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arXiv · 2510.04368

NegotiationGym: Self-Optimizing Agents in a Multi-Agent Social Simulation Environment

Shashank Mangla·Chris Hokamp·Jack Boylan·Demian Gholipour Ghalandari·Yuuv Jauhari·Lauren Cassidy·Oisin Duffy
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Abstract

We design and implement NegotiationGym, an API and user interface for configuring and running multi-agent social simulations focused upon negotiation and cooperation. The NegotiationGym codebase offers a user-friendly, configuration-driven API that enables easy design and customization of simulation scenarios. Agent-level utility functions encode optimization criteria for each agent, and agents can self-optimize by conducting multiple interaction rounds with other agents, observing outcomes, and modifying their strategies for future rounds.

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Shashank Mangla, Chris Hokamp, Jack Boylan, Demian Gholipour Ghalandari, Yuuv Jauhari, Lauren Cassidy, Oisin Duffy. 2025-10-05. NegotiationGym: Self-Optimizing Agents in a Multi-Agent Social Simulation Environment. https://arxiv.org/abs/2510.04368

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