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...

We consider the situation in which individuals in a finite population must repeatedly choose an action yielding an uncertain payoff. Between choices, each individual may observe the performance of one other individual. We search for rules of behavior with limited memory that increase expected...

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...

The evolutionary basis for predicting the backwards induction solution in generic finite extensive-form games with perfect information is examined. Evolution is modelled using the replicator dynamic in combination with rare mutations that introduce a small change in the proportion of each...

We extend the notions of evolutionary stability and, for the first time, that of neutral stability to asymmetric games played between two populations. Stability with respect to simultaneous entry of a small proportion of mutants into both populations is considered. Allocations where neither...

We call a set of strategies "uniformly evolutionary stable" if the following holds after a small mutation of a monomorphic population playing a strategy in the set: a) No mutant strategy can spread. b) Mutant strategies not in the set will be driven out. c) The meaning of a "small mutation" can...

This paper presents a simple repeated-game model of interaction between the government and the private sector where, at each repetition, the government first makes a non-binding announcement about its future actions. The private sector, unsure whether or not this announcement will be respected,...

This paper considers the dynamic evolution of algorithmic (recursive) learning rules in a normal form game. It is shown that the system - the population frequencies - is globally stable for any arbitrary N-player normal form game, if the evolutionary process is algorithmic and the "birth...