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来源类型Working Paper
规范类型报告
DOI10.3386/w22448
来源IDWorking Paper 22448
Can Market Participants Report their Preferences Accurately (Enough)?
Eric Budish; Judd B. Kessler
发表日期2016-07-25
出版年2016
语种英语
摘要In mechanism design theory it is common to assume that agents can perfectly report their preferences, even in complex settings where this assumption strains reality. We experimentally test whether real market participants can report their real preferences for course schedules “accurately enough” for a novel course allocation mechanism, approximate competitive equilibrium from equal incomes (A-CEEI), to realize its theoretical benefits. To use market participants’ real preferences (i.e., rather than artificial “induced preferences” as is typical in market design experiments), we developed a new experimental method. Our method, the “elicited preferences” approach, generates preference data from subjects through a series of binary choices. These binary choices revealed that subjects preferred their schedules constructed under A-CEEI to their schedules constructed under the incumbent mechanism, a bidding points auction, and that A-CEEI reduced envy, suggesting subjects were able to report their preferences accurately enough to realize the efficiency and fairness benefits of A-CEEI. However, preference reporting mistakes did meaningfully harm mechanism performance. One identifiable pattern of mistakes was that subjects had relatively more difficulty reporting cardinal as opposed to ordinal preference information. The experiment helped to persuade the Wharton School to adopt the new mechanism and helped guide aspects of its practical implementation, especially around preference reporting.
主题Microeconomics ; Game Theory ; Econometrics ; Experimental Design ; Market Structure and Distribution
URLhttps://www.nber.org/papers/w22448
来源智库National Bureau of Economic Research (United States)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/580122
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GB/T 7714
Eric Budish,Judd B. Kessler. Can Market Participants Report their Preferences Accurately (Enough)?. 2016.
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