AAAI Publications, Twenty-Fourth International FLAIRS Conference

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Aggregating Forecasts Using a Learned Bayesian Network
Suzanne Mitchell Mahoney, Ethan Comstock, Bradley deBlois, Steven Darcy

Last modified: 2011-03-21

Abstract


Under the Defense Advanced Research Project Agency's (DARPA) Integrated Crisis Early Warning System (ICEWS), Innovative Decisions, Inc. (IDI) constructed a Bayesian network to combine forecasts produced by a set of social science models. We used Bayesian network structure learning with political science variables to produce meaningful priors. We employed a naive Bayes structure to aggregate the forecasts. In both cases, IDI improved classification by intelligently discretizing continuous variables. The resulting network not only met performance criteria set by DARPA, but also out-performed each of the social science models across all types of forecasted events. We describe the construction of the aggregator as well as a set of experiments performed to explore the nature of the Bayesian EOI Aggregator's performance.

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