Beyond Arrow's Impossibility: Fairness as an Emergent Property of Multi-Agent Collaboration

• URL: http://arxiv.org/abs/2604.13705v1
• Date: Wed, 15 Apr 2026 10:34:35 GMT
• Title: Beyond Arrow's Impossibility: Fairness as an Emergent Property of Multi-Agent Collaboration
• Abstract summary: As large language models become agentic, we propose that fairness emerges through interaction and exchange.<n>We study this via a controlled hospital triage framework in which two agents negotiate over three structured debate rounds.
• Score: 3.4539478661465766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fairness in language models is typically studied as a property of a single, centrally optimized model. As large language models become increasingly agentic, we propose that fairness emerges through interaction and exchange. We study this via a controlled hospital triage framework in which two agents negotiate over three structured debate rounds. One agent is aligned to a specific ethical framework via retrieval-augmented generation (RAG), while the other is either unaligned or adversarially prompted to favor demographic groups over clinical need. We find that alignment systematically shapes negotiation strategies and allocation patterns, and that neither agent's allocation is ethically adequate in isolation, yet their joint final allocation can satisfy fairness criteria that neither would have reached alone. Aligned agents partially moderate bias through contestation rather than override, acting as corrective patches that restore access for marginalized groups without fully converting a biased counterpart. We further observe that even explicitly aligned agents exhibit intrinsic biases toward certain frameworks, consistent with known left-leaning tendencies in LLMs. We connect these limits to Arrow's Impossibility Theorem: no aggregation mechanism can simultaneously satisfy all desiderata of collective rationality, and multi-agent deliberation navigates rather than resolves this constraint. Our results reposition fairness as an emergent, procedural property of decentralized agent interaction, and the system rather than the individual agent as the appropriate unit of evaluation.

Related papers

• Mechanism Design for Alignment and Control [0.6372261626436676]
  We develop a framework for mechanism design with AI agents whose alignment and capabilities are unknown.<n>We want such agents to act on our behalf so mechanisms must incentivize both honesty and obedience.
  arXiv  Detail & Related papers  (2026-09-01T17:57:50Z)
• TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning [52.67756371231985]
  Reinforcement learning with verifiable rewards (RLVR) is a promising approach for enhancing reasoning and agentic behavior in large language models.<n>We introduce Tree Rollout Allocation for Contrastive Exploration (TRACE), a unified rollout allocation framework.<n>Technically, TRACE allocates rollout budget to both prompt roots and intermediate prefixes that are most likely to yield mixed terminal rewards.
  arXiv  Detail & Related papers  (2026-06-09T17:16:03Z)
• Scalable Constrained Multi-Agent Reinforcement Learning via State Augmentation and Consensus for Separable Dynamics [4.278821961637313]
  We present a distributed approach for constrained Multi-Agent Reinforcement Learning (MARL)<n>Our method targets systems where agents have separable dynamics but must coordinate to satisfy global resource constraints.<n>We show that lightweight neighbor-to-neighbor consensus over Lagrange multipliers suffices for globally coordinated constraint enforcement.
  arXiv  Detail & Related papers  (2026-05-28T18:37:16Z)
• Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment [59.536125286960186]
  Multi-agent frameworks assigning specialized roles are increasingly adopted to enable self-reflection and mutual auditing.<n>We find it simultaneously induces a human-like cognitive bias known as Actor-Observer Asymmetry.<n>ReTAS guides agents to synthesize conflicting viewpoints into an objective consensus.
  arXiv  Detail & Related papers  (2026-04-21T15:05:58Z)
• The Consensus Trap: Rescuing Multi-Agent LLMs from Adversarial Majorities via Token-Level Collaboration [32.17285446393425]
  Response-level aggregation collapses when corrupted agents form a local majority.<n>We propose the Token-Level Round-Robin (RR) Collaboration, where agents sequentially interleave generation within a shared auto-regressive context.<n>We prove that RR maintains robust accuracy well beyond this critical threshold.
  arXiv  Detail & Related papers  (2026-04-18T20:31:37Z)
• AffectAgent: Collaborative Multi-Agent Reasoning for Retrieval-Augmented Multimodal Emotion Recognition [62.16431420189863]
  LLM-based multimodal emotion recognition relies on static parametric memory and often hallucinates when interpreting nuanced affective states.<n>We introduce AffectAgent, an affect-oriented multi-agent retrieval-augmented generation framework.<n>AffectAgent comprises three jointly optimized agents, namely a query planner, an evidence filter, and an emotion generator.
  arXiv  Detail & Related papers  (2026-04-14T13:49:19Z)
• When Is Collective Intelligence a Lottery? Multi-Agent Scaling Laws for Memetic Drift in LLMs [19.42697228981973]
  We show that in naming games, even when no individual agent favors any label a priori, populations rapidly break symmetry and reach consensus.<n>We reveal the mechanism by introducing a minimal model, Quantized Simplex Gossip (QSG), and trace the microscopic origin of this agreement to mutual in-context learning.<n>We derive scaling laws for drift-induced polarization as a function of population size, communication bandwidth, in-context adaptation rate, and agents' internal uncertainty.
  arXiv  Detail & Related papers  (2026-03-25T18:00:29Z)
• The Coordination Gap: Alternation Metrics for Temporal Dynamics in Multi-Agent Battle of the Exes [0.0]
  We study a BoE-derived multi-agent variant of the Battle of the Exes as a Markov game.<n>We show that high aggregate payoffs can coexist with poor temporal coordination.
  arXiv  Detail & Related papers  (2026-03-06T00:43:53Z)
• Intersectional Fairness via Mixed-Integer Optimization [2.664154603948153]
  We argue that true fairness requires addressing bias at the intersections of protected groups.<n>We propose a unified framework that leverages Mixed-Integer Optimization (MIO) to train intersectionally fair and intrinsically interpretable classifiers.
  arXiv  Detail & Related papers  (2026-01-27T13:29:25Z)
• Emergent Coordination in Multi-Agent Language Models [2.504366738288215]
  We introduce an information-theoretic framework to test whether multi-agent systems show signs of higher-order structure.<n>This information decomposition lets us measure whether dynamical emergence is present in multi-agent LLM systems.<n>We apply our framework to experiments using a simple guessing game without direct agent communication.
  arXiv  Detail & Related papers  (2025-10-05T11:26:41Z)
• When Disagreements Elicit Robustness: Investigating Self-Repair Capabilities under LLM Multi-Agent Disagreements [56.29265568399648]
  We argue that disagreements prevent premature consensus and expand the explored solution space.<n>Disagreements on task-critical steps can derail collaboration depending on the topology of solution paths.
  arXiv  Detail & Related papers  (2025-02-21T02:24:43Z)
• Pure Exploration under Mediators' Feedback [63.56002444692792]
  Multi-armed bandits are a sequential-decision-making framework, where, at each interaction step, the learner selects an arm and observes a reward. We consider the scenario in which the learner has access to a set of mediators, each of which selects the arms on the agent's behalf according to a and possibly unknown policy. We propose a sequential decision-making strategy for discovering the best arm under the assumption that the mediators' policies are known to the learner.
  arXiv  Detail & Related papers  (2023-08-29T18:18:21Z)
• On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring [105.13668993076801]
  A central problem in the theory of multi-agent reinforcement learning (MARL) is to understand what structural conditions and algorithmic principles lead to sample-efficient learning guarantees. We study this question in a general framework for interactive decision making with multiple agents. We show that characterizing the statistical complexity for multi-agent decision making is equivalent to characterizing the statistical complexity of single-agent decision making.
  arXiv  Detail & Related papers  (2023-05-01T06:46:22Z)
• VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback [104.06766271716774]
  We study a multi-round welfare-maximising mechanism design problem in instances where agents do not know their values. We first define three notions of regret for the welfare, the individual utilities of each agent and that of the mechanism. Our framework also provides flexibility to control the pricing scheme so as to trade-off between the agent and seller regrets.
  arXiv  Detail & Related papers  (2020-04-19T18:00:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.