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Incentivising cooperation by judging a group's performance by its weakest member in neuroevolution and reinforcement
Jory Schossau1, Bamshad Shirmohammadi2, Arend Hintze1,2
1BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI, United States.
Frontiers in Robotics and AI
|August 11, 2025
Summary
Rewarding autonomous agents based on their weakest member promotes fairness and improves group performance. This weakest-link strategy encourages equitable outcomes, benefiting both the collective and individual agents.
Area of Science:
- Artificial Intelligence
- Multi-agent Systems
- Behavioral Economics
Background:
- Autonomous agents are increasingly deployed in social domains, necessitating decision-making strategies that balance individual goals with group welfare.
- Individually greedy strategies can lead to suboptimal group performance, as seen in traffic congestion and network failures.
- A need exists for methods that promote equitable outcomes among autonomous agents.
Purpose of the Study:
- To introduce a novel reward system for autonomous agents that prioritizes group equitability.
- To investigate the impact of a "weakest-link" reward metric on agent behavior within evolutionary and reinforcement learning frameworks.
- To demonstrate how aligning agent incentives with the weakest member's performance can enhance collective outcomes.
Main Methods:
- Implemented a "weakest-link" reward system in evolutionary and reinforcement learning.
- Agents' rewards were explicitly based on the performance of the group's least successful member.
- Compared outcomes against individually optimized reward strategies.
Main Results:
- The weakest-member reward system effectively promoted equitable behavior among autonomous agents.
- Agents utilizing this system balanced collective benefit with individual performance, leading to fairer group outcomes.
- This approach enhanced overall group efficiency, stability, and individual agent success compared to selfish strategies.
Conclusions:
- The weakest-link reward framework successfully encourages equitable and cooperative behavior in autonomous agents.
- This methodology offers a scalable approach to optimizing multi-agent systems for both fairness and performance.
- Findings suggest that incentive structures focusing on collective well-being can improve outcomes in complex social domains.
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