 Now that we have seen the prominent approaches to both norm emergence and norm stability, we can turn to some general interpretive considerations of evolutionary models. An evolutionary approach is based on the principle that strategies with higher current payoffs will be retained, while strategies that lead to failure will be abandoned. The success of a strategy is measured by its relative frequency in the population at any given time. This is most easily seen in a game theoretic framework. A game is repeated a finite number of times with randomly selected opponents. After each round of the game, the actual payoffs and strategies of the players become public knowledge; on the basis of this information, each player adjusts her strategy for the next round. The payoff to an individual player depends on her choice as well as on the choices of the other players in the game, and players are rational in the sense that they are payoff-maximizers. In an evolutionary model, however, players learn and adapt in a non-Bayesian way, that is, they do not condition on past experience using Bayes’ Rule. In this sense, they are not typical rational learners (Nachbar 1990; Binmore &amp; Samuelson 1992).