 In the first interpretation, we have agents who employ learning rules that are less than fully rational, as defined by what a Bayesian agent would have, both in terms of computational ability and memory. As such, these rules tend to be classified as adaptive strategies: they are reacting to a more limited set of data, with lower cognitive resources than what a fully rational learner would possess. However, there are many different adaptive mechanisms we may attribute to the players. One realistic adaptive mechanism is learning by trial and error; another plausible mechanism is imitation: those who do best are observed by others who subsequently emulate their behavior (Hardin 1982). Reinforcement learning is another class of adaptive behavior, in which agents tweak their probabilities of choosing one strategy over another based on the payoffs they just received.