---
title: "Quantifying the Self-Interest Level of Markov Social Dilemmas"
authors:
  - "Richard Willis"
  - "Yali Du"
  - "Joel Z. Leibo"
  - "Michael Luck"
doi: "10.24963/ijcai.2025/33"
url: "https://ijcai.org/proceedings/2025/33"
pdf: "https://ijcai.org/proceedings/2025/0033.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "284-292"
track: "Main Track"
keywords:
  - "Agent-based and Multi-agent Systems: MAS: Coordination and cooperation"
  - "Game Theory and Economic Paradigms: GTEP: Mechanism design"
  - "Game Theory and Economic Paradigms: GTEP: Cooperative games"
  - "Agent-based and Multi-agent Systems: MAS: Multi-agent learning"
---

# Quantifying the Self-Interest Level of Markov Social Dilemmas

Richard Willis, Yali Du, Joel Z. Leibo, Michael Luck

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 284-292. DOI: [10.24963/ijcai.2025/33](https://doi.org/10.24963/ijcai.2025/33)

## Abstract

This paper introduces a novel method for estimating the self-interest level of Markov social dilemmas.
We extend the concept of self-interest level from normal-form games to Markov games, providing a quantitative measure of the minimum reward exchange required to align individual and collective interests.
We demonstrate our method on three environments from the Melting Pot suite, representing either common-pool resources or public goods.
Our results illustrate how reward exchange can enable agents to transition from selfish to collective equilibria in a Markov social dilemma.
This work contributes to multi-agent reinforcement learning by providing a practical tool for analysing complex, multistep social dilemmas.
Our findings offer insights into how reward structures can promote or hinder cooperation, with potential applications in areas such as mechanism design.