We analyze a repeated first-price auction in which the types of the players are determined before the first round. It is proved that if every player is using either a belief-based learning scheme with bounded recall or a generalized fictitious play learning scheme, then for sufficiently large...

A learning process is belief affirming if for each player, the difference between her expected payoff in the next period, and the average of her past payoffs converges to zero. We show that every smooth discrete fictitious play and every continuous fictitious play is belief affirming. We also...

The Internet exhibits forms of interactions which are not captured by existing models in economics, artificial intelligence and game theory. New models are needed to deal with these multi-agent interactions. In this paper we present a new model--distributed games. In such a model each players...

The model of a non-Bayesian agent who faces a repeated game with incomplete informationagainst Nature is an appropriate tool for modeling general agent- environment interactions. In such a modelthe environment state (controlled by Nature) may change arbitrarily and the reward function is...