Optimal Dynamic Information Provision

TL;DR

This paper models a dynamic information provision scenario where an advisor controls information flow to influence a decision maker's actions, analyzing the optimality of greedy disclosure policies under evolving states.

Contribution

It introduces a stylized dynamic model of information provision and characterizes when greedy policies are optimal or suboptimal.

Findings

Greedy policy is optimal in many cases.

Optimality of greedy policy depends on specific conditions.

The model provides insights into dynamic information disclosure strategies.

Abstract

We study a dynamic model of information provision. A state of nature evolves according to a Markov chain. An informed advisor decides how much information to provide to an uninformed decision maker, so as to influence his short-term decisions. We deal with a stylized class of situations, in which the decision maker has a risky action and a safe action, and the payoff to the advisor only depends on the action chosen by the decision maker. The greedy disclosure policy is the policy which, at each round, minimizes the amount of information being disclosed in that round, under the constraint that it maximizes the current payoff of the advisor. We prove that the greedy policy is optimal in many cases -- but not always.