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Boundedly rational quasi-Bayesian learning in coordination games with imperfect monitoring

Part of: Stochastic processes Game theory Limit theorems Special processes

Published online by Cambridge University Press:  14 July 2016

Hsiao-Chi Chen ,
Yunshyong Chow  and
June Hsieh
Hsiao-Chi Chen*
Affiliation:
National Taipei University
Yunshyong Chow*
Affiliation:
Academia Sinica, Taipei
June Hsieh*
Affiliation:
Academia Sinica, Taipei
*
∗Postal address: Department of Economics, National Taipei University, 67, Section 3, Min-Sheng E. Road, Taipei, Taiwan 104, Republic of China. Email address: [email protected]
∗∗∗Postal address: Institute of Mathematics, Academia Sinica, Taipei, Taiwan 115, Republic of China.
∗∗∗Postal address: Institute of Mathematics, Academia Sinica, Taipei, Taiwan 115, Republic of China.
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Abstract

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In this paper we study players' long-run behaviors in evolutionary coordination games with imperfect monitoring. In each time period, signals corresponding to players' underlying actions, instead of the actions themselves, are observed. A boundedly rational quasi-Bayesian learning process is proposed to extract information from the realized signals. We find that players' long-run behaviors depend not only on the correlations between actions and signals, but on the initial probabilities of risk-dominant and non-risk-dominant equilibria being chosen. The conditions under which risk-dominant equilibrium, non-risk-dominant equilibrium, and the coexistence of both equilibria emerges in the long run are shown. In some situations, the number of limiting distributions grows unboundedly as the population size grows to infinity.

Keywords

Coordination gamequasi-Bayesian learningimperfect monitoring

MSC classification

Primary: 91A22: Evolutionary games 60G99: None of the above, but in this section 60K99: None of the above, but in this section 60F99: None of the above, but in this section
Type
Research Papers
Information
Journal of Applied Probability , Volume 43 , Issue 2 , June 2006 , pp. 335 - 350
Copyright
© Applied Probability Trust 2006 

Footnotes

Funding from the National Science Council (project no. NSC 92-2415-H-305-001) is gratefully acknowledged.

Funding from the National Science Council (project no. NSC 93-2115-M-001-011) is gratefully acknowledged.

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