Cycling and Categorical Learning in Decentralized Adverse Selection Economies

Philippe Jehiel () and Erik Mohlin ()

Working Papers from HAL

Abstract: 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 nontraded 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.

Keywords: Adverse selection; Bounded rationality; Categorization; Learning; Model misspeciÖcation; OTC markets Adverse selection; Model misspecification; OTC markets (search for similar items in EconPapers)
Date: 2022-07
New Economics Papers: this item is included in nep-gth and nep-mic
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03754118v1
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://shs.hal.science/halshs-03754118v1/document (application/pdf)

Related works:
Working Paper: Cycling and Categorical Learning in Decentralized Adverse Selection Economies (2022)
Working Paper: Cycling and Categorical Learning in Decentralized Adverse Selection Economies (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:halshs-03754118

Access Statistics for this paper

More papers in Working Papers from HAL
Bibliographic data for series maintained by CCSD ().