This thesis consists of five papers, presented as separate chapters within three parts: Industrial Organization, Evolutionary Game Theory and Game Theory. The common basis of these parts is research in the field of game theory and more specifically, equilibrium selection in different frameworks. The first part, Industrial Organization, consists of one paper co-authored with Prajit Dutta and Jörgen Weibull. Forward-looking consumers are analysed in a Bertrand framework. It is assumed that if firms can anticipate a price war and act accordingly, so can consumers. The second part, Evolutionary Game Theory, contains three chapters. All models in these papers are based on Young’s (1993, 1998) approach. In Chapter 2, the Saez Marti and Weibull’s (1999) model is generalized from the Nash Demand Game to generic two-player games. In Chapter 3, co-authored with Jens Josephson, a special set of stochastically stable states is introduced, minimal construction, which is the long-run prediction under imitation behavior in normal form games. In Chapter 4, best reply and imitation rules are considered on extensive form games with perfect information. / Diss. Stockholm : Handelshögsk., 2001

This thesis contains four essays that analyze the behaviors that evolve when populations of boundedly rational individuals interact strategically for a long period of time. Individuals are boundedly rational in the sense that their strategy choices are determined by simple rules of adaptation -- learning rules. Convergence results for general finite games are first obtained in a homogenous setting, where all populations consist either of stochastic imitators, who almost always imitate the most successful strategy in a sample from their own population's past strategy choices, or stochastic better repliers, who almost always play a strategy that gives at least as high expected payoff as a sample distribution of all populations' past play. Similar results are then obtained in a heterogeneous setting, where both of these learning rules are represented in each population. It is found that only strategies in certain sets are played in the limit, as time goes to infinity and the mutation rate tends to zero. Sufficient conditions for the selection of a Pareto efficient such set are also provided. Finally, the analysis is extended to natural selection among learning rules. The question is whether there exists a learning rule that is evolutionarily stable, in the sense that a population employing this learning rule cannot be invaded by individuals using a different rule. Monte Carlo simulations for a large class of learning rules and four different games indicate that only a learning rule that takes full account of hypothetical payoffs to strategies that are not played is evolutionarily stable in almost all cases. / Diss. Stockholm : Handelshögsk., 2001