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http://hdl.handle.net/10419/56285
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| Title: | | A numerical analysis of the evolutionary stability of learning rules  |
| Authors: | | Josephson, Jens |
| Issue Date: | | 2001 |
| Series/Report no.: | | SSE/EFI Working Paper Series in Economics and Finance 474 |
| 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. |
| Subjects: | | Bounded rationality Evolutionary game theory Evolutionary Stability Learning in games Belief learning Reinforcement learning |
| JEL: | | C72 C73 |
| Document Type: | | Working Paper |
| Appears in Collections: | | SSE/EFI Working Paper Series in Economics and Finance, EFI - The Economic Research Institute, Stockholm School of Economics
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