# NomicLaw: Emergent Trust and Strategic Argumentation in LLMs During Collaborative Law-Making

arXiv: https://arxiv.org/abs/2508.05344 · HTML: https://mlsanity.com/paper/2508.05344/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents](https://mlsanity.com/paper/2404.16698/) (2024)
- [The Generative AI Paradox: "What It Can Create, It May Not Understand"](https://mlsanity.com/paper/2311.00059/) (2023)
- [Legal Syllogism Prompting: Teaching Large Language Models for Legal Judgment Prediction](https://mlsanity.com/paper/2307.08321/) (2023)
- [LawLLM: Law Large Language Model for the US Legal System](https://mlsanity.com/paper/2407.21065/) (2024)
- [Generative to Agentic AI: Survey, Conceptualization, and Challenges](https://mlsanity.com/paper/2504.18875/) (2025)
- [Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues](https://mlsanity.com/paper/2402.13550/) (2024)
- [How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law](https://mlsanity.com/paper/2404.12762/) (2024)
- [SocialJax: An Evaluation Suite for Multi-agent Reinforcement Learning in Sequential Social Dilemmas](https://mlsanity.com/paper/2503.14576/) (2025)
- [Legal Minds, Algorithmic Decisions: How LLMs Apply Constitutional Principles in Complex Scenarios](https://mlsanity.com/paper/2407.19760/) (2024)

## Built on by (most cited first, 4 of 4)

- [Social Theory Should Be a Structural Prior for Agentic AI: A Formal Framework for Multi-Agent Social Systems](https://mlsanity.com/paper/2605.07069/) (2026)
- [Adaptive Collaboration of Arena-Based Argumentative LLMs for Explainable and Contestable Legal Reasoning](https://mlsanity.com/paper/2602.18916/) (2026)
- [Implicature in Interaction: Understanding Implicature Improves Alignment in Human-LLM Interaction](https://mlsanity.com/paper/2510.25426/) (2025)
- [GPS-Bench: A Governance Policy Benchmark for Automating Policy Analysis](https://mlsanity.com/paper/2609.03553/) (2026)

## Abstract (arXiv)

Recent advancements in large language models (LLMs) have extended their capabilities from basic text processing to complex reasoning tasks, including legal interpretation, argumentation, and strategic interaction. However, empirical understanding of LLM behavior in open-ended, multi-agent settings especially those involving deliberation over legal and ethical dilemmas remains limited. We introduce NomicLaw, a structured multi-agent simulation where LLMs engage in collaborative law-making, responding to complex legal vignettes by proposing rules, justifying them, and voting on peer proposals. We quantitatively measure trust and reciprocity via voting patterns and qualitatively assess how agents use strategic language to justify proposals and influence outcomes. Experiments involving homogeneous and heterogeneous LLM groups demonstrate how agents spontaneously form alliances, betray trust, and adapt their rhetoric to shape collective decisions. Our results highlight the latent social reasoning and persuasive capabilities of ten open-source LLMs and provide insights into the design of future AI systems capable of autonomous negotiation, coordination and drafting legislation in legal settings.