mlsanityData to 7 Oct 2026

Welfare Diplomacy: Benchmarking Language Model Cooperation

arXiv 2310.08901 ↗First posted 13 Oct 2023cs.MA cs.AI cs.CLcited by 43

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Cited by 27 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The growing capabilities and increasingly widespread deployment of AI systems necessitate robust benchmarks for measuring their cooperative capabilities. Unfortunately, most multi-agent benchmarks are either zero-sum or purely cooperative, providing limited opportunities for such measurements. We introduce a general-sum variant of the zero-sum board game Diplomacy -- called Welfare Diplomacy -- in which players must balance investing in military conquest and domestic welfare. We argue that Welfare Diplomacy facilitates both a clearer assessment of and stronger training incentives for cooperative capabilities. Our contributions are: (1) proposing the Welfare Diplomacy rules and implementing them via an open-source Diplomacy engine; (2) constructing baseline agents using zero-shot prompted language models; and (3) conducting experiments where we find that baselines using state-of-the-art models attain high social welfare but are exploitable. Our work aims to promote societal safety by aiding researchers in developing and assessing multi-agent AI systems. Code to evaluate Welfare Diplomacy and reproduce our experiments is available at https://github.com/mukobi/welfare-diplomacy.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). Builds on 42 of its 72 references that are in the map. First posted on arXiv 13 Oct 2023.

Builds on

42 of 72 references.

References · 42

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,053
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  2. 2019On the Utility of Learning about Humans for Human-AI Coordinationcited by 345
  3. 2023Playing repeated games with Large Language Modelscited by 213
  4. 2017Deal or No Deal? End-to-End Learning for Negotiation Dialoguescited by 202
  5. 2023Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedbackcited by 202
  6. 2019The Hanabi Challenge: A New Frontier for AI Researchcited by 194
  7. 2023AI Deception: A Survey of Examples, Risks, and Potential Solutionscited by 185
  8. 2020Open Problems in Cooperative AIcited by 167
  9. 2023Model evaluation for extreme riskscited by 167
  10. 2018Decoupling Strategy and Generation in Negotiation Dialoguescited by 156
  11. 2023Taken out of context: On measuring situational awareness in LLMscited by 131
  12. 2021Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Potcited by 97
  13. 2023Frontier AI Regulation: Managing Emerging Risks to Public Safetycited by 95
  14. 2023Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiationcited by 94
  15. 2020The Turking Test: Can Language Models Understand Instructions?cited by 82
  16. 2023Strategic Reasoning with Language Modelscited by 64
  17. 2023Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmarkcited by 62
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  19. 2017Cooperating with Machinescited by 39
  20. 2019No Press Diplomacy: Modeling Multi-Agent Gameplaycited by 37
  21. 2021No-Press Diplomacy from Scratchcited by 31
  22. 2020Too many cooks: Bayesian inference for coordinating multi-agent collaborationcited by 29
  23. 2020Learning to Play No-Press Diplomacy with Best Response Policy Iterationcited by 25
  24. 2022A Hazard Analysis Framework for Code Synthesis Large Language Modelscited by 22
  25. 2023Towards the Scalable Evaluation of Cooperativeness in Language Modelscited by 12
  26. 2020Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Gamescited by 9
  27. 2021Normative Disagreement as a Challenge for Cooperative AIcited by 7
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Citing papers per year: 2023: 1; 2024: 15; 2025: 20; 2026: 7.

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  27. 2025Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Reviewcited by 0
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  30. 2026Symbolic Reasoning Frameworks Trigger Memory-Mediated Ecosystem Dynamics in Multi-Agent LLM Systemscited by 0
  31. 2026Financial Fragility in Societies of LLM Agents: Coordination Failures and Stabilizing Mechanismscited by 0

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