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Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation

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  • Robin Hanson
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    Abstract

    In practice, scoring rules elicit good probability estimates from individuals, while betting markets elicit good consensus estimates from groups. Market scoring rules combine these features, eliciting estimates from individuals or groups, with groups costing no more than individuals. Regarding a bet on one event given another event, only logarithmic versions preserve the probability of the given event. Logarithmic versions also preserve the conditional probabilities of other events, and so preserve conditional independence relations. Given logarithmic rules that elicit relative probabilities of base event pairs, it costs no more to elicit estimates on all combinations of these base events.

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    File URL: http://www.ingentaconnect.com/content/ubpl/jpm/2007/00000001/00000001/art00002
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    Bibliographic Info

    Article provided by University of Buckingham Press in its journal Journal of Prediction Markets.

    Volume (Year): 1 (2007)
    Issue (Month): 1 (February)
    Pages: 3-15

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    Handle: RePEc:buc:jpredm:v:1:y:2007:i:1:p:3-15

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    Web: http://www.predictionmarketjournal.com/index_files/Page418.htm

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    Cited by:
    1. Nicolas Della Penna & Mark D. Reid, 2011. "Bandit Market Makers," Papers 1112.0076, arXiv.org, revised Aug 2013.
    2. Erik Snowberg & Justin Wolfers & Eric Zitzewitz, 2012. "Prediction Markets for Economic Forecasting," NBER Working Papers 18222, National Bureau of Economic Research, Inc.
    3. Mikuláš Gangur & Miroslav Plevný, 2014. "Tools for Consumer Rights Protection in the Prediction of Electronic Virtual Market and Technological Changes," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 36(16), pages 578, May.
    4. Yiling Chen & Mike Ruberry & Jennifer Wortman Vaughan, 2012. "Designing Informative Securities," Papers 1210.4837, arXiv.org.
    5. Jinli Hu, 2012. "Combinatorial Modelling and Learning with Prediction Markets," Papers 1201.3851, arXiv.org.
    6. Ledyard, John & Hanson, Robin & Ishikida, Takashi, 2009. "An experimental test of combinatorial information markets," Journal of Economic Behavior & Organization, Elsevier, vol. 69(2), pages 182-189, February.
    7. David Kelly & David Letson & Forest Nelson & David S. Nolan & Daniel Solis, 2009. "Evolution of Subjective Hurricane Risk Perceptions: A Bayesian Approach," Working Papers 0905, University of Miami, Department of Economics.
    8. Abraham Othman & Tuomas Sandholm, 2013. "The Gates Hillman prediction market," Review of Economic Design, Springer, vol. 17(2), pages 95-128, June.
    9. Riekhof, Hans-Christian & Riekhof, Marie-Catherine & Brinkhoff, Stefan, 2012. "Predictive Markets: Ein vielversprechender Weg zur Verbesserung der Prognosequalität im Unternehmen?," PFH Forschungspapiere/Research Papers 2012/07, PFH Private University of Applied Sciences, Göttingen.
    10. Yiling Chen & David M Pennock, 2012. "A Utility Framework for Bounded-Loss Market Makers," Papers 1206.5252, arXiv.org.
    11. Wei Sun & Robin Hanson & Kathryn Blackmond Laskey & Charles Twardy, 2012. "Probability and Asset Updating using Bayesian Networks for Combinatorial Prediction Markets," Papers 1210.4900, arXiv.org.
    12. Papakonstantinou, A. & Rogers, A & Gerding, E. H. & Jennings, N. R., 2010. "Mechanism Design for the truthful elicitation of costly probabilistic estimates in Distributed Information Systems," MPRA Paper 43324, University Library of Munich, Germany.

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