Learning about Initial Play Determines Average Cooperation in Repeated Games
Title: Learning about Initial Play Determines Average Cooperation in Repeated Games
(joint with Drew Fudenberg (MIT))
Abstract: We propose a simple learning model to predict cooperation rates across treatments in the experimental play of the indefinitely repeated prisoner’s dilemma. Although the model has only 4 parameters, it performs almost as well as more complicated models and machine learning algorithms. We find that learning has the most effect on choices in the initial round of each supergame and that whether cooperation rises or falls in the course of a session depends on the way the initial choices in a supergame determine play in subsequent rounds. Our results also explain past findings on the impact of the risk dominance considerations.
A current working paper version can be found on the website: gustavkarreskog.com