Incentivising cooperation by judging a group's performance by its weakest member in neuroevolution and reinforcement learning

Jory Schossau¹Bamshad Shirmohammadi²Arend Hintze^2*

• ¹Michigan State University, East Lansing, Michigan, United States
• ²Dalarna University, Falun, Dalarna, Sweden

Autonomous agents that act with each other on behalf of humans are becoming more common in many social domains, such as customer service, transportation, and health care. In such social situations, greedy strategies can reduce the positive outcome for all agents, such as leading to stop-and-go traffic on highways or causing a denial of service on a communications channel.Instead, we desire autonomous decision-making for efficient performance while also considering the equitability of the group to avoid these pitfalls. Unfortunately, in complex situations, it is far easier to design machine learning objectives for selfish strategies than for equitable behaviors.Here we present a simple way to reward groups of agents in both evolution and reinforcement learning domains by the performance of their weakest member. We show how this yields "fairer" more equitable behavior while also maximizing individual outcomes, and we show the relationship to biological selection mechanisms of group-level selection and inclusive fitness theory.