Quadratic Multi-Dimensional Signaling Games and Affine Equilibria

TL;DR

This paper extends quadratic signaling game analysis to multi-dimensional and noisy environments, revealing that equilibrium policies are quantized in Nash cases but fully informative in Stackelberg cases, with new conditions for affine policy optimality.

Contribution

It introduces multi-dimensional and noisy channel extensions to signaling games, characterizes equilibrium types, and provides conditions for affine policy optimality in strategic settings.

Findings

Nash equilibria are quantized for scalar sources with misalignment.

Stackelberg equilibria are fully informative for scalar sources.

In noisy setups, the linear equilibrium is unique for scalar variables.

Abstract

This paper studies the decentralized quadratic cheap talk and signaling game problems when an encoder and a decoder, viewed as two decision makers, have misaligned objective functions. The main contributions of this study are the extension of Crawford and Sobel's cheap talk formulation to multi-dimensional sources and to noisy channel setups. We consider both (simultaneous) Nash equilibria and (sequential) Stackelberg equilibria. We show that for arbitrary scalar sources, in the presence of misalignment, the quantized nature of all equilibrium policies holds for Nash equilibria in the sense that all Nash equilibria are equivalent to those achieved by quantized encoder policies. On the other hand, all Stackelberg equilibria policies are fully informative. For multi-dimensional setups, unlike the scalar case, Nash equilibrium policies may be of non-quantized nature, and even linear. In the noisy setup, a Gaussian source is to be transmitted over an additive Gaussian channel. The goals of the encoder and the decoder are misaligned by a bias term and encoder's cost also includes a penalty term on signal power. Conditions for the existence of affine Nash equilibria as well as general informative equilibria are presented. For the noisy setup, the only Stackelberg equilibrium is the linear equilibrium when the variables are scalar. Our findings provide further conditions on when affine policies may be optimal in decentralized multi-criteria control problems and lead to conditions for the presence of active information transmission in strategic environments.