# Evaluating Agents using Social Choice Theory

arXiv: https://arxiv.org/abs/2312.03121 · HTML: https://mlsanity.com/paper/2312.03121/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 7 of 7)

- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [OpenSpiel: A Framework for Reinforcement Learning in Games](https://mlsanity.com/paper/1908.09453/) (2019)
- [Open-Ended Learning Leads to Generally Capable Agents](https://mlsanity.com/paper/2107.12808/) (2021)
- [Human-Timescale Adaptation in an Open-Ended Task Space](https://mlsanity.com/paper/2301.07608/) (2023)
- [Human-Level Performance in No-Press Diplomacy via Equilibrium Search](https://mlsanity.com/paper/2010.02923/) (2020)
- [On the Limitations of Elo: Real-World Games, are Transitive, not Additive](https://mlsanity.com/paper/2206.12301/) (2022)
- [Game Theoretic Rating in N-player general-sum games with Equilibria](https://mlsanity.com/paper/2210.02205/) (2022)

## Built on by (most cited first, 11 of 11)

- [The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models](https://mlsanity.com/paper/2404.16019/) (2024)
- [Magnetic Preference Optimization: Achieving Last-iterate Convergence for Language Model Alignment](https://mlsanity.com/paper/2410.16714/) (2024)
- [Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia](https://mlsanity.com/paper/2512.03318/) (2025)
- [Re-evaluating Open-ended Evaluation of Large Language Models](https://mlsanity.com/paper/2502.20170/) (2025)
- [A Survey of RWKV](https://mlsanity.com/paper/2412.14847/) (2024)
- [Soft Condorcet Optimization for Ranking of General Agents](https://mlsanity.com/paper/2411.00119/) (2024)
- [AI Alignment From Social Choice Perspectives](https://mlsanity.com/paper/2606.21550/) (2026)
- [Game Arena: Strategic LLM Evaluation in Competitive Environments](https://mlsanity.com/paper/2609.31473/) (2026)
- [Beyond Arrow: From Impossibility to Possibilities in Multi-Criteria Benchmarking](https://mlsanity.com/paper/2602.07593/) (2026)
- [How Hard is it to Rig a Benchmark? A Social Choice Analysis of Leaderboard Robustness](https://mlsanity.com/paper/2605.23628/) (2026)
- [Context-dependent agent evaluation with orthogonal equilibrium learning](https://mlsanity.com/paper/2609.31897/) (2026)

## Abstract (arXiv)

We argue that many general evaluation problems can be viewed through the lens of voting theory. Each task is interpreted as a separate voter, which requires only ordinal rankings or pairwise comparisons of agents to produce an overall evaluation. By viewing the aggregator as a social welfare function, we are able to leverage centuries of research in social choice theory to derive principled evaluation frameworks with axiomatic foundations. These evaluations are interpretable and flexible, while avoiding many of the problems currently facing cross-task evaluation. We apply this Voting-as-Evaluation (VasE) framework across multiple settings, including reinforcement learning, large language models, and humans. In practice, we observe that VasE can be more robust than popular evaluation frameworks (Elo and Nash averaging), discovers properties in the evaluation data not evident from scores alone, and can predict outcomes better than Elo in a complex seven-player game. We identify one particular approach, maximal lotteries, that satisfies important consistency properties relevant to evaluation, is computationally efficient (polynomial in the size of the evaluation data), and identifies game-theoretic cycles.