发表机构
Swissi Institute for AI; Hochschule für Wirtschaft und Umwelt Nürtingen-Geislingen; Faculty of Information Technology and Engineering (FITI), Belgrade（Swissi人工智能研究所; 叙廷根-盖斯林根经济与环境应用科学大学; 贝尔格莱德信息技术与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种模型无关的嵌套委托-代理与约束联合最大化框架，用于私人财富管理中的AI授权，并通过模拟验证其能揭示授权选择中的违规与权衡，有助于改善监督和客户结果。
AI 中文摘要
在私人财富管理中，管理者将任务委托给人工智能（AI）时，既充当客户的代理人，又充当该系统的委托人。我们提出了一种与模型无关的表述，将嵌套的委托-代理关系与约束联合最大化相结合，作为分配给AI系统的任务。目标函数分别针对投资组合-工作流对，表示客户和管理者的结果。法律义务、授权要求和证据充分性决定了可采纳性，其中瑞士、德国和奥地利提供了法律背景。权重和参考服务下限使权衡变得明确；让步核算将其对客户的影响分开。分析性构建和使用公开市场观察数据的模拟说明了该方法。在来自四个构建授权的八个决策状态中，遗漏客户负债导致两次流动性违规，遗漏管理者条款导致两次容量违规，而错误翻译的权重在忠实优化下改变了四个原本可采纳的选择。在声明的权重下，六个状态选择了比客户最优替代方案更高的服务层级，客户让步金额为1,178欧元至2,264欧元，管理者收益为3,062欧元至10,381欧元。三种指令形式在共享的数字、证据和模拟审批控制下均达到了全部32个指定决策；专业指令在准确性和澄清次数上与显式嵌套委托相匹配。随后的2022年汇率和收益率路径，结合构建的增长情景，产生了低于参考服务的客户结果，尽管所选服务达到了决策时的预测基准。这些例子表明，该方法可能有助于更容易地审查授权选择及其后果。专业和实地研究可以评估这是否能改善监督和客户结果。
英文摘要
In private wealth management, a manager delegating to artificial intelligence (AI) acts as the client's agent and the system's principal. We introduce a model-independent formulation that combines nested principal--agent delegation with constrained joint maximisation as the task assigned to the AI system. The objective represents client and manager outcomes separately over portfolio--workflow pairs. Legal duties, mandate requirements and evidence sufficiency determine admissibility, with Switzerland, Germany and Austria supplying the legal context. Weights and reference-service floors make the trade-off explicit; concession accounting separates their effects on the client. Analytical constructions and a simulation using public-market observations illustrate the approach. Across eight decision states from four constructed mandates, omitted client liabilities caused two liquidity violations, omitted manager terms caused two capacity violations, and mistranslated weights changed four otherwise admissible choices under faithful optimisation. At the declared weights, six states selected a higher service tier than the client-best alternative, with client concessions of EUR 1,178 to EUR 2,264 and manager gains of EUR 3,062 to EUR 10,381. Three instruction forms each reached all 32 specified decisions under shared numerical, evidence and simulated approval controls; professional instructions matched explicit nested delegation on accuracy and clarification count. Subsequent 2022 exchange-rate and yield paths, combined with constructed growth scenarios, produced lower client outcomes than the reference service although the selected services met the decision-time forecast benchmarks. These examples suggest that the approach could help make mandate choices and their consequences easier to examine. Professional and field studies could assess whether this improves oversight and client outcomes.
CommentsQuantitative formulation of client-manager-AI delegation and constrained joint decision objectives in private wealth management. 30 pages, 8 figures, 11 tables. Published in Swissi AI Journal under CC BY 4.0. Journal record: https://journal.swissi-ai.institute/en/doi/guex5c23zfm5
Journal refSwissi AI Journal, Volume 2026, Article SAIJ-guex5c23zfm5 (2026)
