What to Remember for Cooperation: The Content and Timescale of Memory in Sequential Social Dilemmas

In shared environments, self-interested individuals repeatedly pursue their own objectives, yet locally rational choices can erode cooperation and reduce collective welfare. Sequential social dilemmas (SSDs) provide a formal setting for studying this tension. Existing approaches in SSDs often promote cooperation by modifying incentives or modeling opponents’ learning. A more basic question is whether and under what conditions interaction history itself can support learned cooperation. Here, we augment agents’ current observations with compact summaries of past interactions and systematically examine how memory representation and horizon shape conditional cooperation. Theoretically, we establish sufficient conditions under which a memory-based strategy achieves a strict local return advantage over any memoryless decision rule. Experiments in Coin Game and CleanUp-lite show that memory representations that link outcomes to the agents that produced them improve cooperation. Extending the memory horizon can further improve cooperation, whereas shuffling events within longer memory sequences weakens it.