This paper examines the convergence of payoffs and strategies in Erev and Roth`s model of reinforcement learning. When all players use this rule it eliminates iteratively dominated strategies and in two-person constant-sum games average payoffs converge to the value of the game. Strategies...

This paper studies learning in monotone Bayesian games with one-dimensional types and finitely many actions. Players switch between actions at a set of thresholds.  A learning algorithm under which players adjust their strategies in the direction of better ones using payoffs received at...

We axiomatize preferences that can be represented by a monotonic aggregation of subjective expected utilities generated by a utility function and some set of i.i.d. probability measures over a product state space, S1. For such preferences, we define relevant measures, show that they are treated...

If players learn to play an infinitely repeated game using Bayesian learning, it is known that their strategies eventually approximate Nash equilibria of the repeated game under an absolute-continuity assumption on their prior beliefs.  We suppose here that Bayesian learners do not start with...

A seller wishes to prevent the discovery of rival offers by its prospective customers.  We study sales techniques which serve this purpose by making it harder for a customer to return to buy later after a search for alternatives.  These include making an exploding offer, offering a "buy-now"...

This paper presents a model of a rational seller who is actively learning the slope of his demand curve via his pricing strategy.  Consequently, this seller optimally experiments with his price.  Resulting price patterns show a lot of discreteness (as observed in the data), which has proved to...