Adaptive Punishment Algorithm Boosts Cooperation in Mixed-Motive Games

Researchers have introduced Adaptive Punishment for Cooperation (APC), a distributed method designed to promote cooperation in mixed-motive scenarios where self-interested agents often defect for immediate rewards. APC determines punishment intensity based on a dynamic punishment probability and the severity of defection, reducing costly and ineffective punishment while encouraging cooperation. The method uses a defection awareness module to accurately assess defection severity, with learning guided by game theory principles. This approach addresses the challenge of costly second-order altruism in peer punishment, balancing efficacy and cost to improve long-term gains and collective welfare. The work is detailed in a paper on arXiv (2605.24516).