How well can large language model agents simulate complex legal dispute resolution?

Abstract Legal dispute resolution hinges on the weighing of evidence and arguments across parties, yet human biases and bounded rationality can influence outcomes. Simulating this process with large language models can clarify both the capabilities and the anthropomorphic behavior. Most existing studies focus on algorithmic support for adjudication, such as online courts, risk assessment, and sentencing tools. At the same time, the role of AI agents in mediation and conciliatory negotiation remains largely untested. To address this gap, the study designs six scenario types and evaluates them on a multi-agent platform. It addresses two questions: (i) To what extent can LLM agents produce realistic simulations from a juridical perspective? We find that LLMs can reproduce diverse dispute scenarios, but performance varies by role; in particular, judge agents sometimes commit serious legal errors when interpreting clauses and may infer property rights rather than apply the correct rules. (ii) Do sensitive factors and intrinsic properties affect legal dispute resolution? Sensitivity analyses across model-level configurations, framework design choices, and procedural settings confirm the robustness of our findings; we further observe irrational patterns yielding mechanical or unexpected decisions and measurable effects from intrinsic properties. These findings show strengths in fact-heavy money bargaining and mixed remedies, while highlighting limits where careful discretion and normative justification are required. Code and scenarios are publicly available for replication ( https://github.com/TOM-ZHOUch/Legal_Dispute_Resolution ).