International Journal of Game Theory

, Volume 43, Issue 4, pp 767–789

Repeated games of incomplete information with large sets of states

Article

DOI: 10.1007/s00182-013-0404-8

Cite this article as:
Sandomirskiy, F. Int J Game Theory (2014) 43: 767. doi:10.1007/s00182-013-0404-8

Abstract

The famous theorem of R. Aumann and M. Maschler states that the sequence of values of an \(N\)-stage zero-sum game \(\varGamma _N(\rho )\) with incomplete information on one side and prior distribution \(\rho \) converges as \(N\rightarrow \infty \), and that the error term \({\mathrm {err}}[\varGamma _N(\rho )]={\mathrm {val}}[\varGamma _N(\rho )]- \lim _{M\rightarrow \infty }{\mathrm {val}}[\varGamma _{M}(\rho )]\) is bounded by \(C N^{-\frac{1}{2}}\) if the set of states \(K\) is finite. The paper deals with the case of infinite \(K\). It turns out that, if the prior distribution \(\rho \) is countably-supported and has heavy tails, then the error term can be of the order of \(N^{\alpha }\) with \(\alpha \in \left( -\frac{1}{2},0\right) \), i.e., the convergence can be anomalously slow. The maximal possible \(\alpha \) for a given \(\rho \) is determined in terms of entropy-like family of functionals. Our approach is based on the well-known connection between the behavior of the maximal variation of measure-valued martingales and asymptotic properties of repeated games with incomplete information.

Keywords

Repeated games with incomplete information Error term  Bayesian learning Maximal variation of martingales Entropy 

JEL Classification

C73 

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Chebyshev Laboratory, Faculty of Mathematics and MechanicsSt. Petersburg State UniversitySt. PetersburgRussia
  2. 2.Faculty of PhysicsSt. Petersburg State UniversitySt. PetersburgRussia

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