用博弈论方法动态分配多智能体决策权重，提升投资组合系统性能与可解释性。
Market Regime Council for Dynamic Credit Assignment in Multi-Agent LLM Decision Systems
- 基于合作博弈的谢帕利值计算各智能体贡献，实时调整权重。
- 1037天测试中收益达440.1%，夏普比率达1.51，回撤最低。
- 适合关注智能体协作、量化交易透明性的研究者与从业者。
用于投资组合管理的多智能体大模型决策系统仍缺乏对专业智能体进行合理信用分配的方法，面对市场结构变化时易受冷启动影响，且难以揭示最终配置形成过程。本文提出市场体制委员会（MRC），一种协作式多智能体决策系统，能够在线计算所有单个、成对及全联盟输出的精确谢帕利信用值，用于智能体权重更新。在包含13种加密资产、5个随机种子、共计1037个交易日的实验中，当N=3个专业智能体时，MRC实现夏普比率1.51，累计回报440.1%，在主动基线中排名CR、SR、IR第一，最大回撤最低。消融实验表明收益主要来自联盟输出的谢帕利加权融合，而非单一阶段。附录包含代码与演示数据。
原文摘要 · Abstract (English)
Multi-agent LLM decision systems for portfolio management still lack a principled way to assign credit across specialist agents, remain vulnerable to cold-start dominance under regime shifts, and offer limited transparency into how final allocations are formed. We propose Market Regime Council (MRC), a cooperative multi-agent decision system that computes exact Shapley credits across all single, pairwise, and Grand-coalition outputs for online agent weighting. Instantiated with N=3 specialist agents, at each trading period, MRC recomputes coalition-based Shapley weights from exponentially weighted performance histories, uses a Bayesian adaptive mixture to stabilize early periods, applies regime-dependent multipliers to adjust agent authority, and records each rebalance through a five-layer causal trace. Over 1,037 trading days across 13 crypto assets and five seeds, MRC achieves a Sharpe ratio of 1.51 and a cumulative return of 440.1%, ranking first on CR, SR, and IR among active baselines and attaining the lowest MDD among active methods. Ablation results show that the gains come from Shapley-weighted integration across coalition outputs rather than from any single stage in isolation. Code and demo data are included in the supplementary material.
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