More and more decision-making problems are being solved by groups. Collective intelligence is the ability of groups to perform well when solving complex problems. Thus, it is important to encourage collective intelligence to emerge from groups. In this study, we explore how two critical characteristics of groups, that is, group structure and individual knowledge in groups, influence the emergence of collective intelligence. To do this, we propose a measure for group structure using the collaboration network of a group and a measure for the distribution of individual knowledge in groups. Group structure is measured based on the intensities of links and whether the network is hierarchical or flat. The distribution of individual knowledge is measured from the perspective of whether group information is shared or unique. Social interactions among group members and individual changes in opinion are modeled based on a simulation technique. We find that unbalanced information distribution undermines group performance, whereas group structure can modify the effect of information distribution. We also find that groups with broadly distributed knowledge are good at solving complex problems. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72171158, 71771156 and 71971145].
{"title":"Group Structure and Information Distribution on the Emergence of Collective Intelligence","authors":"Ming Tang, Huchang Liao","doi":"10.1287/deca.2022.0466","DOIUrl":"https://doi.org/10.1287/deca.2022.0466","url":null,"abstract":"More and more decision-making problems are being solved by groups. Collective intelligence is the ability of groups to perform well when solving complex problems. Thus, it is important to encourage collective intelligence to emerge from groups. In this study, we explore how two critical characteristics of groups, that is, group structure and individual knowledge in groups, influence the emergence of collective intelligence. To do this, we propose a measure for group structure using the collaboration network of a group and a measure for the distribution of individual knowledge in groups. Group structure is measured based on the intensities of links and whether the network is hierarchical or flat. The distribution of individual knowledge is measured from the perspective of whether group information is shared or unique. Social interactions among group members and individual changes in opinion are modeled based on a simulation technique. We find that unbalanced information distribution undermines group performance, whereas group structure can modify the effect of information distribution. We also find that groups with broadly distributed knowledge are good at solving complex problems. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72171158, 71771156 and 71971145].","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":"16 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86182083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Triangular and PERT (Program Evaluation Review Technique) distribution probability density functions are commonly used in decision and risk analyses. These distributions are popular because they are each specified by only three points (two support bounds and the mode) that are believed to be easy to assess from experts or data. In this paper, we carefully analyze how close the Triangular and PERT distributions are to other distributions sharing the same support and mode and show that the errors induced by the Triangular and PERT distributions are significant. We further show that distributions that are characterized by the median tend to provide a better fit than do those that are characterized by the mode. Funding: This research was supported by the Equinor Fellows Program and the Operating System 2.0 research program developed by the Construction Industry Institute.
{"title":"Determining the Accuracy of the Triangular and PERT Distributions","authors":"Imran A. Khan, J. Bickel, Robert K. Hammond","doi":"10.1287/deca.2022.0464","DOIUrl":"https://doi.org/10.1287/deca.2022.0464","url":null,"abstract":"The Triangular and PERT (Program Evaluation Review Technique) distribution probability density functions are commonly used in decision and risk analyses. These distributions are popular because they are each specified by only three points (two support bounds and the mode) that are believed to be easy to assess from experts or data. In this paper, we carefully analyze how close the Triangular and PERT distributions are to other distributions sharing the same support and mode and show that the errors induced by the Triangular and PERT distributions are significant. We further show that distributions that are characterized by the median tend to provide a better fit than do those that are characterized by the mode. Funding: This research was supported by the Equinor Fellows Program and the Operating System 2.0 research program developed by the Construction Industry Institute.","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":"158 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86352218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Information value has been proposed and used as a probabilistic sensitivity measure, the idea being that uncertain parameters having higher information value are precisely those to which an optimal decision is more sensitive. In this paper, we study the notion of information density as a graphical complement to information value analysis, one that augments an information value calculation with associated directions of information gain. We formally examine mathematical details absent from its earlier presentation that guarantee information density exists and is well posed and describe its relationship to alternate measures of information value. We present its application in the context of a realistic case study and discuss the associated insights.
{"title":"Information Density in Decision Analysis","authors":"Gordon B. Hazen, E. Borgonovo, Xuefei Lu","doi":"10.1287/deca.2022.0465","DOIUrl":"https://doi.org/10.1287/deca.2022.0465","url":null,"abstract":"Information value has been proposed and used as a probabilistic sensitivity measure, the idea being that uncertain parameters having higher information value are precisely those to which an optimal decision is more sensitive. In this paper, we study the notion of information density as a graphical complement to information value analysis, one that augments an information value calculation with associated directions of information gain. We formally examine mathematical details absent from its earlier presentation that guarantee information density exists and is well posed and describe its relationship to alternate measures of information value. We present its application in the context of a realistic case study and discuss the associated insights.","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":"25 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91219821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vicki Bier, the Editor-in-Chief of Decision Analysis, would like to thank the referees who generously provide expert counsel and guidance on a voluntary basis. Without them, the journal could not function. The following list acknowledges those individuals who acted as referees for papers considered during calendar year 2022.
{"title":"Appreciation to Referees, 2022","authors":"","doi":"10.1287/deca.2022.0459","DOIUrl":"https://doi.org/10.1287/deca.2022.0459","url":null,"abstract":"Vicki Bier, the Editor-in-Chief of Decision Analysis, would like to thank the referees who generously provide expert counsel and guidance on a voluntary basis. Without them, the journal could not function. The following list acknowledges those individuals who acted as referees for papers considered during calendar year 2022.","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":"119 Suppl 3 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85319767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For media or digital products with quality uncertainty like online games, movies, theater plays, software, and smartphone applications, online customers may strategically delay their purchase waiting for online reviews and their peers’ purchase decisions. Thus, a firm needs to consider both social learning and positive network externality to anticipate the customers’ purchasing decisions and set a good pricing strategy over time. This paper investigates how these dual concerns affect the strategic interaction between a firm using preannounced pricing or responsive pricing and strategic customers in a two-period game-theoretic model. Deviating from conventional wisdom suggesting that social learning and externality work in a similar way, our results highlight their differences and provide valuable managerial insights. Although social learning and externality play a similar role in expanding the increasing-price-optimal region, they are different in other aspects: The firm will be worse off with learning if the externality gets stronger, whereas it will be worse off or better off with learning if learning gets stronger. In addition, we characterize the condition under which responsive pricing may outperform preannounced pricing. We further find that the firm’s discount factor has an influence on the firm’s pricing strategy selection. Funding: X. Wang and Y. Yang acknowledge financial support from the National Natural Science Foundation of China [Grant 72071204]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2022.0463 .
{"title":"Pricing Decisions with Social Interactions: A Game-Theoretic Model","authors":"Xiaofang Wang, Yaoyao Yang, Jun Zhuang","doi":"10.1287/deca.2022.0463","DOIUrl":"https://doi.org/10.1287/deca.2022.0463","url":null,"abstract":"For media or digital products with quality uncertainty like online games, movies, theater plays, software, and smartphone applications, online customers may strategically delay their purchase waiting for online reviews and their peers’ purchase decisions. Thus, a firm needs to consider both social learning and positive network externality to anticipate the customers’ purchasing decisions and set a good pricing strategy over time. This paper investigates how these dual concerns affect the strategic interaction between a firm using preannounced pricing or responsive pricing and strategic customers in a two-period game-theoretic model. Deviating from conventional wisdom suggesting that social learning and externality work in a similar way, our results highlight their differences and provide valuable managerial insights. Although social learning and externality play a similar role in expanding the increasing-price-optimal region, they are different in other aspects: The firm will be worse off with learning if the externality gets stronger, whereas it will be worse off or better off with learning if learning gets stronger. In addition, we characterize the condition under which responsive pricing may outperform preannounced pricing. We further find that the firm’s discount factor has an influence on the firm’s pricing strategy selection. Funding: X. Wang and Y. Yang acknowledge financial support from the National Natural Science Foundation of China [Grant 72071204]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2022.0463 .","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":"167 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84976718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peter Fishburn has had a tremendous impact on the field of decision analysis, developing ideas that would come to be foundational across decision analysis and that would impact the literature on decision making in economics, psychology, finance, engineering, and mathematics. This paper provides an overview of his legacy. We summarize 11 of his influential papers. We then trace his impact on recent research in topics including preference representation and elicitation, risk attitudes, time preferences, health preferences, behavioral decision making, social choice and voting, and geometric analyses. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2022.0461 .
{"title":"The Legacy of Peter Fishburn: Foundational Work and Lasting Impact","authors":"Andrea C. Hupman, Jay R. Simon","doi":"10.1287/deca.2022.0461","DOIUrl":"https://doi.org/10.1287/deca.2022.0461","url":null,"abstract":"Peter Fishburn has had a tremendous impact on the field of decision analysis, developing ideas that would come to be foundational across decision analysis and that would impact the literature on decision making in economics, psychology, finance, engineering, and mathematics. This paper provides an overview of his legacy. We summarize 11 of his influential papers. We then trace his impact on recent research in topics including preference representation and elicitation, risk attitudes, time preferences, health preferences, behavioral decision making, social choice and voting, and geometric analyses. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2022.0461 .","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":"51 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74971859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Health remains one of the most challenging realms for decision makers and policy making while critical for the well-being of humans, the stability of societies, and the development of economies. Decision making in this field ranges from medical doctors identifying the best treatments for patients, healthcare companies selecting the most promising drugs for development, healthcare providers deciding for adequate levels of resourcing, health regulators deciding whether to approve a new medicine or health technology, to regional and national health departments identifying how to increase the health security of regions and countries. In this positioning paper, and introduction to this Special Issue, we present the history, evolution, and trends of health decision analysis and suggest that these developments and news trends can be conceptualized as an emerging field of applied research for our discipline: Health Decision Analysis.
{"title":"Health Decision Analysis: Evolution, Trends, and Emerging Topics","authors":"Elisa F. Long, G. Montibeller, Jun Zhuang","doi":"10.1287/deca.2022.0460","DOIUrl":"https://doi.org/10.1287/deca.2022.0460","url":null,"abstract":"Health remains one of the most challenging realms for decision makers and policy making while critical for the well-being of humans, the stability of societies, and the development of economies. Decision making in this field ranges from medical doctors identifying the best treatments for patients, healthcare companies selecting the most promising drugs for development, healthcare providers deciding for adequate levels of resourcing, health regulators deciding whether to approve a new medicine or health technology, to regional and national health departments identifying how to increase the health security of regions and countries. In this positioning paper, and introduction to this Special Issue, we present the history, evolution, and trends of health decision analysis and suggest that these developments and news trends can be conceptualized as an emerging field of applied research for our discipline: Health Decision Analysis.","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":"65 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91391156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
William N. Caballero, Ethan Gharst, David L. Banks, J. Weir
In an increasingly competitive environment, defense organizations are met with more difficult decisions than in years past. This problem is especially apparent in security cooperation, that is, defense diplomacy, conducted by the United States. Both the United States and its competitors offer military assistance to third-party nations who, in turn, select an offer based on their own self-interest. Unfortunately, current security cooperation planning practices adopt an ad hoc approach to such problems. Therefore, we set forth herein a decision-analytic-planning framework by (1) provisioning a generic utility model for security cooperation planning applicable to myriad stakeholders and (2) developing a Bayesian solution that allows the stakeholder to select an action that maximizes their expected utility. This combination of value-focused thinking and adversarial risk analysis improves upon standard U.S. defense practices; it tractably encodes planning assumptions and more comprehensively considers the relevant uncertainties. The efficacy of this planning approach is illustrated on a notional U.S. Air Force case study in which a host nation must choose between security assistance from the United States or a competing nation.
{"title":"Multipolar Security Cooperation Planning: A Multiobjective, Adversarial-Risk-Analysis Approach","authors":"William N. Caballero, Ethan Gharst, David L. Banks, J. Weir","doi":"10.1287/deca.2022.0458","DOIUrl":"https://doi.org/10.1287/deca.2022.0458","url":null,"abstract":"In an increasingly competitive environment, defense organizations are met with more difficult decisions than in years past. This problem is especially apparent in security cooperation, that is, defense diplomacy, conducted by the United States. Both the United States and its competitors offer military assistance to third-party nations who, in turn, select an offer based on their own self-interest. Unfortunately, current security cooperation planning practices adopt an ad hoc approach to such problems. Therefore, we set forth herein a decision-analytic-planning framework by (1) provisioning a generic utility model for security cooperation planning applicable to myriad stakeholders and (2) developing a Bayesian solution that allows the stakeholder to select an action that maximizes their expected utility. This combination of value-focused thinking and adversarial risk analysis improves upon standard U.S. defense practices; it tractably encodes planning assumptions and more comprehensively considers the relevant uncertainties. The efficacy of this planning approach is illustrated on a notional U.S. Air Force case study in which a host nation must choose between security assistance from the United States or a competing nation.","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":"186 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76930736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Erik T. Rosenstrom, Sareh Meshkinfam, J. Ivy, Shadi Hassani Goodarzi, M. Capan, J. Huddleston, S. Romero-Brufau
Sepsis is considered a medical emergency where delays in initial treatment are associated with increased morbidity and mortality, yet there is no gold standard for identifying sepsis onset and thus treatment timing. We leverage electronic health record (EHR) data with clinical expertise to develop a continuous-time Markov decision process (MDP) optimal stopping model that identifies the optimal first intervention action (anti-infective, fluid, or wait). To study the impact of initial treatment of patients at risk for developing sepsis, we define the delayed treatment population who received delayed treatment upon admission or during hospitalization and serves as an approximation of the natural history of sepsis. We apply the optimal first treatment policy to sample patient visits from the nondelayed treatment population. This analysis indicates the average risk of death could be reduced by approximately 2.2%, the average time until treatment could be reduced by 106 minutes, and the average severity of the treatment state could be reduced by 15.5% compared with the treatment they received in the hospital. We study the properties of the optimal policy to define an easily interpretable initial treatment heuristic that considers a patient’s organ dysfunction, location, and septic shock status. This generalizable framework can inform personalized treatment of patients at risk for sepsis.
{"title":"Optimizing the First Response to Sepsis: An Electronic Health Record-Based Markov Decision Process Model","authors":"Erik T. Rosenstrom, Sareh Meshkinfam, J. Ivy, Shadi Hassani Goodarzi, M. Capan, J. Huddleston, S. Romero-Brufau","doi":"10.1287/deca.2022.0455","DOIUrl":"https://doi.org/10.1287/deca.2022.0455","url":null,"abstract":"Sepsis is considered a medical emergency where delays in initial treatment are associated with increased morbidity and mortality, yet there is no gold standard for identifying sepsis onset and thus treatment timing. We leverage electronic health record (EHR) data with clinical expertise to develop a continuous-time Markov decision process (MDP) optimal stopping model that identifies the optimal first intervention action (anti-infective, fluid, or wait). To study the impact of initial treatment of patients at risk for developing sepsis, we define the delayed treatment population who received delayed treatment upon admission or during hospitalization and serves as an approximation of the natural history of sepsis. We apply the optimal first treatment policy to sample patient visits from the nondelayed treatment population. This analysis indicates the average risk of death could be reduced by approximately 2.2%, the average time until treatment could be reduced by 106 minutes, and the average severity of the treatment state could be reduced by 15.5% compared with the treatment they received in the hospital. We study the properties of the optimal policy to define an easily interpretable initial treatment heuristic that considers a patient’s organ dysfunction, location, and septic shock status. This generalizable framework can inform personalized treatment of patients at risk for sepsis.","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80033102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When creating mathematical models for forecasting and decision making, there is a tendency to include more complexity than necessary, in the belief that higher-fidelity models are more accurate than simpler ones. In this paper, we analyze the performance of models that submitted COVID-19 forecasts to the U.S. Centers for Disease Control and Prevention and evaluate them against a simple two-equation model that is specified using simple linear regression. We find that our simple model was comparable in accuracy to highly publicized models and had among the best-calibrated forecasts. This result may be surprising given the complexity of many COVID-19 models and their support by large forecasting teams. However, our result is consistent with the body of research that suggests that simple models perform very well in a variety of settings.
{"title":"Model Complexity and Accuracy: A COVID-19 Case Study","authors":"Colin Small, J. Bickel","doi":"10.1287/deca.2022.0457","DOIUrl":"https://doi.org/10.1287/deca.2022.0457","url":null,"abstract":"When creating mathematical models for forecasting and decision making, there is a tendency to include more complexity than necessary, in the belief that higher-fidelity models are more accurate than simpler ones. In this paper, we analyze the performance of models that submitted COVID-19 forecasts to the U.S. Centers for Disease Control and Prevention and evaluate them against a simple two-equation model that is specified using simple linear regression. We find that our simple model was comparable in accuracy to highly publicized models and had among the best-calibrated forecasts. This result may be surprising given the complexity of many COVID-19 models and their support by large forecasting teams. However, our result is consistent with the body of research that suggests that simple models perform very well in a variety of settings.","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":"155 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73446081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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