Recency, Records and Recaps: Learning and Non-Equilibrium Behavior in a Simple Decision Problem

13 Pages Posted: 14 Oct 2013 Last revised: 3 Dec 2016

Drew Fudenberg

Massachusetts Institute of Technology (MIT)

Alexander Peysakhovich

Yale University - Human Cooperation Lab

Date Written: October 13, 2013

Abstract

Nash equilibrium takes optimization as a primitive, but suboptimal behavior can persist in simple stochastic decision problems. This has motivated the development of other equilibrium concepts such as cursed equilibrium and behavioral equilibrium. We experimentally study a simple adverse selection (or “lemons”) problem and find that learning models that heavily discount past information (i.e. display recency bias) explain patterns of behavior better than Nash, cursed or behavioral equilibrium. Providing counterfactual information or a record of past outcomes does little to aid convergence to optimal strategies, but providing sample averages (“recaps”) gets individuals most of the way to optimality. Thus recency effects are not solely due to limited memory but stem from some other form of cognitive constraints. Our results show the importance of going beyond static optimization and incorporating features of human learning into economic models.

Keywords: learning, decision problems, behavioral economics

JEL Classification: C70, D1, D8

Suggested Citation

Fudenberg , Drew and Peysakhovich, Alexander, Recency, Records and Recaps: Learning and Non-Equilibrium Behavior in a Simple Decision Problem (October 13, 2013). Available at SSRN: https://ssrn.com/abstract=2339862 or http://dx.doi.org/10.2139/ssrn.2339862

Drew Fudenberg

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Alexander Peysakhovich (Contact Author)

Yale University - Human Cooperation Lab ( email )

New Haven, CT
United States

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