# Resolving social dilemmas with minimal reward transfer

arXiv: https://arxiv.org/abs/2310.12928 · HTML: https://mlsanity.com/paper/2310.12928/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2023.

## Built on by (most cited first, 6 of 6)

- [A Review of Cooperation in Multi-agent Learning](https://mlsanity.com/paper/2312.05162/) (2023)
- [Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts](https://mlsanity.com/paper/2407.18074/) (2024)
- [Mechanism Design Is Not Enough: Prosocial Agents for Cooperative AI](https://mlsanity.com/paper/2605.08426/) (2026)
- [Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2405.02654/) (2024)
- [Preference-based opponent shaping in differentiable games](https://mlsanity.com/paper/2412.03072/) (2024)
- [Hybrid Human-Agent Social Dilemmas in Energy Markets](https://mlsanity.com/paper/2603.11834/) (2026)

## Abstract (arXiv)

Social dilemmas present a significant challenge in multi-agent cooperation because individuals are incentivised to behave in ways that undermine socially optimal outcomes. Consequently, self-interested agents often avoid collective behaviour. In response, we formalise social dilemmas and introduce a novel metric, the general self-interest level, to quantify the disparity between individual and group rationality in such scenarios. This metric represents the maximum proportion of their individual rewards that agents can retain while ensuring that a social welfare optimum becomes a dominant strategy. Our approach diverges from traditional concepts of altruism, instead focusing on strategic reward redistribution. By transferring rewards among agents in a manner that aligns individual and group incentives, rational agents will maximise collective welfare while pursuing their own interests. We provide an algorithm to compute efficient transfer structures for an arbitrary number of agents, and introduce novel multi-player social dilemma games to illustrate the effectiveness of our method. This work provides both a descriptive tool for analysing social dilemmas and a prescriptive solution for resolving them via efficient reward transfer contracts. Applications include mechanism design, where we can assess the impact on collaborative behaviour of modifications to models of environments.