Price updating in combinatorial prediction markets with Bayesian networks

David M. Pennock, Lirong Xia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

To overcome the #P-hardness of computing/ updating prices in logarithm market scoring rule-based (LMSR-based) combinatorial prediction markets, Chen et al. [5] recently used a simple Bayesian network to represent the prices of securities in combinatorial predictionmarkets for tournaments, and showed that two types of popular securities are structure preserving. In this paper, we significantly extend this idea by employing Bayesian networks in general combinatorial prediction markets. We reveal a very natural connection between LMSR-based combinatorial prediction markets and probabilistic belief aggregation, which leads to a complete characterization of all structure preserving securities for decomposable network structures. Notably, the main results by Chen et al. [5] are corollaries of our characterization. We then prove that in order for a very basic set of securities to be structure preserving, the graph of the Bayesian network must be decomposable. We also discuss some approximation techniques for securities that are not structure preserving.

Original languageEnglish (US)
Title of host publicationProceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011
PublisherAUAI Press
Pages581-588
Number of pages8
StatePublished - 2011
Externally publishedYes

Publication series

NameProceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Price updating in combinatorial prediction markets with Bayesian networks'. Together they form a unique fingerprint.

Cite this