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

Michael P. Wellman, Lu Hong, Scott E. Page
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:800-808, 2011.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR9-wellman11a, title = {The Structure of Signals: Causal Interdependence Models for Games of Incomplete Information}, author = {Wellman, Michael P. and Hong, Lu and Page, Scott E.}, booktitle = {Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence}, pages = {800--808}, year = {2011}, editor = {Cozman, Fabio and Pfeffer, Avi}, volume = {R9}, series = {Proceedings of Machine Learning Research}, month = {14--17 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r9/main/assets/wellman11a/wellman11a.pdf}, url = {https://proceedings.mlr.press/r9/wellman11a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T The Structure of Signals: Causal Interdependence Models for Games of Incomplete Information %A Michael P. Wellman %A Lu Hong %A Scott E. Page %B Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2011 %E Fabio Cozman %E Avi Pfeffer %F pmlr-vR9-wellman11a %I PMLR %P 800--808 %U https://proceedings.mlr.press/r9/wellman11a.html %V R9 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Wellman, M.P., Hong, L. & Page, S.E.. (2011). The Structure of Signals: Causal Interdependence Models for Games of Incomplete Information. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R9:800-808 Available from https://proceedings.mlr.press/r9/wellman11a.html. Reissued by PMLR on 04 October 2026.

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