. The choosiness of searchers increases with the variability of the quality of prospective mates under conditions of the original model, but under conditions of uncertainty the choosiness of searchers may increase or decrease with the variability of inspected male attributes. The behavioral response depends on the functional relationship between observed male attributes and the fitness return to searchers and on costs associated with the search process. Higher uncertainty often induces searchers to pay more for information and under conditions of uncertainty the fitness return to searchers is never higher than under conditions of the original model. Further studies of the performance of alternative search strategies under conditions of uncertainty may consequently be necessary to identify search strategies likely to be used under natural conditions. PMID:19499223

  18. Assessment of distance-based multi-attribute group decision-making methods from a maintenance strategy perspective

    NASA Astrophysics Data System (ADS)

    Ding, Siew-Hong; Kamaruddin, Shahrul

    2015-07-01

    Maintenance has been acknowledged by industrial management as a significant influencing factor of plant performance. Effective plant maintenance can be realized by developing a proper maintenance strategy. However, selecting an appropriate maintenance strategy is difficult because maintenance is a non-repetitive task such as production activity. Maintenance also does not leave a consistent traceable record that can be referred to during the decision-making process. The involvement of tangible and intangible factors in the assessment process further increases the complexity of the decision-making process. The technique of preference order by similarity to ideal solution (TOPSIS) is one of the most well-known decision-making methods and has been widely used by organizations to conduct effective decisions regarding maintenance issues. TOPSIS has also evolved by integrating different approaches such as the fuzzy concept. Although numerous TOPSIS applications for maintenance decision making have been published, the effectiveness of crisp TOPSIS and fuzzy TOPSIS needs to be investigated further. This paper attempts to present a comparison between conventional crisp TOPSIS and fuzzy TOPSIS from a group maintenance decision-making perspective by an empirical illustration. Sensitivity analysis is conducted to demonstrate further the resilience of crisp TOPSIS and fuzzy TOPSIS.

  19. Hierarchical competitions subserving multi-attribute choice.

    PubMed

    Hunt, Laurence T; Dolan, Raymond J; Behrens, Timothy E J

    2014-11-01

    Valuation is a key tenet of decision neuroscience, where it is generally assumed that different attributes of competing options are assimilated into unitary values. Such values are central to current neural models of choice. By contrast, psychological studies emphasize complex interactions between choice and valuation. Principles of neuronal selection also suggest that competitive inhibition may occur in early valuation stages, before option selection. We found that behavior in multi-attribute choice is best explained by a model involving competition at multiple levels of representation. This hierarchical model also explains neural signals in human brain regions previously linked to valuation, including striatum, parietal and prefrontal cortex, where activity represents within-attribute competition, competition between attributes and option selection. This multi-layered inhibition framework challenges the assumption that option values are computed before choice. Instead, our results suggest a canonical competition mechanism throughout all stages of a processing hierarchy, not simply at a final choice stage. PMID:25306549

  20. Multi-Attribute Consensus Building Tool

    ERIC Educational Resources Information Center

    Shyyan, Vitaliy; Christensen, Laurene; Thurlow, Martha; Lazarus, Sheryl

    2013-01-01

    The Multi-Attribute Consensus Building (MACB) method is a quantitative approach for determining a group's opinion about the importance of each item (strategy, decision, recommendation, policy, priority, etc.) on a list (Vanderwood, & Erickson, 1994). This process enables a small or large group of participants to generate and discuss a set…

  1. Multi-Attribute Sequential Search

    ERIC Educational Resources Information Center

    Bearden, J. Neil; Connolly, Terry

    2007-01-01

    This article describes empirical and theoretical results from two multi-attribute sequential search tasks. In both tasks, the DM sequentially encounters options described by two attributes and must pay to learn the values of the attributes. In the "continuous" version of the task the DM learns the precise numerical value of an attribute when she…

  2. Experimental analysis of multi-attribute decision-making based on Atanassov intuitionistic fuzzy sets: a discussion of anchor dependency and accuracy functions

    NASA Astrophysics Data System (ADS)

    Chen, Ting-Yu

    2012-06-01

    This article presents a useful method for relating anchor dependency and accuracy functions to multiple attribute decision-making (MADM) problems in the context of Atanassov intuitionistic fuzzy sets (A-IFSs). Considering anchored judgement with displaced ideals and solution precision with minimal hesitation, several auxiliary optimisation models have proposed to obtain the optimal weights of the attributes and to acquire the corresponding TOPSIS (the technique for order preference by similarity to the ideal solution) index for alternative rankings. Aside from the TOPSIS index, as a decision-maker's personal characteristics and own perception of self may also influence the direction in the axiom of choice, the evaluation of alternatives is conducted based on distances of each alternative from the positive and negative ideal alternatives, respectively. This article originates from Li's [Li, D.-F. (2005), 'Multiattribute Decision Making Models and Methods Using Intuitionistic Fuzzy Sets', Journal of Computer and System Sciences, 70, 73-85] work, which is a seminal study of intuitionistic fuzzy decision analysis using deduced auxiliary programming models, and deems it a benchmark method for comparative studies on anchor dependency and accuracy functions. The feasibility and effectiveness of the proposed methods are illustrated by a numerical example. Finally, a comparative analysis is illustrated with computational experiments on averaging accuracy functions, TOPSIS indices, separation measures from positive and negative ideal alternatives, consistency rates of ranking orders, contradiction rates of the top alternative and average Spearman correlation coefficients.

  3. Multi-Attribute Tradespace Exploration in Space System Design

    NASA Astrophysics Data System (ADS)

    Ross, A. M.; Hastings, D. E.

    2002-01-01

    The complexity inherent in space systems necessarily requires intense expenditures of resources both human and monetary. The high level of ambiguity present in the early design phases of these systems causes long, highly iterative, and costly design cycles. This paper looks at incorporating decision theory methods into the early design processes to streamline communication of wants and needs among stakeholders and between levels of design. Communication channeled through formal utility interviews and analysis enables engineers to better understand the key drivers for the system and allows a more thorough exploration of the design tradespace. Multi-Attribute Tradespace Exploration (MATE), an evolving process incorporating decision theory into model and simulation- based design, has been applied to several space system case studies at MIT. Preliminary results indicate that this process can improve the quality of communication to more quickly resolve project ambiguity, and enable the engineer to discover better value designs for multiple stakeholders. MATE is also being integrated into a concurrent design environment to facilitate the transfer knowledge of important drivers into higher fidelity design phases. Formal utility theory provides a mechanism to bridge the language barrier between experts of different backgrounds and differing needs (e.g. scientists, engineers, managers, etc). MATE with concurrent design couples decision makers more closely to the design, and most importantly, maintains their presence between formal reviews.

  4. Using Consumer Behavior and Decision Models to Aid Students in Choosing a Major.

    ERIC Educational Resources Information Center

    Kaynama, Shohreh A.; Smith, Louise W.

    1996-01-01

    A study found that using consumer behavior and decision models to guide students to a major can be useful and enjoyable for students. Students consider many of the basic parameters through multi-attribute and decision-analysis models, so time with professors, who were found to be the most influential group, can be used for more individual and…

  5. Production Task Queue Optimization Based on Multi-Attribute Evaluation for Complex Product Assembly Workshop.

    PubMed

    Li, Lian-Hui; Mo, Rong

    2015-01-01

    The production task queue has a great significance for manufacturing resource allocation and scheduling decision. Man-made qualitative queue optimization method has a poor effect and makes the application difficult. A production task queue optimization method is proposed based on multi-attribute evaluation. According to the task attributes, the hierarchical multi-attribute model is established and the indicator quantization methods are given. To calculate the objective indicator weight, criteria importance through intercriteria correlation (CRITIC) is selected from three usual methods. To calculate the subjective indicator weight, BP neural network is used to determine the judge importance degree, and then the trapezoid fuzzy scale-rough AHP considering the judge importance degree is put forward. The balanced weight, which integrates the objective weight and the subjective weight, is calculated base on multi-weight contribution balance model. The technique for order preference by similarity to an ideal solution (TOPSIS) improved by replacing Euclidean distance with relative entropy distance is used to sequence the tasks and optimize the queue by the weighted indicator value. A case study is given to illustrate its correctness and feasibility. PMID:26414758

  6. Production Task Queue Optimization Based on Multi-Attribute Evaluation for Complex Product Assembly Workshop

    PubMed Central

    Li, Lian-hui; Mo, Rong

    2015-01-01

    The production task queue has a great significance for manufacturing resource allocation and scheduling decision. Man-made qualitative queue optimization method has a poor effect and makes the application difficult. A production task queue optimization method is proposed based on multi-attribute evaluation. According to the task attributes, the hierarchical multi-attribute model is established and the indicator quantization methods are given. To calculate the objective indicator weight, criteria importance through intercriteria correlation (CRITIC) is selected from three usual methods. To calculate the subjective indicator weight, BP neural network is used to determine the judge importance degree, and then the trapezoid fuzzy scale-rough AHP considering the judge importance degree is put forward. The balanced weight, which integrates the objective weight and the subjective weight, is calculated base on multi-weight contribution balance model. The technique for order preference by similarity to an ideal solution (TOPSIS) improved by replacing Euclidean distance with relative entropy distance is used to sequence the tasks and optimize the queue by the weighted indicator value. A case study is given to illustrate its correctness and feasibility. PMID:26414758

  7. Weight sensitivity measurement, analysis, and application in multi-attribute evaluation

    NASA Astrophysics Data System (ADS)

    Zhao, Yong; Huang, Chongyin; Chen, Yang

    2013-11-01

    Weights are used to measure relative importance of multiple attributes or objectives, which influence evaluation or decision results to a great degree. Thus, analyzing weight sensitivity is an important work for a multi-attribute evaluation or decision. A measuring method based on the inclined angle of two vectors is proposed in this paper in order to solve the weight sensitivity of a multi-attribute evaluation with isotonicity characteristic. This method uses the cosine of the inclined angle to measure the weight sensitivity based on preferences or preference combinations. Concepts of sensitivity space, degree, and angle are given, and the relevant measurement method is discussed and proved. Also, this method is used for the choice of the water environment protection projects in Heyuan City.

  8. Zoning of an agricultural field using a fuzzy indicator model in combination with tool for multi-attributed decision-making

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Zoning of agricultural fields is an important task for utilization of precision farming technology. This paper extends previously published work entitled “Zoning of an agricultural field using a fuzzy indicator model” to a general case where there is disagreement between groups of managers or expert...

  9. Comparison of Decision Models

    NASA Technical Reports Server (NTRS)

    Feinberg, A.; Miles, J. R. F.; Smith, J. H.; Scheuer, E. M.

    1986-01-01

    Two methods of multiattribute decision analysis compared in report. One method employs linear utility model. Other utilizes multiplicative utility model. Report based on interviews with experts in automotive technology to obtain their preferences regarding 10 new types of vehicles.

  10. LineUp: Visual Analysis of Multi-Attribute Rankings

    PubMed Central

    Gratzl, Samuel; Lex, Alexander; Gehlenborg, Nils; Pfister, Hanspeter; Streit, Marc

    2014-01-01

    Rankings are a popular and universal approach to structuring otherwise unorganized collections of items by computing a rank for each item based on the value of one or more of its attributes. This allows us, for example, to prioritize tasks or to evaluate the performance of products relative to each other. While the visualization of a ranking itself is straightforward, its interpretation is not, because the rank of an item represents only a summary of a potentially complicated relationship between its attributes and those of the other items. It is also common that alternative rankings exist which need to be compared and analyzed to gain insight into how multiple heterogeneous attributes affect the rankings. Advanced visual exploration tools are needed to make this process efficient. In this paper we present a comprehensive analysis of requirements for the visualization of multi-attribute rankings. Based on these considerations, we propose LineUp - a novel and scalable visualization technique that uses bar charts. This interactive technique supports the ranking of items based on multiple heterogeneous attributes with different scales and semantics. It enables users to interactively combine attributes and flexibly refine parameters to explore the effect of changes in the attribute combination. This process can be employed to derive actionable insights as to which attributes of an item need to be modified in order for its rank to change. Additionally, through integration of slope graphs, LineUp can also be used to compare multiple alternative rankings on the same set of items, for example, over time or across different attribute combinations. We evaluate the effectiveness of the proposed multi-attribute visualization technique in a qualitative study. The study shows that users are able to successfully solve complex ranking tasks in a short period of time. PMID:24051794

  11. Decision Impact Assessment Model

    Energy Science and Technology Software Center (ESTSC)

    1991-08-01

    DIAMOND represents the decision-making environment that utility planners and executives face. Users interact with the model after every year or two of simulation, which provides an opportunity to modify past decisions as well as to make new decisions. For example, construction of a power plant can be started one year, and if circumstances change, the plant can be accelerated, mothballed, cancelled, or continued as originally planned. Similarly, the marketing and financial incentives for demand-side managementmore » programs can be changed from year to year. This frequent user interaction with the model, an operational game, should build greater understanding and insights among utility planners about the risks associated with different types of resources.« less

  12. Visual analytics techniques for large multi-attribute time series data

    NASA Astrophysics Data System (ADS)

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

    2008-01-01

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

  13. Automated modeling of medical decisions.

    PubMed Central

    Egar, J. W.; Musen, M. A.

    1993-01-01

    We have developed a graph grammar and a graph-grammar derivation system that, together, generate decision-theoretic models from unordered lists of medical terms. The medical terms represent considerations in a dilemma that confronts the patient and the health-care provider. Our current grammar ensures that several desirable structural properties are maintained in all derived decision models. PMID:8130509

  14. Measurement of patient-derived utility values for periodontal health using a multi-attribute scale.

    PubMed

    Bellamy, C A; Brickley, M R; McAndrew, R

    1996-09-01

    Periodontal health states are difficult to quantify and no formal scale quantifying patients' utilities for periodontal health states exits. Multi-attribute utility (MAU) techniques were used to develop such a scale. The MAU scale may be used to quantify patients' assessment of their current periodontal health and that of possible treatment outcomes. Such data, combined with probability values in formal decision analysis techniques would result in improved rationality of treatment planning for periodontal disease. 20 patients attending for routine undergraduate care were interviewed. Data from these interviews were sorted into groups of common interest (domains). Intra-domain health statements were complied from the interview content. 21 patients ranked the intra-domain statements on a scale of 0-100. This same group of patients also performed an inter-domain weighting. Mean results showed that patients were 2X as concerned with how they felt and with the prognosis of possible outcomes, than with how they looked and what facts they knew about their oral health. However, the real value of utilities research lies in application of individual results to treatment planning as there is a wide range of opinion regarding outcome health states. PMID:8891929

  15. Models of Affective Decision Making

    PubMed Central

    Charpentier, Caroline J.; De Neve, Jan-Emmanuel; Li, Xinyi; Roiser, Jonathan P.; Sharot, Tali

    2016-01-01

    Intuitively, how you feel about potential outcomes will determine your decisions. Indeed, an implicit assumption in one of the most influential theories in psychology, prospect theory, is that feelings govern choice. Surprisingly, however, very little is known about the rules by which feelings are transformed into decisions. Here, we specified a computational model that used feelings to predict choices. We found that this model predicted choice better than existing value-based models, showing a unique contribution of feelings to decisions, over and above value. Similar to the value function in prospect theory, our feeling function showed diminished sensitivity to outcomes as value increased. However, loss aversion in choice was explained by an asymmetry in how feelings about losses and gains were weighted when making a decision, not by an asymmetry in the feelings themselves. The results provide new insights into how feelings are utilized to reach a decision. PMID:27071751

  16. Multi-Attribute Utility Theory and Adaptive Techniques for Intelligent Web-Based Educational Software

    ERIC Educational Resources Information Center

    Kabassi, K.; Virvou, M.

    2006-01-01

    This paper describes how the Multi-Attribute Utility Theory can be combined with adaptive techniques to improve individualised teaching in an Intelligent Learning Environment (ILE). The ILE is called Web F-SMILE, it operates over the Web and is meant to help novice users learn basic skills of computer use. Tutoring is dynamically adapted to the…

  17. MULTI-ATTRIBUTE SEISMIC/ROCK PHYSICS APPROACH TO CHARACTERIZING FRACTURED RESERVOIRS

    SciTech Connect

    Gary Mavko

    2000-10-01

    This project consists of three key interrelated Phases, each focusing on the central issue of imaging and quantifying fractured reservoirs, through improved integration of the principles of rock physics, geology, and seismic wave propagation. This report summarizes the results of Phase I of the project. The key to successful development of low permeability reservoirs lies in reliably characterizing fractures. Fractures play a crucial role in controlling almost all of the fluid transport in tight reservoirs. Current seismic methods to characterize fractures depend on various anisotropic wave propagation signatures that can arise from aligned fractures. We are pursuing an integrated study that relates to high-resolution seismic images of natural fractures to the rock parameters that control the storage and mobility of fluids. Our goal is to go beyond the current state-of-the art to develop and demonstrate next generation methodologies for detecting and quantitatively characterizing fracture zones using seismic measurements. Our study incorporates 3 key elements: (1) Theoretical rock physics studies of the anisotropic viscoelastic signatures of fractured rocks, including up scaling analysis and rock-fluid interactions to define the factors relating fractures in the lab and in the field. (2) Modeling of optimal seismic attributes, including offset and azimuth dependence of travel time, amplitude, impedance and spectral signatures of anisotropic fractured rocks. We will quantify the information content of combinations of seismic attributes, and the impact of multi-attribute analyses in reducing uncertainty in fracture interpretations. (3) Integration and interpretation of seismic, well log, and laboratory data, incorporating field geologic fracture characterization and the theoretical results of items 1 and 2 above. The focal point for this project is the demonstration of these methodologies in the Marathon Oil Company Yates Field in West Texas.

  18. The Application of a Multi-Attribute Decision-Making Algorithm to Learning Management Systems Evaluation

    ERIC Educational Resources Information Center

    Cavus, Nadire

    2011-01-01

    Recently, because of the rapid increase in the popularity of the Internet, the delivery of learning programmes has gradually shifted from local desktop to online-based applications. As more and more technological tools become available for online education, there is an increasing interest among educators and other professionals in the application…

  19. How Well Do the Generic Multi-attribute Utility Instruments Incorporate Patient and Public Views Into Their Descriptive Systems?

    PubMed

    Stevens, Katherine J

    2016-02-01

    Multi-attribute utility instruments (MAUIs) are increasingly being used to generate utility data, which can be used to calculate quality-adjusted life-years (QALYs). These QALY data can then be incorporated into a cost-utility analysis as part of an economic evaluation, to inform health care resource allocation decisions. Many health care decision-making bodies around the world, such as the National Institute for Health and Care Excellence, require the use of generic MAUIs. Recently, there has been a call for greater input of patients into the development of patient-reported outcome measures, and this is now actively encouraged. By incorporating the views of patients, greater validity of an instrument is expected and it is more likely that patients will be able to self-complete the instrument, which is the ideal when obtaining information about a patient's health-related quality of life. This paper examines the stages of MAUI development and the scope for patient and/or public involvement at each stage. The paper then reviews how much the main generic MAUIs have incorporated the views of patients/the public into the development of their descriptive systems at each of these stages, and the implications of this. The review finds that the majority of MAUIs had very little input from patients/the public. Instead, existing literature and/or the views of experts were used. If we wish to incorporate patient/public views into future development of MAUIs, qualitative methods are recommended. PMID:25663152

  20. A decision-analytic model for early stage breast cancer: lumpectomy vs mastectomy.

    PubMed

    Büyükdamgaci-Alogan, G; Elele, T; Hayran, M; Erman, M; Kiliçkap, S

    2008-01-01

    The purpose was to construct a decision model that incorporated patient preferences over differing health state prospects and to analyze the decision context of early stage breast cancer patients in relation to two main surgical treatment options. A Markov chain was constructed to project the clinical history of breast carcinoma following surgery. A Multi Attribute Utility Model was developed for outcome evaluation. Transition probabilities were obtained by using subjective probability assessment. This study was performed on the sample population of female university students and utilities were elicited from these healthy volunteers. The results were validated by using Standard Gamble technique. Finally, Monte Carlo Simulation was utilized in Treeage-Pro 2006-Suit software program in order to calculate expected utility generated by each treatment option. The results showed that, if the subject had mastectomy, mean value for the quality adjusted life years gained was 6.42; on the other hand, if the preference was lumpectomy, it was 7.00 out of a possible 10 years. Sensitivity analysis on transition probabilities to local recurrence and salvaged states was performed and two threshold values were observed. Additionally, sensitivity analysis on utilities showed that the model was more sensitive to no evidence of disease state; however, was not sensitive to utilities of local recurrence and salvaged states. The decision model was developed with reasonable success for early stage breast cancer patients, and tested by using general public data. The results obtained from these data showed that lumpectomy was more favourable for these participants. PMID:18348655

  1. Decision tree modeling using R.

    PubMed

    Zhang, Zhongheng

    2016-08-01

    In machine learning field, decision tree learner is powerful and easy to interpret. It employs recursive binary partitioning algorithm that splits the sample in partitioning variable with the strongest association with the response variable. The process continues until some stopping criteria are met. In the example I focus on conditional inference tree, which incorporates tree-structured regression models into conditional inference procedures. While growing a single tree is subject to small changes in the training data, random forests procedure is introduced to address this problem. The sources of diversity for random forests come from the random sampling and restricted set of input variables to be selected. Finally, I introduce R functions to perform model based recursive partitioning. This method incorporates recursive partitioning into conventional parametric model building. PMID:27570769

  2. Decision tree modeling using R

    PubMed Central

    2016-01-01

    In machine learning field, decision tree learner is powerful and easy to interpret. It employs recursive binary partitioning algorithm that splits the sample in partitioning variable with the strongest association with the response variable. The process continues until some stopping criteria are met. In the example I focus on conditional inference tree, which incorporates tree-structured regression models into conditional inference procedures. While growing a single tree is subject to small changes in the training data, random forests procedure is introduced to address this problem. The sources of diversity for random forests come from the random sampling and restricted set of input variables to be selected. Finally, I introduce R functions to perform model based recursive partitioning. This method incorporates recursive partitioning into conventional parametric model building. PMID:27570769

  3. MAT - MULTI-ATTRIBUTE TASK BATTERY FOR HUMAN OPERATOR WORKLOAD AND STRATEGIC BEHAVIOR RESEARCH

    NASA Technical Reports Server (NTRS)

    Comstock, J. R.

    1994-01-01

    MAT, a Multi-Attribute Task battery, gives the researcher the capability of performing multi-task workload and performance experiments. The battery provides a benchmark set of tasks for use in a wide range of laboratory studies of operator performance and workload. MAT incorporates tasks analogous to activities that aircraft crew members perform in flight, while providing a high degree of experiment control, performance data on each subtask, and freedom to use non-pilot test subjects. The MAT battery primary display is composed of four separate task windows which are as follows: a monitoring task window which includes gauges and warning lights, a tracking task window for the demands of manual control, a communication task window to simulate air traffic control communications, and a resource management task window which permits maintaining target levels on a fuel management task. In addition, a scheduling task window gives the researcher information about future task demands. The battery also provides the option of manual or automated control of tasks. The task generates performance data for each subtask. The task battery may be paused and onscreen workload rating scales presented to the subject. The MAT battery was designed to use a serially linked second computer to generate the voice messages for the Communications task. The MATREMX program and support files, which are included in the MAT package, were designed to work with the Heath Voice Card (Model HV-2000, available through the Heath Company, Benton Harbor, Michigan 49022); however, the MATREMX program and support files may easily be modified to work with other voice synthesizer or digitizer cards. The MAT battery task computer may also be used independent of the voice computer if no computer synthesized voice messages are desired or if some other method of presenting auditory messages is devised. MAT is written in QuickBasic and assembly language for IBM PC series and compatible computers running MS-DOS. The

  4. Values in Decision Making: The INVOLVE Model.

    ERIC Educational Resources Information Center

    Elfrink, Victoria L.; Coldwell, LuAnn Linson

    1993-01-01

    Describes the function of values and participatory decision making within the student affairs profession. Introduces a process model as a means of operationalizing values in the decision-making process and uses case studies to illustrate how the model can be used to improve student affairs professionals' decision making. (RJM)

  5. Modelling decision-making by pilots

    NASA Technical Reports Server (NTRS)

    Patrick, Nicholas J. M.

    1993-01-01

    Our scientific goal is to understand the process of human decision-making. Specifically, a model of human decision-making in piloting modern commercial aircraft which prescribes optimal behavior, and against which we can measure human sub-optimality is sought. This model should help us understand such diverse aspects of piloting as strategic decision-making, and the implicit decisions involved in attention allocation. Our engineering goal is to provide design specifications for (1) better computer-based decision-aids, and (2) better training programs for the human pilot (or human decision-maker, DM).

  6. The KONVERGENCE Model for Sustainable Decisions

    SciTech Connect

    Kerr, Thomas A; Dakins, Maxine; Gibson, Patrick Lavern; Joe, Jeffrey Clark; Nitschke, Robert Leon; Piet, Steven James

    2002-08-01

    The KONVERGENCE Model for Sustainable Decisions is a new way of viewing, developing, organizing, and evaluating alternatives for decisions that may affect a wide range of interests and that must factor in long timeframes, enduring hazards, and/or continuing responsibilities. It differs from other models in that it addresses the need for decisions to continue to "work" over long time periods in an ever-changing decision environment. The authors show that the model contains three major universes - knowledge, values, and resources (the K, V, and R in KONVERGENCE)- that interact and overlap throughout the effective lifetime of a decision. They discuss how decision-makers and decision participants can use the model to craft and analyze decisions and decision processes that stand the test of time. The authors use the U.S. moon-landing program as an example of a major decision process that was sustained over time. They use the model to explain why events unfolded in the way that they did - and why we are where we are today in that program. The authors believe that this model will be especially useful in long-term decision processes such as those that address contamination cleanup programs, long-term environmental stewardship, and the initial siting of facilities with long-term objectives. Companion papers describe the KONVERGENCE Model process steps and implications for intractable cleanup decisions.

  7. Model choice for decision making under uncertainty

    NASA Astrophysics Data System (ADS)

    Bàrdossy, Andràs

    2015-04-01

    Present and future water management decisions are often supported by modelling. The choice of the appropriate model and model parameters depend on the decision related question, the quality of the model and the available information. While spatially detailed physics based models might seem very transferable, the uncertainty of the parametrization and of the input may lead to highly diverging results, which are of no use for decision making. The optimal model choice requires a quantification of the input/natural parameter uncertainty. As a next step the influence of this uncertainty on predictions using models with different complexity has to be quantified. Finally the influence of this prediction uncertainty on the decisions to be taken has to be assessed. Different data/information availability and modelling questions thus might require different modelling approaches. A framework for this model choice and parametrization problem will be presented together with examples from regions with very different data availability and data quality.

  8. Shared Problem Models and Crew Decision Making

    NASA Technical Reports Server (NTRS)

    Orasanu, Judith; Statler, Irving C. (Technical Monitor)

    1994-01-01

    The importance of crew decision making to aviation safety has been well established through NTSB accident analyses: Crew judgment and decision making have been cited as causes or contributing factors in over half of all accidents in commercial air transport, general aviation, and military aviation. Yet the bulk of research on decision making has not proven helpful in improving the quality of decisions in the cockpit. One reason is that traditional analytic decision models are inappropriate to the dynamic complex nature of cockpit decision making and do not accurately describe what expert human decision makers do when they make decisions. A new model of dynamic naturalistic decision making is offered that may prove more useful for training or aiding cockpit decision making. Based on analyses of crew performance in full-mission simulation and National Transportation Safety Board accident reports, features that define effective decision strategies in abnormal or emergency situations have been identified. These include accurate situation assessment (including time and risk assessment), appreciation of the complexity of the problem, sensitivity to constraints on the decision, timeliness of the response, and use of adequate information. More effective crews also manage their workload to provide themselves with time and resources to make good decisions. In brief, good decisions are appropriate to the demands of the situation and reflect the crew's metacognitive skill. Effective crew decision making and overall performance are mediated by crew communication. Communication contributes to performance because it assures that all crew members have essential information, but it also regulates and coordinates crew actions and is the medium of collective thinking in response to a problem. This presentation will examine the relation between communication that serves to build performance. Implications of these findings for crew training will be discussed.

  9. Research on the Comparability of Multi-attribute Evaluation Methods for Academic Journals

    NASA Astrophysics Data System (ADS)

    Liping, Yu

    This paper first constructs a classification framework for multi-attribute evaluation methods oriented to academic journals, and then discusses the comparability of the vast majority of non-linear evaluation methods and the majority of linear evaluation methods theoretically, taking the TOPSIS method as an example and the evaluation data on agricultural journals as an exercise of validation. The analysis result shows that we should attach enough importance to the comparability of evaluation methods for academic journals; the evaluation objectives are closely related to the choice of evaluation methods, and also relevant to the comparability of evaluation methods; the specialized organizations for journal evaluation had better release the evaluation data, evaluation methods and evaluation results to the best of their abilities; only purely subjective evaluation method is of broad comparability.

  10. Recognition of human-vehicle interactions in group activities via multi-attributed semantic message generation

    NASA Astrophysics Data System (ADS)

    Elangovan, Vinayak; Shirkhodaie, Amir

    2015-05-01

    Improved Situational awareness is a vital ongoing research effort for the U.S. Homeland Security for the past recent years. Many outdoor anomalous activities involve vehicles as their primary source of transportation to and from the scene where a plot is executed. Analysis of dynamics of Human-Vehicle Interaction (HVI) helps to identify correlated patterns of activities representing potential threats. The objective of this paper is bi-folded. Primarily, we discuss a method for temporal HVI events detection and verification for generation of HVI hypotheses. To effectively recognize HVI events, a Multi-attribute Vehicle Detection and Identification technique (MVDI) for detection and classification of stationary vehicles is presented. Secondly, we describe a method for identification of pertinent anomalous behaviors through analysis of state transitions between two successively detected events. Finally, we present a technique for generation of HVI semantic messages and present our experimental results to demonstrate the effectiveness of semantic messages for discovery of HVI in group activities.

  11. Disentangling Decision Models: From Independence to Competition

    ERIC Educational Resources Information Center

    Teodorescu, Andrei R.; Usher, Marius

    2013-01-01

    A multitude of models have been proposed to account for the neural mechanism of value integration and decision making in speeded decision tasks. While most of these models account for existing data, they largely disagree on a fundamental characteristic of the choice mechanism: independent versus different types of competitive processing. Five…

  12. A Structured Decision Approach for Integrating and Analyzing Community Perspectives in Re-Use Planning of Vacant Properties in Cleveland, Ohio

    EPA Science Inventory

    An integrated GIS-based, multi-attribute decision model deployed in a web-based platform is presented enabling an iterative, spatially explicit and collaborative analysis of relevant and available information for repurposing vacant land. The process incorporated traditional and ...

  13. Optimal Decision Making in Neural Inhibition Models

    ERIC Educational Resources Information Center

    van Ravenzwaaij, Don; van der Maas, Han L. J.; Wagenmakers, Eric-Jan

    2012-01-01

    In their influential "Psychological Review" article, Bogacz, Brown, Moehlis, Holmes, and Cohen (2006) discussed optimal decision making as accomplished by the drift diffusion model (DDM). The authors showed that neural inhibition models, such as the leaky competing accumulator model (LCA) and the feedforward inhibition model (FFI), can mimic the…

  14. The multi-attribute task battery for human operator workload and strategic behavior research

    NASA Technical Reports Server (NTRS)

    Comstock, J. Raymond, Jr.; Arnegard, Ruth J.

    1992-01-01

    The Multi-Attribute Task (MAT) Battery provides a benchmark set of tasks for use in a wide range of lab studies of operator performance and workload. The battery incorporates tasks analogous to activities that aircraft crewmembers perform in flight, while providing a high degree of experimenter control, performance data on each subtask, and freedom to use nonpilot test subjects. Features not found in existing computer based tasks include an auditory communication task (to simulate Air Traffic Control communication), a resource management task permitting many avenues or strategies of maintaining target performance, a scheduling window which gives the operator information about future task demands, and the option of manual or automated control of tasks. Performance data are generated for each subtask. In addition, the task battery may be paused and onscreen workload rating scales presented to the subject. The MAT Battery requires a desktop computer with color graphics. The communication task requires a serial link to a second desktop computer with a voice synthesizer or digitizer card.

  15. A visual analysis of multi-attribute data using pixel matrix displays

    NASA Astrophysics Data System (ADS)

    Hao, Ming C.; Dayal, Umeshwar; Keim, Daniel; Schreck, Tobias

    2007-01-01

    Charts and tables are commonly used to visually analyze data. These graphics are simple and easy to understand, but charts show only highly aggregated data and present only a limited number of data values while tables often show too many data values. As a consequence, these graphics may either lose or obscure important information, so different techniques are required to monitor complex datasets. Users need more powerful visualization techniques to digest and compare detailed multi-attribute data to analyze the health of their business. This paper proposes an innovative solution based on the use of pixel-matrix displays to represent transaction-level information. With pixelmatrices, users can visualize areas of importance at a glance, a capability not provided by common charting techniques. We present our solutions to use colored pixel-matrices in (1) charts for visualizing data patterns and discovering exceptions, (2) tables for visualizing correlations and finding root-causes, and (3) time series for visualizing the evolution of long-running transactions. The solutions have been applied with success to product sales, Internet network performance analysis, and service contract applications demonstrating the benefits of our method over conventional graphics. The method is especially useful when detailed information is a key part of the analysis.

  16. Computer modeling of human decision making

    NASA Technical Reports Server (NTRS)

    Gevarter, William B.

    1991-01-01

    Models of human decision making are reviewed. Models which treat just the cognitive aspects of human behavior are included as well as models which include motivation. Both models which have associated computer programs, and those that do not, are considered. Since flow diagrams, that assist in constructing computer simulation of such models, were not generally available, such diagrams were constructed and are presented. The result provides a rich source of information, which can aid in construction of more realistic future simulations of human decision making.

  17. EXPERIMENTING WITH MULTI-ATTRIBUTE UTILITY SURVEY METHODS IN A MULTI-DIMENSIONAL VALUATION PROBLEM. (R824699)

    EPA Science Inventory

    Abstract

    The use of willingness-to-pay (WTP) survey techniques based on multi-attribute utility (MAU) approaches has been recommended by some authors as a way to deal simultaneously with two difficulties that increasingly plague environmental valuation. The first of th...

  18. Diffusion Decision Model: Current Issues and History.

    PubMed

    Ratcliff, Roger; Smith, Philip L; Brown, Scott D; McKoon, Gail

    2016-04-01

    There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in psychology. They view decision making as a process of noisy accumulation of evidence from a stimulus. The standard model assumes that evidence accumulates at a constant rate during the second or two it takes to make a decision. This process can be linked to the behaviors of populations of neurons and to theories of optimality. Diffusion models have been used successfully in a range of cognitive tasks and as psychometric tools in clinical research to examine individual differences. In this review, we relate the models to both earlier and more recent research in psychology. PMID:26952739

  19. Comprehensive Decision Tree Models in Bioinformatics

    PubMed Central

    Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

    2012-01-01

    Purpose Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. Methods This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. Results The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. Conclusions The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class

  20. A decision model for planetary missions

    NASA Technical Reports Server (NTRS)

    Hazelrigg, G. A., Jr.; Brigadier, W. L.

    1976-01-01

    Many techniques developed for the solution of problems in economics and operations research are directly applicable to problems involving engineering trade-offs. This paper investigates the use of utility theory for decision making in planetary exploration space missions. A decision model is derived that accounts for the objectives of the mission - science - the cost of flying the mission and the risk of mission failure. A simulation methodology for obtaining the probability distribution of science value and costs as a function spacecraft and mission design is presented and an example application of the decision methodology is given for various potential alternatives in a comet Encke mission.

  1. A Stochastic Multi-Attribute Assessment of Energy Options for Fairbanks, Alaska

    NASA Astrophysics Data System (ADS)

    Read, L.; Madani, K.; Mokhtari, S.; Hanks, C. L.; Sheets, B.

    2012-12-01

    Many competing projects have been proposed to address Interior Alaska's high cost of energy—both for electricity production and for heating. Public and private stakeholders are considering the costs associated with these competing projects which vary in fuel source, subsidy requirements, proximity, and other factors. As a result, the current projects under consideration involve a complex cost structure of potential subsidies and reliance on present and future market prices, introducing a significant amount of uncertainty associated with each selection. Multi-criteria multi-decision making (MCMDM) problems of this nature can benefit from game theory and systems engineering methods, which account for behavior and preferences of stakeholders in the analysis to produce feasible and relevant solutions. This work uses a stochastic MCMDM framework to evaluate the trade-offs of each proposed project based on a complete cost analysis, environmental impact, and long-term sustainability. Uncertainty in the model is quantified via a Monte Carlo analysis, which helps characterize the sensitivity and risk associated with each project. Based on performance measures and criteria outlined by the stakeholders, a decision matrix will inform policy on selecting a project that is both efficient and preferred by the constituents.

  2. Multi-attributed tagged big data exploitation for hidden concepts discovery

    NASA Astrophysics Data System (ADS)

    Habibi, Mohammad S.; Shirkhodaie, Amir

    2014-06-01

    Analysts who are using visualization methods for big data concept exploration increasingly expect to comprehend more distinct relationships and prominent concepts in support of their hypotheses or decisions. To expedite this knowledge discovery process, Vector Space Modeling (VSM) in conjunction with probabilistic analysis enables rapid knowledgebased relationship discovery while allowing for exploration of multi-embedded concepts than otherwise it is difficult to perceive. In this paper, we present a technique for intrinsic ontology concepts similarity matching based on VSM for exploitation and knowledge discovery from multimodality sensors metadata generated in Persistent Surveillance Systems (PSS). To reduce data dimensionality, Principal Component Analysis (PCA) and Latent Dirichlet Allocation (LDA) is applied to arrive at more abstract concepts. The proposed technique is able to reveal intrinsic concept relationships from multi-dimensional metadata structures. Experimental results demonstrate effectiveness of this approach for analytical ontological patterns exploitation. In this paper, the expediency of this technique for Visual Analytics application is demonstrated. The result indicates that the newly developed system can significantly enhance situation awareness and expedite actionable decision making.

  3. Modeling Choice and Valuation in Decision Experiments

    ERIC Educational Resources Information Center

    Loomes, Graham

    2010-01-01

    This article develops a parsimonious descriptive model of individual choice and valuation in the kinds of experiments that constitute a substantial part of the literature relating to decision making under risk and uncertainty. It suggests that many of the best known "regularities" observed in those experiments may arise from a tendency for…

  4. Neurally Constrained Modeling of Perceptual Decision Making

    ERIC Educational Resources Information Center

    Purcell, Braden A.; Heitz, Richard P.; Cohen, Jeremiah Y.; Schall, Jeffrey D.; Logan, Gordon D.; Palmeri, Thomas J.

    2010-01-01

    Stochastic accumulator models account for response time in perceptual decision-making tasks by assuming that perceptual evidence accumulates to a threshold. The present investigation mapped the firing rate of frontal eye field (FEF) visual neurons onto perceptual evidence and the firing rate of FEF movement neurons onto evidence accumulation to…

  5. Decision models for capital investment and financing decisions in hospitals.

    PubMed Central

    Vraciu, R A

    1980-01-01

    The literature on capital investment and financing decisions for hospitals has suggested several approaches to analyzing sets of options. In this paper, I present a taxonomy of the different approaches; analyze and compare the different elements of the taxonomy; and illustrate and discuss the information that can be gained by using each approach. I view these different analytic methods as complementary rather than competing methods of providing information to decision makers, and argue that the complex nature of hospital objectives demands the use of more than one approach. Failure to do this may lead to biased evaluations and poor decision making. PMID:6768699

  6. Variable Perceptions of Decision: An Operationalization of Four Models.

    ERIC Educational Resources Information Center

    Benjamin, Beverly P.; Kerchner, Charles T.

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

  7. Adaptive Decision Modeling in Wisconsin River Islands

    NASA Astrophysics Data System (ADS)

    Gyawali, R.; Greb, S. R.; Watkins, D. W., Jr.; Block, P.

    2014-12-01

    River islands in Wisconsin are of high ecological significance. Understanding of climate change impacts and appropriate management alternatives in these islands are of great interest to all stakeholders, including the State of Wisconsin and Bureau of Land Management (BLM) who have jurisdiction of these islands in WI. We use historical areal imagery to describe island dynamics and river morphometry, such as changes in island shape and size. Relationships of related changes are explored with concurrent changes in river flow regimes. In an effort to integrate climate change uncertainties into decision making, we demonstrate an application of a multistage adaptive decision making framework to Wisconsin River islands, with a particular emphasis on flood management and planning. The framework is comprised of hydro-climatic ensemble projections generated from CMIP5 climate model outputs and multiple hydrologic models, including statistical and physically based approaches.

  8. Modelling and Decision Support of Clinical Pathways

    NASA Astrophysics Data System (ADS)

    Gabriel, Roland; Lux, Thomas

    The German health care market is under a rapid rate of change, forcing especially hospitals to provide high-quality services at low costs. Appropriate measures for more effective and efficient service provision are process orientation and decision support by information technology of clinical pathway of a patient. The essential requirements are adequate modelling of clinical pathways as well as usage of adequate systems, which are capable of assisting the complete path of a patient within a hospital, and preferably also outside of it, in a digital way. To fulfil these specifications the authors present a suitable concept, which meets the challenges of well-structured clinical pathways as well as rather poorly structured diagnostic and therapeutic decisions, by interplay of process-oriented and knowledge-based hospital information systems.

  9. Flood Impact Modelling to support decision making

    NASA Astrophysics Data System (ADS)

    Owen, Gareth; Quinn, Paul; O'Donnell, Greg

    2015-04-01

    Much of what is known about the impacts of landuse change and Natural Flood Management (NFM) is at the local/plot scale. Evidence of the downstream impacts at the larger catchment scale is limited. However, the strategic and financial decisions of land managers, stakeholders and policy makers are made at the larger scale. There are a number of techniques that have the potential to scale local impacts to the catchment scale. This poster will show findings for the 30km2 Leven catchment, North Yorkshire, England. A NFM approach has been adopted by the Environment Agency to reduce flood risk within the catchment. A dense network of stream level gauges were installed in the catchment at the commencement of this project to gain a detailed understanding of the catchment behaviour during storm events. A novel Flood Impact Modelling (FIM) approach has been adopted which uses the network of gauges to disaggregate the outlet hydrograph in terms of source locations. Using a combination of expert opinion and local evidence, the model can be used to assess the impacts of distributed changes in land use management and NFM on flood events. A number of potential future landuse and NFM scenarios have been modelled to investigate their impact on flood peaks. These modelled outcomes are mapped to a simple Decision Support Matrix (DSM). The DSM encourages end users (e.g. land managers and policy makers) to develop an NFM scheme by studying the degree to which local runoff can be attenuated and how that flow will propagate through the network to the point of impact. The DSM relates the impact on flood peaks in terms of alterations to soil management practices and landscape flow connectivity (e.g. soil underdrainage), which can be easily understood by farmers and land managers. The DSM and the FIM together provide a simple to use and transparent modelling tool, making best use of expert knowledge, to support decision making.

  10. A control-theory model for human decision-making

    NASA Technical Reports Server (NTRS)

    Levison, W. H.; Tanner, R. B.

    1971-01-01

    A model for human decision making is an adaptation of an optimal control model for pilot/vehicle systems. The models for decision and control both contain concepts of time delay, observation noise, optimal prediction, and optimal estimation. The decision making model was intended for situations in which the human bases his decision on his estimate of the state of a linear plant. Experiments are described for the following task situations: (a) single decision tasks, (b) two-decision tasks, and (c) simultaneous manual control and decision making. Using fixed values for model parameters, single-task and two-task decision performance can be predicted to within an accuracy of 10 percent. Agreement is less good for the simultaneous decision and control situation.

  11. Decision Tree Modeling for Ranking Data

    NASA Astrophysics Data System (ADS)

    Yu, Philip L. H.; Wan, Wai Ming; Lee, Paul H.

    Ranking/preference data arises from many applications in marketing, psychology, and politics. We establish a new decision tree model for the analysis of ranking data by adopting the concept of classification and regression tree. The existing splitting criteria are modified in a way that allows them to precisely measure the impurity of a set of ranking data. Two types of impurity measures for ranking data are introduced, namelyg-wise and top-k measures. Theoretical results show that the new measures exhibit properties of impurity functions. In model assessment, the area under the ROC curve (AUC) is applied to evaluate the tree performance. Experiments are carried out to investigate the predictive performance of the tree model for complete and partially ranked data and promising results are obtained. Finally, a real-world application of the proposed methodology to analyze a set of political rankings data is presented.

  12. A consensus reaching model for 2-tuple linguistic multiple attribute group decision making with incomplete weight information

    NASA Astrophysics Data System (ADS)

    Zhang, Wancheng; Xu, Yejun; Wang, Huimin

    2016-01-01

    The aim of this paper is to put forward a consensus reaching method for multi-attribute group decision-making (MAGDM) problems with linguistic information, in which the weight information of experts and attributes is unknown. First, some basic concepts and operational laws of 2-tuple linguistic label are introduced. Then, a grey relational analysis method and a maximising deviation method are proposed to calculate the incomplete weight information of experts and attributes respectively. To eliminate the conflict in the group, a weight-updating model is employed to derive the weights of experts based on their contribution to the consensus reaching process. After conflict elimination, the final group preference can be obtained which will give the ranking of the alternatives. The model can effectively avoid information distortion which is occurred regularly in the linguistic information processing. Finally, an illustrative example is given to illustrate the application of the proposed method and comparative analysis with the existing methods are offered to show the advantages of the proposed method.

  13. Difficult Budgetary Decisions: A Desk-Top Calculator Model to Facilitate Executive Decisions.

    ERIC Educational Resources Information Center

    Tweddale, R. Bruce

    Presented is a budgetary decision model developed to aid the executive officers in arriving at tentative decisions on enrollment, tuition rates, increased compensation, and level of staffing as they affect the total institutional budget. The model utilizes a desk-top programmable calculator (in this case, a Burroughs Model C 3660). The model…

  14. Contribution of the Multi Attribute Value Theory to conflict resolution in groundwater management. Application to the Mancha Oriental groundwater system, Spain

    NASA Astrophysics Data System (ADS)

    Apperl, B.; Andreu, J.; Karjalainen, T. P.; Pulido-Velazquez, M.

    2014-09-01

    The implementation of the EU Water Framework Directive demands participatory water resource management approaches. Decision making in groundwater quantity and quality management is complex because of the existence of many independent actors, heterogeneous stakeholder interests, multiple objectives, different potential policies, and uncertain outcomes. Conflicting stakeholder interests have been often identified as an impediment to the realization and success of water regulations and policies. The management of complex groundwater systems requires clarifying stakeholders' positions (identifying stakeholders preferences and values), improving transparency with respect to outcomes of alternatives, and moving the discussion from the selection of alternatives towards definition of fundamental objectives (value-thinking approach), what facilitates negotiation. The aims of the study are to analyse the potential of the multi attribute value theory for conflict resolution in groundwater management and to evaluate the benefit of stakeholder incorporation in the different stages of the planning process to find an overall satisfying solution for groundwater management. The research was conducted in the Mancha Oriental groundwater system (Spain), subject to an intensive use of groundwater for irrigation. A complex set of objectives and attributes were defined, and the management alternatives were created by a combination of different fundamental actions, considering different implementation stages and future changes in water resources availability. Interviews were conducted with representative stakeholder groups using an interactive platform, showing simultaneously the consequences of changes of preferences to the alternative ranking. Results show that the acceptation of alternatives depends strongly on the combination of measures and the implementation stages. Uncertainties of the results were notable but did not influence heavily on the alternative ranking. The expected

  15. Contribution of the multi-attribute value theory to conflict resolution in groundwater management - application to the Mancha Oriental groundwater system, Spain

    NASA Astrophysics Data System (ADS)

    Apperl, B.; Pulido-Velazquez, M.; Andreu, J.; Karjalainen, T. P.

    2015-03-01

    The implementation of the EU Water Framework Directive demands participatory water resource management approaches. Decision making in groundwater quantity and quality management is complex because of the existence of many independent actors, heterogeneous stakeholder interests, multiple objectives, different potential policies, and uncertain outcomes. Conflicting stakeholder interests have often been identified as an impediment to the realisation and success of water regulations and policies. The management of complex groundwater systems requires the clarification of stakeholders' positions (identifying stakeholder preferences and values), improving transparency with respect to outcomes of alternatives, and moving the discussion from the selection of alternatives towards the definition of fundamental objectives (value-thinking approach), which facilitates negotiation. The aims of the study are to analyse the potential of the multi-attribute value theory for conflict resolution in groundwater management and to evaluate the benefit of stakeholder incorporation into the different stages of the planning process, to find an overall satisfying solution for groundwater management. The research was conducted in the Mancha Oriental groundwater system (Spain), subject to intensive use of groundwater for irrigation. A complex set of objectives and attributes was defined, and the management alternatives were created by a combination of different fundamental actions, considering different implementation stages and future changes in water resource availability. Interviews were conducted with representative stakeholder groups using an interactive platform, showing simultaneously the consequences of changes in preferences to the alternative ranking. Results show that the approval of alternatives depends strongly on the combination of measures and the implementation stages. Uncertainties in the results were notable, but did not influence the alternative ranking heavily. The

  16. Subjective Expected Utility: A Model of Decision-Making.

    ERIC Educational Resources Information Center

    Fischoff, Baruch; And Others

    1981-01-01

    Outlines a model of decision making known to researchers in the field of behavioral decision theory (BDT) as subjective expected utility (SEU). The descriptive and predictive validity of the SEU model, probability and values assessment using SEU, and decision contexts are examined, and a 54-item reference list is provided. (JL)

  17. A communication model of shared decision making: accounting for cancer treatment decisions.

    PubMed

    Siminoff, Laura A; Step, Mary M

    2005-07-01

    The authors present a communication model of shared decision making (CMSDM) that explicitly identifies the communication process as the vehicle for decision making in cancer treatment. In this view, decision making is necessarily a sociocommunicative process whereby people enter into a relationship, exchange information, establish preferences, and choose a course of action. The model derives from contemporary notions of behavioral decision making and ethical conceptions of the doctor-patient relationship. This article briefly reviews the theoretical approaches to decision making, notes deficiencies, and embeds a more socially based process into the dynamics of the physician-patient relationship, focusing on cancer treatment decisions. In the CMSDM, decisions depend on (a) antecedent factors that have potential to influence communication, (b) jointly constructed communication climate, and (c) treatment preferences established by the physician and the patient. PMID:16045427

  18. High dimensional decision dilemmas in climate models

    NASA Astrophysics Data System (ADS)

    Bracco, A.; Neelin, J. D.; Luo, H.; McWilliams, J. C.; Meyerson, J. E.

    2013-05-01

    An important source of uncertainty in climate models is linked to the calibration of model parameters. Interest in systematic and automated parameter optimization procedures stems from the desire to improve the model climatology and to quantify the average sensitivity associated with potential changes in the climate system. Neelin et al. (2010) used a quadratic metamodel to objectively calibrate an atmospheric circulation model (AGCM) around four adjustable parameters. The metamodel accurately estimates global spatial averages of common fields of climatic interest, from precipitation, to low and high level winds, from temperature at various levels to sea level pressure and geopotential height, while providing a computationally cheap strategy to explore the influence of parameter settings. Here, guided by the metamodel, the ambiguities or dilemmas related to the decision making process in relation to model sensitivity and optimization are examined. Simulations of current climate are subject to considerable regional-scale biases. Those biases may vary substantially depending on the climate variable considered, and/or on the performance metric adopted. Common dilemmas are associated with model revisions yielding improvement in one field or regional pattern or season, but degradation in another, or improvement in the model climatology but degradation in the interannual variability representation. Challenges are posed to the modeler by the high dimensionality of the model output fields and by the large number of adjustable parameters. The use of the metamodel in the optimization strategy helps visualize trade-offs at a regional level, e.g. how mismatches between sensitivity and error spatial fields yield regional errors under minimization of global objective functions.

  19. High dimensional decision dilemmas in climate models

    NASA Astrophysics Data System (ADS)

    Bracco, A.; Neelin, J. D.; Luo, H.; McWilliams, J. C.; Meyerson, J. E.

    2013-10-01

    An important source of uncertainty in climate models is linked to the calibration of model parameters. Interest in systematic and automated parameter optimization procedures stems from the desire to improve the model climatology and to quantify the average sensitivity associated with potential changes in the climate system. Building upon on the smoothness of the response of an atmospheric circulation model (AGCM) to changes of four adjustable parameters, Neelin et al. (2010) used a quadratic metamodel to objectively calibrate the AGCM. The metamodel accurately estimates global spatial averages of common fields of climatic interest, from precipitation, to low and high level winds, from temperature at various levels to sea level pressure and geopotential height, while providing a computationally cheap strategy to explore the influence of parameter settings. Here, guided by the metamodel, the ambiguities or dilemmas related to the decision making process in relation to model sensitivity and optimization are examined. Simulations of current climate are subject to considerable regional-scale biases. Those biases may vary substantially depending on the climate variable considered, and/or on the performance metric adopted. Common dilemmas are associated with model revisions yielding improvement in one field or regional pattern or season, but degradation in another, or improvement in the model climatology but degradation in the interannual variability representation. Challenges are posed to the modeler by the high dimensionality of the model output fields and by the large number of adjustable parameters. The use of the metamodel in the optimization strategy helps visualize trade-offs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional errors under minimization of global objective functions.

  20. A Model for Evaluation of Decision Passages.

    ERIC Educational Resources Information Center

    Stimac, Michele

    In an age when decision making is becoming more and more significant for us human beings as we face dilemmas about whether or not to clone, to engineer behavior on mass scale, to expand or to decrease nuclear power, we educators must assist students to increase their decision-making skills. Many of our students will soon be decision makers for…

  1. Modeling boolean decision rules applied to multiple-observer decision strategies.

    PubMed

    Maguire, W

    1996-01-01

    A model that derives multiple-observer decision strategy ROC curves for boolean decision rules applied to binary decisions of two or three observers is presented. It is assumed that covert decision variables consistent with ROC models of observer performance underlie decisions and that readers' decision criteria are in a fixed relationship. The specific parameters of individual ROC curves and the correlational structure that describes interobserver agreement have dramatic effects upon the relative benefits to be derived from different boolean strategies. A common strategy employed in clinical practice, in which the overall decision is positive if any observer makes a positive decision, is most effective when the readers are of similar ability, when they adopt similar decision criteria, when interreader agreement is greater for negative than for positive cases, and when the individual ROC slope is <1.0. Different multiple-observer decision strategies can be evaluated using the model equations. A bootstrap method for testing model-associated hypotheses is described. PMID:8717599

  2. Demographics of reintroduced populations: estimation, modeling, and decision analysis

    USGS Publications Warehouse

    Converse, Sarah J.; Moore, Clinton T.; Armstrong, Doug P.

    2013-01-01

    Reintroduction can be necessary for recovering populations of threatened species. However, the success of reintroduction efforts has been poorer than many biologists and managers would hope. To increase the benefits gained from reintroduction, management decision making should be couched within formal decision-analytic frameworks. Decision analysis is a structured process for informing decision making that recognizes that all decisions have a set of components—objectives, alternative management actions, predictive models, and optimization methods—that can be decomposed, analyzed, and recomposed to facilitate optimal, transparent decisions. Because the outcome of interest in reintroduction efforts is typically population viability or related metrics, models used in decision analysis efforts for reintroductions will need to include population models. In this special section of the Journal of Wildlife Management, we highlight examples of the construction and use of models for informing management decisions in reintroduced populations. In this introductory contribution, we review concepts in decision analysis, population modeling for analysis of decisions in reintroduction settings, and future directions. Increased use of formal decision analysis, including adaptive management, has great potential to inform reintroduction efforts. Adopting these practices will require close collaboration among managers, decision analysts, population modelers, and field biologists.

  3. Application of the Multi-Attribute Value Theory for engaging stakeholders in groundwater protection in the Vosvozis catchment in Greece.

    PubMed

    Stefanopoulos, Kyriakos; Yang, Hong; Gemitzi, Alexandra; Tsagarakis, Konstantinos P

    2014-02-01

    Multi-Attribute Value Theory (MAVT) was used to investigate stakeholders' preferences and beliefs in ameliorating a deteriorating ecosystem, i.e. Vosvozis River and Ismarida Lake in Northeastern Greece. Various monetary and environmental criteria were evaluated with scores and weights by different stakeholder groups and key individuals such as farmers, fishermen, entrepreneurs, residents and ecologists to elicit their preferences concerning alternative protection scenarios. The ultimate objective was to propose policy recommendations for a sustainable water resources management for the case study area. The analysis revealed an overwhelming agreement among stakeholders regarding the dire need for immediate actions in order to preserve and enhance Vosvozis ecosystem. With a two stage evaluation process, the MAVT analysis led to a high consensus among the stakeholders on the alternative that favors water recycling from the wastewater treatment plant combined with small dams for rainwater harvesting. PMID:24121661

  4. The Multi-Attribute Task Battery II (MATB-II) Software for Human Performance and Workload Research: A User's Guide

    NASA Technical Reports Server (NTRS)

    Santiago-Espada, Yamira; Myer, Robert R.; Latorella, Kara A.; Comstock, James R., Jr.

    2011-01-01

    The Multi-Attribute Task Battery (MAT Battery). is a computer-based task designed to evaluate operator performance and workload, has been redeveloped to operate in Windows XP Service Pack 3, Windows Vista and Windows 7 operating systems.MATB-II includes essentially the same tasks as the original MAT Battery, plus new configuration options including a graphical user interface for controlling modes of operation. MATB-II can be executed either in training or testing mode, as defined by the MATB-II configuration file. The configuration file also allows set up of the default timeouts for the tasks, the flow rates of the pumps and tank levels of the Resource Management (RESMAN) task. MATB-II comes with a default event file that an experimenter can modify and adapt

  5. A Bayesian Attractor Model for Perceptual Decision Making

    PubMed Central

    Bitzer, Sebastian; Bruineberg, Jelle; Kiebel, Stefan J.

    2015-01-01

    Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence about a decision. We propose a novel approach of how perceptual decisions are made by combining two influential formalisms into a new model. Specifically, we embed an attractor model of decision making into a probabilistic framework that models decision making as Bayesian inference. We show that the new model can explain decision making behaviour by fitting it to experimental data. In addition, the new model combines for the first time three important features: First, the model can update decisions in response to switches in the underlying stimulus. Second, the probabilistic formulation accounts for top-down effects that may explain recent experimental findings of decision-related gain modulation of sensory neurons. Finally, the model computes an explicit measure of confidence which we relate to recent experimental evidence for confidence computations in perceptual decision tasks. PMID:26267143

  6. Leadership of risk decision making in a complex, technology organization: The deliberative decision making model

    NASA Astrophysics Data System (ADS)

    Flaming, Susan C.

    2007-12-01

    The continuing saga of satellite technology development is as much a story of successful risk management as of innovative engineering. How do program leaders on complex, technology projects manage high stakes risks that threaten business success and satellite performance? This grounded theory study of risk decision making portrays decision leadership practices at one communication satellite company. Integrated product team (IPT) leaders of multi-million dollar programs were interviewed and observed to develop an extensive description of the leadership skills required to navigate organizational influences and drive challenging risk decisions to closure. Based on the study's findings the researcher proposes a new decision making model, Deliberative Decision Making, to describe the program leaders' cognitive and organizational leadership practices. This Deliberative Model extends the insights of prominent decision making models including the rational (or classical) and the naturalistic and qualifies claims made by bounded rationality theory. The Deliberative Model describes how leaders proactively engage resources to play a variety of decision leadership roles. The Model incorporates six distinct types of leadership decision activities, undertaken in varying sequence based on the challenges posed by specific risks. Novel features of the Deliberative Decision Model include: an inventory of leadership methods for managing task challenges, potential stakeholder bias and debates; four types of leadership meta-decisions that guide decision processes, and aligned organizational culture. Both supporting and constraining organizational influences were observed as leaders managed major risks, requiring active leadership on the most difficult decisions. Although the company's engineering culture emphasized the importance of data-based decisions, the uncertainties intrinsic to satellite risks required expert engineering judgment to be exercised throughout. An investigation into

  7. A control theory model for human decision making

    NASA Technical Reports Server (NTRS)

    Levison, W. H.

    1972-01-01

    The optimal control model for pilot-vehicle systems has been extended to handle certain types of human decision tasks. The model for decision making incorporates the observation noise, optimal estimation, and prediction concepts that form the basis of the model for control behavior. Experiments are described for the following task situations: (1) single decision tasks; (2) two decision tasks; and (3) simultaneous manual control and decision tasks. Using fixed values for model parameters, single-task and two-task decision performance scores to within an accuracy of 10 percent can be predicted. The experiment on simultaneous control and decision indicates the presence of task interference in this situation, but the results are not adequate to allow a conclusive test of the predictive capability of the model.

  8. Use of Multi-attribute Utility Functions in Evaluating Security Systems

    SciTech Connect

    Meyers, C; Lamont, A; Sicherman, A

    2008-06-13

    In analyzing security systems, we are concerned with protecting a building or facility from an attack by an adversary. Typically, we address the possibility that an adversary could enter a building and cause damage resulting in an immediate loss of life, or at least substantial disruption in the operations of the facility. In response to this setting, we implement security systems including devices, procedures, and facility upgrades designed to (a) prevent the adversary from entering, (b) detect and neutralize him if he does enter, and (c) harden the facility to minimize damage if an attack is carried out successfully. Although we have cast this in terms of physical protection of a building, the same general approach can be applied to non-physical attacks such as cyber attacks on a computer system. A rigorous analytic process is valuable for quantitatively evaluating an existing system, identifying its weaknesses, and proposing useful upgrades. As such, in this paper we describe an approach to assess the degree of overall protection provided by security measures. Our approach evaluates the effectiveness of the individual components of the system, describes how the components work together, and finally assesses the degree of overall protection achieved. This model can then be used to quantify the amount of protection provided by existing security measures, as well as to address proposed upgrades to the system and help identify a robust and cost effective set of improvements. Within the model, we use multiattribute utility functions to perform the overall evaluations of the system.

  9. Multi-Attribute Seismic/Rock Physics Approach to Characterizing Fractured Reservoirs

    SciTech Connect

    Gary Mavko

    2004-11-30

    is likely to be more intense near faults--sometimes referred to as the damaged zone. Yet another constraint, based on world-wide observations, is that the maximum likely fracture density increases with depth in a well-defined way. Defining these prior constrains has several benefits: they lead to a priori probability distributions of fractures, that are important for objective statistical integration; they limit the number of geologic hypotheses that need to be theoretically modeled; they provide plausible models for fracture distributions below the seismic resolution. The second element was theoretical rock physics modeling of optimal seismic attributes, including offset and azimuth dependence of traveltime, amplitude, and impedance signatures of anisotropic fractured rocks. The suggested workflow is to begin with an elastic earth model, based on well logs, theoretically add fractures to the likely facies as defined by the geologic prior information, and then compute synthetic seismic traces and attributes, including variations in P and S-wave velocities, Poisson's ratio, reflectivity, travel time, attenuation, and anisotropies of these parameters. This workflow is done in a Monte-Carlo fashion, yielding ranges of expected fracture signatures, and allowing realistic assessments of uncertainty to be honored. The third element was statistical integration of the geophysical data and prior constraints to map fracture intensity and orientations, along with uncertainties. A Bayesian framework was developed that allowed systematic integration of the prior constraints, the theoretical relations between fractures and their seismic signatures, and the various observed seismic observations. The integration scheme was successfully applied on an East Texas field site. The primary benefit from the study was the optimization and refinement of practical workflows for improved geophysical characterization of natural fractures and for quantifying the uncertainty of these

  10. Seasonal to Interannual Hydroclimatic Prediction: From Identification of Dynamics to Multi-Attribute Forecasts

    NASA Astrophysics Data System (ADS)

    Lall, U.

    2004-12-01

    Dynamical and Statistical Models for seasonal to interannual forecasts of key hydroclimatic state variables have been explored in recent years. Many authors report success based on typical performance metrics. Thus, a casual external observer may feel that we are at the verge of a breakthrough in hydrologic prediction, and hence in water resource management. This talk explores this notion, with particular regard to the multi-scale (time and space) nature of hydrologic fluxes, and of the management variables and styles that the water resources community has become accustomed to. A conceptual framework for the nascent predictive science of hydroclimatology is developed and exemplified. Aspects of the dynamics that need to be understood, and a unifying estimation/inference framework are proposed.

  11. Seasonal to Interannual Hydroclimatic Prediction: From Identification of Dynamics to Multi-Attribute Forecasts

    NASA Astrophysics Data System (ADS)

    Lall, U.

    2004-05-01

    Dynamical and Statistical Models for seasonal to interannual forecasts of key hydroclimatic state variables have been explored in recent years. Many authors report success based on typical performance metrics. Thus, a casual external observer may feel that we are at the verge of a breakthrough in hydrologic prediction, and hence in water resource management. This talk explores this notion, with particular regard to the multi-scale (time and space) nature of hydrologic fluxes, and of the management variables and styles that the water resources community has become accustomed to. A conceptual framework for the nascent predictive science of hydroclimatology is developed and exemplified. Aspects of the dynamics that need to be understood, and a unifying estimation/inference framework are proposed.

  12. Recognizing Multiple Decision-Making Models: A Guide for Managers.

    ERIC Educational Resources Information Center

    Giesecke, Joan

    1993-01-01

    Provides a theoretical overview of decision-making models which are applicable to libraries; presents a framework for comparing the rational, political bargaining, and garbage can models; describes a case study that applied the models to a decision regarding an academic library governance system; and discusses strategies for library managers for…

  13. A Training Model for School-Based Decision Making.

    ERIC Educational Resources Information Center

    Horgan, Dianne D.

    The development of a comprehensive training model designed specifically for school-based decision making is discussed in this report, with a focus on teaching relevant skills and when to utilize them. Loosely based on Vroom and Yetton's 1973 model of participative decision making, the model is characterized by a general-to-specific continuum and…

  14. Making Insulation Decisions through Mathematical Modeling

    ERIC Educational Resources Information Center

    Yanik, H. Bahadir; Memis, Yasin

    2014-01-01

    Engaging students in studies about conservation and sustainability can support their understanding of making environmental conscious decisions to conserve Earth. This article aims to contribute these efforts and direct students' attention to how they can use mathematics to make environmental decisions. Contributors to iSTEM: Integrating…

  15. Multi-attribute criteria applied to electric generation energy system analysis LDRD.

    SciTech Connect

    Kuswa, Glenn W.; Tsao, Jeffrey Yeenien; Drennen, Thomas E.; Zuffranieri, Jason V.; Paananen, Orman Henrie; Jones, Scott A.; Ortner, Juergen G.; Brewer, Jeffrey D.; Valdez, Maximo M.

    2005-10-01

    This report began with a Laboratory-Directed Research and Development (LDRD) project to improve Sandia National Laboratories multidisciplinary capabilities in energy systems analysis. The aim is to understand how various electricity generating options can best serve needs in the United States. The initial product is documented in a series of white papers that span a broad range of topics, including the successes and failures of past modeling studies, sustainability, oil dependence, energy security, and nuclear power. Summaries of these projects are included here. These projects have provided a background and discussion framework for the Energy Systems Analysis LDRD team to carry out an inter-comparison of many of the commonly available electric power sources in present use, comparisons of those options, and efforts needed to realize progress towards those options. A computer aid has been developed to compare various options based on cost and other attributes such as technological, social, and policy constraints. The Energy Systems Analysis team has developed a multi-criteria framework that will allow comparison of energy options with a set of metrics that can be used across all technologies. This report discusses several evaluation techniques and introduces the set of criteria developed for this LDRD.

  16. Feature Integration in the Mapping of Multi-Attribute Visual Stimuli to Responses

    PubMed Central

    Ishizaki, Takuya; Morita, Hiromi; Morita, Masahiko

    2015-01-01

    In the human visual system, different attributes of an object, such as shape and color, are separately processed in different modules and then integrated to elicit a specific response. In this process, different attributes are thought to be temporarily “bound” together by focusing attention on the object; however, how such binding contributes to stimulus-response mapping remains unclear. Here we report that learning and performance of stimulus-response tasks was more difficult when three attributes of the stimulus determined the correct response than when two attributes did. We also found that spatially separated presentations of attributes considerably complicated the task, although they did not markedly affect target detection. These results are consistent with a paired-attribute model in which bound feature pairs, rather than object representations, are associated with responses by learning. This suggests that attention does not bind three or more attributes into a unitary object representation, and long-term learning is required for their integration. PMID:25762010

  17. Incorporating affective bias in models of human decision making

    NASA Technical Reports Server (NTRS)

    Nygren, Thomas E.

    1991-01-01

    Research on human decision making has traditionally focused on how people actually make decisions, how good their decisions are, and how their decisions can be improved. Recent research suggests that this model is inadequate. Affective as well as cognitive components drive the way information about relevant outcomes and events is perceived, integrated, and used in the decision making process. The affective components include how the individual frames outcomes as good or bad, whether the individual anticipates regret in a decision situation, the affective mood state of the individual, and the psychological stress level anticipated or experienced in the decision situation. A focus of the current work has been to propose empirical studies that will attempt to examine in more detail the relationships between the latter two critical affective influences (mood state and stress) on decision making behavior.

  18. The medical decision model and decision maker tools for management of radiological and nuclear incidents.

    PubMed

    Koerner, John F; Coleman, C Norman; Murrain-Hill, Paula; FitzGerald, Denis J; Sullivan, Julie M

    2014-06-01

    Effective decision making during a rapidly evolving emergency such as a radiological or nuclear incident requires timely interim decisions and communications from onsite decision makers while further data processing, consultation, and review are ongoing by reachback experts. The authors have recently proposed a medical decision model for use during a radiological or nuclear disaster, which is similar in concept to that used in medical care, especially when delay in action can have disastrous effects. For decision makers to function most effectively during a complex response, they require access to onsite subject matter experts who can provide information, recommendations, and participate in public communication efforts. However, in the time before this expertise is available or during the planning phase, just-in-time tools are essential that provide critical overview of the subject matter written specifically for the decision makers. Recognizing the complexity of the science, risk assessment, and multitude of potential response assets that will be required after a nuclear incident, the Office of the Assistant Secretary for Preparedness and Response, in collaboration with other government and non-government experts, has prepared a practical guide for decision makers. This paper illustrates how the medical decision model process could facilitate onsite decision making that includes using the deliberative reachback process from science and policy experts and describes the tools now available to facilitate timely and effective incident management. PMID:24776895

  19. A Layered Decision Model for Cost-Effective System Security

    SciTech Connect

    Wei, Huaqiang; Alves-Foss, James; Soule, Terry; Pforsich, Hugh; Zhang, Du; Frincke, Deborah A.

    2008-10-01

    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 in 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.

  20. Anticipatory network models of multicriteria decision-making processes

    NASA Astrophysics Data System (ADS)

    Skulimowski, Andrzej M. J.

    2014-01-01

    In this article, we will investigate the properties of a compromise solution selection method based on modelling the consequences of a decision as factors influencing the decision making in subsequent problems. Specifically, we assume that the constraints and preference structures in the (k + 1)st multicriteria optimisation problem depend on the values of criteria in the k-th problem. To make a decision in the initial problem, the decision maker should take into account the anticipated outcomes of each linked future decision problem. This model can be extended to a network of linked decision problems, such that causal relations are defined between the time-ordered nodes. Multiple edges starting from a decision node correspond to different future scenarios of consequences at this node. In addition, we will define the relation of anticipatory feedback, assuming that some decision makers take into account the anticipated future consequences of their decisions described by a network of optimisers - a class of information processing units introduced in this article. Both relations (causal and anticipatory) form a feedback information model, which makes possible a selection of compromise solutions taking into account the anticipated consequences. We provide constructive algorithms to solve discrete multicriteria decision problems that admit the above preference information structure. An illustrative example is presented in Section 4. Various applications of the above model, including the construction of technology foresight scenarios, are discussed in the final section of this article.

  1. Evolution of quantum-like modeling in decision making processes

    NASA Astrophysics Data System (ADS)

    Khrennikova, Polina

    2012-12-01

    The application of the mathematical formalism of quantum mechanics to model behavioral patterns in social science and economics is a novel and constantly emerging field. The aim of the so called 'quantum like' models is to model the decision making processes in a macroscopic setting, capturing the particular 'context' in which the decisions are taken. Several subsequent empirical findings proved that when making a decision people tend to violate the axioms of expected utility theory and Savage's Sure Thing principle, thus violating the law of total probability. A quantum probability formula was devised to describe more accurately the decision making processes. A next step in the development of QL-modeling in decision making was the application of Schrödinger equation to describe the evolution of people's mental states. A shortcoming of Schrödinger equation is its inability to capture dynamics of an open system; the brain of the decision maker can be regarded as such, actively interacting with the external environment. Recently the master equation, by which quantum physics describes the process of decoherence as the result of interaction of the mental state with the environmental 'bath', was introduced for modeling the human decision making. The external environment and memory can be referred to as a complex 'context' influencing the final decision outcomes. The master equation can be considered as a pioneering and promising apparatus for modeling the dynamics of decision making in different contexts.

  2. Evolution of quantum-like modeling in decision making processes

    SciTech Connect

    Khrennikova, Polina

    2012-12-18

    The application of the mathematical formalism of quantum mechanics to model behavioral patterns in social science and economics is a novel and constantly emerging field. The aim of the so called 'quantum like' models is to model the decision making processes in a macroscopic setting, capturing the particular 'context' in which the decisions are taken. Several subsequent empirical findings proved that when making a decision people tend to violate the axioms of expected utility theory and Savage's Sure Thing principle, thus violating the law of total probability. A quantum probability formula was devised to describe more accurately the decision making processes. A next step in the development of QL-modeling in decision making was the application of Schroedinger equation to describe the evolution of people's mental states. A shortcoming of Schroedinger equation is its inability to capture dynamics of an open system; the brain of the decision maker can be regarded as such, actively interacting with the external environment. Recently the master equation, by which quantum physics describes the process of decoherence as the result of interaction of the mental state with the environmental 'bath', was introduced for modeling the human decision making. The external environment and memory can be referred to as a complex 'context' influencing the final decision outcomes. The master equation can be considered as a pioneering and promising apparatus for modeling the dynamics of decision making in different contexts.

  3. LATER models of neural decision behavior in choice tasks

    PubMed Central

    Noorani, Imran

    2014-01-01

    Reaction time has been increasingly used over the last few decades to provide information on neural decision processes: it is a direct reflection of decision time. Saccades provide an excellent paradigm for this because many of them can be made in a very short time and the underlying neural pathways are relatively well-known. LATER (linear approach to threshold with ergodic rate) is a model originally devised to explain reaction time distributions in simple decision tasks. Recently, however it is being extended to increasingly more advanced tasks, including those with decision errors and those requiring voluntary control such as the antisaccade task and those where sequential decisions are required. The strength of this modeling approach lies in its detailed, quantitative predictions of behavior, yet LATER models still retain their conceptual simplicity that made LATER initially successful in explaining reaction times in simple decision tasks. PMID:25202242

  4. Modeling Adversaries in Counterterrorism Decisions Using Prospect Theory.

    PubMed

    Merrick, Jason R W; Leclerc, Philip

    2016-04-01

    Counterterrorism decisions have been an intense area of research in recent years. Both decision analysis and game theory have been used to model such decisions, and more recently approaches have been developed that combine the techniques of the two disciplines. However, each of these approaches assumes that the attacker is maximizing its utility. Experimental research shows that human beings do not make decisions by maximizing expected utility without aid, but instead deviate in specific ways such as loss aversion or likelihood insensitivity. In this article, we modify existing methods for counterterrorism decisions. We keep expected utility as the defender's paradigm to seek for the rational decision, but we use prospect theory to solve for the attacker's decision to descriptively model the attacker's loss aversion and likelihood insensitivity. We study the effects of this approach in a critical decision, whether to screen containers entering the United States for radioactive materials. We find that the defender's optimal decision is sensitive to the attacker's levels of loss aversion and likelihood insensitivity, meaning that understanding such descriptive decision effects is important in making such decisions. PMID:25039254

  5. Ethical Decision-Making Models: A Review of the Literature.

    ERIC Educational Resources Information Center

    Cottone, R. Rocco; Claus, Ronald E.

    2000-01-01

    Presents a review of literature on ethical decision-making models in counseling, beginning in the fall of 1984 through the summer of 1998. Surveys theoretically or philosophically based, practice-based, and specialty-relevant approaches. Results indicate the literature is rich with decision-making model descriptions, although few have been…

  6. Relevance of a Managerial Decision-Model to Educational Administration.

    ERIC Educational Resources Information Center

    Lundin, Edward.; Welty, Gordon

    The rational model of classical economic theory assumes that the decision maker has complete information on alternatives and consequences, and that he chooses the alternative that maximizes expected utility. This model does not allow for constraints placed on the decision maker resulting from lack of information, organizational pressures,…

  7. Development of a quantitative mass spectrometry multi-attribute method for characterization, quality control testing and disposition of biologics

    PubMed Central

    Rogers, Richard S; Nightlinger, Nancy S; Livingston, Brittney; Campbell, Phil; Bailey, Robert; Balland, Alain

    2015-01-01

    Regulatory agencies have recently recommended a Quality by Design (QbD) approach for the manufacturing of therapeutic molecules. A QbD strategy requires deep understanding at the molecular level of the attributes that are crucial for safety and efficacy and for insuring that the desired quality of the purified protein drug product is met at the end of the manufacturing process. A mass spectrometry (MS)-based approach to simultaneously monitor the extensive array of product quality attributes (PQAs) present on therapeutic molecules has been developed. This multi-attribute method (MAM) uses a combination of high mass accuracy / high resolution MS data generated by Orbitrap technology and automated identification and relative quantification of PQAs with dedicated software (Pinpoint). The MAM has the potential to replace several conventional electrophoretic and chromatographic methods currently used in Quality Control to release therapeutic molecules. The MAM represents an optimized analytical solution to focus on the attributes of the therapeutic molecule essential for function and implement QbD principles across process development, manufacturing and drug disposition. PMID:26186204

  8. Cotton Modeling for Climate Change, On-farm Decision Support, and Policy Decisions

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Crop simulation models are valuable tools that scientists can use in testing hypothesis. Models also are used to identify the areas where knowledge is lacking, indicating the needs for future research activities. In addition, models are being used as decision support systems at the farm level to opt...

  9. Modeling uncertainty in requirements engineering decision support

    NASA Technical Reports Server (NTRS)

    Feather, Martin S.; Maynard-Zhang, Pedrito; Kiper, James D.

    2005-01-01

    One inherent characteristic of requrements engineering is a lack of certainty during this early phase of a project. Nevertheless, decisions about requirements must be made in spite of this uncertainty. Here we describe the context in which we are exploring this, and some initial work to support elicitation of uncertain requirements, and to deal with the combination of such information from multiple stakeholders.

  10. Models of Affective Decision Making: How Do Feelings Predict Choice?

    PubMed

    Charpentier, Caroline J; De Neve, Jan-Emmanuel; Li, Xinyi; Roiser, Jonathan P; Sharot, Tali

    2016-06-01

    Intuitively, how you feel about potential outcomes will determine your decisions. Indeed, an implicit assumption in one of the most influential theories in psychology, prospect theory, is that feelings govern choice. Surprisingly, however, very little is known about the rules by which feelings are transformed into decisions. Here, we specified a computational model that used feelings to predict choices. We found that this model predicted choice better than existing value-based models, showing a unique contribution of feelings to decisions, over and above value. Similar to the value function in prospect theory, our feeling function showed diminished sensitivity to outcomes as value increased. However, loss aversion in choice was explained by an asymmetry in how feelings about losses and gains were weighted when making a decision, not by an asymmetry in the feelings themselves. The results provide new insights into how feelings are utilized to reach a decision. PMID:27071751

  11. Adaptive Dimensionality Reduction with Semi-Supervision (AdDReSS): Classifying Multi-Attribute Biomedical Data

    PubMed Central

    Romo Bucheli, David Edmundo; Madabhushi, Anant

    2016-01-01

    Medical diagnostics is often a multi-attribute problem, necessitating sophisticated tools for analyzing high-dimensional biomedical data. Mining this data often results in two crucial bottlenecks: 1) high dimensionality of features used to represent rich biological data and 2) small amounts of labelled training data due to the expense of consulting highly specific medical expertise necessary to assess each study. Currently, no approach that we are aware of has attempted to use active learning in the context of dimensionality reduction approaches for improving the construction of low dimensional representations. We present our novel methodology, AdDReSS (Adaptive Dimensionality Reduction with Semi-Supervision), to demonstrate that fewer labeled instances identified via AL in embedding space are needed for creating a more discriminative embedding representation compared to randomly selected instances. We tested our methodology on a wide variety of domains ranging from prostate gene expression, ovarian proteomic spectra, brain magnetic resonance imaging, and breast histopathology. Across these various high dimensional biomedical datasets with 100+ observations each and all parameters considered, the median classification accuracy across all experiments showed AdDReSS (88.7%) to outperform SSAGE, a SSDR method using random sampling (85.5%), and Graph Embedding (81.5%). Furthermore, we found that embeddings generated via AdDReSS achieved a mean 35.95% improvement in Raghavan efficiency, a measure of learning rate, over SSAGE. Our results demonstrate the value of AdDReSS to provide low dimensional representations of high dimensional biomedical data while achieving higher classification rates with fewer labelled examples as compared to without active learning. PMID:27421116

  12. A Framework and Model for Evaluating Clinical Decision Support Architectures

    PubMed Central

    Wright, Adam; Sittig, Dean F.

    2008-01-01

    In this paper, we develop a four-phase model for evaluating architectures for clinical decision support that focuses on: defining a set of desirable features for a decision support architecture; building a proof-of-concept prototype; demonstrating that the architecture is useful by showing that it can be integrated with existing decision support systems and comparing its coverage to that of other architectures. We apply this framework to several well-known decision support architectures, including Arden Syntax, GLIF, SEBASTIAN and SAGE PMID:18462999

  13. Medical Specialty Decision Model: Utilizing Social Cognitive Career Theory

    ERIC Educational Resources Information Center

    Gibson, Denise D.; Borges, Nicole J.

    2004-01-01

    Objectives: The purpose of this study was to develop a working model to explain medical specialty decision-making. Using Social Cognitive Career Theory, we examined personality, medical specialty preferences, job satisfaction, and expectations about specialty choice to create a conceptual framework to guide specialty choice decision-making.…

  14. A quantitative risk model for early lifecycle decision making

    NASA Technical Reports Server (NTRS)

    Feather, M. S.; Cornford, S. L.; Dunphy, J.; Hicks, K.

    2002-01-01

    Decisions made in the earliest phases of system development have the most leverage to influence the success of the entire development effort, and yet must be made when information is incomplete and uncertain. We have developed a scalable cost-benefit model to support this critical phase of early-lifecycle decision-making.

  15. A Decision Support Model and Tool to Assist Financial Decision-Making in Universities

    ERIC Educational Resources Information Center

    Bhayat, Imtiaz; Manuguerra, Maurizio; Baldock, Clive

    2015-01-01

    In this paper, a model and tool is proposed to assist universities and other mission-based organisations to ascertain systematically the optimal portfolio of projects, in any year, meeting the organisations risk tolerances and available funds. The model and tool presented build on previous work on university operations and decision support systems…

  16. Social influence and perceptual decision making: a diffusion model analysis.

    PubMed

    Germar, Markus; Schlemmer, Alexander; Krug, Kristine; Voss, Andreas; Mojzisch, Andreas

    2014-02-01

    Classic studies on social influence used simple perceptual decision-making tasks to examine how the opinions of others change individuals' judgments. Since then, one of the most fundamental questions in social psychology has been whether social influence can alter basic perceptual processes. To address this issue, we used a diffusion model analysis. Diffusion models provide a stochastic approach for separating the cognitive processes underlying speeded binary decisions. Following this approach, our study is the first to disentangle whether social influence on decision making is due to altering the uptake of available sensory information or due to shifting the decision criteria. In two experiments, we found consistent evidence for the idea that social influence alters the uptake of available sensory evidence. By contrast, participants did not adjust their decision criteria. PMID:24154917

  17. Rationality Validation of a Layered Decision Model for Network Defense

    SciTech Connect

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

    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 LDM rationality through simulation.

  18. A Representation for Gaining Insight into Clinical Decision Models

    PubMed Central

    Jimison, Holly B.

    1988-01-01

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

  19. Reduced model-based decision-making in schizophrenia.

    PubMed

    Culbreth, Adam J; Westbrook, Andrew; Daw, Nathaniel D; Botvinick, Matthew; Barch, Deanna M

    2016-08-01

    Individuals with schizophrenia have a diminished ability to use reward history to adaptively guide behavior. However, tasks traditionally used to assess such deficits often rely on multiple cognitive and neural processes, leaving etiology unresolved. In the current study, we adopted recent computational formalisms of reinforcement learning to distinguish between model-based and model-free decision-making in hopes of specifying mechanisms associated with reinforcement-learning dysfunction in schizophrenia. Under this framework, decision-making is model-free to the extent that it relies solely on prior reward history, and model-based if it relies on prospective information such as motivational state, future consequences, and the likelihood of obtaining various outcomes. Model-based and model-free decision-making was assessed in 33 schizophrenia patients and 30 controls using a 2-stage 2-alternative forced choice task previously demonstrated to discern individual differences in reliance on the 2 forms of reinforcement-learning. We show that, compared with controls, schizophrenia patients demonstrate decreased reliance on model-based decision-making. Further, parameter estimates of model-based behavior correlate positively with IQ and working memory measures, suggesting that model-based deficits seen in schizophrenia may be partially explained by higher-order cognitive deficits. These findings demonstrate specific reinforcement-learning and decision-making deficits and thereby provide valuable insights for understanding disordered behavior in schizophrenia. (PsycINFO Database Record PMID:27175984

  20. Decision making in bipolar disorder: a cognitive modeling approach.

    PubMed

    Yechiam, Eldad; Hayden, Elizabeth P; Bodkins, Misty; O'Donnell, Brian F; Hetrick, William P

    2008-11-30

    A formal modeling approach was used to characterize decision-making processes in bipolar disorder. Decision making was examined in 28 bipolar patients (14 acute and 14 remitted) and 25 controls using the Iowa Gambling Task (Bechara et al., 1994), a decision-making task used for assessing cognitive impulsivity. To disentangle motivational and cognitive aspects of decision-making processes, we applied a formal cognitive model to the performance on the Iowa Gambling Task. The model has three parameters: The relative impact of rewards and punishments on evaluations, the impact of recent and past payoffs, and the degree of choice consistency. The results indicated that acute bipolar patients were characterized by low choice consistency, or a tendency to make erratic choices. Low choice consistency improved the prediction of acute bipolar disorder beyond that provided by cognitive functioning and self-report measures of personality and temperament. PMID:18848361

  1. Healthcare ethics: a patient-centered decision model.

    PubMed

    Oddo, A R

    2001-01-01

    A common financial model used in business decisions is the cost/benefit comparison. The costs of a proposed project are compared with the benefits, and if the benefits outweigh the costs, the project is accepted; if the costs exceed the benefits, the project is rejected. This model is applicable when tangible costs and benefits can be reasonably measured in monetary units. However, it is difficult to consider intangible factors in this model because intangible factors cannot be readily quantified in money. While some might argue that the financial model should not apply to healthcare decisions, the fact is that costs do enter into the picture. People may decide to forego needed healthcare because they cannot afford it. Healthcare providers may make choices based in part on the costs of diagnosis and treatment, rather than solely on medical information and what is best for the patient. Should financial issues enter into healthcare decisions--decisions about human health and well being? If so, how should the costs and benefits be measured and evaluated? What are some ethical issues and dilemmas involved in such decisions. This paper addresses ethical dilemmas and financial issues in healthcare. A healthcare decision model, which considers medical information, financial information, as well as ethical and other intangible factors, is proposed. PMID:12530441

  2. RNA search with decision trees and partial covariance models.

    PubMed

    Smith, Jennifer A

    2009-01-01

    The use of partial covariance models to search for RNA family members in genomic sequence databases is explored. The partial models are formed from contiguous subranges of the overall RNA family multiple alignment columns. A binary decision-tree framework is presented for choosing the order to apply the partial models and the score thresholds on which to make the decisions. The decision trees are chosen to minimize computation time subject to the constraint that all of the training sequences are passed to the full covariance model for final evaluation. Computational intelligence methods are suggested to select the decision tree since the tree can be quite complex and there is no obvious method to build the tree in these cases. Experimental results from seven RNA families shows execution times of 0.066-0.268 relative to using the full covariance model alone. Tests on the full sets of known sequences for each family show that at least 95 percent of these sequences are found for two families and 100 percent for five others. Since the full covariance model is run on all sequences accepted by the partial model decision tree, the false alarm rate is at least as low as that of the full model alone. PMID:19644178

  3. Climate change decision-making: Model & parameter uncertainties explored

    SciTech Connect

    Dowlatabadi, H.; Kandlikar, M.; Linville, C.

    1995-12-31

    A critical aspect of climate change decision-making is uncertainties in current understanding of the socioeconomic, climatic and biogeochemical processes involved. Decision-making processes are much better informed if these uncertainties are characterized and their implications understood. Quantitative analysis of these uncertainties serve to inform decision makers about the likely outcome of policy initiatives, and help set priorities for research so that outcome ambiguities faced by the decision-makers are reduced. A family of integrated assessment models of climate change have been developed at Carnegie Mellon. These models are distinguished from other integrated assessment efforts in that they were designed from the outset to characterize and propagate parameter, model, value, and decision-rule uncertainties. The most recent of these models is ICAM 2.1. This model includes representation of the processes of demographics, economic activity, emissions, atmospheric chemistry, climate and sea level change and impacts from these changes and policies for emissions mitigation, and adaptation to change. The model has over 800 objects of which about one half are used to represent uncertainty. In this paper we show, that when considering parameter uncertainties, the relative contribution of climatic uncertainties are most important, followed by uncertainties in damage calculations, economic uncertainties and direct aerosol forcing uncertainties. When considering model structure uncertainties we find that the choice of policy is often dominated by model structure choice, rather than parameter uncertainties.

  4. A Conditional Model of Evidence-Based Decision Making

    PubMed Central

    Falzer, Paul R.; Garman, D. Melissa

    2009-01-01

    Rationale Efforts to describe how individual treatment decisions are informed by systematic knowledge have been hindered by a standard that gauges the quality of clinical decisions by their adherence to guidelines and evidence-based practices. This paper tests a new contextual standard that gauges the incorporation of knowledge into practice and develops a model of evidence-based decision making. Aims and objectives Previous work found that the forecasted outcome of a treatment guideline exerts a highly significant influence on how it is used in making decisions. This study proposed that forecasted outcomes affect the recognition of a treatment scenario, and this recognition triggers distinct contextual decision strategies. Method N=21 volunteers from a psychiatric residency program responded to 64 case vignettes, 16 in each of four treatment scenarios. The vignettes represented a fully balanced within-subjects design that included guideline switching criteria and patient-specific factors. For each vignette, participants indicated whether they endorsed the guideline’s recommendation. Results Clinicians employed consistent contextual decision strategies in responding to clearly positive or negative forecasts. When forecasts were more ambiguous or risky, their strategies became complex and relatively inconsistent. Conclusion The results support a three step model of evidence-based decision making, in which clinicians recognize a decision scenario, apply a simple contextual strategy, then if necessary engage a more complex strategy to resolve discrepancies between general guidelines and specific cases. The paper concludes by noting study limitations and discussing implications of the model for future research in clinical and shared decision making, training, and guideline development. PMID:20367718

  5. Evaluation information integration model on book purchasing bids

    NASA Astrophysics Data System (ADS)

    Wang, Wei; Jiao, Yang

    2011-12-01

    The multi attributes decision model is presented basing on a number of indicators of book procurement bidders, and by the characteristics of persons to engage in joint decision-making. For each evaluation to define the ideal solution and negative ideal solution, further the relative closeness of each evaluation person and each supplier. The ideal solution and negative ideal solution of the evaluation committee is defined based on the group closeness matrix, and then the results of the ultimate supplier evaluation are calculated by decision-making groups. In this paper, the model is through the application of experimental data.

  6. Crop Simulation Models and Decision Support Systems

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The first computer simulation models for agricultural systems were developed in the 1970s. These early models simulated potential production for major crops as a function of weather conditions, especially temperature and solar radiation. At a later stage, the water component was added to be able to ...

  7. Critical infrastructure protection decision support system decision model : overview and quick-start user's guide.

    SciTech Connect

    Samsa, M.; Van Kuiken, J.; Jusko, M.; Decision and Information Sciences

    2008-12-01

    The Critical Infrastructure Protection Decision Support System Decision Model (CIPDSS-DM) is a useful tool for comparing the effectiveness of alternative risk-mitigation strategies on the basis of CIPDSS consequence scenarios. The model is designed to assist analysts and policy makers in evaluating and selecting the most effective risk-mitigation strategies, as affected by the importance assigned to various impact measures and the likelihood of an incident. A typical CIPDSS-DM decision map plots the relative preference of alternative risk-mitigation options versus the annual probability of an undesired incident occurring once during the protective life of the investment, assumed to be 20 years. The model also enables other types of comparisons, including a decision map that isolates a selected impact variable and displays the relative preference for the options of interest--parameterized on the basis of the contribution of the isolated variable to total impact, as well as the likelihood of the incident. Satisfaction/regret analysis further assists the analyst or policy maker in evaluating the confidence with which one option can be selected over another.

  8. Measuring and modeling behavioral decision dynamics in collective evacuation.

    PubMed

    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

  9. 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

  10. A model of the human observer and decision maker

    NASA Technical Reports Server (NTRS)

    Wewerinke, P. H.

    1981-01-01

    The decision process is described in terms of classical sequential decision theory by considering the hypothesis that an abnormal condition has occurred by means of a generalized likelihood ratio test. For this, a sufficient statistic is provided by the innovation sequence which is the result of the perception an information processing submodel of the human observer. On the basis of only two model parameters, the model predicts the decision speed/accuracy trade-off and various attentional characteristics. A preliminary test of the model for single variable failure detection tasks resulted in a very good fit of the experimental data. In a formal validation program, a variety of multivariable failure detection tasks was investigated and the predictive capability of the model was demonstrated.

  11. Modeling Lexical Decision: The Form of Frequency and Diversity Effects

    ERIC Educational Resources Information Center

    Adelman, James S.; Brown, Gordon D. A.

    2008-01-01

    What is the root cause of word frequency effects on lexical decision times? W. S. Murray and K. I. Forster (2004) argued that such effects are linear in rank frequency, consistent with a serial search model of lexical access. In this article, the authors (a) describe a method of testing models of such effects that takes into account the…

  12. A Transcultural Integrative Model for Ethical Decision Making in Counseling.

    ERIC Educational Resources Information Center

    Garcia, Jorge, G.; Cartwright, Brenda; Winston, Stacey M.; Borzuchowska, Barbara

    2003-01-01

    The Transcultural Integrative Ethical Decision-Making Model in counseling addresses the need for including cultural factors in the process of ethical dilemma resolution. The proposed model is presented in a step-by-step, linear format that can be used by counselors facing ethical dilemmas in a variety of settings and with different cultural…

  13. Coming Apart: A Model of the Marital Dissolution Decision.

    ERIC Educational Resources Information Center

    Edwards, John N.; Saunders, Janice M.

    1981-01-01

    Proposes a social-psychological model of the dissolution decision in marriage based on prior theoretical formulations. Sequential in character and emphasizing the duality of the marital relationship, the model modifies and refines previous theoretical efforts, and seeks to extend their explanatory power by incorporating various principles of…

  14. 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)

  15. A sequential decision-theoretic model for medical diagnostic system.

    PubMed

    Li, Aiping; Jin, Songchang; Zhang, Lumin; Jia, Yan

    2015-01-01

    Although diagnostic expert systems using a knowledge base which models decision-making of traditional experts can provide important information to non-experts, they tend to duplicate the errors made by experts. Decision-Theoretic Model (DTM) is therefore very useful in expert system since they prevent experts from incorrect reasoning under uncertainty. For the diagnostic expert system, corresponding DTM and arithmetic are studied and a sequential diagnostic decision-theoretic model based on Bayesian Network is given. In the model, the alternative features are categorized into two classes (including diseases features and test features), then an arithmetic for prior of test is provided. The different features affect other features weights are also discussed. Bayesian Network is adopted to solve uncertainty presentation and propagation. The model can help knowledge engineers model the knowledge involved in sequential diagnosis and decide evidence alternative priority. A practical example of the models is also presented: at any time of the diagnostic process the expert is provided with a dynamically updated list of suggested tests in order to support him in the decision-making problem about which test to execute next. The results show it is better than the traditional diagnostic model which is based on experience. PMID:26410326

  16. A Utility Model for Teaching Load Decisions in Academic Departments.

    ERIC Educational Resources Information Center

    Massey, William F.; Zemsky, Robert

    1997-01-01

    Presents a utility model for academic department decision making and describes the structural specifications for analyzing it. The model confirms the class-size utility asymmetry predicted by the authors' academic rachet theory, but shows that marginal utility associated with college teaching loads is always negative. Curricular structure and…

  17. Dissolving decision making? Models and their roles in decision-making processes and policy at large.

    PubMed

    Zeiss, Ragna; van Egmond, Stans

    2014-12-01

    This article studies the roles three science-based models play in Dutch policy and decision making processes. Key is the interaction between model construction and environment. Their political and scientific environments form contexts that shape the roles of models in policy decision making. Attention is paid to three aspects of the wider context of the models: a) the history of the construction process; b) (changes in) the political and scientific environments; and c) the use in policy processes over longer periods of time. Models are more successfully used when they are constructed in a stable political and scientific environment. Stability and certainty within a scientific field seems to be a key predictor for the usefulness of models for policy making. The economic model is more disputed than the ecology-based model and the model that has its theoretical foundation in physics and chemistry. The roles models play in policy processes are too complex to be considered as straightforward technocratic powers. PMID:25549446

  18. A decision model for environmental R and D

    SciTech Connect

    Chao, H.P.; Peck, S.

    1999-09-01

    In this paper, a simple decision model is formulated to investigate the optimal funding level for an environmental R and D activity. The Bayesian approach is extended to research in situations where the researchers have prior beliefs different from the ultimate decision makers. Sensitivity analysis demonstrated how the optimal level of R and D varies with assumptions about: (1) the shadow price of R and D resources, which is a measure of the scarcity of funds; (2) the technical difficulty of the research area; (3) the potential payoffs involved in the decision that may be influenced by the research outcome; and (4) the prior knowledge of the key decision makers. Among other insights, analysis of the model demonstrated that the optimal research expenditure jumped from zero to a significant amount or conversely with small changes in parameters. The jump occurred when the expected cost of an uniformed decision equals the cost of research plus the expected cost of the contingent decision. In addition, the optimal research expenditure was sensitive to the technical difficulty of the research area.

  19. Beyond pain: modeling decision-making deficits in chronic pain

    PubMed Central

    Hess, Leonardo Emanuel; Haimovici, Ariel; Muñoz, Miguel Angel; Montoya, Pedro

    2014-01-01

    Risky decision-making seems to be markedly disrupted in patients with chronic pain, probably due to the high cost that impose pain and negative mood on executive control functions. Patients’ behavioral performance on decision-making tasks such as the Iowa Gambling Task (IGT) is characterized by selecting cards more frequently from disadvantageous than from advantageous decks, and by switching often between competing responses in comparison with healthy controls (HCs). In the present study, we developed a simple heuristic model to simulate individuals’ choice behavior by varying the level of decision randomness and the importance given to gains and losses. The findings revealed that the model was able to differentiate the behavioral performance of patients with chronic pain and HCs at the group, as well as at the individual level. The best fit of the model in patients with chronic pain was yielded when decisions were not based on previous choices and when gains were considered more relevant than losses. By contrast, the best account of the available data in HCs was obtained when decisions were based on previous experiences and losses loomed larger than gains. In conclusion, our model seems to provide useful information to measure each individual participant extensively, and to deal with the data on a participant-by-participant basis. PMID:25136301

  20. Beyond pain: modeling decision-making deficits in chronic pain.

    PubMed

    Hess, Leonardo Emanuel; Haimovici, Ariel; Muñoz, Miguel Angel; Montoya, Pedro

    2014-01-01

    Risky decision-making seems to be markedly disrupted in patients with chronic pain, probably due to the high cost that impose pain and negative mood on executive control functions. Patients' behavioral performance on decision-making tasks such as the Iowa Gambling Task (IGT) is characterized by selecting cards more frequently from disadvantageous than from advantageous decks, and by switching often between competing responses in comparison with healthy controls (HCs). In the present study, we developed a simple heuristic model to simulate individuals' choice behavior by varying the level of decision randomness and the importance given to gains and losses. The findings revealed that the model was able to differentiate the behavioral performance of patients with chronic pain and HCs at the group, as well as at the individual level. The best fit of the model in patients with chronic pain was yielded when decisions were not based on previous choices and when gains were considered more relevant than losses. By contrast, the best account of the available data in HCs was obtained when decisions were based on previous experiences and losses loomed larger than gains. In conclusion, our model seems to provide useful information to measure each individual participant extensively, and to deal with the data on a participant-by-participant basis. PMID:25136301

  1. Consumer's Online Shopping Influence Factors and Decision-Making Model

    NASA Astrophysics Data System (ADS)

    Yan, Xiangbin; Dai, Shiliang

    Previous research on online consumer behavior has mostly been confined to the perceived risk which is used to explain those barriers for purchasing online. However, perceived benefit is another important factor which influences consumers’ decision when shopping online. As a result, an integrated consumer online shopping decision-making model is developed which contains three elements—Consumer, Product, and Web Site. This model proposed relative factors which influence the consumers’ intention during the online shopping progress, and divided them into two different dimensions—mentally level and material level. We tested those factors with surveys, from both online volunteers and offline paper surveys with more than 200 samples. With the help of SEM, the experimental results show that the proposed model and method can be used to analyze consumer’s online shopping decision-making process effectively.

  2. 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.

  3. Interactive agent based modeling of public health decision-making.

    PubMed

    Parks, Amanda L; Walker, Brett; Pettey, Warren; Benuzillo, Jose; Gesteland, Per; Grant, Juliana; Koopman, James; Drews, Frank; Samore, Matthew

    2009-01-01

    Agent-based models have yielded important insights regarding the transmission dynamics of communicable diseases. To better understand how these models can be used to study decision making of public health officials, we developed a computer program that linked an agent-based model of pertussis with an agent-based model of public health management. The program, which we call the Public Health Interactive Model & simulation (PHIMs) encompassed the reporting of cases to public health, case investigation, and public health response. The user directly interacted with the model in the role of the public health decision-maker. In this paper we describe the design of our model, and present the results of a pilot study to assess its usability and potential for future development. Affinity for specific tools was demonstrated. Participants ranked the program high in usability and considered it useful for training. Our ultimate goal is to achieve better public health decisions and outcomes through use of public health decision support tools. PMID:20351907

  4. Emergent collective decision-making: Control, model and behavior

    NASA Astrophysics Data System (ADS)

    Shen, Tian

    In this dissertation we study emergent collective decision-making in social groups with time-varying interactions and heterogeneously informed individuals. First we analyze a nonlinear dynamical systems model motivated by animal collective motion with heterogeneously informed subpopulations, to examine the role of uninformed individuals. We find through formal analysis that adding uninformed individuals in a group increases the likelihood of a collective decision. Secondly, we propose a model for human shared decision-making with continuous-time feedback and where individuals have little information about the true preferences of other group members. We study model equilibria using bifurcation analysis to understand how the model predicts decisions based on the critical threshold parameters that represent an individual's tradeoff between social and environmental influences. Thirdly, we analyze continuous-time data of pairs of human subjects performing an experimental shared tracking task using our second proposed model in order to understand transient behavior and the decision-making process. We fit the model to data and show that it reproduces a wide range of human behaviors surprisingly well, suggesting that the model may have captured the mechanisms of observed behaviors. Finally, we study human behavior from a game-theoretic perspective by modeling the aforementioned tracking task as a repeated game with incomplete information. We show that the majority of the players are able to converge to playing Nash equilibrium strategies. We then suggest with simulations that the mean field evolution of strategies in the population resemble replicator dynamics, indicating that the individual strategies may be myopic. Decisions form the basis of control and problems involving deciding collectively between alternatives are ubiquitous in nature and in engineering. Understanding how multi-agent systems make decisions among alternatives also provides insight for designing

  5. Landslide risk models for decision making.

    PubMed

    Bonachea, Jaime; Remondo, Juan; de Terán, José Ramón Díaz; González-Díez, Alberto; Cendrero, Antonio

    2009-11-01

    This contribution presents a quantitative procedure for landslide risk analysis and zoning considering hazard, exposure (or value of elements at risk), and vulnerability. The method provides the means to obtain landslide risk models (expressing expected damage due to landslides on material elements and economic activities in monetary terms, according to different scenarios and periods) useful to identify areas where mitigation efforts will be most cost effective. It allows identifying priority areas for the implementation of actions to reduce vulnerability (elements) or hazard (processes). The procedure proposed can also be used as a preventive tool, through its application to strategic environmental impact analysis (SEIA) of land-use plans. The underlying hypothesis is that reliable predictions about hazard and risk can be made using models based on a detailed analysis of past landslide occurrences in connection with conditioning factors and data on past damage. The results show that the approach proposed and the hypothesis formulated are essentially correct, providing estimates of the order of magnitude of expected losses for a given time period. Uncertainties, strengths, and shortcomings of the procedure and results obtained are discussed and potential lines of research to improve the models are indicated. Finally, comments and suggestions are provided to generalize this type of analysis. PMID:19732396

  6. A neuro-computational model of economic decisions.

    PubMed

    Rustichini, Aldo; Padoa-Schioppa, Camillo

    2015-09-01

    Neuronal recordings and lesion studies indicate that key aspects of economic decisions take place in the orbitofrontal cortex (OFC). Previous work identified in this area three groups of neurons encoding the offer value, the chosen value, and the identity of the chosen good. An important and open question is whether and how decisions could emerge from a neural circuit formed by these three populations. Here we adapted a biophysically realistic neural network previously proposed for perceptual decisions (Wang XJ. Neuron 36: 955-968, 2002; Wong KF, Wang XJ. J Neurosci 26: 1314-1328, 2006). The domain of economic decisions is significantly broader than that for which the model was originally designed, yet the model performed remarkably well. The input and output nodes of the network were naturally mapped onto two groups of cells in OFC. Surprisingly, the activity of interneurons in the network closely resembled that of the third group of cells, namely, chosen value cells. The model reproduced several phenomena related to the neuronal origins of choice variability. It also generated testable predictions on the excitatory/inhibitory nature of different neuronal populations and on their connectivity. Some aspects of the empirical data were not reproduced, but simple extensions of the model could overcome these limitations. These results render a biologically credible model for the neuronal mechanisms of economic decisions. They demonstrate that choices could emerge from the activity of cells in the OFC, suggesting that chosen value cells directly participate in the decision process. Importantly, Wang's model provides a platform to investigate the implications of neuroscience results for economic theory. PMID:26063776

  7. Weighted Hybrid Decision Tree Model for Random Forest Classifier

    NASA Astrophysics Data System (ADS)

    Kulkarni, Vrushali Y.; Sinha, Pradeep K.; Petare, Manisha C.

    2016-06-01

    Random Forest is an ensemble, supervised machine learning algorithm. An ensemble generates many classifiers and combines their results by majority voting. Random forest uses decision tree as base classifier. In decision tree induction, an attribute split/evaluation measure is used to decide the best split at each node of the decision tree. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation among them. The work presented in this paper is related to attribute split measures and is a two step process: first theoretical study of the five selected split measures is done and a comparison matrix is generated to understand pros and cons of each measure. These theoretical results are verified by performing empirical analysis. For empirical analysis, random forest is generated using each of the five selected split measures, chosen one at a time. i.e. random forest using information gain, random forest using gain ratio, etc. The next step is, based on this theoretical and empirical analysis, a new approach of hybrid decision tree model for random forest classifier is proposed. In this model, individual decision tree in Random Forest is generated using different split measures. This model is augmented by weighted voting based on the strength of individual tree. The new approach has shown notable increase in the accuracy of random forest.

  8. Integrated Environmental Modelling: human decisions, human challenges

    USGS Publications Warehouse

    Glynn, Pierre D.

    2015-01-01

    Integrated Environmental Modelling (IEM) is an invaluable tool for understanding the complex, dynamic ecosystems that house our natural resources and control our environments. Human behaviour affects the ways in which the science of IEM is assembled and used for meaningful societal applications. In particular, human biases and heuristics reflect adaptation and experiential learning to issues with frequent, sharply distinguished, feedbacks. Unfortunately, human behaviour is not adapted to the more diffusely experienced problems that IEM typically seeks to address. Twelve biases are identified that affect IEM (and science in general). These biases are supported by personal observations and by the findings of behavioural scientists. A process for critical analysis is proposed that addresses some human challenges of IEM and solicits explicit description of (1) represented processes and information, (2) unrepresented processes and information, and (3) accounting for, and cognizance of, potential human biases. Several other suggestions are also made that generally complement maintaining attitudes of watchful humility, open-mindedness, honesty and transparent accountability. These suggestions include (1) creating a new area of study in the behavioural biogeosciences, (2) using structured processes for engaging the modelling and stakeholder communities in IEM, and (3) using ‘red teams’ to increase resilience of IEM constructs and use.

  9. Model-based decision support in diabetes care.

    PubMed

    Salzsieder, E; Vogt, L; Kohnert, K-D; Heinke, P; Augstein, P

    2011-05-01

    The model-based Karlsburg Diabetes Management System (KADIS®) has been developed as a patient-focused decision-support tool to provide evidence-based advice for physicians in their daily efforts to optimize metabolic control in diabetes care of their patients on an individualized basis. For this purpose, KADIS® was established in terms of a personalized, interactive in silico simulation procedure, implemented into a problem-related diabetes health care network and evaluated under different conditions by conducting open-label mono- and polycentric trials, and a case-control study, and last but not least, by application in routine diabetes outpatient care. The trial outcomes clearly show that the recommendations provided to the physicians by KADIS® lead to significant improvement of metabolic control. This model-based decision-support system provides an excellent tool to effectively guide physicians in personalized decision-making to achieve optimal metabolic control for their patients. PMID:20621384

  10. Simple model for multiple-choice collective decision making.

    PubMed

    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. PMID:25493831

  11. 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.

  12. Overcoming barriers to development of cooperative medical decision support models.

    PubMed

    Hudson, Donna L; Cohen, Maurice E

    2012-01-01

    Attempts to automate the medical decision making process have been underway for the at least fifty years, beginning with data-based approaches that relied chiefly on statistically-based methods. Approaches expanded to include knowledge-based systems, both linear and non-linear neural networks, agent-based systems, and hybrid methods. While some of these models produced excellent results none have been used extensively in medical practice. In order to move these methods forward into practical use, a number of obstacles must be overcome, including validation of existing systems on large data sets, development of methods for including new knowledge as it becomes available, construction of a broad range of decision models, and development of non-intrusive methods that allow the physician to use these decision aids in conjunction with, not instead of, his or her own medical knowledge. None of these four requirements will come easily. A cooperative effort among researchers, including practicing MDs, is vital, particularly as more information on diseases and their contributing factors continues to expand resulting in more parameters than the human decision maker can process effectively. In this article some of the basic structures that are necessary to facilitate the use of an automated decision support system are discussed, along with potential methods for overcoming existing barriers. PMID:23366358

  13. Emulation Modeling with Bayesian Networks for Efficient Decision Support

    NASA Astrophysics Data System (ADS)

    Fienen, M. N.; Masterson, J.; Plant, N. G.; Gutierrez, B. T.; Thieler, E. R.

    2012-12-01

    Bayesian decision networks (BDN) have long been used to provide decision support in systems that require explicit consideration of uncertainty; applications range from ecology to medical diagnostics and terrorism threat assessments. Until recently, however, few studies have applied BDNs to the study of groundwater systems. BDNs are particularly useful for representing real-world system variability by synthesizing a range of hydrogeologic situations within a single simulation. Because BDN output is cast in terms of probability—an output desired by decision makers—they explicitly incorporate the uncertainty of a system. BDNs can thus serve as a more efficient alternative to other uncertainty characterization methods such as computationally demanding Monte Carlo analyses and others methods restricted to linear model analyses. We present a unique application of a BDN to a groundwater modeling analysis of the hydrologic response of Assateague Island, Maryland to sea-level rise. Using both input and output variables of the modeled groundwater response to different sea-level (SLR) rise scenarios, the BDN predicts the probability of changes in the depth to fresh water, which exerts an important influence on physical and biological island evolution. Input variables included barrier-island width, maximum island elevation, and aquifer recharge. The variability of these inputs and their corresponding outputs are sampled along cross sections in a single model run to form an ensemble of input/output pairs. The BDN outputs, which are the posterior distributions of water table conditions for the sea-level rise scenarios, are evaluated through error analysis and cross-validation to assess both fit to training data and predictive power. The key benefit for using BDNs in groundwater modeling analyses is that they provide a method for distilling complex model results into predictions with associated uncertainty, which is useful to decision makers. Future efforts incorporate

  14. The Decision/Adoption Model as a Heuristic Program Theory.

    ERIC Educational Resources Information Center

    Tedrick, William E.

    The emerging concept of program theory and its function in program evaluation practice is the central focus of this paper. It appears that the traditional decision/adoption model, when considered in conjunction with the institutionalized beliefs, values, and methodological procedures of the state Cooperative Extension Services, meets the criteria…

  15. OASIS: A GRAPHICAL DECISION SUPPORT SYSTEM FOR GROUNDWATER CONTAMINANT MODELING

    EPA Science Inventory

    Three new software technologies were applied to develop an efficient and easy to use decision support system far ground-water contaminant modeling. raphical interfaces create a more intuitive and effective form of communication with the computer compared to text-based interfaces....

  16. Modelling Human Emotions for Tactical Decision-Making Games

    ERIC Educational Resources Information Center

    Visschedijk, Gillian C.; Lazonder, Ard W.; van der Hulst, Anja; Vink, Nathalie; Leemkuil, Henny

    2013-01-01

    The training of tactical decision making increasingly occurs through serious computer games. A challenging aspect of designing such games is the modelling of human emotions. Two studies were performed to investigate the relation between fidelity and human emotion recognition in virtual human characters. Study 1 compared five versions of a virtual…

  17. A group decision-making model for siting LULUs

    SciTech Connect

    Juang, C.H.; Wu, S.; Sheu, H.J.

    1995-03-01

    This paper presents a group decision-making model for siting locally unwanted land uses (LULUs) such as hazardous and nonhazardous solid waste landfills. The new model is based on fuzzy set theory, which has been proven to be effective and efficient in handling ambiguous information such as opinions expressed by a panel of representatives with diverse backgrounds. The model involves an operation called fuzzy weighted average (FWA) for aggregating opinions. The weights used in the FWA operation are determined by the entropy method. Entropy is a measure of the uncertainty associated with a piece of information such as an opinion. By measuring the entropy of each of the opinions to be aggregated, the weight of each opinion may be objectively determined. The result of the FWA operation may be used to aid in making decisions. Examples are presented to illustrate the proposed model.

  18. How to Say "No" to a Nonword: A Leaky Competing Accumulator Model of Lexical Decision

    ERIC Educational Resources Information Center

    Dufau, Stephane; Grainger, Jonathan; Ziegler, Johannes C.

    2012-01-01

    We describe a leaky competing accumulator (LCA) model of the lexical decision task that can be used as a response/decision module for any computational model of word recognition. The LCA model uses evidence for a word, operationalized as some measure of lexical activity, as input to the "YES" decision node. Input to the "NO" decision node is…

  19. A decision-making process model of young online shoppers.

    PubMed

    Lin, Chin-Feng; Wang, Hui-Fang

    2008-12-01

    Based on the concepts of brand equity, means-end chain, and Web site trust, this study proposes a novel model called the consumption decision-making process of adolescents (CDMPA) to understand adolescents' Internet consumption habits and behavioral intention toward particular sporting goods. The findings of the CDMPA model can help marketers understand adolescents' consumption preferences and habits for developing effective Internet marketing strategies. PMID:19025465

  20. Robust Decision-making Applied to Model Selection

    SciTech Connect

    Hemez, Francois M.

    2012-08-06

    The scientific and engineering communities are relying more and more on numerical models to simulate ever-increasingly complex phenomena. Selecting a model, from among a family of models that meets the simulation requirements, presents a challenge to modern-day analysts. To address this concern, a framework is adopted anchored in info-gap decision theory. The framework proposes to select models by examining the trade-offs between prediction accuracy and sensitivity to epistemic uncertainty. The framework is demonstrated on two structural engineering applications by asking the following question: Which model, of several numerical models, approximates the behavior of a structure when parameters that define each of those models are unknown? One observation is that models that are nominally more accurate are not necessarily more robust, and their accuracy can deteriorate greatly depending upon the assumptions made. It is posited that, as reliance on numerical models increases, establishing robustness will become as important as demonstrating accuracy.

  1. Towards a controlled sensitivity analysis of model development decisions

    NASA Astrophysics Data System (ADS)

    Clark, Martyn; Nijssen, Bart

    2016-04-01

    The current generation of hydrologic models have followed a myriad of different development paths, making it difficult for the community to test underlying hypotheses and identify a clear path to model improvement. Model comparison studies have been undertaken to explore model differences, but these studies have not been able to meaningfully attribute inter-model differences in predictive ability to individual model components because there are often too many structural and implementation differences among the models considered. As a consequence, model comparison studies to date have provided limited insight into the causes of differences in model behavior, and model development has often relied on the inspiration and experience of individual modelers rather than a systematic analysis of model shortcomings. This presentation will discuss a unified approach to process-based hydrologic modeling to enable controlled and systematic analysis of multiple model representations (hypotheses) of hydrologic processes and scaling behavior. Our approach, which we term the Structure for Unifying Multiple Modeling Alternatives (SUMMA), formulates a general set of conservation equations, providing the flexibility to experiment with different spatial representations, different flux parameterizations, different model parameter values, and different time stepping schemes. We will discuss the use of SUMMA to systematically analyze different model development decisions, focusing on both analysis of simulations for intensively instrumented research watersheds as well as simulations across a global dataset of FLUXNET sites. The intent of the presentation is to demonstrate how the systematic analysis of model shortcomings can help identify model weaknesses and inform future model development priorities.

  2. On anthropomorphic decision making in a model observer

    NASA Astrophysics Data System (ADS)

    Avanaki, Ali R. N.; Espig, Kathryn S.; Kimpe, Tom R. L.; Maidment, Andrew D. A.

    2015-03-01

    By analyzing human readers' performance in detecting small round lesions in simulated digital breast tomosynthesis background in a location known exactly scenario, we have developed a model observer that is a better predictor of human performance with different levels of background complexity (i.e., anatomical and quantum noise). Our analysis indicates that human observers perform a lesion detection task by combining a number of sub-decisions, each an indicator of the presence of a lesion in the image stack. This is in contrast to a channelized Hotelling observer, where the detection task is conducted holistically by thresholding a single decision variable, made from an optimally weighted linear combination of channels. However, it seems that the sub-par performance of human readers compared to the CHO cannot be fully explained by their reliance on sub-decisions, or perhaps we do not consider a sufficient number of subdecisions. To bridge the gap between the performances of human readers and the model observer based upon subdecisions, we use an additive noise model, the power of which is modulated with the level of background complexity. The proposed model observer better predicts the fast drop in human detection performance with background complexity.

  3. Modeling Common-Sense Decisions in Artificial Intelligence

    NASA Technical Reports Server (NTRS)

    Zak, Michail

    2010-01-01

    A methodology has been conceived for efficient synthesis of dynamical models that simulate common-sense decision- making processes. This methodology is intended to contribute to the design of artificial-intelligence systems that could imitate human common-sense decision making or assist humans in making correct decisions in unanticipated circumstances. This methodology is a product of continuing research on mathematical models of the behaviors of single- and multi-agent systems known in biology, economics, and sociology, ranging from a single-cell organism at one extreme to the whole of human society at the other extreme. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the three most recent and relevant being Characteristics of Dynamics of Intelligent Systems (NPO -21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; Self-Supervised Dynamical Systems (NPO-30634), NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72; and Complexity for Survival of Living Systems (NPO- 43302), NASA Tech Briefs, Vol. 33, No. 7 (July 2009), page 62. The methodology involves the concepts reported previously, albeit viewed from a different perspective. One of the main underlying ideas is to extend the application of physical first principles to the behaviors of living systems. Models of motor dynamics are used to simulate the observable behaviors of systems or objects of interest, and models of mental dynamics are used to represent the evolution of the corresponding knowledge bases. For a given system, the knowledge base is modeled in the form of probability distributions and the mental dynamics is represented by models of the evolution of the probability densities or, equivalently, models of flows of information. Autonomy is imparted to the decisionmaking process by feedback from mental to motor dynamics. This feedback replaces unavailable external information by information stored in the internal knowledge base. Representation

  4. Aggregation of Environmental Model Data for Decision Support

    NASA Astrophysics Data System (ADS)

    Alpert, J. C.

    2013-12-01

    Weather forecasts and warnings must be prepared and then delivered so as to reach their intended audience in good time to enable effective decision-making. An effort to mitigate these difficulties was studied at a Workshop, 'Sustaining National Meteorological Services - Strengthening WMO Regional and Global Centers' convened, June , 2013, by the World Bank, WMO and the US National Weather Service (NWS). The skill and accuracy of atmospheric forecasts from deterministic models have increased and there are now ensembles of such models that improve decisions to protect life, property and commerce. The NWS production of numerical weather prediction products result in model output from global and high resolution regional ensemble forecasts. Ensembles are constructed by changing the initial conditions to make a 'cloud' of forecasts that attempt to span the space of possible atmospheric realizations which can quantify not only the most likely forecast, but also the uncertainty. This has led to an unprecedented increase in data production and information content from higher resolution, multi-model output and secondary calculations. One difficulty is to obtain the needed subset of data required to estimate the probability of events, and report the information. The calibration required to reliably estimate the probability of events, and honing of threshold adjustments to reduce false alarms for decision makers is also needed. To meet the future needs of the ever-broadening user community and address these issues on a national and international basis, the weather service implemented the NOAA Operational Model Archive and Distribution System (NOMADS). NOMADS provides real-time and retrospective format independent access to climate, ocean and weather model data and delivers high availability content services as part of NOAA's official real time data dissemination at its new NCWCP web operations center. An important aspect of the server's abilities is to aggregate the matrix of

  5. A Product Development Decision Model for Cockpit Weather Information Systems

    NASA Technical Reports Server (NTRS)

    Sireli, Yesim; Kauffmann, Paul; Gupta, Surabhi; Kachroo, Pushkin

    2003-01-01

    There is a significant market demand for advanced cockpit weather information products. However, it is unclear how to identify the most promising technological options that provide the desired mix of consumer requirements by employing feasible technical systems at a price that achieves market success. This study develops a unique product development decision model that employs Quality Function Deployment (QFD) and Kano's model of consumer choice. This model is specifically designed for exploration and resolution of this and similar information technology related product development problems.

  6. A Product Development Decision Model for Cockpit Weather Information System

    NASA Technical Reports Server (NTRS)

    Sireli, Yesim; Kauffmann, Paul; Gupta, Surabhi; Kachroo, Pushkin; Johnson, Edward J., Jr. (Technical Monitor)

    2003-01-01

    There is a significant market demand for advanced cockpit weather information products. However, it is unclear how to identify the most promising technological options that provide the desired mix of consumer requirements by employing feasible technical systems at a price that achieves market success. This study develops a unique product development decision model that employs Quality Function Deployment (QFD) and Kano's model of consumer choice. This model is specifically designed for exploration and resolution of this and similar information technology related product development problems.

  7. A vertical handoff decision algorithm based on ARMA prediction model

    NASA Astrophysics Data System (ADS)

    Li, Ru; Shen, Jiao; Chen, Jun; Liu, Qiuhuan

    2011-12-01

    With the development of computer technology and the increasing demand for mobile communications, the next generation wireless networks will be composed of various wireless networks (e.g., WiMAX and WiFi). Vertical handoff is a key technology of next generation wireless networks. During the vertical handoff procedure, handoff decision is a crucial issue for an efficient mobility. Based on auto regression moving average (ARMA) prediction model, we propose a vertical handoff decision algorithm, which aims to improve the performance of vertical handoff and avoid unnecessary handoff. Based on the current received signal strength (RSS) and the previous RSS, the proposed approach adopt ARMA model to predict the next RSS. And then according to the predicted RSS to determine whether trigger the link layer triggering event and complete vertical handoff. The simulation results indicate that the proposed algorithm outperforms the RSS-based scheme with a threshold in the performance of handoff and the number of handoff.

  8. A vertical handoff decision algorithm based on ARMA prediction model

    NASA Astrophysics Data System (ADS)

    Li, Ru; Shen, Jiao; Chen, Jun; Liu, Qiuhuan

    2012-01-01

    With the development of computer technology and the increasing demand for mobile communications, the next generation wireless networks will be composed of various wireless networks (e.g., WiMAX and WiFi). Vertical handoff is a key technology of next generation wireless networks. During the vertical handoff procedure, handoff decision is a crucial issue for an efficient mobility. Based on auto regression moving average (ARMA) prediction model, we propose a vertical handoff decision algorithm, which aims to improve the performance of vertical handoff and avoid unnecessary handoff. Based on the current received signal strength (RSS) and the previous RSS, the proposed approach adopt ARMA model to predict the next RSS. And then according to the predicted RSS to determine whether trigger the link layer triggering event and complete vertical handoff. The simulation results indicate that the proposed algorithm outperforms the RSS-based scheme with a threshold in the performance of handoff and the number of handoff.

  9. 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. We provide efficient algorithms for manipulating EVMDDs and review 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. Compared to the CUDD package, our tool is several orders of magnitude faster

  10. Markov Modeling with Soft Aggregation for Safety and Decision Analysis

    SciTech Connect

    COOPER,J. ARLIN

    1999-09-01

    The methodology in this report improves on some of the limitations of many conventional safety assessment and decision analysis methods. A top-down mathematical approach is developed for decomposing systems and for expressing imprecise individual metrics as possibilistic or fuzzy numbers. A ''Markov-like'' model is developed that facilitates combining (aggregating) inputs into overall metrics and decision aids, also portraying the inherent uncertainty. A major goal of Markov modeling is to help convey the top-down system perspective. One of the constituent methodologies allows metrics to be weighted according to significance of the attribute and aggregated nonlinearly as to contribution. This aggregation is performed using exponential combination of the metrics, since the accumulating effect of such factors responds less and less to additional factors. This is termed ''soft'' mathematical aggregation. Dependence among the contributing factors is accounted for by incorporating subjective metrics on ''overlap'' of the factors as well as by correspondingly reducing the overall contribution of these combinations to the overall aggregation. Decisions corresponding to the meaningfulness of the results are facilitated in several ways. First, the results are compared to a soft threshold provided by a sigmoid function. Second, information is provided on input ''Importance'' and ''Sensitivity,'' in order to know where to place emphasis on considering new controls that may be necessary. Third, trends in inputs and outputs are tracked in order to obtain significant information% including cyclic information for the decision process. A practical example from the air transportation industry is used to demonstrate application of the methodology. Illustrations are given for developing a structure (along with recommended inputs and weights) for air transportation oversight at three different levels, for developing and using cycle information, for developing Importance and

  11. An environmentally sustainable decision model for urban solid waste management.

    PubMed

    Costi, P; Minciardi, R; Robba, M; Rovatti, M; Sacile, R

    2004-01-01

    The aim of this work is to present the structure and the application of a decision support system (DSS) designed to help decision makers of a municipality in the development of incineration, disposal, treatment and recycling integrated programs. Specifically, within a MSW management system, several treatment plants and facilities can generally be found: separators, plants for production of refuse derived fuel (RDF), incinerators with energy recovery, plants for treatment of organic material, and sanitary landfills. The main goal of the DSS is to plan the MSW management, defining the refuse flows that have to be sent to recycling or to different treatment or disposal plants, and suggesting the optimal number, the kinds, and the localization of the plants that have to be active. The DSS is based on a decision model that requires the solution of a constrained non-linear optimization problem, where some decision variables are binary and other ones are continuous. The objective function takes into account all possible economic costs, whereas constraints arise from technical, normative, and environmental issues. Specifically, pollution and impacts, induced by the overall solid waste management system, are considered through the formalization of constraints on incineration emissions and on negative effects produced by disposal or other particular treatments. PMID:15016417

  12. An environmentally sustainable decision model for urban solid waste management

    SciTech Connect

    Costi, P.; Minciardi, R.; Robba, M.; Rovatti, M.; Sacile, R

    2004-07-01

    The aim of this work is to present the structure and the application of a decision support system (DSS) designed to help decision makers of a municipality in the development of incineration, disposal, treatment and recycling integrated programs. Specifically, within a MSW management system, several treatment plants and facilities can generally be found: separators, plants for production of refuse derived fuel (RDF), incinerators with energy recovery, plants for treatment of organic material, and sanitary landfills. The main goal of the DSS is to plan the MSW management, defining the refuse flows that have to be sent to recycling or to different treatment or disposal plants, and suggesting the optimal number, the kinds, and the localization of the plants that have to be active. The DSS is based on a decision model that requires the solution of a constrained non-linear optimization problem, where some decision variables are binary and other ones are continuous. The objective function takes into account all possible economic costs, whereas constraints arise from technical, normative, and environmental issues. Specifically, pollution and impacts, induced by the overall solid waste management system, are considered through the formalization of constraints on incineration emissions and on negative effects produced by disposal or other particular treatments.

  13. Reverse engineering cellular decisions for hybrid reconfigurable network modeling

    NASA Astrophysics Data System (ADS)

    Blair, Howard A.; Saranak, Jureepan; Foster, Kenneth W.

    2011-06-01

    Cells as microorganisms and within multicellular organisms make robust decisions. Knowing how these complex cells make decisions is essential to explain, predict or mimic their behavior. The discovery of multi-layer multiple feedback loops in the signaling pathways of these modular hybrid systems suggests their decision making is sophisticated. Hybrid systems coordinate and integrate signals of various kinds: discrete on/off signals, continuous sensory signals, and stochastic and continuous fluctuations to regulate chemical concentrations. Such signaling networks can form reconfigurable networks of attractors and repellors giving them an extra level of organization that has resilient decision making built in. Work on generic attractor and repellor networks and on the already identified feedback networks and dynamic reconfigurable regulatory topologies in biological cells suggests that biological systems probably exploit such dynamic capabilities. We present a simple behavior of the swimming unicellular alga Chlamydomonas that involves interdependent discrete and continuous signals in feedback loops. We show how to rigorously verify a hybrid dynamical model of a biological system with respect to a declarative description of a cell's behavior. The hybrid dynamical systems we use are based on a unification of discrete structures and continuous topologies developed in prior work on convergence spaces. They involve variables of discrete and continuous types, in the sense of type theory in mathematical logic. A unification such as afforded by convergence spaces is necessary if one wants to take account of the affect of the structural relationships within each type on the dynamics of the system.

  14. Decision Consequence Model (DCM): Integrating environmental data and analysis into real time decision making

    SciTech Connect

    Cimorelli, A.J.; Stahl, C.H.; Chow, A.H.; Fernandez, C.

    1999-07-01

    A critical evaluation of the many environmental issues facing EPA Region 3 has established five major priorities: (1) ozone pollution (and its precursors); (2) impacts of acidification (acid deposition and acid mine drainage); (3) eutrophication of the Chesapeake Bay from atmospheric nitrogen deposition; (4) Cities/Urban Environment (ozone, particulate matter (PM), air toxics are some of the air components); and (5) Climate Change. Recognizing the complex nature of the systems controlling these issues, Region III's Air Protection Division (APD) is developing a decision support tool, i.e., the Decision Consequence Model (DCM), that will integrate and automate the analysis of environmental impacts in a manner that allows them to holistically address these regional priorities. Using this tool the authors intend to consider the interdependency of pollutants and their environmental impacts in order to support real-time decision making. The purpose of this paper is to outline the basic concept of the DCM and to present an example set of environmental indicators to illustrate how the DCM will be used to evaluate environmental impacts. The authors will discuss their process of indicator development, and present an example suite of indicators to provide a concrete example of the concepts presented above and, to illustrate the utility of the DCM to simultaneously evaluate multiple effects of a single pollutant. They will discuss the type of indicators chosen for this example as well as the general criteria the DCM indicators must satisfy. The framework that was developed to construct the indicators is discussed and used to calculate the example indicators. The yearly magnitudes of these example indicators are calculated for various multi-year periods to show their behavior over time.

  15. Behavioural modelling of irrigation decision making under water scarcity

    NASA Astrophysics Data System (ADS)

    Foster, T.; Brozovic, N.; Butler, A. P.

    2013-12-01

    Providing effective policy solutions to aquifer depletion caused by abstraction for irrigation is a key challenge for socio-hydrology. However, most crop production functions used in hydrological models do not capture the intraseasonal nature of irrigation planning, or the importance of well yield in land and water use decisions. Here we develop a method for determining stochastic intraseasonal water use that is based on observed farmer behaviour but is also theoretically consistent with dynamically optimal decision making. We use the model to (i) analyse the joint land and water use decision by farmers; (ii) to assess changes in behaviour and production risk in response to water scarcity; and (iii) to understand the limits of applicability of current methods in policy design. We develop a biophysical model of water-limited crop yield building on the AquaCrop model. The model is calibrated and applied to case studies of irrigated corn production in Nebraska and Texas. We run the model iteratively, using long-term climate records, to define two formulations of the crop-water production function: (i) the aggregate relationship between total seasonal irrigation and yield (typical of current approaches); and (ii) the stochastic response of yield and total seasonal irrigation to the choice of an intraseasonal soil moisture target and irrigated area. Irrigated area (the extensive margin decision) and per-area irrigation intensity (the intensive margin decision) are then calculated for different seasonal water restrictions (corresponding to regulatory policies) and well yield constraints on intraseasonal abstraction rates (corresponding to aquifer system limits). Profit- and utility-maximising decisions are determined assuming risk neutrality and varying degrees of risk aversion, respectively. Our results demonstrate that the formulation of the production function has a significant impact on the response to water scarcity. For low well yields, which are the major concern

  16. Economic decision making and the application of nonparametric prediction models

    USGS Publications Warehouse

    Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

    2007-01-01

    Sustained increases in energy prices have focused attention on gas resources in low permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are large. Planning and development decisions for extraction of such resources must be area-wide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm the decision to enter such plays depends on reconnaissance level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional scale cost functions. The context of the worked example is the Devonian Antrim shale gas play, Michigan Basin. One finding relates to selection of the resource prediction model to be used with economic models. Models which can best predict aggregate volume over larger areas (many hundreds of sites) may lose granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined by extraneous factors. The paper also shows that when these simple prediction models are used to strategically order drilling prospects, the gain in gas volume over volumes associated with simple random site selection amounts to 15 to 20 percent. It also discusses why the observed benefit of updating predictions from results of new drilling, as opposed to following static predictions, is somewhat smaller. Copyright 2007, Society of Petroleum Engineers.

  17. Economic decision making and the application of nonparametric prediction models

    USGS Publications Warehouse

    Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

    2008-01-01

    Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly. Copyright ?? 2008 Society of Petroleum Engineers.

  18. Modeling human decision making behavior in supervisory control

    NASA Technical Reports Server (NTRS)

    Tulga, M. K.; Sheridan, T. B.

    1977-01-01

    An optimal decision control model was developed, which is based primarily on a dynamic programming algorithm which looks at all the available task possibilities, charts an optimal trajectory, and commits itself to do the first step (i.e., follow the optimal trajectory during the next time period), and then iterates the calculation. A Bayesian estimator was included which estimates the tasks which might occur in the immediate future and provides this information to the dynamic programming routine. Preliminary trials comparing the human subject's performance to that of the optimal model show a great similarity, but indicate that the human skips certain movements which require quick change in strategy.

  19. Real-Time Decision Making and Aggressive Behavior in Youth: A Heuristic Model of Response Evaluation and Decision (RED)

    PubMed Central

    Fontaine, Reid Griffith; Dodge, Kenneth A.

    2009-01-01

    Considerable scientific and intervention attention has been paid to judgment and decision-making systems associated with aggressive behavior in youth. However, most empirical studies have investigated social-cognitive correlates of stable child and adolescent aggressiveness, and less is known about real-time decision making to engage in aggressive behavior. A model of real-time decision making must incorporate both impulsive actions and rational thought. The present paper advances a process model (response evaluation and decision; RED) of real-time behavioral judgments and decision making in aggressive youths with mathematic representations that may be used to quantify response strength. These components are a heuristic to describe decision making, though it is doubtful that individuals always mentally complete these steps. RED represents an organization of social–cognitive operations believed to be active during the response decision step of social information processing. The model posits that RED processes can be circumvented through impulsive responding. This article provides a description and integration of thoughtful, rational decision making and nonrational impulsivity in aggressive behavioral interactions. PMID:20802851

  20. A Spiking Network Model of Decision Making Employing Rewarded STDP

    PubMed Central

    Bazhenov, Maxim

    2014-01-01

    Reward-modulated spike timing dependent plasticity (STDP) combines unsupervised STDP with a reinforcement signal that modulates synaptic changes. It was proposed as a learning rule capable of solving the distal reward problem in reinforcement learning. Nonetheless, performance and limitations of this learning mechanism have yet to be tested for its ability to solve biological problems. In our work, rewarded STDP was implemented to model foraging behavior in a simulated environment. Over the course of training the network of spiking neurons developed the capability of producing highly successful decision-making. The network performance remained stable even after significant perturbations of synaptic structure. Rewarded STDP alone was insufficient to learn effective decision making due to the difficulty maintaining homeostatic equilibrium of synaptic weights and the development of local performance maxima. Our study predicts that successful learning requires stabilizing mechanisms that allow neurons to balance their input and output synapses as well as synaptic noise. PMID:24632858

  1. Quantum-Like Bayesian Networks for Modeling Decision Making

    PubMed Central

    Moreira, Catarina; Wichert, Andreas

    2016-01-01

    In this work, we explore an alternative quantum structure to perform quantum probabilistic inferences to accommodate the paradoxical findings of the Sure Thing Principle. We propose a Quantum-Like Bayesian Network, which consists in replacing classical probabilities by quantum probability amplitudes. However, since this approach suffers from the problem of exponential growth of quantum parameters, we also propose a similarity heuristic that automatically fits quantum parameters through vector similarities. This makes the proposed model general and predictive in contrast to the current state of the art models, which cannot be generalized for more complex decision scenarios and that only provide an explanatory nature for the observed paradoxes. In the end, the model that we propose consists in a nonparametric method for estimating inference effects from a statistical point of view. It is a statistical model that is simpler than the previous quantum dynamic and quantum-like models proposed in the literature. We tested the proposed network with several empirical data from the literature, mainly from the Prisoner's Dilemma game and the Two Stage Gambling game. The results obtained show that the proposed quantum Bayesian Network is a general method that can accommodate violations of the laws of classical probability theory and make accurate predictions regarding human decision-making in these scenarios. PMID:26858669

  2. A naturalistic decision making model for simulated human combatants

    SciTech Connect

    HUNTER,KEITH O.; HART,WILLIAM E.; FORSYTHE,JAMES C.

    2000-05-01

    The authors describe a naturalistic behavioral model for the simulation of small unit combat. This model, Klein's recognition-primed decision making (RPD) model, is driven by situational awareness rather than a rational process of selecting from a set of action options. They argue that simulated combatants modeled with RPD will have more flexible and realistic responses to a broad range of small-scale combat scenarios. Furthermore, they note that the predictability of a simulation using an RPD framework can be easily controlled to provide multiple evaluations of a given combat scenario. Finally, they discuss computational issues for building an RPD-based behavior engine for fully automated combatants in small conflict scenarios, which are being investigated within Sandia's Next Generation Site Security project.

  3. A new framework for modeling decisions about changing information: The Piecewise Linear Ballistic Accumulator model.

    PubMed

    Holmes, William R; Trueblood, Jennifer S; Heathcote, Andrew

    2016-03-01

    In the real world, decision making processes must be able to integrate non-stationary information that changes systematically while the decision is in progress. Although theories of decision making have traditionally been applied to paradigms with stationary information, non-stationary stimuli are now of increasing theoretical interest. We use a random-dot motion paradigm along with cognitive modeling to investigate how the decision process is updated when a stimulus changes. Participants viewed a cloud of moving dots, where the motion switched directions midway through some trials, and were asked to determine the direction of motion. Behavioral results revealed a strong delay effect: after presentation of the initial motion direction there is a substantial time delay before the changed motion information is integrated into the decision process. To further investigate the underlying changes in the decision process, we developed a Piecewise Linear Ballistic Accumulator model (PLBA). The PLBA is efficient to simulate, enabling it to be fit to participant choice and response-time distribution data in a hierarchal modeling framework using a non-parametric approximate Bayesian algorithm. Consistent with behavioral results, PLBA fits confirmed the presence of a long delay between presentation and integration of new stimulus information, but did not support increased response caution in reaction to the change. We also found the decision process was not veridical, as symmetric stimulus change had an asymmetric effect on the rate of evidence accumulation. Thus, the perceptual decision process was slow to react to, and underestimated, new contrary motion information. PMID:26760448

  4. A National Modeling Framework for Water Management Decisions

    NASA Astrophysics Data System (ADS)

    Bales, J. D.; Cline, D. W.; Pietrowsky, R.

    2013-12-01

    The National Weather Service (NWS), the U.S. Army Corps of Engineers (USACE), and the U.S. Geological Survey (USGS), all Federal agencies with complementary water-resources activities, entered into an Interagency Memorandum of Understanding (MOU) "Collaborative Science Services and Tools to Support Integrated and Adaptive Water Resources Management" to collaborate in activities that are supportive to their respective missions. One of the interagency activities is the development of a highly integrated national water modeling framework and information services framework. Together these frameworks establish a common operating picture, improve modeling and synthesis, support the sharing of data and products among agencies, and provide a platform for incorporation of new scientific understanding. Each of the agencies has existing operational systems to assist in carrying out their respective missions. The systems generally are designed, developed, tested, fielded, and supported by specialized teams. A broader, shared approach is envisioned and would include community modeling, wherein multiple independent investigators or teams develop and contribute new modeling capabilities based on science advances; modern technology in coupling model components and visualizing results; and a coupled atmospheric - hydrologic model construct such that the framework could be used in real-time water-resources decision making or for long-term management decisions. The framework also is being developed to account for organizational structures of the three partners such that, for example, national data sets can move down to the regional scale, and vice versa. We envision the national water modeling framework to be an important element of North American Water Program, to contribute to goals of the Program, and to be informed by the science and approaches developed as a part of the Program.

  5. Effects of dynamic agricultural decision making in an ecohydrological model

    NASA Astrophysics Data System (ADS)

    Reichenau, T. G.; Krimly, T.; Schneider, K.

    2012-04-01

    Due to various interdependencies between the cycles of water, carbon, nitrogen, and energy the impacts of climate change on ecohydrological systems can only be investigated in an integrative way. Furthermore, the human intervention in the environmental processes makes the system even more complex. On the one hand human impact affects natural systems. On the other hand the changing natural systems have a feedback on human decision making. One of the most important examples for this kind of interaction can be found in the agricultural sector. Management dates (planting, fertilization, harvesting) are chosen based on meteorological conditions and yield expectations. A faster development of crops under a warmer climate causes shorter cropping seasons. The choice of crops depends on their profitability, which is mainly determined by market prizes, the agro-political framework, and the (climate dependent) crop yield. This study investigates these relations for the district Günzburg located in the Upper Danube catchment in southern Germany. The modeling system DANUBIA was used to perform dynamically coupled simulations of plant growth, surface and soil hydrological processes, soil nitrogen transformations, and agricultural decision making. The agro-economic model simulates decisions on management dates (based on meteorological conditions and the crops' development state), on fertilization intensities (based on yield expectations), and on choice of crops (based on profitability). The environmental models included in DANUBIA are to a great extent process based to enable its use in a climate change scenario context. Scenario model runs until 2058 were performed using an IPCC A1B forcing. In consecutive runs, dynamic crop management, dynamic crop selection, and a changing agro-political framework were activated. Effects of these model features on hydrological and ecological variables were analyzed separately by comparing the results to a model run with constant crop

  6. Using health economic models to help guide healthcare decisions.

    PubMed

    Charokopou, M

    2016-01-01

    This editorial accompanies a research article being published by Clinical Medical Research and Opinion (CMRO) journal, entitled "Methods applied in cost-effectiveness models for treatment strategies in type 2 diabetes mellitus and their use in Health Technology Assessments: a systematic review of the literature from 2008 to 2013". The importance and the contribution of this research to the scientific community are presented on the grounds of serving the decision-making process of evaluating and approving T2DM treatments for public funding. PMID:26473453

  7. 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.

  8. Collocated cokriging and neural-network multi-attribute transform in the prediction of effective porosity: A comparative case study for the Second Wall Creek Sand of the Teapot Dome field, Wyoming, USA

    NASA Astrophysics Data System (ADS)

    Moon, Seonghoon; Lee, Gwang H.; Kim, Hyeonju; Choi, Yosoon; Kim, Han-Joon

    2016-08-01

    Collocated cokriging (CCK) and neural-network multi-attribute transform (NN-MAT) are widely used in the prediction of reservoir properties because they can integrate sparsely-distributed, high-resolution well-log data and densely-sampled, low-resolution seismic data. CCK is a linear-weighted averaging method based on spatial covariance model. NN-MAT, based on a nonlinear relationship between seismic attributes and log values, treats data as spatially independent observations. In this study, we analyzed 3-D seismic and well-log data from the Second Wall Creek Sand of the Teapot Dome field, Wyoming, USA to investigate: (1) how CCK and NN-MAT perform in the prediction of porosity and (2) how the number of wells affects the results. Among a total of 64 wells, 25 wells were selected for CCK and NN-MAT and 39 wells were withheld for validation. We examined four cases: 25, 20, 15, and 10 wells. CCK overpredicted the porosity in the validation wells for all cases likely due to the strong influence of high values, but failed to predict very large porosities. Overprediction of CCK porosity becomes more pronounced with decreasing number of wells. NN-MAT largely underpredicted the porosity for all cases probably due to the band-limited nature of seismic data. The performance of CCK appears to be not affected significantly by the number of wells. Overall, NN-MAT performed better than CCK although its performance decreases continuously with decreasing number of wells.

  9. Modeling violations of the race model inequality in bimodal paradigms: co-activation from decision and non-decision components

    PubMed Central

    Zehetleitner, Michael; Ratko-Dehnert, Emil; Müller, Hermann J.

    2015-01-01

    The redundant-signals paradigm (RSP) is designed to investigate response behavior in perceptual tasks in which response-relevant targets are defined by either one or two features, or modalities. The common finding is that responses are speeded for redundantly compared to singly defined targets. This redundant-signals effect (RSE) can be accounted for by race models if the response times do not violate the race model inequality (RMI). When there are violations of the RMI, race models are effectively excluded as a viable account of the RSE. The common alternative is provided by co-activation accounts, which assume that redundant target signals are integrated at some processing stage. However, “co-activation” has mostly been only indirectly inferred and the accounts have only rarely been explicitly modeled; if they were modeled, the RSE has typically been assumed to have a decisional locus. Yet, there are also indications in the literature that the RSE might originate, at least in part, at a non-decisional or motor stage. In the present study, using a distribution analysis of sequential-sampling models (ex-Wald and Ratcliff Diffusion model), the locus of the RSE was investigated for two bimodal (audio-visual) detection tasks that strongly violated the RMI, indicative of substantial co-activation. Three model variants assuming different loci of the RSE were fitted to the quantile reaction time proportions: a decision, a non-decision, and a combined variant both to vincentized group as well as individual data. The results suggest that for the two bimodal detection tasks, co-activation has a shared decisional and non-decisional locus. These findings point to the possibility that the mechanisms underlying the RSE depend on the specifics (task, stimulus, conditions, etc.) of the experimental paradigm. PMID:25805987

  10. Decision Models for Determining the Optimal Life Test Sampling Plans

    NASA Astrophysics Data System (ADS)

    Nechval, Nicholas A.; Nechval, Konstantin N.; Purgailis, Maris; Berzins, Gundars; Strelchonok, Vladimir F.

    2010-11-01

    Life test sampling plan is a technique, which consists of sampling, inspection, and decision making in determining the acceptance or rejection of a batch of products by experiments for examining the continuous usage time of the products. In life testing studies, the lifetime is usually assumed to be distributed as either a one-parameter exponential distribution, or a two-parameter Weibull distribution with the assumption that the shape parameter is known. Such oversimplified assumptions can facilitate the follow-up analyses, but may overlook the fact that the lifetime distribution can significantly affect the estimation of the failure rate of a product. Moreover, sampling costs, inspection costs, warranty costs, and rejection costs are all essential, and ought to be considered in choosing an appropriate sampling plan. The choice of an appropriate life test sampling plan is a crucial decision problem because a good plan not only can help producers save testing time, and reduce testing cost; but it also can positively affect the image of the product, and thus attract more consumers to buy it. This paper develops the frequentist (non-Bayesian) decision models for determining the optimal life test sampling plans with an aim of cost minimization by identifying the appropriate number of product failures in a sample that should be used as a threshold in judging the rejection of a batch. The two-parameter exponential and Weibull distributions with two unknown parameters are assumed to be appropriate for modelling the lifetime of a product. A practical numerical application is employed to demonstrate the proposed approach.

  11. The collaborative autonomy model of medical decision-making.

    PubMed

    Rubin, Michael A

    2014-04-01

    While the bioethical principle of beneficence originated in antiquity, the ascension of autonomy, or "self-rule," has redefined the physician-patient relationship to the extent that autonomy often dominates medical decision-making. Philosophical and social movements, medical research atrocities, consumerism, and case law have all had their influence on this paradigm shift. Consequently, the contemporary physician encounters an uncertainty in medical practice on how to resolve conflicts that arise in the pursuit of valuing both autonomy and beneficence. This is especially true in the practice of neurologic critical care where physicians may be advising comfort care measures for neurologically devastated patients while surrogates request physiologically futile interventions. This conundrum has been an important subject of the bioethics and social science literature but often this discourse is not disseminated to the clinicians confronting these issues. The purpose of this essay is to present a history of the principles of autonomy and beneficence and then present a shared medical decision-making model, collaborative autonomy, to provide guidance to neurologic critical care providers in how to resolve such dilemmas. Clinical vignettes will help illustrate the model. PMID:24233814

  12. Classification images in a very general decision model.

    PubMed

    Murray, Richard F

    2016-06-01

    Most of the theory supporting our understanding of classification images relies on standard signal detection models and the use of normally distributed stimulus noise. Here I show that the most common methods of calculating classification images by averaging stimulus noise samples within stimulus-response classes of trials are much more general than has previously been demonstrated, and that they give unbiased estimates of an observer's template for a wide range of decision rules and non-Gaussian stimulus noise distributions. These results are similar to findings on reverse correlation and related methods in the neurophysiology literature, but here I formulate them in terms that are tailored to signal detection analyses of visual tasks, in order to make them more accessible and useful to visual psychophysicists. I examine 2AFC and yes-no designs. These findings make it possible to use and interpret classification images in tasks where observers' decision strategies may not conform to classic signal detection models such as the difference rule, and in tasks where the stimulus noise is non-Gaussian. PMID:27174841

  13. Assembling Tools and Data for Climate Model Decision Support

    NASA Astrophysics Data System (ADS)

    Batcheller, A. L.; VanWijngaarden, F.

    2011-12-01

    The Global Earth Observation System of Systems (GEOSS) effort has identified nine areas in which society benefits from appropriate environmental information. We have targeted specific issues within these societal benefit areas by determining appropriate data sets needed and transforming these data into information useable by decision makers. Here we describe the service-oriented architecture that allows us to ingest real-time or static data into a database with a spatial data engine, make appropriate manipulations to the data using domain knowledge relevant to the problem, and expose the data as services. We then build custom portals using a library of common widgets to display and overlay the data for users to analyze. By using portals and a service-oriented architecture we can reuse services and widgets to rapidly assemble a view of geographic data, and assist decision-makers in applying and interpreting the latest scientific results. As a case study with our system, we have integrated data from Intergovernmental Panel on Climate Change (IPCC) climate models, crop yields, and environmental thresholds for crops to present a first level analysis of the impact of climate change on key crops grown in Mexico. Knowledge about changes in the regions that are favorable for crop growth is important for many stakeholders, ranging from individual farmers, to governments, to scientists working to create new seed varieties. Our work also highlights research opportunities in climate science by identifying the types and resolution of parameters modeled.

  14. Distributed Hydrologic Modeling Apps for Decision Support in the Cloud

    NASA Astrophysics Data System (ADS)

    Swain, N. R.; Latu, K.; Christiensen, S.; Jones, N.; Nelson, J.

    2013-12-01

    Advances in computation resources and greater availability of water resources data represent an untapped resource for addressing hydrologic uncertainties in water resources decision-making. The current practice of water authorities relies on empirical, lumped hydrologic models to estimate watershed response. These models are not capable of taking advantage of many of the spatial datasets that are now available. Physically-based, distributed hydrologic models are capable of using these data resources and providing better predictions through stochastic analysis. However, there exists a digital divide that discourages many science-minded decision makers from using distributed models. This divide can be spanned using a combination of existing web technologies. The purpose of this presentation is to present a cloud-based environment that will offer hydrologic modeling tools or 'apps' for decision support and the web technologies that have been selected to aid in its implementation. Compared to the more commonly used lumped-parameter models, distributed models, while being more intuitive, are still data intensive, computationally expensive, and difficult to modify for scenario exploration. However, web technologies such as web GIS, web services, and cloud computing have made the data more accessible, provided an inexpensive means of high-performance computing, and created an environment for developing user-friendly apps for distributed modeling. Since many water authorities are primarily interested in the scenario exploration exercises with hydrologic models, we are creating a toolkit that facilitates the development of a series of apps for manipulating existing distributed models. There are a number of hurdles that cloud-based hydrologic modeling developers face. One of these is how to work with the geospatial data inherent with this class of models in a web environment. Supporting geospatial data in a website is beyond the capabilities of standard web frameworks and it

  15. Decision Making Methods in Space Economics and Systems Engineering

    NASA Technical Reports Server (NTRS)

    Shishko, Robert

    2006-01-01

    This viewgraph presentation reviews various methods of decision making and the impact that they have on space economics and systems engineering. Some of the methods discussed are: Present Value and Internal Rate of Return (IRR); Cost-Benefit Analysis; Real Options; Cost-Effectiveness Analysis; Cost-Utility Analysis; Multi-Attribute Utility Theory (MAUT); and Analytic Hierarchy Process (AHP).

  16. Influence of proxy attributes on multiattribute decision analysis: An empirical investigation in the context of air pollution control

    SciTech Connect

    Damodaran, N.

    1988-01-01

    Three separate studies in decision analysis were conducted in the context of air pollution control wherein the preferences of informed subjects were individually assessed. The first study was designed to develop a decision model for the control of sulfur dioxide emissions by incorporating multi-media effects of pollution control using both fundamental and proxy attributes. The second study specifically compared fundamental and proxy attributes and tested the hypothesis that proxy attributes lead to biased decisions. The third study validated the results of the previous one and was extended to examine the hypothesis that proxy bias could be reduced by appropriate elicitation techniques. The findings of this study indicated that subjects behaved according to the norms of expected utility theory when the unidimensional utility function for the proxy attribute was assessed. However, subjects exhibited a near universal bias to overweight the proxy attribute, relative to prescriptions of expected utility theory, in a multi-attribute scenario.

  17. Modelling sustainable development planning: a multicriteria decision conferencing approach.

    PubMed

    Quaddus, M A; Siddique, M A

    2001-09-01

    Development planning is multidimensional in nature. On the one hand, it addresses economic growth, and on the other, it deals with economic development of the whole nation. Sustainable development, on the other hand, emphasizes the need for integration of economics and environment, as well as promoting intra- and intergenerational equity. While the literature deals extensively with the issues of sustainable development, it lacks a prescription of an easy-to-use, yet rigorous, methodology for it. In this paper, we offer a decision conferencing approach to sustainable development planning based on a multicriteria model. The integrated model is presented and applied to a sustainable development planning exercise in a third world country. Sample results are presented and detailed sensitivity analyses show the environmental variables that are of major concern. PMID:11697680

  18. Prediction model based on decision tree analysis for laccase mediators.

    PubMed

    Medina, Fabiola; Aguila, Sergio; Baratto, Maria Camilla; Martorana, Andrea; Basosi, Riccardo; Alderete, Joel B; Vazquez-Duhalt, Rafael

    2013-01-10

    A Structure Activity Relationship (SAR) study for laccase mediator systems was performed in order to correctly classify different natural phenolic mediators. Decision tree (DT) classification models with a set of five quantum-chemical calculated molecular descriptors were used. These descriptors included redox potential (ɛ°), ionization energy (E(i)), pK(a), enthalpy of formation of radical (Δ(f)H), and OH bond dissociation energy (D(O-H)). The rationale for selecting these descriptors is derived from the laccase-mediator mechanism. To validate the DT predictions, the kinetic constants of different compounds as laccase substrates, their ability for pesticide transformation as laccase-mediators, and radical stability were experimentally determined using Coriolopsis gallica laccase and the pesticide dichlorophen. The prediction capability of the DT model based on three proposed descriptors showed a complete agreement with the obtained experimental results. PMID:23199741

  19. Trade-offs underlying maternal breastfeeding decisions: a conceptual model.

    PubMed

    Tully, Kristin P; Ball, Helen L

    2013-01-01

    This paper presents a new conceptual model that generates predictions about breastfeeding decisions and identifies interactions that affect outcomes. We offer a contextual approach to infant feeding that models multi-directional influences by expanding on the evolutionary parent-offspring conflict and situation-specific breastfeeding theories. The main hypothesis generated from our framework suggests that simultaneously addressing breastfeeding costs and benefits, in relation to how they are interpreted by mothers, will be most effective. Our approach focuses on contributors to the attitudes and commitment underlying breastfeeding outcomes, beginning in the prenatal period. We conclude that some maternal-offspring conflict is inherent with the dynamic infant feeding relationship. Guidance that anticipates and addresses family trade-offs over time can be incorporated into breastfeeding support for families. PMID:22188564

  20. CASE STUDIES IN THE APPLICATION OF AIR QUALITY MODELING IN ENVIRONMENTAL DECISION MAKING: SUMMARY AND RECOMMENDATIONS

    EPA Science Inventory

    Eleven case studies of the application of air quality models were undertaken in order to examine the problems encountered when trying to use these models in making environmental policy decisions. The case studies of air pollution control decisions describe the decision process, t...

  1. A queueing model of pilot decision making in a multi-task flight management situation

    NASA Technical Reports Server (NTRS)

    Walden, R. S.; Rouse, W. B.

    1977-01-01

    Allocation of decision making responsibility between pilot and computer is considered and a flight management task, designed for the study of pilot-computer interaction, is discussed. A queueing theory model of pilot decision making in this multi-task, control and monitoring situation is presented. An experimental investigation of pilot decision making and the resulting model parameters are discussed.

  2. Representation requirements for supporting knowledge-based construction of decision models in medicine.

    PubMed Central

    Leong, T. Y.

    1991-01-01

    This paper analyzes the medical knowledge required for formulating decision models in the domain of pulmonary infectious diseases (PIDs) with acquired immunodeficiency syndrome (AIDS). Aiming to support dynamic decision-modeling, the knowledge characterization focuses on the ontology of the clinical decision problem. Relevant inference patterns and knowledge types are identified. PMID:1807680

  3. Decoding Problem Gamblers' Signals: A Decision Model for Casino Enterprises.

    PubMed

    Ifrim, Sandra

    2015-12-01

    The aim of the present study is to offer a validated decision model for casino enterprises. The model enables those users to perform early detection of problem gamblers and fulfill their ethical duty of social cost minimization. To this end, the interpretation of casino customers' nonverbal communication is understood as a signal-processing problem. Indicators of problem gambling recommended by Delfabbro et al. (Identifying problem gamblers in gambling venues: final report, 2007) are combined with Viterbi algorithm into an interdisciplinary model that helps decoding signals emitted by casino customers. Model output consists of a historical path of mental states and cumulated social costs associated with a particular client. Groups of problem and non-problem gamblers were simulated to investigate the model's diagnostic capability and its cost minimization ability. Each group consisted of 26 subjects and was subsequently enlarged to 100 subjects. In approximately 95% of the cases, mental states were correctly decoded for problem gamblers. Statistical analysis using planned contrasts revealed that the model is relatively robust to the suppression of signals performed by casino clientele facing gambling problems as well as to misjudgments made by staff regarding the clients' mental states. Only if the last mentioned source of error occurs in a very pronounced manner, i.e. judgment is extremely faulty, cumulated social costs might be distorted. PMID:24938732

  4. Composite tissue allotransplantation of the face: Decision analysis model

    PubMed Central

    Cugno, Sabrina; Sprague, Sheila; Duku, Eric; Thoma, Achilleas

    2007-01-01

    BACKGROUND: Facial composite tissue allotransplantation is a potential reconstructive option for severe facial disfigurement. The purpose of the present investigation was to use decision analysis modelling to ascertain the expected quality-adjusted life years (QALYs) gained with face transplantation (versus remaining in a disfigured state) in an effort to assist surgeons with the decision of whether to adopt this procedure. STUDY DESIGN: The probabilities of potential complications associated with facial allotransplantation were identified by a comprehensive review of kidney and hand transplant literature. A decision analysis tree illustrating possible health states for face allotransplantation was then constructed. Utilities were obtained from 30 participants, using the standard gamble and time trade-off measures. The utilities were then translated into QALYs, and the expected QALYs gained with transplantation were computed. RESULTS: Severe facial deformity was associated with an average of 7.34 QALYs. Allotransplantation of the face imparted an expected gain in QALYs of between 16.2 and 27.3 years. CONCLUSIONS: The current debate within the medical community surrounding facial composite tissue allotransplantation has centred on the issue of inducing a state of immunocompromise in a physically healthy individual for a non-life-saving procedure. However, the latter must be weighed against the potential social and psychological benefits that transplantation would confer. As demonstrated by a gain of 26.9 QALYs, participants’ valuation of quality of life is notably greater for face transplantation with its side effects of immunosuppression than for a state of uncompromised physical health with severe facial disfigurement. PMID:19554146

  5. Accounting for uncertainty in health economic decision models by using model averaging.

    PubMed

    Jackson, Christopher H; Thompson, Simon G; Sharples, Linda D

    2009-04-01

    Health economic decision models are subject to considerable uncertainty, much of which arises from choices between several plausible model structures, e.g. choices of covariates in a regression model. Such structural uncertainty is rarely accounted for formally in decision models but can be addressed by model averaging. We discuss the most common methods of averaging models and the principles underlying them. We apply them to a comparison of two surgical techniques for repairing abdominal aortic aneurysms. In model averaging, competing models are usually either weighted by using an asymptotically consistent model assessment criterion, such as the Bayesian information criterion, or a measure of predictive ability, such as Akaike's information criterion. We argue that the predictive approach is more suitable when modelling the complex underlying processes of interest in health economics, such as individual disease progression and response to treatment. PMID:19381329

  6. Qualitative modeling of the decision-making process using electrooculography.

    PubMed

    Marandi, Ramtin Zargari; Sabzpoushan, S H

    2015-12-01

    A novel method based on electrooculography (EOG) has been introduced in this work to study the decision-making process. An experiment was designed and implemented wherein subjects were asked to choose between two items from the same category that were presented within a limited time. The EOG and voice signals of the subjects were recorded during the experiment. A calibration task was performed to map the EOG signals to their corresponding gaze positions on the screen by using an artificial neural network. To analyze the data, 16 parameters were extracted from the response time and EOG signals of the subjects. Evaluation and comparison of the parameters, together with subjects' choices, revealed functional information. On the basis of this information, subjects switched their eye gazes between items about three times on average. We also found, according to statistical hypothesis testing-that is, a t test, t(10) = 71.62, SE = 1.25, p < .0001-that the correspondence rate of a subjects' gaze at the moment of selection with the selected item was significant. Ultimately, on the basis of these results, we propose a qualitative choice model for the decision-making task. PMID:25515839

  7. Pharmaceutical expenditure forecast model to support health policy decision making

    PubMed Central

    Rémuzat, Cécile; Urbinati, Duccio; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

    2014-01-01

    Background and objective With constant incentives for healthcare payers to contain their pharmaceutical budgets, modelling policy decision impact became critical. The objective of this project was to test the impact of various policy decisions on pharmaceutical budget (developed for the European Commission for the project ‘European Union (EU) Pharmaceutical expenditure forecast’ – http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). Methods A model was built to assess policy scenarios’ impact on the pharmaceutical budgets of seven member states of the EU, namely France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. The following scenarios were tested: expanding the UK policies to EU, changing time to market access, modifying generic price and penetration, shifting the distribution chain of biosimilars (retail/hospital). Results Applying the UK policy resulted in dramatic savings for Germany (10 times the base case forecast) and substantial additional savings for France and Portugal (2 and 4 times the base case forecast, respectively). Delaying time to market was found be to a very powerful tool to reduce pharmaceutical expenditure. Applying the EU transparency directive (6-month process for pricing and reimbursement) increased pharmaceutical expenditure for all countries (from 1.1 to 4 times the base case forecast), except in Germany (additional savings). Decreasing the price of generics and boosting the penetration rate, as well as shifting distribution of biosimilars through hospital chain were also key methods to reduce pharmaceutical expenditure. Change in the level of reimbursement rate to 100% in all countries led to an important increase in the pharmaceutical budget. Conclusions Forecasting pharmaceutical expenditure is a critical exercise to inform policy decision makers. The most important leverages identified by the model on pharmaceutical budget were driven by generic and biosimilar prices, penetration rate

  8. A Stochastic Decision Model for Experiments in Discrimination Learning.

    ERIC Educational Resources Information Center

    Egelston, Richard L.

    Decisions to terminate training for subjects involved with discrete trial experiments in discrimination learning should utilize a probability criterion rather than a deterministic criterion. Furthermore, these decisions should be based upon the number of correct and error responses made by the subject, with the decision made to terminate training…

  9. Decision-making models of Finnish nurses and public health nurses.

    PubMed

    Lauri, S; Salanterä, S

    1995-03-01

    This study described nursing decision-making models and variables related to these models. For this purpose a 56-item Likert-type questionnaire was constructed according to the Dreyfus model of skill acquisition as applied to nursing by Benner and information processing theory. The target group consisted of 100 registered nurses working in inpatient clinics and 100 public health nurses working in preventive health care. The decision-making variables explored were nurses' experience, education and knowledge as well as the nature of the nursing task and context. The results revealed four different types of decision-making: (a) unquestioning/questioning decision-making, (b) creative-diversive decision-making, (c) patient/nurse-oriented decision-making, and (d) rule- and situation-based decision-making. The most important factors related to decision-making were experience and the nature of the nursing task and context. PMID:7745207

  10. An Integrated Decision-Making Model for Categorizing Weather Products and Decision Aids

    NASA Technical Reports Server (NTRS)

    Elgin, Peter D.; Thomas, Rickey P.

    2004-01-01

    The National Airspace System s capacity will experience considerable growth in the next few decades. Weather adversely affects safe air travel. The FAA and NASA are working to develop new technologies that display weather information to support situation awareness and optimize pilot decision-making in avoiding hazardous weather. Understanding situation awareness and naturalistic decision-making is an important step in achieving this goal. Information representation and situation time stress greatly influence attentional resource allocation and working memory capacity, potentially obstructing accurate situation awareness assessments. Three naturalistic decision-making theories were integrated to provide an understanding of the levels of decision making incorporated in three operational situations and two conditions. The task characteristics associated with each phase of flight govern the level of situation awareness attained and the decision making processes utilized. Weather product s attributes and situation task characteristics combine to classify weather products according to the decision-making processes best supported. In addition, a graphical interface is described that affords intuitive selection of the appropriate weather product relative to the pilot s current flight situation.

  11. Agricultural Model for the Nile Basin Decision Support System

    NASA Astrophysics Data System (ADS)

    van der Bolt, Frank; Seid, Abdulkarim

    2014-05-01

    To analyze options for increasing food supply in the Nile basin the Nile Agricultural Model (AM) was developed. The AM includes state-of-the-art descriptions of biophysical, hydrological and economic processes and realizes a coherent and consistent integration of hydrology, agronomy and economics. The AM covers both the agro-ecological domain (water, crop productivity) and the economic domain (food supply, demand, and trade) and allows to evaluate the macro-economic and hydrological impacts of scenarios for agricultural development. Starting with the hydrological information from the NileBasin-DSS the AM calculates the available water for agriculture, the crop production and irrigation requirements with the FAO-model AquaCrop. With the global commodity trade model MAGNET scenarios for land development and conversion are evaluated. The AM predicts consequences for trade, food security and development based on soil and water availability, crop allocation, food demand and food policy. The model will be used as a decision support tool to contribute to more productive and sustainable agriculture in individual Nile countries and the whole region.

  12. Selection of Representative Models for Decision Analysis Under Uncertainty

    NASA Astrophysics Data System (ADS)

    Meira, Luis A. A.; Coelho, Guilherme P.; Santos, Antonio Alberto S.; Schiozer, Denis J.

    2016-03-01

    The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request.

  13. Agricultural climate impacts assessment for economic modeling and decision support

    NASA Astrophysics Data System (ADS)

    Thomson, A. M.; Izaurralde, R. C.; Beach, R.; Zhang, X.; Zhao, K.; Monier, E.

    2013-12-01

    A range of approaches can be used in the application of climate change projections to agricultural impacts assessment. Climate projections can be used directly to drive crop models, which in turn can be used to provide inputs for agricultural economic or integrated assessment models. These model applications, and the transfer of information between models, must be guided by the state of the science. But the methodology must also account for the specific needs of stakeholders and the intended use of model results beyond pure scientific inquiry, including meeting the requirements of agencies responsible for designing and assessing policies, programs, and regulations. Here we present methodology and results of two climate impacts studies that applied climate model projections from CMIP3 and from the EPA Climate Impacts and Risk Analysis (CIRA) project in a crop model (EPIC - Environmental Policy Indicator Climate) in order to generate estimates of changes in crop productivity for use in an agricultural economic model for the United States (FASOM - Forest and Agricultural Sector Optimization Model). The FASOM model is a forward-looking dynamic model of the US forest and agricultural sector used to assess market responses to changing productivity of alternative land uses. The first study, focused on climate change impacts on the UDSA crop insurance program, was designed to use available daily climate projections from the CMIP3 archive. The decision to focus on daily data for this application limited the climate model and time period selection significantly; however for the intended purpose of assessing impacts on crop insurance payments, consideration of extreme event frequency was critical for assessing periodic crop failures. In a second, coordinated impacts study designed to assess the relative difference in climate impacts under a no-mitigation policy and different future climate mitigation scenarios, the stakeholder specifically requested an assessment of a

  14. A Markov decision model for determining optimal outpatient scheduling.

    PubMed

    Patrick, Jonathan

    2012-06-01

    Managing an efficient outpatient clinic can often be complicated by significant no-show rates and escalating appointment lead times. One method that has been proposed for avoiding the wasted capacity due to no-shows is called open or advanced access. The essence of open access is "do today's demand today". We develop a Markov Decision Process (MDP) model that demonstrates that a short booking window does significantly better than open access. We analyze a number of scenarios that explore the trade-off between patient-related measures (lead times) and physician- or system-related measures (revenue, overtime and idle time). Through simulation, we demonstrate that, over a wide variety of potential scenarios and clinics, the MDP policy does as well or better than open access in terms of minimizing costs (or maximizing profits) as well as providing more consistent throughput. PMID:22089944

  15. The Role of Intent in Ethical Decision Making: The Ethical Choice Model

    ERIC Educational Resources Information Center

    King, Christine; Powell, Toni

    2007-01-01

    This paper reviews the major theories, studies and models concerning ethical decision making in organizations. The authors drew upon Jones' Model (1991) as the foundation for their Ethical Choice Model, which is designed to further clarify the ethical decision making process as it relates to the construct of intentionality. The model, illustrated…

  16. Time-variant clustering model for understanding cell fate decisions.

    PubMed

    Huang, Wei; Cao, Xiaoyi; Biase, Fernando H; Yu, Pengfei; Zhong, Sheng

    2014-11-01

    Both spatial characteristics and temporal features are often the subjects of concern in physical, social, and biological studies. This work tackles the clustering problems for time course data in which the cluster number and clustering structure change with respect to time, dubbed time-variant clustering. We developed a hierarchical model that simultaneously clusters the objects at every time point and describes the relationships of the clusters between time points. The hidden layer of this model is a generalized form of branching processes. A reversible-jump Markov Chain Monte Carlo method was implemented for model inference, and a feature selection procedure was developed. We applied this method to explore an open question in preimplantation embryonic development. Our analyses using single-cell gene expression data suggested that the earliest cell fate decision could start at the 4-cell stage in mice, earlier than the commonly thought 8- to 16-cell stage. These results together with independent experimental data from single-cell RNA-seq provided support against a prevailing hypothesis in mammalian development. PMID:25339442

  17. Time-variant clustering model for understanding cell fate decisions

    PubMed Central

    Huang, Wei; Cao, Xiaoyi; Biase, Fernando H.; Yu, Pengfei; Zhong, Sheng

    2014-01-01

    Both spatial characteristics and temporal features are often the subjects of concern in physical, social, and biological studies. This work tackles the clustering problems for time course data in which the cluster number and clustering structure change with respect to time, dubbed time-variant clustering. We developed a hierarchical model that simultaneously clusters the objects at every time point and describes the relationships of the clusters between time points. The hidden layer of this model is a generalized form of branching processes. A reversible-jump Markov Chain Monte Carlo method was implemented for model inference, and a feature selection procedure was developed. We applied this method to explore an open question in preimplantation embryonic development. Our analyses using single-cell gene expression data suggested that the earliest cell fate decision could start at the 4-cell stage in mice, earlier than the commonly thought 8- to 16-cell stage. These results together with independent experimental data from single-cell RNA-seq provided support against a prevailing hypothesis in mammalian development. PMID:25339442

  18. Management decision making for fisher populations informed by occupancy modeling

    USGS Publications Warehouse

    Fuller, Angela K.; Linden, Daniel W.; Royle, J. Andrew

    2016-01-01

    Harvest data are often used by wildlife managers when setting harvest regulations for species because the data are regularly collected and do not require implementation of logistically and financially challenging studies to obtain the data. However, when harvest data are not available because an area had not previously supported a harvest season, alternative approaches are required to help inform management decision making. When distribution or density data are required across large areas, occupancy modeling is a useful approach, and under certain conditions, can be used as a surrogate for density. We collaborated with the New York State Department of Environmental Conservation (NYSDEC) to conduct a camera trapping study across a 70,096-km2 region of southern New York in areas that were currently open to fisher (Pekania [Martes] pennanti) harvest and those that had been closed to harvest for approximately 65 years. We used detection–nondetection data at 826 sites to model occupancy as a function of site-level landscape characteristics while accounting for sampling variation. Fisher occupancy was influenced positively by the proportion of conifer and mixed-wood forest within a 15-km2 grid cell and negatively associated with road density and the proportion of agriculture. Model-averaged predictions indicated high occupancy probabilities (>0.90) when road densities were low (<1 km/km2) and coniferous and mixed forest proportions were high (>0.50). Predicted occupancy ranged 0.41–0.67 in wildlife management units (WMUs) currently open to trapping, which could be used to guide a minimum occupancy threshold for opening new areas to trapping seasons. There were 5 WMUs that had been closed to trapping but had an average predicted occupancy of 0.52 (0.07 SE), and above the threshold of 0.41. These areas are currently under consideration by NYSDEC for opening a conservative harvest season. We demonstrate the use of occupancy modeling as an aid to management

  19. Logit Estimation of a Gravity Model of the College Enrollment Decision.

    ERIC Educational Resources Information Center

    Leppel, Karen

    1993-01-01

    A study investigated the factors influencing students' decisions about attending a college to which they had been admitted. Logit analysis confirmed gravity model predictions that geographic distance and student ability would most influence the enrollment decision and found other variables, although affecting earlier stages of decision making, did…

  20. Between Safety and Risk: A Model for Outdoor Adventure Decision Making.

    ERIC Educational Resources Information Center

    Boyes, Michael A.; O'Hare, David

    2003-01-01

    Decision making by outdoor adventure educators revolves around balancing risk and competence. A model of outdoor adventure decision making is presented that draws on naturalistic decision-making processes and emphasizes the importance of situational recognition and prior experience. Leaders draw key information from the natural environment,…

  1. A Critical Analysis of HRD Evaluation Models from a Decision-Making Perspective

    ERIC Educational Resources Information Center

    Holton, Elwood F., III; Naquin, Sharon

    2005-01-01

    HRD evaluation models are recommended for use by organizations to improve decisions made about HRD interventions. However, the organizational decision-making literature has been virtually ignored by evaluation researchers. In this article, we review the organizational decision-making literature and critically review HRD evaluation research through…

  2. An Analysis of Consistency between Team Decisions and Reading Assessment Data within an RTI Model

    ERIC Educational Resources Information Center

    Shapiro, Edward S.; Hilt-Panahon, Alexandra; Gischlar, Karen L.; Semeniak, Kathleen; Leichman, Erin; Bowles, Shelly

    2012-01-01

    Data-based decision making by teams is central to implementation of response to intervention (RTI) models. Few studies have examined the actual decision-making process within RTI systems of service delivery. The purpose of this study was to examine the tier assignment decisions for students across grade-level teams in three K-5 elementary schools…

  3. Decision-Making Models in a Tunisian University: Towards a Framework for Analysis

    ERIC Educational Resources Information Center

    Khefacha, I.; Belkacem, L.

    2014-01-01

    This study investigates how decisions are made in Tunisian public higher education establishments. Some factors are identified as having a potentially significant impact on the odds that the decision-making process follows the characteristics of one of the most well known decision-making models: collegial, political, bureaucratic or anarchical…

  4. A novel sustainable decision making model for municipal solid waste management

    SciTech Connect

    Hung, M.-L. . E-mail: d89541004@ntu.edu.tw; Ma Hwongwen . E-mail: hwma@ntu.edu.tw; Yang, W.-F. . E-mail: wfyang@ntu.edu.tw

    2007-07-01

    This paper reviews several models developed to support decision making in municipal solid waste management (MSWM). The concepts underlying sustainable MSWM models can be divided into two categories: one incorporates social factors into decision making methods, and the other includes public participation in the decision-making process. The public is only apprised or takes part in discussion, and has little effect on decision making in most research efforts. Few studies have considered public participation in the decision-making process, and the methods have sought to strike a compromise between concerned criteria, not between stakeholders. However, the source of the conflict arises from the stakeholders' complex web of value. Such conflict affects the feasibility of implementing any decision. The purpose of this study is to develop a sustainable decision making model for MSWM to overcome these shortcomings. The proposed model combines multicriteria decision making (MCDM) and a consensus analysis model (CAM). The CAM is built up to aid in decision-making when MCDM methods are utilized and, subsequently, a novel sustainable decision making model for MSWM is developed. The main feature of CAM is the assessment of the degree of consensus between stakeholders for particular alternatives. A case study for food waste management in Taiwan is presented to demonstrate the practicality of this model.

  5. Clinical Decision Analysis and Markov Modeling for Surgeons: An Introductory Overview.

    PubMed

    Hogendoorn, Wouter; Moll, Frans L; Sumpio, Bauer E; Hunink, M G Myriam

    2016-08-01

    This study addresses the use of decision analysis and Markov models to make contemplated decisions for surgical problems. Decision analysis and decision modeling in surgical research are increasing, but many surgeons are unfamiliar with the techniques and are skeptical of the results. The goal of this review is to familiarize surgeons with techniques and terminology used in decision analytic papers, to provide the reader a practical guide to read these papers, and to ensure that surgeons can critically appraise the quality of published clinical decision models and draw well founded conclusions from such reports.First, a brief explanation of decision analysis and Markov models is presented in simple steps, followed by an overview of the components of a decision and Markov model. Subsequently, commonly used terms and definitions are described and explained, including quality-adjusted life-years, disability-adjusted life-years, discounting, half-cycle correction, cycle length, probabilistic sensitivity analysis, incremental cost-effectiveness ratio, and the willingness-to-pay threshold.Finally, the advantages and limitations of research with Markov models are described, and new modeling techniques and future perspectives are discussed. It is important that surgeons are able to understand conclusions from decision analytic studies and are familiar with the specific definitions of the terminology used in the field to keep up with surgical research. Decision analysis can guide treatment strategies when complex clinical questions need to be answered and is a necessary and useful addition to the surgical research armamentarium. PMID:26756750

  6. Applications of a simulation model to decisions in mallard management

    USGS Publications Warehouse

    Cowardin, L.M.; Johnson, D.H.; Shaffer, T.L.; Sparling, D.W.

    1988-01-01

    A system comprising simulation models and data bases for habitat availability and nest success rates was used to predict results from a mallard (Anas platyrhynchos) management plan and to compare six management methods with a control. Individual treatments in the applications included land purchase for waterfowl production, wetland easement purchase, lease of uplands for waterfowl management, cropland retirement, use of no-till winter wheat, delayed cutting of alfalfa, installation of nest baskets, nesting island construction, and use of predator-resistant fencing.The simulations predicted that implementation of the management plan would increase recruits by 24%. Nest baskets were the most effective treatment, accounting for 20.4% of the recruits. No-till winter wheat was the second most effective, accounting for 5.9% of the recruits. Wetland loss due to drainage would cause an 11% loss of breeding population in 10 years.The models were modified to account for migrational homing. The modification indicated that migrational homing would enhance the effects of management. Nest success rates were critical contributions to individual management methods. The most effective treatments, such as nest baskets, had high success rates and affected a large portion of the breeding population.Economic analyses indicated that nest baskets would be the most economical of the three techniques tested. The applications indicated that the system is a useful tool to aid management decisions, but data are scarce for several important variables. Basic research will be required to adequately model the effect of migrational homing and density dependence on production. The comprehensive nature of predictions desired by managers will also require that production models like the one described here be extended to encompass the entire annual cycle of waterfowl.

  7. Bayesian Decision Theory Guiding Educational Decision-Making: Theories, Models and Application

    ERIC Educational Resources Information Center

    Pan, Yilin

    2016-01-01

    Given the importance of education and the growing public demand for improving education quality under tight budget constraints, there has been an emerging movement to call for research-informed decisions in educational resource allocation. Despite the abundance of rigorous studies on the effectiveness, cost, and implementation of educational…

  8. Modeling Decision-Specific Stress: Some Methodological Considerations.

    ERIC Educational Resources Information Center

    Holbrook, Morris B.; Ryan, Michael J.

    1982-01-01

    A national questionnaire survey of 324 automotive fleet administrators, examining the effects of three stress variables (decisional ambiguity, conflict, and work overload) in the administrators' 17 decision areas, tested whether managerial stress is decision-specific. Individual-level correlations and aggregate-level multidimensional scaling…

  9. A Four Value Personnel Decision Model: Remedial Placement.

    ERIC Educational Resources Information Center

    Boldt, Robert F.

    The personnel decision principally treated in this paper is that of remedial placement. The decision made with respect to candidates is to accept or reject, and performances by candidates are evaluated as pass or fail. A criterion and a selection variable were assumed, and the problem is to choose a cutting score on the selection variable such…

  10. Modeling Prosecutors' Charging Decisions in Domestic Violence Cases

    ERIC Educational Resources Information Center

    Worrall, John L.; Ross, Jay W.; McCord, Eric S.

    2006-01-01

    Relatively little research explaining prosecutors' charging decisions in criminal cases is available. Even less has focused on charging decisions in domestic violence cases. Past studies have also relied on restrictive definitions of domestic violence, notably cases with male offenders and female victims, and they have not considered prosecutors'…

  11. Determining the optimal planting density and land expectation value -- a numerical evaluation of decision model

    SciTech Connect

    Gong, P. . Dept. of Forest Economics)

    1998-08-01

    Different decision models can be constructed and used to analyze a regeneration decision in even-aged stand management. However, the optimal decision and management outcomes determined in an analysis may depend on the decision model used in the analysis. This paper examines the proper choice of decision model for determining the optimal planting density and land expectation value (LEV) for a Scots pine (Pinus sylvestris L.) plantation in northern Sweden. First, a general adaptive decision model for determining the regeneration alternative that maximizes the LEV is presented. This model recognizes future stand state and timber price uncertainties by including multiple stand state and timber price scenarios, and assumes that the harvest decision in each future period will be made conditional on the observed stand state and timber prices. Alternative assumptions about future stand states, timber prices, and harvest decisions can be incorporated into this general decision model, resulting in several different decision models that can be used to analyze a specific regeneration problem. Next, the consequences of choosing different modeling assumptions are determined using the example Scots pine plantation problem. Numerical results show that the most important sources of uncertainty that affect the optimal planting density and LEV are variations of the optimal clearcut time due to short-term fluctuations of timber prices. It is appropriate to determine the optimal planting density and harvest policy using an adaptive decision model that recognizes uncertainty only in future timber prices. After the optimal decisions have been found, however, the LEV should be re-estimated by incorporating both future stand state and timber price uncertainties.

  12. Exposure models for the prior distribution in bayesian decision analysis for occupational hygiene decision making.

    PubMed

    Lee, Eun Gyung; Kim, Seung Won; Feigley, Charles E; Harper, Martin

    2013-01-01

    This study introduces two semi-quantitative methods, Structured Subjective Assessment (SSA) and Control of Substances Hazardous to Health (COSHH) Essentials, in conjunction with two-dimensional Monte Carlo simulations for determining prior probabilities. Prior distribution using expert judgment was included for comparison. Practical applications of the proposed methods were demonstrated using personal exposure measurements of isoamyl acetate in an electronics manufacturing facility and of isopropanol in a printing shop. Applicability of these methods in real workplaces was discussed based on the advantages and disadvantages of each method. Although these methods could not be completely independent of expert judgments, this study demonstrated a methodological improvement in the estimation of the prior distribution for the Bayesian decision analysis tool. The proposed methods provide a logical basis for the decision process by considering determinants of worker exposure. PMID:23252451

  13. Exposure Models for the Prior Distribution in Bayesian Decision Analysis for Occupational Hygiene Decision Making

    PubMed Central

    Lee, Eun Gyung; Kim, Seung Won; Feigley, Charles E.; Harper, Martin

    2015-01-01

    This study introduces two semi-quantitative methods, Structured Subjective Assessment (SSA) and Control of Substances Hazardous to Health (COSHH) Essentials, in conjunction with two-dimensional Monte Carlo simulations for determining prior probabilities. Prior distribution using expert judgment was included for comparison. Practical applications of the proposed methods were demonstrated using personal exposure measurements of isoamyl acetate in an electronics manufacturing facility and of isopropanol in a printing shop. Applicability of these methods in real workplaces was discussed based on the advantages and disadvantages of each method. Although these methods could not be completely independent of expert judgments, this study demonstrated a methodological improvement in the estimation of the prior distribution for the Bayesian decision analysis tool. The proposed methods provide a logical basis for the decision process by considering determinants of worker exposure. PMID:23252451

  14. Making Risk Models Operational for Situational Awareness and Decision Support

    SciTech Connect

    Paulson, Patrick R.; Coles, Garill A.; Shoemaker, Steven V.

    2012-06-12

    Modernization of nuclear power operations control systems, in particular the move to digital control systems, creates an opportunity to modernize existing legacy infrastructure and extend plant life. We describe here decision support tools that allow the assessment of different facets of risk and support the optimization of available resources to reduce risk as plants are upgraded and maintained. This methodology could become an integrated part of the design review process and a part of the operations management systems. The methodology can be applied to the design of new reactors such as small nuclear reactors (SMR), and be helpful in assessing the risks of different configurations of the reactors. Our tool provides a low cost evaluation of alternative configurations and provides an expanded safety analysis by considering scenarios while early in the implementation cycle where cost impacts can be minimized. The effects of failures can be modeled and thoroughly vetted to understand their potential impact on risk. The process and tools presented here allow for an integrated assessment of risk by supporting traditional defense in depth approaches while taking into consideration the insertion of new digital instrument and control systems.

  15. ICADS: A cooperative decision making model with CLIPS experts

    NASA Technical Reports Server (NTRS)

    Pohl, Jens; Myers, Leonard

    1991-01-01

    A cooperative decision making model is described which is comprised of six concurrently executing domain experts coordinated by a blackboard control expert. The focus application field is architectural design, and the domain experts represent consultants in the area of daylighting, noise control, structural support, cost estimating, space planning, and climate responsiveness. Both the domain experts and the blackboard were implemented as production systems, using an enhanced version of the basic CLIPS package. Acting in unison as an Expert Design Advisor, the domain and control experts react to the evolving design solution progressively developed by the user in a 2-D CAD drawing environment. A Geometry Interpreter maps each drawing action taken by the user to real world objects, such as spaces, walls, windows, and doors. These objects, endowed with geometric and nongeometric attributes, are stored as frames in a semantic network. Object descriptions are derived partly from the geometry of the drawing environment and partly from knowledge bases containing prototypical, generalized information about the building type and site conditions under consideration.

  16. 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. PMID:26033468

  17. Quantifying the Value of Downscaled Climate Model Information for Adaptation Decisions: When is Downscaling a Smart Decision?

    NASA Astrophysics Data System (ADS)

    Terando, A. J.; Wootten, A.; Eaton, M. J.; Runge, M. C.; Littell, J. S.; Bryan, A. M.; Carter, S. L.

    2015-12-01

    Two types of decisions face society with respect to anthropogenic climate change: (1) whether to enact a global greenhouse gas abatement policy, and (2) how to adapt to the local consequences of current and future climatic changes. The practice of downscaling global climate models (GCMs) is often used to address (2) because GCMs do not resolve key features that will mediate global climate change at the local scale. In response, the development of downscaling techniques and models has accelerated to aid decision makers seeking adaptation guidance. However, quantifiable estimates of the value of information are difficult to obtain, particularly in decision contexts characterized by deep uncertainty and low system-controllability. Here we demonstrate a method to quantify the additional value that decision makers could expect if research investments are directed towards developing new downscaled climate projections. As a proof of concept we focus on a real-world management problem: whether to undertake assisted migration for an endangered tropical avian species. We also take advantage of recently published multivariate methods that account for three vexing issues in climate impacts modeling: maximizing climate model quality information, accounting for model dependence in ensembles of opportunity, and deriving probabilistic projections. We expand on these global methods by including regional (Caribbean Basin) and local (Puerto Rico) domains. In the local domain, we test whether a high resolution (2km) dynamically downscaled GCM reduces the multivariate error estimate compared to the original coarse-scale GCM. Initial tests show little difference between the downscaled and original GCM multivariate error. When propagated through to a species population model, the Value of Information analysis indicates that the expected utility that would accrue to the manager (and species) if this downscaling were completed may not justify the cost compared to alternative actions.

  18. Integrated Modelling Frameworks for Environmental Assessment and Decision Support

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Modern management of environmental resources defines problems from a holistic and integrated perspective, imposing strong requirements to Environmental Decision Support Systems (EDSSs) and Integrated Assessment Tools (IATs), which tend to be increasingly complex in terms of software architecture and...

  19. Learning to maximize reward rate: a model based on semi-Markov decision processes

    PubMed Central

    Khodadadi, Arash; Fakhari, Pegah; Busemeyer, Jerome R.

    2014-01-01

    When animals have to make a number of decisions during a limited time interval, they face a fundamental problem: how much time they should spend on each decision in order to achieve the maximum possible total outcome. Deliberating more on one decision usually leads to more outcome but less time will remain for other decisions. In the framework of sequential sampling models, the question is how animals learn to set their decision threshold such that the total expected outcome achieved during a limited time is maximized. The aim of this paper is to provide a theoretical framework for answering this question. To this end, we consider an experimental design in which each trial can come from one of the several possible “conditions.” A condition specifies the difficulty of the trial, the reward, the penalty and so on. We show that to maximize the expected reward during a limited time, the subject should set a separate value of decision threshold for each condition. We propose a model of learning the optimal value of decision thresholds based on the theory of semi-Markov decision processes (SMDP). In our model, the experimental environment is modeled as an SMDP with each “condition” being a “state” and the value of decision thresholds being the “actions” taken in those states. The problem of finding the optimal decision thresholds then is cast as the stochastic optimal control problem of taking actions in each state in the corresponding SMDP such that the average reward rate is maximized. Our model utilizes a biologically plausible learning algorithm to solve this problem. The simulation results show that at the beginning of learning the model choses high values of decision threshold which lead to sub-optimal performance. With experience, however, the model learns to lower the value of decision thresholds till finally it finds the optimal values. PMID:24904252

  20. Decision Support Model for Introduction of Gamification Solution Using AHP

    PubMed Central

    2014-01-01

    Gamification means the use of various elements of game design in nongame contexts including workplace collaboration, marketing, education, military, and medical services. Gamification is effective for both improving workplace productivity and motivating employees. However, introduction of gamification is not easy because the planning and implementation processes of gamification are very complicated and it needs interdisciplinary knowledge such as information systems, organization behavior, and human psychology. Providing a systematic decision making method for gamification process is the purpose of this paper. This paper suggests the decision criteria for selection of gamification platform to support a systematic decision making process for managements. The criteria are derived from previous works on gamification, introduction of information systems, and analytic hierarchy process. The weights of decision criteria are calculated through a survey by the professionals on game, information systems, and business administration. The analytic hierarchy process is used to derive the weights. The decision criteria and weights provided in this paper could support the managements to make a systematic decision for selection of gamification platform. PMID:24892075

  1. Decision support model for introduction of gamification solution using AHP.

    PubMed

    Kim, Sangkyun

    2014-01-01

    Gamification means the use of various elements of game design in nongame contexts including workplace collaboration, marketing, education, military, and medical services. Gamification is effective for both improving workplace productivity and motivating employees. However, introduction of gamification is not easy because the planning and implementation processes of gamification are very complicated and it needs interdisciplinary knowledge such as information systems, organization behavior, and human psychology. Providing a systematic decision making method for gamification process is the purpose of this paper. This paper suggests the decision criteria for selection of gamification platform to support a systematic decision making process for managements. The criteria are derived from previous works on gamification, introduction of information systems, and analytic hierarchy process. The weights of decision criteria are calculated through a survey by the professionals on game, information systems, and business administration. The analytic hierarchy process is used to derive the weights. The decision criteria and weights provided in this paper could support the managements to make a systematic decision for selection of gamification platform. PMID:24892075

  2. Decision-Tree Models of Categorization Response Times, Choice Proportions, and Typicality Judgments

    ERIC Educational Resources Information Center

    Lafond, Daniel; Lacouture, Yves; Cohen, Andrew L.

    2009-01-01

    The authors present 3 decision-tree models of categorization adapted from T. Trabasso, H. Rollins, and E. Shaughnessy (1971) and use them to provide a quantitative account of categorization response times, choice proportions, and typicality judgments at the individual-participant level. In Experiment 1, the decision-tree models were fit to…

  3. Methodology for the use of DSSAT Models for Precision Agriculture Decision Support

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A prototype decision support system (DSS) called Apollo was developed to assist researchers in using the Decision Support System for Agrotechnology Transfer (DSSAT) crop growth models to analyze precision farming datasets. Because the DSSAT models are written to simulate crop growth and development...

  4. A Model of Decision-Making for Teachers Engaged in Developmental Research.

    ERIC Educational Resources Information Center

    Impey, William D.

    An analysis of the rationale, concepts and procedures for designing and implementing a model of decision-making for teachers engaged in developmental research is presented. The decision-making model is conceptualized as the intersection of four sets of data sources derived from the performance of preactive/planning tasks,…

  5. A Review of Contemporary Ethical Decision-Making Models for Mental Health Professionals

    ERIC Educational Resources Information Center

    Francis, Perry C.

    2015-01-01

    Mental health professionals are faced with increasingly complex ethical decisions that are impacted by culture, personal and professional values, and the contexts in which they and their clients inhabit. This article presents the reasons for developing and implementing multiple ethical decision making models and reviews four models that address…

  6. Decision-making Strategies and Performance among Seniors1

    PubMed Central

    Besedeš, Tibor; Deck, Cary; Sarangi, Sudipta; Shor, Mikhael

    2011-01-01

    Using paper and pencil experiments administered in senior centers, we examine decision-making performance in multi-attribute decision problems. We differentiate the effects of declining cognitive performance and changing cognitive process on decision-making performance of seniors as they age. We find a significant decline in performance with age due to reduced reliance on common heuristics and increased decision-making randomness among our oldest subjects. However, we find that increasing the number of options in a decision problem increases the number of heuristics brought to the task. This challenges the choice overload view that people give up when confronted with too much choice. PMID:22408282

  7. Impact of model development, calibration and validation decisions on hydrological simulations in West Lake Erie Basin

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Watershed simulation models are used extensively to investigate hydrologic processes, landuse and climate change impacts, pollutant load assessments and best management practices (BMPs). Developing, calibrating and validating these models require a number of critical decisions that will influence t...

  8. Decision-Making Models with Sets of Strategies for Applications to Individuals and Groups in Higher Education.

    ERIC Educational Resources Information Center

    Gill, Wanda E.

    Three decision-making models that have applications for college presidents and administrators are reviewed. While both individual and group decision-making are addressed, emphasis is placed on the importance of group decisions on institutional policy planning. The model of Edmund M. Burke (1979) presents specific decision-making strategies in…

  9. Decision support models for solid waste management: Review and game-theoretic approaches

    SciTech Connect

    Karmperis, Athanasios C.; Aravossis, Konstantinos; Tatsiopoulos, Ilias P.; Sotirchos, Anastasios

    2013-05-15

    Highlights: ► The mainly used decision support frameworks for solid waste management are reviewed. ► The LCA, CBA and MCDM models are presented and their strengths, weaknesses, similarities and possible combinations are analyzed. ► The game-theoretic approach in a solid waste management context is presented. ► The waste management bargaining game is introduced as a specific decision support framework. ► Cooperative and non-cooperative game-theoretic approaches to decision support for solid waste management are discussed. - Abstract: This paper surveys decision support models that are commonly used in the solid waste management area. Most models are mainly developed within three decision support frameworks, which are the life-cycle assessment, the cost–benefit analysis and the multi-criteria decision-making. These frameworks are reviewed and their strengths and weaknesses as well as their critical issues are analyzed, while their possible combinations and extensions are also discussed. Furthermore, the paper presents how cooperative and non-cooperative game-theoretic approaches can be used for the purpose of modeling and analyzing decision-making in situations with multiple stakeholders. Specifically, since a waste management model is sustainable when considering not only environmental and economic but also social aspects, the waste management bargaining game is introduced as a specific decision support framework in which future models can be developed.

  10. A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control

    PubMed Central

    Rigoux, Lionel; Guigon, Emmanuel

    2012-01-01

    Costs (e.g. energetic expenditure) and benefits (e.g. food) are central determinants of behavior. In ecology and economics, they are combined to form a utility function which is maximized to guide choices. This principle is widely used in neuroscience as a normative model of decision and action, but current versions of this model fail to consider how decisions are actually converted into actions (i.e. the formation of trajectories). Here, we describe an approach where decision making and motor control are optimal, iterative processes derived from the maximization of the discounted, weighted difference between expected rewards and foreseeable motor efforts. The model accounts for decision making in cost/benefit situations, and detailed characteristics of control and goal tracking in realistic motor tasks. As a normative construction, the model is relevant to address the neural bases and pathological aspects of decision making and motor control. PMID:23055916