The Structure of Signals: Causal Interdependence Models for Games of Incomplete Information
This work addresses a theoretical problem in economics by clarifying signal structures in games of incomplete information, offering incremental insights for model extension.
The paper tackles the distinction between generated and interpreted signals in economic models by framing it as a causal dependence structure, using graphical models to illustrate differences and extend results to more general situations, with specific insights applied to bidding games in classical auction mechanisms.
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.