Algorithmic Cooperation

Abstract

Algorithms play an increasingly important role in economic situations. Often these situations are strategic, where the artificial intelligence may or may not be cooperative. We study the determinants and forms of algorithmic cooperation in the infinitely repeated prisoner's dilemma. We run a sequence of computational experiments, accompanied by additional repeated prisoner's dilemma games played by humans in the lab. We find that the same factors that increase human cooperation largely also determine the cooperation rates of algorithms. However, algorithms tend to play different strategies than humans. Algorithms cooperate less than humans when cooperation is very risky or not incentive compatible.

Keywords: Artificial Intelligence, Cooperation, Large language models, Q-learning, Repeated Prisoner's Dilemma

JEL Classification: C72, C73, C92, D83