Probability and Asset Updating using Bayesian Networks for
Combinatorial Prediction Markets
Wei Sun, Robin Hanson, Kathryn B. Laskey, Charles Twardy
Abstract:
A market-maker-based prediction market lets
forecasters aggregateinformation by editing a consensus probability distribution either directly or by trading securities that pay off
contingent on an event of interest. Combinatorial prediction
markets allow trading on any event that can be specified as
a combination of a base set of events. However, explicitly
representing the full joint distribution is infeasible for
markets with more than a few base events. A factored representation such as a Bayesian network (BN) can achieve tractable
computation for problems with many related variables.
Standard BN inference algorithms, such as the junction tree
algorithm, can be used to update a representation of the entire joint distribution given a change to any local conditional
probability. However, in order to let traders reuse assets
from prior trades while never allowing assets to become
negative, a BN based prediction market also needs to update
a representation of each user's assets and find the
conditional state in which a user has minimum assets. Users
also find it useful to see their expected assets given an
edit outcome. We show how to generalize the junction tree algorithm to perform all these computations.
Bibtex:
@INPROCEEDINGS{Sunetal2012,
AUTHOR
= {Wei Sun, Robin Hanson, Kathryn B. Laskey, and Charles
Twardy},
TITLE = {Probability and Asset
Updating using Bayesian Networks for Combinatorial Prediction
Markets},
BOOKTITLE = {Proceedings of
the 28th Conference on Uncertainty in Artificial Intelligence
(UAI-2012)},
PUBLISHER = {AUAI
Press},
ADDRESS = {Catalina,
CA},
YEAR =
{2012},
PAGES = {}
}
This year's UAI has 304 submisstions, among them, only 96 were accepted (about 31% acceptance rate). Further, of these 96, only 24 papers were chosen for oral presentation, and our paper is one for oral presentation, yeah~! :-)