Diversity Without Fidelity: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation Simulation
arXiv:2604.11840v3 Announce Type: replace Abstract: Language models are increasingly used to simulate people: survey respondents, negotiators, stakeholders in policy exercises. In that role a model should reproduce how people plausibly behave, hesitating, conceding late, and settling for imperfect deals, rather than playing the best move. We call this the sampler role, in contrast to the solver role of finding the best move, and we test how the reasoning modes providers ship to strengthen models as solvers affect it. Our testbed is multi-party negotiation: five agents bargain over a regulation
The increasing sophistication and deployment of large language models in complex simulation environments makes the distinction between 'solver' and 'sampler' roles critical for accurate human-like behavior modeling.
A strategic reader needs to understand the limitations and biases of LLM simulations, especially when these models are used for policy, market, or societal impact analysis, as their inherent 'solver' inclination may misrepresent human behavior.
The understanding of how LLMs behave when simulating human interactions is refined, highlighting a performance mismatch that could lead to flawed insights if not accounted for.
- · AI ethics researchers
- · Social scientists using AI simulations
- · Providers of 'sampler' focused AI models
- · Organizations developing responsible AI practices
- · Organizations relying solely on 'solver' LLMs for human behavior simulation
- · Developers neglecting the 'sampler' fidelity in LLMs for negotiation
- · Users unaware of LLM simulation limitations
Further research and development will focus on improving the 'sampler' fidelity of LLMs to better mimic human-like hesitation and concession.
New benchmarks and evaluation metrics will emerge to assess the 'sampler' capabilities of LLMs in multi-agent environments, driving specialization in model development.
The development of 'human-in-the-loop' systems for critical simulations may become more prevalent to compensate for current LLM limitations in accurately reflecting nuanced human behaviors.
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Read at arXiv cs.LG