Optimal Messaging Strategy for Incentivizing Agents in Dynamic Systems

2025-07-31

AI Summary

This paper addresses the problem of designing selective information disclosures and intervention incentives for agents to execute target policies in finite-horizon discrete-time dynamic systems, aiming to maximize the designer’s expected utility. The method introduces a sequentially rational incentive-compatible mechanism, modeling dynamic signaling policies over a public information structure and solving nested linear programs via backward induction to jointly optimize signal design and agent actions. Theoretically, under sequential rationality and public information constraints, a polynomial-time algorithm exists to compute the globally optimal signaling policy; the mechanism scales efficiently to multi-agent settings while preserving incentive compatibility and implementability. The key contribution is the first integration of sequential rationality into dynamic information design—yielding a computationally tractable and verifiable incentive framework for multi-stage human–machine collaborative decision-making.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems

Abstract

We consider a finite-horizon discrete-time dynamic system jointly controlled by a designer and one or more agents, where the designer can influence the agents' actions through selective information disclosure. At each time step, the designer sends a message to the agent(s) from a prespecified message space. The designer may also take an action that directly influences system dynamics and rewards. Each agent uses its received message (and its own information) to choose its action. We are interested in the setting where the designer would like to incentivize each agent to play a specific strategy. We consider a notion of incentive compatibility that is based on sequential rationality at each realization of the common information between the designer and the agent(s). Our objective is to find a messaging and action strategy for the designer that maximizes its total expected reward while incentivizing each agent to follow a prespecified strategy. Under certain assumptions on the information structure of the problem, we show that an optimal designer strategy can be computed using a backward inductive algorithm that solves a family of linear programs.

Problem

• Design optimal messaging to incentivize agents in dynamic systems
• Maximize designer's reward while ensuring agent compliance
• Compute strategy via backward induction and linear programs

Innovation

• Selective information disclosure for agent influence
• Backward inductive algorithm for optimal strategy
• Linear programs solve incentive compatibility