Convergence and approximation results for non-cooperative Bayesian games: Learning theorems
نویسندگان
چکیده
Let T deno te a con t inuous t ime hor izon and {Gt:teT} be a net (general ized sequence) of Bayesian games. We show that: (i) if {xt: te T} is a net of Bayes ian Nash Equi l ib r ium (BNE) strategies for G t, we can ext rac t a subsequence which converges to a l imit full in fo rmat ion B N E s t ra tegy for a one shot l imit full in fo rmat ion Bayes ian game. (ii) If {xt:t~T} is a net of a p p r o x i m a t e or et-BNE strategies for the game G t we can still ext rac t a subsequence which converges to the one shot l imit full i n fo rmat ion equi l ib r ium B N E strategy. (iii) G iven a l imit full in fo rmat ion B N E s t ra tegy of a one shot l imit full in fo rmat ion Bayes ian game, we can find a net of et-BNE strategies {xt:t~T} in {Gt:teT} which converges to the l imit full in fo rmat ion B N E s t ra tegy of the one shot game.
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