AI智能体环境中的动态机制与信息设计：一个通用分析框架

中文摘要 AI 辅助

本文开发了一个适用于AI智能体的动态机制与信息设计通用框架，涵盖最优契约，将不断演变的隐藏信息、隐藏行动与部分验证、技术授权及多智能体交互相结合。该框架嵌套了传统的动态筛选、隐藏行动契约及条件动态信息设计，同时允许报告、行动及信息使用决策通过共同的延续机制相互作用。我们建立了动态显示原理与可实施性的递归表征，其中真实报告与报告后服从通过延续激励共同决定。在拟线性最优契约环境中，动态包络与积分单调性方法产生了经验证调整的动态虚拟剩余表征，并确定了三个不同边际：动态信息租金、验证租金及隐藏行动的实施成本。这些边际形成了一个分支层级，区分了传统的动态筛选、隐藏行动契约及其联合问题。我们还研究了监测、信息使用与激励的联合设计：更具信息性的监测能力扩大了设计者的机会集，但完全披露未必最优，且信息使用本身可改变隐藏行动激励。对于多智能体，相关信息、同伴约束、耦合激励、联合可行性及延续机会产生了额外的战略交互。尽管以AI智能体环境为动机，该框架更广泛适用于具有类似经济特征的动态代理环境。

英文摘要

This paper develops a general framework for dynamic mechanism and information design with AI agents, encompassing optimal contracting and integrating evolving hidden information and hidden action with partial verification, technological authorization, and multi-agent interaction. It nests conventional dynamic screening, hidden-action contracting, and conditional dynamic information design, while allowing reporting, action, and information-use decisions to interact through a common continuation mechanism. We establish a dynamic revelation principle and a recursive characterization of implementability in which truthful reporting and post-report obedience are jointly determined through continuation incentives. In quasilinear optimal-contracting environments, dynamic envelope and integral-monotonicity methods yield a verification-adjusted dynamic virtual-surplus representation and identify three distinct margins: dynamic information rents, verification rents, and the implementation cost of hidden action. These margins generate a branched hierarchy separating conventional dynamic screening, hidden-action contracting, and their joint problem. We also study the joint design of monitoring, information use, and incentives: more informative monitoring capacity expands the designer's opportunity set, but full disclosure need not be optimal, and information use can itself alter hidden-action incentives. With multiple agents, correlated information, peer discipline, coupled incentives, joint feasibility, and continuation opportunities create additional strategic interactions. Although motivated by AI-agent environments, the framework applies more broadly to dynamic agency settings with similar economic features.

发表机构

• Texas A&M University（德克萨斯农工大学）

机构由 AI 辅助整理，请以论文原文为准。