This dissertation consists of three independent parts. In the first part, I examine simple binary decisions on whether to adopt a particular behavior, practice, or technology. Agents rely on the decisions made by those surrounding them to inform their own. In particular, I am interested in the relationship between the individual decision and the spread of uniform behavior across a large number of decision makers. The main contribution of the chapter is to propose an analytical framework for the macroscopic diffusion process that accounts for both the decision and the socio-structural circumstances of the agents.

The second part studies the problem of cooperative behavior emerging in an environment where individual behavior and interaction structures both evolve. Players not only learn which strategy to adopt by mimicking the strategy of the best performing players they observe, but also choose with whom to interact on the basis of cost-benefit analysis. We find that scaleable cooperation—that is, high levels of cooperation in large populations—can be achieved in sparse networks, assuming that individuals are able to sever ties unilaterally and new ties can only be created with the mutual consent of both parties.

The third part concerns a class of problems in non-cooperative game theory that is subject to multiple, Pareto-ranked equilibria, where decision makers are faced with strategic uncertainty. I investigate how intervention strategies based on a Schelling-inspired tipping mechanism can efficiently alter the incentives of a small subset of individuals to trigger system-wide coordination on the Pareto-superior outcome. I then apply this framework to two examples, the provision of airline security and reward schemes in organizations.