mlsanityData to 14 Oct 2026

Procedural Fairness in Multi-Agent Bandits

arXiv 2601.10600 ↗First posted 15 Jan 2026cs.MA cs.AI cs.GTcited by 0

Not on the map. Why?

Abstract (arXiv)

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 procedural fairness as equal voice and formalize it for MA-MABs as equal representation within a policy over arms, using a core-stable Nash welfare objective based on representation rather than utility. Empirical results confirm that fairness notions based on optimizing for outcomes sacrifice equal voice and representation, while the sacrifice in outcome-based 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 40 references that are in the map. First posted on arXiv 15 Jan 2026.

Builds on

16 of 40 references.

References · 16

  1. 2016Fairness in Learning: Classic and Contextual Banditscited by 168
  2. 2019Achieving Fairness in the Stochastic Multi-armed Bandit Problemcited by 78
  3. 2020Fair Algorithms for Multi-Agent Multi-Armed Banditscited by 41
  4. 2017Multi-objective Bandits: Optimizing the Generalized Gini Indexcited by 28
  5. 2018Fair Allocation of Indivisible Public Goodscited by 22
  6. 2016Fair Public Decision Makingcited by 21
  7. 2016The Core of the Participatory Budgeting Problemcited by 14
  8. 2022Pareto Regret Analyses in Multi-objective Multi-armed Banditcited by 13
  9. 2014Eliciting Single-Peaked Preferences Using Comparison Queriescited by 12
  10. 2022An Efficient Algorithm for Fair Multi-Agent Multi-Armed Bandit with Low Regretcited by 10
  11. 2019Equitable Allocations of Indivisible Goodscited by 9
  12. 2024No-Regret Learning for Fair Multi-Agent Social Welfare Optimizationcited by 8
4 more
  1. 2024Honor Among Bandits: No-Regret Learning for Online Fair Divisioncited by 7
  2. 2020Funding Public Projects: A Case for the Nash Product Rulecited by 4
  3. 2025Improved Regret Bounds for Online Fair Division with Bandit Learningcited by 3
  4. 2024Fair Lotteries for Participatory Budgetingcited by 2

Built on by

Not cited yet.