{"title":"The Discount Rate for Investment Analysis Applying Expected Utility","authors":"Manel Baucells, Samuel E. Bodily","doi":"10.1287/deca.2022.0059","DOIUrl":"https://doi.org/10.1287/deca.2022.0059","url":null,"abstract":"Decision Analysis, Ahead of Print. <br/>","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139910546","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}
{"title":"How to Control Waste Incineration Pollution? Cost-Sharing or Penalty Mechanism—Based on Two Differential Game Models","authors":"Huijie Li, Deqing Tan","doi":"10.1287/deca.2023.0078","DOIUrl":"https://doi.org/10.1287/deca.2023.0078","url":null,"abstract":"Decision Analysis, Ahead of Print. <br/>","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139754385","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}
Pub Date : 2023-12-12DOI: 10.1287/deca.2023.intro.v20.n4
Kelly F. Robinson, Erin Baker, Elizabeth Ewing, Victoria Hemming, Melissa A. Kenney, Michael C. Runge
Decision Analysis, Volume 20, Issue 4, Page 243-251, December 2023.
决策分析》，第 20 卷第 4 期，第 243-251 页，2023 年 12 月。
{"title":"Decision Analysis to Advance Environmental Sustainability","authors":"Kelly F. Robinson, Erin Baker, Elizabeth Ewing, Victoria Hemming, Melissa A. Kenney, Michael C. Runge","doi":"10.1287/deca.2023.intro.v20.n4","DOIUrl":"https://doi.org/10.1287/deca.2023.intro.v20.n4","url":null,"abstract":"Decision Analysis, Volume 20, Issue 4, Page 243-251, December 2023. <br/>","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138572327","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}
Pub Date : 2023-11-14DOI: 10.1287/deca.2023.reviewthx.v20.n4
Vicki Bier, the Editor-in-Chief of Decision Analysis, thanks 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 2023.
{"title":"Appreciation to Referees, 2023","authors":"","doi":"10.1287/deca.2023.reviewthx.v20.n4","DOIUrl":"https://doi.org/10.1287/deca.2023.reviewthx.v20.n4","url":null,"abstract":"Vicki Bier, the Editor-in-Chief of Decision Analysis, thanks 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 2023.","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134902332","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}
We consider a variant of the hide-and-seek game in which a seeker inspects multiple hiding locations to find multiple items hidden by a hider. Each hiding location has a maximum hiding capacity and a probability of detecting its hidden items when an inspection by the seeker takes place. The objective of the seeker (respectively, hider) is to minimize (respectively, maximize) the expected number of undetected items. This model is motivated by strategic inspection problems, where a security agency is tasked with coordinating multiple inspection resources to detect and seize illegal commodities hidden by a criminal organization. To solve this large-scale zero-sum game, we leverage its structure and show that its mixed-strategy Nash equilibria can be characterized using their unidimensional marginal distributions, which are pure equilibria of a lower dimensional continuous zero-sum game. This leads to a two-step approach for efficiently solving our hide-and-seek game: First, we analytically solve the continuous game and derive closed-form expressions of the equilibrium marginal distributions. Second, we design a combinatorial algorithm to coordinate the players’ resources and compute equilibrium mixed strategies that satisfy the marginal distributions. We show that this solution approach computes a Nash equilibrium of the hide-and-seek game in quadratic time with linear support. Our analysis reveals novel equilibrium behaviors driven by a complex interplay between the game parameters, captured by our closed-form solutions. Funding: This work was supported by the Georgia Tech Stewart Fellowship and the Georgia Tech New Faculty Start Up Grant [for Georgia Tech New Faculty Start Up Grant]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2023.0012 .
{"title":"Hide-and-Seek Game with Capacitated Locations and Imperfect Detection","authors":"Bastián Bahamondes, Mathieu Dahan","doi":"10.1287/deca.2023.0012","DOIUrl":"https://doi.org/10.1287/deca.2023.0012","url":null,"abstract":"We consider a variant of the hide-and-seek game in which a seeker inspects multiple hiding locations to find multiple items hidden by a hider. Each hiding location has a maximum hiding capacity and a probability of detecting its hidden items when an inspection by the seeker takes place. The objective of the seeker (respectively, hider) is to minimize (respectively, maximize) the expected number of undetected items. This model is motivated by strategic inspection problems, where a security agency is tasked with coordinating multiple inspection resources to detect and seize illegal commodities hidden by a criminal organization. To solve this large-scale zero-sum game, we leverage its structure and show that its mixed-strategy Nash equilibria can be characterized using their unidimensional marginal distributions, which are pure equilibria of a lower dimensional continuous zero-sum game. This leads to a two-step approach for efficiently solving our hide-and-seek game: First, we analytically solve the continuous game and derive closed-form expressions of the equilibrium marginal distributions. Second, we design a combinatorial algorithm to coordinate the players’ resources and compute equilibrium mixed strategies that satisfy the marginal distributions. We show that this solution approach computes a Nash equilibrium of the hide-and-seek game in quadratic time with linear support. Our analysis reveals novel equilibrium behaviors driven by a complex interplay between the game parameters, captured by our closed-form solutions. Funding: This work was supported by the Georgia Tech Stewart Fellowship and the Georgia Tech New Faculty Start Up Grant [for Georgia Tech New Faculty Start Up Grant]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2023.0012 .","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135111537","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}
Kelly F. Robinson, Mark R. DuFour, Jason L. Fischer, Seth J. Herbst, Michael L. Jones, Lucas R. Nathan, Tammy J. Newcomb
Management agencies are tasked with difficult decisions for conservation and management of natural resources. These decisions are difficult because of ecological and social uncertainties, the potential for multiple decision makers from multiple jurisdictions, and the need to account for the diverse values of stakeholders. Decision analysis provides a framework for accounting for these difficulties when making conservation and management decisions. We discuss the benefits of the application of decision analysis for these types of issues and provide insights from three case studies from the Laurentian Great Lakes. These case studies describe applications of decision analysis for decisions within an agency (management of double-crested cormorant), among agencies (response to invasive grass carp), and among agencies and stakeholders (sustainable fisheries harvest management). These case studies provide insight into the ways that decision analysis can be useful for conservation and management of natural resources, but we also highlight future needs for decision making for these resources. In particular, applications of decision analysis for conservation and management would benefit from enhanced integration of both ecological and social science, inclusion of a broader base of stakeholders and rightsholders, and better educational opportunities surrounding decision analysis for undergraduates and graduate students of natural resources management programs. Specific lessons from our experiences include the importance of establishing trust and transparency early through the formation of a working group, collaboratively defining objectives and evaluating uncertainties, risks, and tradeoffs, and implementing participatory modeling processes with an independent facilitator with appropriate quantitative skills. History: This paper has been accepted for the Decision Analysis Special Issue on Further Environmental Sustainability. Funding: This study was supported by Great Lakes Restoration Initiative funding provided to the Michigan Department of Natural Resources [Grant F16AP01094] from the U.S. Fish and Wildlife Service and sub-awarded to Michigan State University.
{"title":"Lessons Learned in Applying Decision Analysis to Natural Resource Management for High-Stakes Issues Surrounded by Uncertainty","authors":"Kelly F. Robinson, Mark R. DuFour, Jason L. Fischer, Seth J. Herbst, Michael L. Jones, Lucas R. Nathan, Tammy J. Newcomb","doi":"10.1287/deca.2023.0015","DOIUrl":"https://doi.org/10.1287/deca.2023.0015","url":null,"abstract":"Management agencies are tasked with difficult decisions for conservation and management of natural resources. These decisions are difficult because of ecological and social uncertainties, the potential for multiple decision makers from multiple jurisdictions, and the need to account for the diverse values of stakeholders. Decision analysis provides a framework for accounting for these difficulties when making conservation and management decisions. We discuss the benefits of the application of decision analysis for these types of issues and provide insights from three case studies from the Laurentian Great Lakes. These case studies describe applications of decision analysis for decisions within an agency (management of double-crested cormorant), among agencies (response to invasive grass carp), and among agencies and stakeholders (sustainable fisheries harvest management). These case studies provide insight into the ways that decision analysis can be useful for conservation and management of natural resources, but we also highlight future needs for decision making for these resources. In particular, applications of decision analysis for conservation and management would benefit from enhanced integration of both ecological and social science, inclusion of a broader base of stakeholders and rightsholders, and better educational opportunities surrounding decision analysis for undergraduates and graduate students of natural resources management programs. Specific lessons from our experiences include the importance of establishing trust and transparency early through the formation of a working group, collaboratively defining objectives and evaluating uncertainties, risks, and tradeoffs, and implementing participatory modeling processes with an independent facilitator with appropriate quantitative skills. History: This paper has been accepted for the Decision Analysis Special Issue on Further Environmental Sustainability. Funding: This study was supported by Great Lakes Restoration Initiative funding provided to the Michigan Department of Natural Resources [Grant F16AP01094] from the U.S. Fish and Wildlife Service and sub-awarded to Michigan State University.","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135815674","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}
Julia R. Falconer, Eibe Frank, Devon L. L. Polaschek, Chaitanya Joshi
There are significant limitations to current methods for eliciting the prior beliefs of experts. To combat some of these limitations, this paper proposes an alternative approach that infers an expert’s prior beliefs about an uncertain event, A, from the expert’s past decisions. We show that an analyst can use past information on an expert’s decision-making task, contingent on an expert’s prior of A, to model the decision-making process and infer an approximation of the prior for A. This concept is illustrated by an application to recidivism. We conclude this work by highlighting important directions for future research. Funding: J. R. Falconer’s research is funded through the University of Waikato Doctoral Scholarship.
{"title":"Eliciting Informative Priors by Modeling Expert Decision Making","authors":"Julia R. Falconer, Eibe Frank, Devon L. L. Polaschek, Chaitanya Joshi","doi":"10.1287/deca.2023.0046","DOIUrl":"https://doi.org/10.1287/deca.2023.0046","url":null,"abstract":"There are significant limitations to current methods for eliciting the prior beliefs of experts. To combat some of these limitations, this paper proposes an alternative approach that infers an expert’s prior beliefs about an uncertain event, A, from the expert’s past decisions. We show that an analyst can use past information on an expert’s decision-making task, contingent on an expert’s prior of A, to model the decision-making process and infer an approximation of the prior for A. This concept is illustrated by an application to recidivism. We conclude this work by highlighting important directions for future research. Funding: J. R. Falconer’s research is funded through the University of Waikato Doctoral Scholarship.","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135396908","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}
Ashley B. C. Goode, Erin Rivenbark, Jessica A. Gilbert, Conor P. McGowan
Species status assessments are used to inform U.S. Fish and Wildlife Service (USFWS) decision making for Endangered Species Act (ESA) classification decisions, recovery planning, and more. The large number of species that require assessment and uncertainty in the data available impede the process of assigning and completing the assessments, which makes creating a multiyear work plan extremely difficult. An optimized triaging system that maximizes the use of the best available information while managing the complex ESA workload and meeting deadlines is necessary. We used a structured decision-making framework to approach the problem with the goal of creating a prioritization tool that would be effective at scheduling assessments, given the best information available and priorities of the USFWS. We collected data on the species awaiting assessment and developed a value function that incorporates existing deadlines, taxonomic uncertainty, controversy of the species, and population and habitat data availability and quality. We used a constrained linear optimization algorithm to maximize the value function and ensure that workload capacity was not exceeded. A comparison of model scenarios indicates that imposed deadlines impact the model more than capacity constraints. Additionally, differential weighting of the metrics significantly affected the outcome of the model. In the future, elicitation of metric weights should be done routinely before the model is run for use in official planning to ensure alignment with current USFWS priorities. Output from this optimization can be used to inform a five-year work plan, allocate resources, and discuss workforce decisions. History: This paper has been accepted for the Decision Analysis Special Issue on Decision Analysis to Further Environmental Sustainability. Funding: This work was funded via an inter-agency agreement between the USFWS and the USGS and subsequently by a Research Work Order contract between the USGS and the University of Florida [Grant G21AC00016].
{"title":"Prioritization of Species Status Assessments for Decision Support","authors":"Ashley B. C. Goode, Erin Rivenbark, Jessica A. Gilbert, Conor P. McGowan","doi":"10.1287/deca.2023.0026","DOIUrl":"https://doi.org/10.1287/deca.2023.0026","url":null,"abstract":"Species status assessments are used to inform U.S. Fish and Wildlife Service (USFWS) decision making for Endangered Species Act (ESA) classification decisions, recovery planning, and more. The large number of species that require assessment and uncertainty in the data available impede the process of assigning and completing the assessments, which makes creating a multiyear work plan extremely difficult. An optimized triaging system that maximizes the use of the best available information while managing the complex ESA workload and meeting deadlines is necessary. We used a structured decision-making framework to approach the problem with the goal of creating a prioritization tool that would be effective at scheduling assessments, given the best information available and priorities of the USFWS. We collected data on the species awaiting assessment and developed a value function that incorporates existing deadlines, taxonomic uncertainty, controversy of the species, and population and habitat data availability and quality. We used a constrained linear optimization algorithm to maximize the value function and ensure that workload capacity was not exceeded. A comparison of model scenarios indicates that imposed deadlines impact the model more than capacity constraints. Additionally, differential weighting of the metrics significantly affected the outcome of the model. In the future, elicitation of metric weights should be done routinely before the model is run for use in official planning to ensure alignment with current USFWS priorities. Output from this optimization can be used to inform a five-year work plan, allocate resources, and discuss workforce decisions. History: This paper has been accepted for the Decision Analysis Special Issue on Decision Analysis to Further Environmental Sustainability. Funding: This work was funded via an inter-agency agreement between the USFWS and the USGS and subsequently by a Research Work Order contract between the USGS and the University of Florida [Grant G21AC00016].","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135980782","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. S. John, Robin L. Dillon, William J. Burns, Nicholas Scurich
Benefit–cost analyses are critical to support U.S. agencies’ programmatic decision making. These analyses are particularly challenging when one of the benefits is adversary deterrence. This paper presents a framework for calculating the value of deterrence related to countermeasures implemented to mitigate an attack by an adaptive adversary. We offer an approach for partitioning the benefit of countermeasures into three components: (1) threat reduction (deterrence), (2) vulnerability reduction, and (3) consequence mitigation. The benefit of a countermeasure is measured by the expected value of countermeasure implementation (EVCI) attributable to a specific countermeasure. It is based on the concept of expected value of imperfect control, defined as the difference in the expected values of alternatives with and without countermeasures. The EVCI represents all the benefits of implementing the countermeasure and is derived from three sources: (1) changes in attack probability (threat reduction from deterrence), (2) changes in detection probability (vulnerability reduction), and (3) changes in the distribution of attack outcomes (consequence mitigation). We partition the EVCI and estimate the portion attributable to each of these three sources to quantify the unique benefit of each. We provide two applications of the partitioning methodology using examples from the published literature that examine countermeasures designed to protect commercial aircraft against man-portable air defense systems. The proposed framework provides an approach for explicitly accounting separately for deterrence, vulnerability reduction, and consequence mitigation in benefit–cost analyses. It provides quantifiable insights into how countermeasures reduce terrorism risk. Funding: This material is based upon work supported by the U.S. Department of Homeland Security under [Grant Award 22STESE00001-02-00]. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security. This award was made to Northeastern University and the University of Southern California is a sub-awardee. This work was also supported by the National Science Foundation [Grant 2027296] awarded to Decision Research.
{"title":"Partitioning the Expected Value of Countermeasures with an Application to Terrorism","authors":"R. S. John, Robin L. Dillon, William J. Burns, Nicholas Scurich","doi":"10.1287/deca.2023.0011","DOIUrl":"https://doi.org/10.1287/deca.2023.0011","url":null,"abstract":"Benefit–cost analyses are critical to support U.S. agencies’ programmatic decision making. These analyses are particularly challenging when one of the benefits is adversary deterrence. This paper presents a framework for calculating the value of deterrence related to countermeasures implemented to mitigate an attack by an adaptive adversary. We offer an approach for partitioning the benefit of countermeasures into three components: (1) threat reduction (deterrence), (2) vulnerability reduction, and (3) consequence mitigation. The benefit of a countermeasure is measured by the expected value of countermeasure implementation (EVCI) attributable to a specific countermeasure. It is based on the concept of expected value of imperfect control, defined as the difference in the expected values of alternatives with and without countermeasures. The EVCI represents all the benefits of implementing the countermeasure and is derived from three sources: (1) changes in attack probability (threat reduction from deterrence), (2) changes in detection probability (vulnerability reduction), and (3) changes in the distribution of attack outcomes (consequence mitigation). We partition the EVCI and estimate the portion attributable to each of these three sources to quantify the unique benefit of each. We provide two applications of the partitioning methodology using examples from the published literature that examine countermeasures designed to protect commercial aircraft against man-portable air defense systems. The proposed framework provides an approach for explicitly accounting separately for deterrence, vulnerability reduction, and consequence mitigation in benefit–cost analyses. It provides quantifiable insights into how countermeasures reduce terrorism risk. Funding: This material is based upon work supported by the U.S. Department of Homeland Security under [Grant Award 22STESE00001-02-00]. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security. This award was made to Northeastern University and the University of Southern California is a sub-awardee. This work was also supported by the National Science Foundation [Grant 2027296] awarded to Decision Research.","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78771493","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}
Forough Pourhossein, W. T. Huh, Steven M. Shechter
Limited information, time, or capacity may prevent customers from acting as utility maximizers when making purchase decisions. Rather, they would settle for a good enough option; that is, they stop searching and make a purchase as soon as they find an acceptable alternative. We incorporate this behavior in an assortment-optimization problem. Whereas different approaches to modeling customer choice are adopted in assortment planning, all assume customers are utility maximizers. Our work bridges the research streams of assortment planning and bounded rationality, particularly satisficing behavior. In addition, we define a limit for the search budget of customers, in which customers leave without purchase after examining a certain number of items. This assumption brings a new perspective to the assortment-planning literature, enabling us to capture the choice-overload effect. We prove that the firm’s problem of finding the optimal assortment is NP-hard. We further establish certain structural properties of the optimal decision, which allows us to reformulate the model as a mixed-integer program. We analytically derive a tight upper bound on the percentage loss in the firm’s expected profit for small instances when it assumes incorrectly that customers are utility maximizers. For larger instances, we take a numerical approach to determine the loss. Our results indicate that firms offering low-involvement products, among those dealing with satisficing customers, are more likely to face substantial profit loss if they ignore this behavior. Supplemental Material: The e-companion is available at https://doi.org/10.1287/deca.2022.0063 .
{"title":"Assortment Planning with Satisficing Customers","authors":"Forough Pourhossein, W. T. Huh, Steven M. Shechter","doi":"10.1287/deca.2022.0063","DOIUrl":"https://doi.org/10.1287/deca.2022.0063","url":null,"abstract":"Limited information, time, or capacity may prevent customers from acting as utility maximizers when making purchase decisions. Rather, they would settle for a good enough option; that is, they stop searching and make a purchase as soon as they find an acceptable alternative. We incorporate this behavior in an assortment-optimization problem. Whereas different approaches to modeling customer choice are adopted in assortment planning, all assume customers are utility maximizers. Our work bridges the research streams of assortment planning and bounded rationality, particularly satisficing behavior. In addition, we define a limit for the search budget of customers, in which customers leave without purchase after examining a certain number of items. This assumption brings a new perspective to the assortment-planning literature, enabling us to capture the choice-overload effect. We prove that the firm’s problem of finding the optimal assortment is NP-hard. We further establish certain structural properties of the optimal decision, which allows us to reformulate the model as a mixed-integer program. We analytically derive a tight upper bound on the percentage loss in the firm’s expected profit for small instances when it assumes incorrectly that customers are utility maximizers. For larger instances, we take a numerical approach to determine the loss. Our results indicate that firms offering low-involvement products, among those dealing with satisficing customers, are more likely to face substantial profit loss if they ignore this behavior. Supplemental Material: The e-companion is available at https://doi.org/10.1287/deca.2022.0063 .","PeriodicalId":46460,"journal":{"name":"Decision Analysis","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72746047","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：