Efficient Monte Carlo Decision Tree Solution in Dynamic Purchasing Environments

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TypeArticle
ConferenceThe International Conference on Electronic Commerce (ICEC'03), October 1, 2003., Pittsburgh, Pennsylvania, USA
Subjectbundle purchasing; decision analysis; decision tree; expected utility theory; Monte Carlo
AbstractThis paper considers the problem of making decisions in a dynamic environment wherr one of possible many bundles of items must be purchased and quotes for items open and close over time. Probability measures on item prices are used when exact prices are not yet known. We show that expected utility estimation can be improved by considering how future information can affect the purchasing agent's behaviour. An efficient Monte Carlo simulation method is presented that determines the expected utility of an option in our decision tree, referred to as a QR-tree, where the number of simulations needed is linear in the size of the tree. In our experiments simulating a purchasing agent in a specific market, the expected utility was estimated more than 50 times more accurately than a greedy method that always pursues the bundle with the current highest expected utility.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number46489
NPARC number8914309
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Record identifierbf081e79-cd17-4ba3-bed3-b30963b5421f
Record created2009-04-22
Record modified2016-05-09
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