规范引导的多智能体决策在耦合环境中的应用：再保险约束的多智能体仿真过程（R-CMASP）
Norm-Governed Multi-Agent Decision-Making in Simulator-Coupled Environments:The Reinsurance Constrained Multi-Agent Simulation Process (R-CMASP)
- Reinsurance Analytics（再保险分析）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出R-CMASP模型，通过规范引导和模拟器耦合机制，提升多智能体在再保险决策中的稳定性与合规性。
AI中文摘要：
再保险决策表现出促使多智能体模型的核心结构性特性：分布式和不对称信息、部分可观测性、异质性认知责任、由模拟器驱动的环境动态以及具有约束性的谨慎性和监管约束。确定性工作流程自动化无法满足这些要求，因为它缺乏所需的认知灵活性、协作协调机制和规范敏感行为。我们提出了再保险约束的多智能体仿真过程（R-CMASP），一种扩展随机游戏和Dec-POMDPs的正式模型，通过添加三个缺失元素：（i）基于灾难、资本和投资组合引擎的耦合模拟器转移动态；（ii）具有结构化可观测性、信念更新和类型化通信的角色专业化智能体；以及（iii）将偿付能力、监管和组织规则编码为联合行动的可接受性约束的规范可行性层。使用具有工具访问和类型化消息协议的LLM智能体，在一个经过领域校准的合成环境中，我们表明在受控的多智能体协调中，比确定性自动化或单体LLM基线更稳定、一致且符合规范的行为——减少定价波动，提高资本效率，增加条款解释准确性。将审慎规范作为可接受性约束嵌入，并将通信结构化为类型化行为，显著增强了均衡稳定性。总体而言，结果表明，受监管的、由模拟器驱动的决策环境最自然地建模为规范引导的、耦合的多智能体系统。
英文摘要：
Reinsurance decision-making exhibits the core structural properties that motivate multi-agent models: distributed and asymmetric information, partial observability, heterogeneous epistemic responsibilities, simulator-driven environment dynamics, and binding prudential and regulatory constraints. Deterministic workflow automation cannot meet these requirements, as it lacks the epistemic flexibility, cooperative coordination mechanisms, and norm-sensitive behaviour required for institutional risk-transfer. We propose the Reinsurance Constrained Multi-Agent Simulation Process (R-CMASP), a formal model that extends stochastic games and Dec-POMDPs by adding three missing elements: (i) simulator-coupled transition dynamics grounded in catastrophe, capital, and portfolio engines; (ii) role-specialized agents with structured observability, belief updates, and typed communication; and (iii) a normative feasibility layer encoding solvency, regulatory, and organizational rules as admissibility constraints on joint actions. Using LLM-based agents with tool access and typed message protocols, we show in a domain-calibrated synthetic environment that governed multi-agent coordination yields more stable, coherent, and norm-adherent behaviour than deterministic automation or monolithic LLM baselines--reducing pricing variance, improving capital efficiency, and increasing clause-interpretation accuracy. Embedding prudential norms as admissibility constraints and structuring communication into typed acts measurably enhances equilibrium stability. Overall, the results suggest that regulated, simulator-driven decision environments are most naturally modelled as norm-governed, simulator-coupled multi-agent systems.
