用大模型模拟服务系统中人的行为，自动优化设计。
Optimizing Service Operations via LLM-Powered Multi-Agent Simulation
- 将设计参数嵌入提示词，让大模型代理自动生成响应行为。
- 单次仿真即可估计梯度并更新参数，提升优化效率。
- 适合想低成本试错复杂服务设计的研究者与从业者。
服务系统性能取决于参与者对设计决策的反应，但人类行为复杂，建模困难。本文提出基于大模型的多智能体仿真框架（LLM-MAS），将问题建模为决策依赖不确定性的随机优化：设计决策被嵌入提示词，影响大模型代理交互结果的分布。通过在提示词中嵌入关键数值信息，并从大模型生成的文本中提取，我们将不确定性建模为可控马尔可夫链。开发了一种轨迹学习算法，在单次仿真中同时构建零阶梯度估计并更新设计参数，以优化稳态性能，并引入方差减少技术。在可持续供应链应用中，该方法优于黑盒优化及将大模型作为数值求解器或角色扮演设计师的基准方案。结合真实行为数据的竞赛设计案例研究显示，LLM-MAS既能高效评估已有设计，也能发现传统方法忽略的优秀设计。
原文摘要 · Abstract (English)
Service system performance depends on how participants respond to design choices, but modeling these responses is hard due to the complexity of human behavior. We introduce an LLM-powered multi-agent simulation (LLM-MAS) framework for optimizing service operations. We pose the problem as stochastic optimization with decision-dependent uncertainty: design choices are embedded in prompts and shape the distribution of outcomes from interacting LLM-powered agents. By embedding key numerical information in prompts and extracting it from LLM-generated text, we model this uncertainty as a controlled Markov chain. We develop an on-trajectory learning algorithm that, on a single simulation run, simultaneously constructs zeroth-order gradient estimates and updates design parameters to optimize steady-state performance. We also incorporate variance reduction techniques. In a sustainable supply chain application, our method outperforms benchmarks, including blackbox optimization and using LLMs as numerical solvers or as role-playing system designers. A case study on optimal contest design with real behavioral data shows that LLM-MAS is both as a cost-effective evaluator of known designs and an exploratory tool that can uncover strong designs overlooked by traditional approaches.
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