Learning, Matching and Aggregation

Ed Hopkins

Edinburgh School of Economics Discussion Paper Series from Edinburgh School of Economics, University of Edinburgh

Abstract: Fictitious play and "gradient" learning are examined in the context of a large population where agents are repeatedly randomly matched. We show that the aggregation of this learning behaviour can be qualitatively different from learning at the level of the individual. This aggregate dynamic belongs to the same class of simply defined dynamic as do several formulations of evolutionary dynamics. We obtain sufficient conditions for convergence and divergence which are valid for the whole class of dynamics. These results are therefore robust to most specifications of adaptive behaviour.

Keywords: games; fictitious play; reinforcement learning; evolution (search for similar items in EconPapers)
JEL-codes: C72 D83 (search for similar items in EconPapers)
Pages: 32
Date: 1995-07
New Economics Papers: this item is included in nep-evo, nep-gth and nep-mic
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Citations: View citations in EconPapers (4)

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http://www.econ.ed.ac.uk/papers/id2_esedps.pdf

Related works:
Journal Article: Learning, Matching, and Aggregation (1999)
Working Paper: Learning, Matching and Aggregation (1995)
Working Paper: Learning, Matching and Aggregation (1995)
Working Paper: Learning, Matching and Aggregation
Working Paper: Learning, Matching and Aggregation
Working Paper: Learning, Matching and Aggregation
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Persistent link: https://EconPapers.repec.org/RePEc:edn:esedps:2

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