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Learning Across Games

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Author Info
Friederike Mengel () (Universidad de Alicante)

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Abstract

In this paper (reinforcement) learning of decision makers that face many different games is studied. As learning separately for all games can be too costly (require too much reasoning resources) agents are assumed to partition the set of all games into analogy classes. Partitions of higher cardinality are more costly. A process of simultaneous learning of actions and partitions is presented and equilibrium partitions and action choices characterized. The model is able to explain deviations from subgame perfection that are sometimes observed in experiments even for vanishingly small reasoning costs. Furthermore it is shown that learning across games can stabilize mixed equilibria in 2×2 Coordination and Anti-Coordination games and destabilize strict Nash equilibria under certain conditions.

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File URL: http://www.ivie.es/downloads/docs/wpasad/wpasad-2007-05.pdf
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File Function: Fisrt version / Primera version, 2007
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Publisher Info
Paper provided by Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie) in its series Working Papers. Serie AD with number 2007-05.

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Length: 35 pages
Date of creation: Apr 2007
Date of revision:
Publication status: Published by Ivie
Handle: RePEc:ivi:wpasad:2007-05

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Related research
Keywords: Game Theory; Bounded Rationality; Reinforcement Learning; Analogies.;

Find related papers by JEL classification:
C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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References listed on IDEAS
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Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Edward W. Piotrowski & Jan Sladkowski & Anna Szczypinska, . "Reinforcement Learning in Market Games," Departmental Working Papers 30, University of Bialtystok, Department of Theoretical Physics. [Downloadable!]
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