Game Theory in Nature: From Optimality to Equilibrium

This workshop explores nature as a massive distributed learning system to bridge the gap between biological adaptation and multi-agent machine learning. While modern AI often relies on centralized optimization and global objectives, natural systems like microbial colonies and animal societies attain stability and collective intelligence through local strategic interactions. As the machine learning community moves toward decentralized environments and uses large scale models for ecological data, understanding the tension between individual goals and population level stability becomes critical. By investigating core themes such as evolutionary stability, environmental feedback, and emergent communication, this workshop aims to identify biological mechanisms that can inform the design of more efficient and scalable artificial systems. We bring together researchers in game theory, animal behavior, and machine learning to address the challenges of collective decision making and systemic robustness in the face of the current explosion in ecological sensor data.