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Abstract

We consider the situation in which individuals in a finite population must repeatedly choose an action yielding an uncertain payoff. Between choices, each individual may observe the performance of one other individual. We search for rules of behavior with limited memory that increase expected payoffs for any underlying payoff distribution. It is shown that the rule that outperforms all other rules with this property is the one that specifies imitation of the action of an individual that performed better with a probability proportional to how much better she performed. When each individual uses this best rule, the aggregate population behavior can be approximated by the replicator dynamic.

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Bibliographic Info

Paper provided by University of Bonn, Germany in its series Discussion Paper Serie B with number 361.

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Length: pages
Date of creation: Dec 1995
Date of revision: Mar 1996
Handle: RePEc:bon:bonsfb:361

Contact details of provider:
Postal: Bonn Graduate School of Economics, University of Bonn, Adenauerallee 24 - 26, 53113 Bonn, Germany
Fax: +49 228 73 6884
Web page: http://www.bgse.uni-bonn.de/index.php?id=517

Related research

Keywords: social learning; bounded rationality; imitation; multi-armed bandit; random matching; payoff increasing; replicator dynamic.;

Other versions of this item:

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

References

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25. repec:att:wimass:9325 is not listed on IDEAS

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