Procedural Fairness in Multi-Agent Bandits

TL;DR

This paper introduces procedural fairness in multi-agent bandits, emphasizing equal decision-making power and normative fairness, contrasting outcome-based fairness, and providing a framework for implementation.

Contribution

It proposes a new fairness objective focusing on process and decision-making power, supported by empirical and theoretical analysis in multi-agent bandit settings.

Findings

Procedural fairness maintains equal voice among agents.

Outcome-based fairness sacrifices some fairness for efficiency.

Different fairness notions reflect incompatible values.

Abstract

In the context of multi-agent multi-armed bandits (MA-MAB), fairness is often reduced to outcomes: maximizing welfare, reducing inequality, or balancing utilities. However, evidence in psychology, economics, and Rawlsian theory suggests that fairness is also about process and who gets a say in the decisions being made. We introduce a new fairness objective, procedural fairness, which provides equal decision-making power for all agents, lies in the core, and provides for proportionality in outcomes. Empirical results confirm that fairness notions based on optimizing for outcomes sacrifice equal voice and representation, while the sacrifice in outcome-based fairness objectives (like equality and utilitarianism) is minimal under procedurally fair policies. We further prove that different fairness notions prioritize fundamentally different and incompatible values, highlighting that fairness requires explicit normative choices. This paper argues that procedural legitimacy deserves greater focus as a fairness objective, and provides a framework for putting procedural fairness into practice.