We examine an evolutionary model in which the primary source of "noise" that moves the model between equilibria is not random, arbitrarily improbable mutations but mistakes in learning. We find conditions under which the payoff-dominant equilibrium in a 2x2 game is selected by the model as well...

We formulate a dynamic learning-and-adjustment model of a market in which sellers choose signals that potentially reveal their types. If the dynamic process selects a unique limiting outcome, then that outcome must be an undefeated equilibrium; though to be undefeated does not suffice to be the...

We examine an Outside Option Game in which player I submits a claim for a share of a cake while player II simultaneously either makes a claim or chooses to opt out. If player II opts out, then she receives an opt-out payment while player I receives nothing. If player II opts in and if the claims...

We examine the evolutionary foundations of common equilibrium refinement ideas for extensive form games, such as backward and forward induction, by examining the limiting outcome of an evolutionary process driven by stochastic learning and (rare) mutations. We show that the limiting outcome in a...