The Structure of Signals: Causal Interdependence Models for Games of Incomplete Information

TL;DR

This paper explores how signals in games of incomplete information can be modeled using causal dependence structures and graphical models, providing new insights into auction bidding behaviors.

Contribution

It introduces a causal dependence framework for signals, contrasting it with traditional models, and demonstrates its application to auction game analysis.

Findings

Graphical models clarify signal interpretation differences.

Causal dependence structures extend analysis of auction games.

Insights into bidding strategies in classical auctions.

Abstract

Traditional economic models typically treat private information, or signals, as generated from some underlying state. Recent work has explicated alternative models, where signals correspond to interpretations of available information. We show that the difference between these formulations can be sharply cast in terms of causal dependence structure, and employ graphical models to illustrate the distinguishing characteristics. The graphical representation supports inferences about signal patterns in the interpreted framework, and suggests how results based on the generated model can be extended to more general situations. Specific insights about bidding games in classical auction mechanisms derive from qualitative graphical models.