Value of Information in Games with Multiple Strategic Information Providers

TL;DR

This paper analyzes a hierarchical game with multiple strategic information providers and a single decision maker, revealing how their interactions affect information disclosure, equilibrium outcomes, and the receiver's advantage, especially under Gaussian and dynamic settings.

Contribution

It introduces a comprehensive framework for multi-sender signaling games with non-cooperative strategies, characterizes equilibrium sets, and extends analysis to dynamic and multi-receiver scenarios with algorithms.

Findings

Competition among senders benefits the receiver.

Linear policies can achieve minimal equilibrium elements.

Multiple equilibria exist in the game.

Abstract

In the classical communication setting multiple senders having access to the same source of information and transmitting it over channel(s) to a receiver in general leads to a decrease in estimation error at the receiver as compared with the single sender case. However, if the objectives of the information providers are different from that of the estimator, this might result in interesting strategic interactions and outcomes. In this work, we consider a hierarchical signaling game between multiple senders (information designers) and a single receiver (decision maker) each having their own, possibly misaligned, objectives. The senders lead the game by committing to individual information disclosure policies simultaneously, within the framework of a non-cooperative Nash game among themselves. This is followed by the receiver's action decision. With Gaussian information structure and quadratic objectives (which depend on underlying state and receiver's action) for all the players, we show that in general the equilibrium is not unique. We hence identify a set of equilibria and further show that linear noiseless policies can achieve a minimal element of this set. Additionally, we show that competition among the senders is beneficial to the receiver, as compared with cooperation among the senders. Further, we extend our analysis to a dynamic signaling game of finite horizon with Markovian information evolution. We show that linear memoryless policies can achieve equilibrium in this dynamic game. We also consider an extension to a game with multiple receivers having coupled objectives. We provide algorithms to compute the equilibrium strategies in all these cases. Finally, via extensive simulations, we analyze the effects of multiple senders in varying degrees of alignment among their objectives.