A Numerical Analysis of the Evolutionary Stability of Learning Rules

Jens Josephson

No 474, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics

Abstract: In this paper I define an evolutionary stability criterion for learning rules. Using Monte Carlo simulations, I then apply this criterion to a class of learning rules that can be represented by Camerer and Ho's (1999) model of learning. This class contains perturbed versions of reinforcement and belief learning as special cases. A large population of individuals with learning rules in this class are repeatedly rematched for a finite number of periods and play one out of four symmetric two-player games. Belief learning is the only learning rule which is evolutionarily stable in almost all cases, whereas reinforcement learning is unstable in almost all cases. I also find that in certain games, the stability of intermediate learning rules hinges critically on a parameter of the model and the relative payoffs.

Keywords: Bounded rationality; Evolutionary game theory; Evolutionary Stability; Learning in games; Belief learning; Reinforcement learning. (search for similar items in EconPapers)
JEL-codes: C72 C73 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2001-11-15
New Economics Papers: this item is included in nep-cbe and nep-gth
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: A numerical analysis of the evolutionary stability of learning rules (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:hastef:0474

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