Arbeitspapier
Cycling and Categorical Learning in Decentralized Adverse Selection Economies
We study learning in a decentralized pairwise adverse selection economy, where buyers have access to the quality of traded goods but not to the quality of non- traded goods. Buyers categorize ask prices in order to predict quality as a function of ask price. The categorization is endogenously determined so that outcomes that are observed more often are categorized more finely, and within each category beliefs reflect the empirical average. This leads buyers to have a very fine understanding of the relationship between qualities and ask prices for prices below the current market price, but only a coarse understanding above that price. We find that this induces a price cycle involving the Nash equilibrium price, and one or more higher prices.
- Language
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Englisch
- Bibliographic citation
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Series: Working Paper ; No. 2021:11
- Classification
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Wirtschaft
Game Theory and Bargaining Theory: General
Stochastic and Dynamic Games; Evolutionary Games; Repeated Games
Asymmetric and Private Information; Mechanism Design
Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making‡
- Subject
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Adverse selection
Bounded rationality
Categorization
Learning
Model misspecification
OTC markets
- Event
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Geistige Schöpfung
- (who)
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Jehiel, Philippe
Mohlin, Erik
- Event
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Veröffentlichung
- (who)
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Lund University, School of Economics and Management, Department of Economics
- (where)
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Lund
- (when)
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2021
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Jehiel, Philippe
- Mohlin, Erik
- Lund University, School of Economics and Management, Department of Economics
Time of origin
- 2021