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Affecting Policy by Manipulating Prediction Markets: Experimental Evidence

Author

Listed:
  • Cary Deck

    (University of Arkansas and Economic Science Institute)

  • Shengle Lin

    (Economic Science Institute, Chapman University)

  • David Porter

    (Economic Science Institute, Chapman University)

Abstract

Documented results indicate prediction markets effectively aggregate information and form accurate predictions. This has led to a proliferation of markets predicting everything from the results of elections to a company’s sales to movie box office receipts. Recent research suggests prediction markets are robust to manipulation attacks and resulting market outcomes improve forecast accuracy. However, we present evidence from the lab indicating that well funded, single minded manipulators can in fact destroy a prediction market’s ability to aggregate information. Our results clearly indicate that the usefulness of prediction markets as inputs to decision making may be limited.

Suggested Citation

  • Cary Deck & Shengle Lin & David Porter, 2010. "Affecting Policy by Manipulating Prediction Markets: Experimental Evidence," Working Papers 10-15, Chapman University, Economic Science Institute.
  • Handle: RePEc:chu:wpaper:10-15
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    References listed on IDEAS

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    Cited by:

    1. Tai, Chung-Ching & Lin, Hung-Wen & Chie, Bin-Tzong & Tung, Chen-Yuan, 2019. "Predicting the failures of prediction markets: A procedure of decision making using classification models," International Journal of Forecasting, Elsevier, vol. 35(1), pages 297-312.
    2. Florian Teschner & David Rothschild & Henner Gimpel, 2017. "Manipulation in Conditional Decision Markets," Group Decision and Negotiation, Springer, vol. 26(5), pages 953-971, September.
    3. Brice Corgnet & Cary Deck & Mark DeSantis & Kyle Hampton & Erik O. Kimbrough, 2023. "When Do Security Markets Aggregate Dispersed Information?," Management Science, INFORMS, vol. 69(6), pages 3697-3729, June.
    4. Lawrence Choo & Todd R. Kaplan & Ro’i Zultan, 2022. "Manipulation and (Mis)trust in Prediction Markets," Management Science, INFORMS, vol. 68(9), pages 6716-6732, September.
    5. Boulu-Reshef, Béatrice & Comeig, Irene & Donze, Robert & Weiss, Gregory D., 2016. "Risk aversion in prediction markets: A framed-field experiment," Journal of Business Research, Elsevier, vol. 69(11), pages 5071-5075.
    6. Brice Corgnet & Cary Deck & Mark DeSantis & David Porter, 2022. "Forecasting Skills in Experimental Markets: Illusion or Reality?," Management Science, INFORMS, vol. 68(7), pages 5216-5232, July.
    7. Bin-Tzong Chie & Chih-Hwa Yang, 2021. "Efficiency of the Experimental Prediction Market: Public Information, Belief Evolution, and Personality Traits," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 11(4), pages 1-3.
    8. Ahrash Dianat & Christoph Siemroth, 2021. "Improving decisions with market information: an experiment on corporate prediction markets," Experimental Economics, Springer;Economic Science Association, vol. 24(1), pages 143-176, March.
    9. Boris Maciejovsky & David V. Budescu, 2020. "Too Much Trust in Group Decisions: Uncovering Hidden Profiles by Groups and Markets," Organization Science, INFORMS, vol. 31(6), pages 1497-1514, November.
    10. Alba Ruiz-Buforn & Simone Alfarano & Eva Camacho-Cuena & Andrea Morone, 2022. "Single vs. multiple disclosures in an experimental asset market with information acquisition," The European Journal of Finance, Taylor & Francis Journals, vol. 28(13-15), pages 1513-1539, October.
    11. Bergemann, Dirk & Ottaviani, Marco, 2021. "Information Markets and Nonmarkets," CEPR Discussion Papers 16459, C.E.P.R. Discussion Papers.
    12. Ruiz-Buforn, Alba & Camacho-Cuena, Eva & Morone, Andrea & Alfarano, Simone, 2021. "Overweighting of public information in financial markets: A lesson from the lab," Journal of Banking & Finance, Elsevier, vol. 133(C).
    13. Patrick Buckley & Fergal O’Brien, 0. "The effect of malicious manipulations on prediction market accuracy," Information Systems Frontiers, Springer, vol. 0, pages 1-13.
    14. Bregu, Klajdi, 2020. "Overconfidence and (Over)Trading: The Effect of Feedback on Trading Behavior," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 88(C).
    15. Lionel Page & Christoph Siemroth, 2021. "How Much Information Is Incorporated into Financial Asset Prices? Experimental Evidence," Review of Financial Studies, Society for Financial Studies, vol. 34(9), pages 4412-4449.
    16. Deck, Cary & Hao, Li & Porter, David, 2015. "Do prediction markets aid defenders in a weak-link contest?," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 248-258.
    17. Lunawat, Radhika, 2021. "Learning from trading activity in laboratory security markets with higher-order uncertainty," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 90(C).
    18. Patrick Buckley & Fergal O’Brien, 2017. "The effect of malicious manipulations on prediction market accuracy," Information Systems Frontiers, Springer, vol. 19(3), pages 611-623, June.

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    More about this item

    Keywords

    Information Aggregation; Prediction Markets; Manipulation;
    All these keywords.

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • G1 - Financial Economics - - General Financial Markets

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