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Attitude as An Interactional Concept:

In an evolutionary approach behavior is adaptive, so that a strategy that did work well in the past is retained, and one that fared poorly will be changed. This can be interpreted in two ways: either the evolution of strategies is the consequence of adaptation by individual agents, or the evolution of strategies is understood as the differential reproduction of agents based on their success rates in their interactions. The former interpretation assumes short timescales for interactions: many iterations of the game over time thus represent no more than a few decades in time in total. The latter interpretation assumes rather longer timescales: each instance of strategy adjustment represents a new generation of agents coming into the population, with the old generation dying simultaneously. Let us consider the ramifications of each interpretation in turn.

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.