Individuals repeatedly face a multi-decision task with unknown payoff distributions. They have minimal memory and update their strategy by observing previous play (and not strategy) of someone else. We select behavior rules that increase average payoffs as often as possible in a large population...

We analyze the evolution of behavioral rules for learning how to play a two-armed bandit. Individuals have no information about the underlying pay-off distributions and have limited memory about their own past experience. Instead they must rely on information obtained trough observing the...

Consider a large population of individuals that are repeatedly randomly matched to play a cyclic 2x2 game such as Matching Pennies with fixed roles assigned in the game. Some learn by sampling previous play of a finite number of other individuals in the same role. We analyze population dynamics...