We generalize results of earlier work on learning in
Bayesian games by allowing players to make decisions
in a nonmyopic fashion. In particular, we address the
issue of nonmyopic Bayesian learning with an arbitrary number of
bounded rational players, i.e., players who choose approximate best-response
strategies for the entire horizon (rather than the current
period). We show that, by repetition, nonmyopic bounded rational players
can reach a limit full-information nonmyopic Bayesian Nash equilibrium
(NBNE) strategy. The converse is also proved: Given a limit full-information
NBNE strategy, one can find a sequence of nonmyopic bounded
rational plays that converges to that strategy.
Bayesian Nash equilibrium existence in (almost continuous) contests
