Toward a Market Model for Bayesian Inference

David Pennock, Michael Wellman

Proceedings of the Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96) 405-41, 1996.

Abstract

We present a methodology for representing probabilistic relationships in a general-equilibrium economic model. Specifically, we define a precise mapping from a Bayesian network with binary nodes to a market price system where consumers and producers trade in uncertain propositions. We demonstrate the correspondence between the equilibrium prices of goods in this economy and the probabilities represented by the Bayesian network. A computational market model such as this may provide a useful framework for investigations of belief aggregation, distributed probabilistic inference, resource allocation under uncertainty, and other problems of decentralized uncertainty.