Slow learning in the market for lemons: A note on reinforcement learning and the winner's circle

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

3 Citations (Scopus)

Abstract

Human-participants experiments using markets with asymmetric information typically exhibit a "winner's curse," wherein bidders systematically bid more than their optimal amount. The winner's curse is very persistent; even when participants are able to make decisions repeatedly in the same situation, they repeatedly overbid. Why do people keep making the same mistakes over and over again? In this chapter, we consider a class of one-player decision problems, which generalize Akerlof's (1970) market-forlemons model. We show that if decision makers learn via reinforcement, specifically by the reference point model of Erev and Roth (1996), their behavior typically changes very slowly, and persistent mistakes are likely. We also develop testable predictions regarding when individuals ought to be able to learn more quickly.

Original languageEnglish
Title of host publicationComputational Economics
Subtitle of host publicationA Perspective from Computational Intelligence
PublisherIGI Global
Pages149-160
Number of pages12
ISBN (Print)9781591406495
DOIs
Publication statusPublished - 1 Dec 2005
Externally publishedYes

Cite this

Feltovich, N. (2005). Slow learning in the market for lemons: A note on reinforcement learning and the winner's circle. In Computational Economics: A Perspective from Computational Intelligence (pp. 149-160). IGI Global. https://doi.org/10.4018/978-1-59140-649-5.ch007