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

Drew Fudenberg and Alexander Peysakhovich

Scholarly Articles from Harvard University Department of Economics

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.

Date: 2014
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Citations: View citations in EconPapers (13)

Published in Proceedings of the Fifteenth ACM Conference on Economics and Computation - EC '14

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Working Paper: Recency, Records and Recaps: Learning and Non-Equilibrium Behavior in a Simple Decision Problem (2014)
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