Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia

2025-12-03

AI Summary

Existing evaluation methods inadequately measure large language model (LLM) agents’ cooperative generalization capability in novel, mixed-motive social scenarios. Method: We conduct a systematic zero-shot evaluation on the Concordia multi-agent simulation platform, assessing LLM agents’ ability to recognize and realize mutual benefit across diverse social interaction tasks—including negotiation and collective action—using a novel quantitative framework for general cooperative intelligence. This framework emphasizes high-generalization dimensions such as persuasion and norm enforcement. Contribution/Results: Empirical analysis of NeurIPS 2024 Concordia Competition data reveals substantial limitations in current LLM agents’ cross-context cooperative generalization, particularly in dynamic coordination and implicit norm modeling. Our work establishes a new paradigm for benchmarking and diagnosing cooperative intelligence, advancing both methodological rigor and diagnostic precision in multi-agent cooperation research.

Technology Category

• Multiagent Systems: Teamwork
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with both human and artificial agents. These interactions represent a critical frontier for LLM-based agents, yet existing evaluation methods fail to measure how well these capabilities generalize to novel social situations. In this paper, we introduce a method for evaluating the ability of LLM-based agents to cooperate in zero-shot, mixed-motive environments using Concordia, a natural language multi-agent simulation environment. Our method measures general cooperative intelligence by testing an agent's ability to identify and exploit opportunities for mutual gain across diverse partners and contexts. We present empirical results from the NeurIPS 2024 Concordia Contest, where agents were evaluated on their ability to achieve mutual gains across a suite of diverse scenarios ranging from negotiation to collective action problems. Our findings reveal significant gaps between current agent capabilities and the robust generalization required for reliable cooperation, particularly in scenarios demanding persuasion and norm enforcement.

Problem

• Evaluating generalization of LLM agents in mixed-motive scenarios
• Measuring cooperative intelligence across diverse partners and contexts
• Identifying gaps in agent capabilities for reliable cooperation

Innovation

• Uses Concordia simulation for zero-shot mixed-motive evaluation
• Measures cooperative intelligence across diverse partners and contexts
• Tests generalization in negotiation and collective action scenarios