We analyze the evolution of behavioral rules for learning how to play a two-armed bandit. Individuals have no information about the underlying pay-off distributions and have limited memory about their own past experience. Instead they must rely on information obtained trough observing the performance of other individuals. Evolution is modelled using the replicator dynamic with the revision behaviors as replicators. We find that evolution favors a special class of imitative rules. These so-called strictly improving rules, that also play an important role in a bounded rational selection approach (Schlag [16]), are found to be neutrally stable when facing any two-armed bandit.
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Paper provided by University of Bonn, Germany in its series Discussion Paper Serie B with number
378.
Length: pages Date of creation: Jul 1996 Date of revision: Handle: RePEc:bon:bonsfb:378
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Find related papers by JEL classification: C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games C79 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Other
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