Sequential Social Dilemma

Sequential social dilemmas explore how self-interested agents make decisions over time in situations where individual gains conflict with collective well-being. Current research focuses on developing reinforcement learning algorithms, often incorporating economic principles like principal-agent theory and Pigovian taxation, to incentivize cooperation and achieve optimal outcomes. These methods are being tested in various multi-agent environments, including those with heterogeneous agent behaviors and complex interaction structures, such as circular dependencies. This research is significant for advancing our understanding of cooperation in complex systems and has potential applications in areas like AI coordination, resource management, and the design of incentive mechanisms.