In strategic situations, people, firms, or nations care about what others are likely to do. Examples of such situations include bargaining, business decisions which require coordinating various activities, or 'signaling games' in which the actions people take signal something about their abilities or intentions. In the last few decades, a large body of mathematical 'game theory' has developed about how people will make choices in these situations. However, these theories generally assume that people know or can figure out how other people in the strategic situation are likely to behave. In fact, people usually figure out what others are likely to do by learning from experience. Our project proposes a general theory of how this learning occurs. The theory combines two very different forces - 'reinforcement,' which means that successful strategies will be repeated, and 'belief learning,' which means that players keep track of what other people have done to figure out what those people will do in the future, then they choose strategies which will give the biggest payoff if their guesses are right. These different types of learning were thought to be different for about 50 years. In earlier NSF-funded research, we discovered that the two theories are actually special kinds of a single kind of learning, 'experience weighted attraction' (EWA) learning. The current research proposes to extend the EWA theory in three ways -- to incorporate the obvious fact that different people may learn in different ways; to extend the theory to cases where people aren't sure what the payoffs from different choices are (which is of course more realistic); and to allow the possibility that people realize, as they learn about what their opponents do, that their opponents are learning also. When the extensions are incorporated we will have a very general theory of learning which can explain the way people bargain, coordinate, and signal to each other changes over time in response to experience.
