Learning Collective Action under Risk Diversity

TL;DR

This paper explores how risk diversity among agents in collective risk dilemmas impacts cooperation, revealing that increased risk diversity reduces overall cooperation and causes asymmetrical contribution behaviors, with RL agents tending towards fairness.

Contribution

It is the first study to analyze the effects of risk diversity on multi-agent learning in collective risk dilemmas, highlighting new challenges and potential solutions.

Findings

Risk diversity decreases overall cooperation.

High-risk individuals contribute more, low-risk contribute less.

RL agents tend to adopt fairer contribution strategies.

Abstract

Collective risk dilemmas (CRDs) are a class of n-player games that represent societal challenges where groups need to coordinate to avoid the risk of a disastrous outcome. Multi-agent systems incurring such dilemmas face difficulties achieving cooperation and often converge to sub-optimal, risk-dominant solutions where everyone defects. In this paper we investigate the consequences of risk diversity in groups of agents learning to play CRDs. We find that risk diversity places new challenges to cooperation that are not observed in homogeneous groups. We show that increasing risk diversity significantly reduces overall cooperation and hinders collective target achievement. It leads to asymmetrical changes in agents' policies -- i.e. the increase in contributions from individuals at high risk is unable to compensate for the decrease in contributions from individuals at low risk -- which overall reduces the total contributions in a population. When comparing RL behaviors to rational individualistic and social behaviors, we find that RL populations converge to fairer contributions among agents. Our results highlight the need for aligning risk perceptions among agents or develop new learning techniques that explicitly account for risk diversity.