Chapter 66 Learning and Equilibrium in Games This chapter describes a very general approach to learning in games: experience weighted attraction (EWA) learning. This approach strives to explain for every choice in an experiment how that choice arose from players previous behavior and experience using a general model which can be applied to most games with minimal customization and which predicts well out of sample. Sophisticated EWA includes important equilibrium concepts and many other learning models as special cases. The model therefore allows one stop shopping for learning about the latest statistical comparisons of many different learning and equilibrium models . The model can also be adapted to field applications in which strategies and payoffs are often poorly specified. The EWA model presented is a simplification (as are all the other adaptive models) because it does not permit players to anticipate learning by others. Omitting anticipation logically implies that players do not use information about the payoffs of other players and that whether players are matched together repeatedly or randomly re matched should not matter.