Econometrica: Sep, 2013, Volume 81, Issue 5

Efficiency in Games With Markovian Private Information

https://doi.org/10.3982/ECTA9557
p. 1887-1934

Juan F. Escobar, Juuso Toikka

We study repeated Bayesian games with communication and observable actions in which the players' privately known payoffs evolve according to an irreducible Markov chain whose transitions are independent across players. Our main result implies that, generically, any Pareto‐efficient payoff vector above a stationary minmax value can be approximated arbitrarily closely in a perfect Bayesian equilibrium as the discount factor goes to 1. As an intermediate step, we construct an approximately efficient dynamic mechanism for long finite horizons without assuming transferable utility.