Muhammad Ejaz, Nisho Rani, Dr Muhammad Ramzan Sheikh
Bidders’ bidding behavior is analyzed in first price sealed-bid (FPSB) auctions using an adversarial risk analysis (ARA) framework. However, using nonstrategic play and level-k thinking solution concepts, modeling is performed by assuming only two bidders. Also, the aleatory and concept uncertainties have not been yet taken into account by using an ARA framework for these auctions. In this paper, we apply an ARA approach to model bidders’ bidding behavior in a more realistic way for FPSB auctions. We assume n bidders that may have different wealth and heterogeneous risk behaviors. We use nonstrategic play and level-k thinking solution concepts, and we take into account aleatory uncertainty in addition to epistemic uncertainty. Finally, concept uncertainty is taken into account to find ARA solutions for these auctions. We also provide numerical examples to illustrate our methodology.
{"title":"First Price Sealed-Bid Auctions with Bidders’ Heterogeneous Risk Behavior: An Adversarial Risk Analysis Approach","authors":"Muhammad Ejaz, Nisho Rani, Dr Muhammad Ramzan Sheikh","doi":"10.1287/deca.2023.0469","DOIUrl":"https://doi.org/10.1287/deca.2023.0469","url":null,"abstract":"Bidders’ bidding behavior is analyzed in first price sealed-bid (FPSB) auctions using an adversarial risk analysis (ARA) framework. However, using nonstrategic play and level-k thinking solution concepts, modeling is performed by assuming only two bidders. Also, the aleatory and concept uncertainties have not been yet taken into account by using an ARA framework for these auctions. In this paper, we apply an ARA approach to model bidders’ bidding behavior in a more realistic way for FPSB auctions. We assume n bidders that may have different wealth and heterogeneous risk behaviors. We use nonstrategic play and level-k thinking solution concepts, and we take into account aleatory uncertainty in addition to epistemic uncertainty. Finally, concept uncertainty is taken into account to find ARA solutions for these auctions. We also provide numerical examples to illustrate our methodology.","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86169069","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}
R. Dillon, V. Bier, R. S. John, Abdullah Althenayyan
Decision analysis (DA) is an explicitly prescriptive discipline that separates beliefs about uncertainties from value preferences in modeling to support decision making. Researchers have been advancing DA tools for the last 60 years to support decision makers handling complex decisions requiring subjective judgments. Recently, some DA researchers and practitioners wondered whether the difficult decisions made during the COVID-19 pandemic regarding testing, masking, closing and reopening businesses, allocating ventilators, and prioritizing vaccines would have been improved with more DA involvement. With its focus on quantifying uncertainties, value trade-offs, and risk attitudes, DA should have been a valuable tool for decision makers during the pandemic. To influence decisions, DA applications require interactions with policymakers and experts to construct formal representations of the decision frame, elicit uncertainties, and assess risk tolerances and trade-offs among competing objectives. Unfortunately, such involvement of decision analysts in the process of decision making and policy setting did not occur during much of the COVID-19 pandemic. This lack of participation may have been partly because many decision makers were unaware of when DA could be valuable in helping with the challenges of the COVID-19 pandemic. In addition, decision analysts were perhaps not sufficiently adept at inserting themselves into the policy process at critical junctures when their expertise could have been helpful. Funding: This research was partially supported by the U.S. Department of Homeland Security through the Center for Accelerating Operational Efficiency at Arizona State University.
{"title":"Closing the Gap Between Decision Analysis and Policy Analysts Before the Next Pandemic","authors":"R. Dillon, V. Bier, R. S. John, Abdullah Althenayyan","doi":"10.1287/deca.2023.0468","DOIUrl":"https://doi.org/10.1287/deca.2023.0468","url":null,"abstract":"Decision analysis (DA) is an explicitly prescriptive discipline that separates beliefs about uncertainties from value preferences in modeling to support decision making. Researchers have been advancing DA tools for the last 60 years to support decision makers handling complex decisions requiring subjective judgments. Recently, some DA researchers and practitioners wondered whether the difficult decisions made during the COVID-19 pandemic regarding testing, masking, closing and reopening businesses, allocating ventilators, and prioritizing vaccines would have been improved with more DA involvement. With its focus on quantifying uncertainties, value trade-offs, and risk attitudes, DA should have been a valuable tool for decision makers during the pandemic. To influence decisions, DA applications require interactions with policymakers and experts to construct formal representations of the decision frame, elicit uncertainties, and assess risk tolerances and trade-offs among competing objectives. Unfortunately, such involvement of decision analysts in the process of decision making and policy setting did not occur during much of the COVID-19 pandemic. This lack of participation may have been partly because many decision makers were unaware of when DA could be valuable in helping with the challenges of the COVID-19 pandemic. In addition, decision analysts were perhaps not sufficiently adept at inserting themselves into the policy process at critical junctures when their expertise could have been helpful. Funding: This research was partially supported by the U.S. Department of Homeland Security through the Center for Accelerating Operational Efficiency at Arizona State University.","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89974744","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}
Cybersecurity planning supports the selection of and implementation of security controls in resource-constrained settings to manage risk. Doing so requires considering adaptive adversaries with different levels of strategic sophistication in modeling efforts to support risk management. However, most models in the literature only consider rational or nonstrategic adversaries. Therefore, we study how to inform defensive decision making to mitigate the risk from boundedly rational players, with a particular focus on making integrated, interdependent planning decisions. To achieve this goal, we introduce a modeling framework for selecting a portfolio of security mitigations that interdict adversarial attack plans that uses a structured approach for risk analysis. Our approach adapts adversarial risk analysis and cognitive hierarchy theory to consider a maximum-reliability path interdiction problem with a single defender and multiple attackers who have different goals and levels of strategic sophistication. Instead of enumerating all possible attacks and defenses, we introduce a solution technique based on integer programming and approximation algorithms to iteratively solve the defender’s and attackers’ problems. A case study illustrates the proposed models and provides insights into defensive planning. Funding: A. Peper and L. A. Albert were supported in part by the National Science Foundation [Grant 2000986].
网络安全规划支持在资源受限的环境中选择和实施安全控制措施，以管理风险。这样做需要在建模工作中考虑具有不同战略成熟度级别的自适应对手，以支持风险管理。然而，文献中的大多数模型只考虑理性的或非战略性的对手。因此，我们研究如何为防御性决策提供信息，以减轻有限理性参与者的风险，并特别关注制定综合的、相互依赖的规划决策。为了实现这一目标，我们引入了一个建模框架，用于选择一组安全缓解措施，以阻止使用结构化方法进行风险分析的对抗性攻击计划。我们的方法采用对抗风险分析和认知层次理论来考虑具有不同目标和战略复杂程度的单个防御者和多个攻击者的最大可靠性路径拦截问题。我们不是列举所有可能的攻击和防御，而是引入一种基于整数规划和近似算法的求解技术来迭代解决防御者和攻击者的问题。一个案例研究说明了所提出的模型，并提供了对防御计划的见解。资助:A. Peper和L. A. Albert得到了美国国家科学基金会的部分支持[Grant 2000986]。
{"title":"Interdicting Attack Plans with Boundedly Rational Players and Multiple Attackers: An Adversarial Risk Analysis Approach","authors":"Eric B. DuBois, Ashley Peper, Laura A. Albert","doi":"10.1287/deca.2023.0471","DOIUrl":"https://doi.org/10.1287/deca.2023.0471","url":null,"abstract":"Cybersecurity planning supports the selection of and implementation of security controls in resource-constrained settings to manage risk. Doing so requires considering adaptive adversaries with different levels of strategic sophistication in modeling efforts to support risk management. However, most models in the literature only consider rational or nonstrategic adversaries. Therefore, we study how to inform defensive decision making to mitigate the risk from boundedly rational players, with a particular focus on making integrated, interdependent planning decisions. To achieve this goal, we introduce a modeling framework for selecting a portfolio of security mitigations that interdict adversarial attack plans that uses a structured approach for risk analysis. Our approach adapts adversarial risk analysis and cognitive hierarchy theory to consider a maximum-reliability path interdiction problem with a single defender and multiple attackers who have different goals and levels of strategic sophistication. Instead of enumerating all possible attacks and defenses, we introduce a solution technique based on integer programming and approximation algorithms to iteratively solve the defender’s and attackers’ problems. A case study illustrates the proposed models and provides insights into defensive planning. Funding: A. Peper and L. A. Albert were supported in part by the National Science Foundation [Grant 2000986].","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78453746","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}
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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
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