I investigate the decision problem which arises in a game of incomplete information under two different types of uncertainty - uncertainty about other players’ type distributions and about other players’ strategies. I propose a new solution concept which works in two steps. First, I assume common knowledge of rationality and eliminate all strategies which are not best replies. Second, I apply the maximin expected utility criterion. Using this solution concept, one can derive predictions about outcomes and recommendations for players facing uncertainty. In its application to first-price auction the solution concept yields to strategies where bidders bid low. Intuitively, bidders expect their competitors to bid the highest rationalizable bid given their valuation. As a consequence, bidders resort to win against lower types with certainty.


Mass, Helene


Auctions, Incomplete Information, Informational Robustness, Belief-Free Rationalizability