MAFE: Multi-Agent Fair Environments for Decision-Making Systems

2025-02-25

AI Summary

Existing fairness research predominantly focuses on static, single-agent settings, failing to capture cumulative unfairness arising from dynamic, multi-stakeholder interactions in real-world social systems. To address this gap, we propose the Multi-Agent Fairness Environment (MAFE) paradigm—a novel framework for modeling long-term fairness as an emergent property of multi-agent cooperation and competition. MAFE comprises three data-driven, reproducible social-system simulation environments, integrating finite real-world data modeling, time-varying fairness metrics, and causal intervention analysis. Empirical evaluation demonstrates that MAFE significantly enhances the comparability, interpretability, and real-world transferability of fairness algorithm assessments. As a benchmark platform, it establishes foundational infrastructure and standardized evaluation criteria for multi-agent fairness learning—enabling rigorous, longitudinal study of fairness dynamics in complex, interactive systems.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Fair Division
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Fairness constraints applied to machine learning (ML) models in static contexts have been shown to potentially produce adverse outcomes among demographic groups over time. To address this issue, emerging research focuses on creating fair solutions that persist over time. While many approaches treat this as a single-agent decision-making problem, real-world systems often consist of multiple interacting entities that influence outcomes. Explicitly modeling these entities as agents enables more flexible analysis of their interventions and the effects they have on a system's underlying dynamics. A significant challenge in conducting research on multi-agent systems is the lack of realistic environments that leverage the limited real-world data available for analysis. To address this gap, we introduce the concept of a Multi-Agent Fair Environment (MAFE) and present and analyze three MAFEs that model distinct social systems. Experimental results demonstrate the utility of our MAFEs as testbeds for developing multi-agent fair algorithms.

Problem

• Address fairness in multi-agent decision-making systems
• Model distinct social systems for fairness analysis
• Develop testbeds for multi-agent fair algorithms

Innovation

• Multi-Agent Fair Environments
• Fairness in dynamic systems
• Realistic social system modeling