Optimal Messaging Strategy for Incentivizing Agents in Dynamic Systems

TL;DR

This paper develops a method for a designer to optimally influence agents' actions in a dynamic system through messaging and direct actions, ensuring incentive compatibility and maximizing overall reward.

Contribution

It introduces a backward inductive algorithm to compute optimal messaging and action strategies under incentive compatibility in dynamic systems.

Findings

Optimal strategies can be computed via linear programs.

The approach ensures incentive compatibility at each stage.

The method applies under specific information structure assumptions.

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.