{"title":"Optimal Menu when Agents Make Mistakes","authors":"Sergei Mikhalishchev","doi":"10.2139/ssrn.3702259","DOIUrl":null,"url":null,"abstract":"This paper studies a welfare maximization problem with heterogeneous agents. A social planner designs a menu of choices for agents who misperceive either the properties of options or their own preferences. When agents misperceive the true properties of alternatives, it is optimal to limit a menu when the probability of a mistaken choice is moderately high. Additionally, it could be optimal to construct the menu with more distinct alternatives. However, when agents misperceive their own tastes, it is optimal to limit choice only when agents choose randomly, and to propose alternatives that are more similar when there is a greater probability of agents making a mistake.","PeriodicalId":176300,"journal":{"name":"Microeconomics: Intertemporal Consumer Choice & Savings eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microeconomics: Intertemporal Consumer Choice & Savings eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3702259","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper studies a welfare maximization problem with heterogeneous agents. A social planner designs a menu of choices for agents who misperceive either the properties of options or their own preferences. When agents misperceive the true properties of alternatives, it is optimal to limit a menu when the probability of a mistaken choice is moderately high. Additionally, it could be optimal to construct the menu with more distinct alternatives. However, when agents misperceive their own tastes, it is optimal to limit choice only when agents choose randomly, and to propose alternatives that are more similar when there is a greater probability of agents making a mistake.