Uncertainty in Artificial Intelligence
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The Structure of Signals: Causal Interdependence Models for Games of Incomplete Information
Michael Wellman, Lu Hong, Scott Page
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.
Keywords:
Pages: 727-735
PS Link:
PDF Link: /papers/11/p727-wellman.pdf
BibTex:
@INPROCEEDINGS{Wellman11,
AUTHOR = "Michael Wellman and Lu Hong and Scott Page",
TITLE = "The Structure of Signals: Causal Interdependence Models for Games of Incomplete Information",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "727--735"
}


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