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

2025-10-05

AI Summary

This study addresses the limited flexibility in modeling negotiation and cooperation, as well as weak strategy evolution mechanisms, in multi-agent social simulation. Methodologically, we propose a configuration-driven multi-agent social simulation framework: (1) agents are modeled via utility functions, enabling multi-round interaction and feedback-driven autonomous strategy optimization; (2) a modular API and interactive visualization interface support low-code scenario configuration and dynamic runtime execution. Our key contribution lies in the deep integration of self-optimizing agent architectures with a configurable simulation framework—achieving, for the first time within a unified platform, the organic unification of social behavior modeling, dynamic strategy evolution, and human-in-the-loop experimentation. Empirical evaluation demonstrates that agents consistently improve both collective cooperation efficiency and individual utility across repeated negotiation episodes.

Technology Category

• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Social networks and social learning

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.

Problem

• Designing configurable multi-agent negotiation simulation environment
• Enabling agent self-optimization through iterative strategy refinement
• Developing user-friendly API for customizable social interaction scenarios

Innovation

• Multi-agent social simulation environment for negotiation
• Configuration-driven API for customizable scenario design
• Self-optimizing agents through iterative strategy modification