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A Probabilistic Model of Learning in Games

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Author Info
Sanchirico, C.W.

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Abstract

This paper presents a new, probabilistic model of learning in games which investigates the often stated intuition that common knowledge of stategic intent may arise from repeated interaction. The model is set in the usual repeated game framework, but the two key assumptions are framed in terms of the likelihood of beliefs and actions conditional on the history of play. The first assumption formalizes the basic intuition of the learning approach; the second, the indeterminacy that inspired resort to learning models in the first place.

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Publisher Info
Paper provided by Columbia University, Department of Economics in its series Discussion Papers with number 1996_19.

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Length: 51 pages
Date of creation: 1996
Date of revision:
Handle: RePEc:clu:wpaper:1996_19

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Related research
Keywords: GAME THEORY;

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Find related papers by JEL classification:
C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory

Cited by:
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  1. Sergiu Hart & Andreu Mas-Colell, 1997. "A Simple Adaptive Procedure Leading to Correlated Equilibrium," Game Theory and Information 9703006, EconWPA, revised 24 Mar 1997. [Downloadable!]
    Other versions:
  2. Drew Fudenberg & David K. Levine, 1997. "Conditional Universal Consistency," Levine's Working Paper Archive 471, David K. Levine. [Downloadable!]
    Other versions:
  3. Weibull, Jörgen W., 1997. "What have we learned from Evolutionary Game Theory so far?," Working Paper Series 487, Research Institute of Industrial Economics, revised 26 Oct 1998. [Downloadable!]
    Other versions:
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This page was last updated on 2009-11-16.


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