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Recursive Methods in Discounted Stochastic Games: An Algorithm for δ → 1 and a Folk Theorem


Johannes Horner


Yale University - Cowles Foundation

Takuo Sugaya


affiliation not provided to SSRN

Satoru Takahashi


National University of Singapore (NUS) - Department of Economics

Nicolas Vieille


HEC Paris - Economics & Decision Sciences

August 20, 2010

Economic Theory Center Working Paper No. 005-2010

Abstract:     
We present an algorithm to compute the set of perfect public equilibrium payoffs as the discount factor tends to one for stochastic games with observable states and public (but not necessarily perfect) monitoring when the limiting set of (long-run players’) equilibrium payoffs is independent of the initial state. This is the case, for instance, if the Markov chain induced by any Markov strategy profile is irreducible. We then provide conditions under which a folk theorem obtains: if in each state the joint distribution over the public signal and next period’s state satisfies some rank condition, every feasible payoff vector above the minmax payoff is sustained by a perfect public equilibrium with low discounting.

Number of Pages in PDF File: 46

Keywords: Stochastic Games

JEL Classification: C72, C73

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Date posted: December 22, 2010  

Suggested Citation

Horner, Johannes, Sugaya, Takuo, Takahashi, Satoru and Vieille, Nicolas, Recursive Methods in Discounted Stochastic Games: An Algorithm for δ → 1 and a Folk Theorem (August 20, 2010). Economic Theory Center Working Paper No. 005-2010. Available at SSRN: http://ssrn.com/abstract=1729299 or http://dx.doi.org/10.2139/ssrn.1729299

Contact Information

Johannes Horner (Contact Author)
Yale University - Cowles Foundation ( email )
Box 208281
New Haven, CT 06520-8281
United States

Takuo Sugaya
affiliation not provided to SSRN ( email )
Satoru Takahashi
National University of Singapore (NUS) - Department of Economics ( email )
1 Arts Link, AS2 #06-02
Singapore 117570, Singapore 119077
Singapore
Nicolas Vieille
HEC Paris (Groupe HEC) - Economics & Decision Sciences ( email )
Paris
France

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