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Computer Science > Multiagent Systems

arXiv:2412.10270 (cs)
[Submitted on 13 Dec 2024]

Title:Cultural Evolution of Cooperation among LLM Agents

Authors:Aron Vallinder, Edward Hughes
View a PDF of the paper titled Cultural Evolution of Cooperation
among LLM Agents, by Aron Vallinder and Edward Hughes
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    Abstract:Large language models (LLMs) provide a compelling
    foundation for building generally-capable AI agents. These agents
    may soon be deployed at scale in the real world, representing the
    interests of individual humans (e.g., AI assistants) or groups of
    humans (e.g., AI-accelerated corporations). At present,
    relatively little is known about the dynamics of multiple LLM
    agents interacting over many generations of iterative deployment.
    In this paper, we examine whether a "society" of LLM agents can
    learn mutually beneficial social norms in the face of incentives
    to defect, a distinctive feature of human sociality that is
    arguably crucial to the success of civilization. In particular,
    we study the evolution of indirect reciprocity across generations
    of LLM agents playing a classic iterated Donor Game in which
    agents can observe the recent behavior of their peers. We find
    that the evolution of cooperation differs markedly across base
    models, with societies of Claude 3.5 Sonnet agents achieving
    significantly higher average scores than Gemini 1.5 Flash, which,
    in turn, outperforms GPT-4o. Further, Claude 3.5 Sonnet can make
    use of an additional mechanism for costly punishment to achieve
    yet higher scores, while Gemini 1.5 Flash and GPT-4o fail to do
    so. For each model class, we also observe variation in emergent
    behavior across random seeds, suggesting an understudied
    sensitive dependence on initial conditions. We suggest that our
    evaluation regime could inspire an inexpensive and informative
    new class of LLM benchmarks, focussed on the implications of LLM
    agent deployment for the cooperative infrastructure of society.

Comments: 15 pages, 6 figures
Subjects: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
Cite as:  arXiv:2412.10270 [cs.MA]
          (or arXiv:2412.10270v1 [cs.MA] for this version)
          https://doi.org/10.48550/arXiv.2412.10270
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Edward Hughes [view email]
[v1] Fri, 13 Dec 2024 16:45:49 UTC (1,544 KB)
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