 We begin with a sketch of the framework for modelling learning rules, and then give several examples of individual learning rules and the evolutionary dynamics which result at the population-level. For simplicity, all of the mathematical details are suppressed in the following discussion; for elaboration, see Sandholm (2010). (Sandholm’s framework, in general, allows for games featuring multiple nonoverlapping populations. One of the simplifying assumptions made here is that there is only one population.) Assume that we have a symmetric game \(G\) with \(n\) strategies \(S_1,\dots, S_n\). (A symmetric game is one where the payoff for playing a particular strategy depends only on the strategies used by the other players, not on who is playing what strategy.) In addition, assume that the only possible information which could be taken into account by individuals is the following: (1) the current state of the population, represented as a distribution over the pure strategies of the game, and (2) the expected payoffs of each strategy, given the current state of the population. 