Abstract
This doctoral thesis contains three essays, all studying different aspects of learning and bounded rationality in strategic interactions. The first chapter presents and tests a theory of human behavior in one-shot games due to the rational use of heuristics. It shows that by assuming that humans use simple heuristics adapted to the environment, we can accurately predict strategic behavior and how it changes across environments. The second chapter presents a simple learning model that can predict average cooperation rates across different treatments of the indefinitely repeated prisoner's dilemma. It is evaluated and tested on an extensive data set containing data from 17 papers and shown to predict cooperation rates at least as well as more complicated models and machine learning algorithms. The last chapter is a theoretical investigation of a recency weighted sampling dynamics designed to capture the long-run stochastic stability of conventions.