# Incentivising cooperation by rewarding the weakest member

arXiv: https://arxiv.org/abs/2212.00119 · HTML: https://mlsanity.com/paper/2212.00119/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 4 Oct 2022.

## Builds on (most cited first, 3 of 3)

- [Shapley Q-value: A Local Reward Approach to Solve Global Reward Games](https://mlsanity.com/paper/1907.05707/) (2019)
- [Faster Deep Q-learning using Neural Episodic Control](https://mlsanity.com/paper/1801.01968/) (2018)
- [Markov Brains: A Technical Introduction](https://mlsanity.com/paper/1709.05601/) (2017)

## Abstract (arXiv)

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 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.