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dc.contributor.authorChen, Weiyun
dc.contributor.authorLi, Xin
dc.contributor.authorZeng, Daniel
dc.date.accessioned2017-08-10T22:17:06Z
dc.date.available2017-08-10T22:17:06Z
dc.date.issued2017-06
dc.identifier.citationChen, W., Li, X., & Zeng, D. (2017). MODELING FIXED ODDS BETTING FOR FUTURE EVENT PREDICTION. MIS Quarterly, 41(2), 645-A6.en
dc.identifier.issn0276-7783
dc.identifier.urihttp://hdl.handle.net/10150/625249
dc.description.abstractPrediction markets provide a promising approach for future event prediction. Most existing prediction market approaches are based on auction mechanisms. Despite their theoretical appeal and success in various application settings, these mechanisms suffer from several major drawbacks. First, opinions from experts and amateurs are treated equally. Second, continuous attention from participants is assumed. Third, such mechanisms are subject to various forms of market manipulation. To alleviate these limitations, we propose to employ the classic fixed odds betting as an alternative prediction market mechanism. We build a structural model based on a belief-decision framework as the event probability estimator. This belief-decision framework models bettors' beliefs with mixed beta distributions and bettors' decisions with prospect theory. A maximum likelihood approach is applied to estimate the model parameters. We conducted experiments on three real-world betting datasets to evaluate our proposed approach. Experimental results show that fixed odds betting based prediction outperforms the reduced form models based on odds and betting results, and achieves a comparable performance with auction-based prediction markets. The results suggest the possibility of employing fixed odds betting as a prediction market in a variety of application contexts where the assumptions made by auction-based approaches do not hold.
dc.description.sponsorshipNational Natural Science Foundation of China [71025001, 71621002, 71572169]; Guang Dong Natural Science Foundation [2015A030313876]; Research Grants Council of the Hong Kong Special Administrative Region, China [CityU 11503115]; Peak Discipline Construction Project of Education at East China Normal Universityen
dc.language.isoenen
dc.publisherSOC INFORM MANAGE-MIS RES CENTen
dc.relation.urlhttp://misq.org/modeling-fixed-odds-betting-for-future-event-prediction.html?SID=ke47j8g6v47ngppd0eisqn12h7en
dc.rightsCopyright © of MIS Quarterly.en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectPrediction marketen
dc.subjectfixed odds bettingen
dc.subjectcrowd intelligenceen
dc.subjectprospect theoryen
dc.subjectdecision supporten
dc.titleModelling Fixed Odds Betting for Future Event Predictionen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Management Informat Systen
dc.identifier.journalMIS QUARTERLYen
dc.description.note60 month embargo; Published online: June 2017en
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
html.description.abstractPrediction markets provide a promising approach for future event prediction. Most existing prediction market approaches are based on auction mechanisms. Despite their theoretical appeal and success in various application settings, these mechanisms suffer from several major drawbacks. First, opinions from experts and amateurs are treated equally. Second, continuous attention from participants is assumed. Third, such mechanisms are subject to various forms of market manipulation. To alleviate these limitations, we propose to employ the classic fixed odds betting as an alternative prediction market mechanism. We build a structural model based on a belief-decision framework as the event probability estimator. This belief-decision framework models bettors' beliefs with mixed beta distributions and bettors' decisions with prospect theory. A maximum likelihood approach is applied to estimate the model parameters. We conducted experiments on three real-world betting datasets to evaluate our proposed approach. Experimental results show that fixed odds betting based prediction outperforms the reduced form models based on odds and betting results, and achieves a comparable performance with auction-based prediction markets. The results suggest the possibility of employing fixed odds betting as a prediction market in a variety of application contexts where the assumptions made by auction-based approaches do not hold.


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