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Slow Learning in the Market fopr Lemons: A Note on Reinforcement Learning and the Winner's Circle

Slow Learning in the Market fopr Lemons: A Note on Reinforcement Learning and the Winner's Circle

N. Feltovich
Copyright: © 2006 |Pages: 12
ISBN13: 9781591406495|ISBN10: 1591406498|ISBN13 Softcover: 9781591406501|EISBN13: 9781591406518
DOI: 10.4018/978-1-59140-649-5.ch007
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MLA

Feltovich, N. "Slow Learning in the Market fopr Lemons: A Note on Reinforcement Learning and the Winner's Circle." Computational Economics: A Perspective from Computational Intelligence, edited by Shu-Heng Chen, et al., IGI Global, 2006, pp. 149-160. https://doi.org/10.4018/978-1-59140-649-5.ch007

APA

Feltovich, N. (2006). Slow Learning in the Market fopr Lemons: A Note on Reinforcement Learning and the Winner's Circle. In S. Chen, L. Jain, & C. Tai (Eds.), Computational Economics: A Perspective from Computational Intelligence (pp. 149-160). IGI Global. https://doi.org/10.4018/978-1-59140-649-5.ch007

Chicago

Feltovich, N. "Slow Learning in the Market fopr Lemons: A Note on Reinforcement Learning and the Winner's Circle." In Computational Economics: A Perspective from Computational Intelligence, edited by Shu-Heng Chen, Lakhmi Jain, and Chung-Ching Tai, 149-160. Hershey, PA: IGI Global, 2006. https://doi.org/10.4018/978-1-59140-649-5.ch007

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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-for-lemons 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.

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