IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v56y2010i11p1977-1996.html
   My bibliography  Save this article

Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders

Author

Listed:
  • Paul J. Healy

    (Department of Economics, The Ohio State University, Columbus, Ohio 43210)

  • Sera Linardi

    (Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California 91125)

  • J. Richard Lowery

    (Finance Department, McCombs School of Business, The University of Texas at Austin, Austin, Texas 78712)

  • John O. Ledyard

    (Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California 91125)

Abstract

Double auction prediction markets have proven successful in large-scale applications such as elections and sporting events. Consequently, several large corporations have adopted these markets for smaller-scale internal applications where information may be complex and the number of traders is small. Using laboratory experiments, we test the performance of the double auction in complex environments with few traders and compare it to three alternative mechanisms. When information is complex we find that an iterated poll (or Delphi method) outperforms the double auction mechanism. We present five behavioral observations that may explain why the poll performs better in these settings.

Suggested Citation

  • Paul J. Healy & Sera Linardi & J. Richard Lowery & John O. Ledyard, 2010. "Prediction Markets: Alternative Mechanisms for Complex Environments with Few Traders," Management Science, INFORMS, vol. 56(11), pages 1977-1996, November.
  • Handle: RePEc:inm:ormnsc:v:56:y:2010:i:11:p:1977-1996
    DOI: 10.1287/mnsc.1100.1226
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.1100.1226
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.1100.1226?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Manski, Charles F., 2006. "Interpreting the predictions of prediction markets," Economics Letters, Elsevier, vol. 91(3), pages 425-429, June.
    2. Colin F. Camerer, 1998. "Can Asset Markets Be Manipulated? A Field Experiment with Racetrack Betting," Journal of Political Economy, University of Chicago Press, vol. 106(3), pages 457-482, June.
    3. De Long, J Bradford, et al, 1990. "Positive Feedback Investment Strategies and Destabilizing Rational Speculation," Journal of Finance, American Finance Association, vol. 45(2), pages 379-395, June.
    4. Plott, Charles R & Sunder, Shyam, 1982. "Efficiency of Experimental Security Markets with Insider Information: An Application of Rational-Expectations Models," Journal of Political Economy, University of Chicago Press, vol. 90(4), pages 663-698, August.
    5. Berg, Joyce & Forsythe, Robert & Nelson, Forrest & Rietz, Thomas, 2008. "Results from a Dozen Years of Election Futures Markets Research," Handbook of Experimental Economics Results, in: Charles R. Plott & Vernon L. Smith (ed.), Handbook of Experimental Economics Results, edition 1, volume 1, chapter 80, pages 742-751, Elsevier.
    6. Robert Bloomfield & Maureen O'Hara & Gideon Saar, 2009. "How Noise Trading Affects Markets: An Experimental Analysis," Review of Financial Studies, Society for Financial Studies, vol. 22(6), pages 2275-2302, June.
    7. Plott, Charles R & Sunder, Shyam, 1988. "Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets," Econometrica, Econometric Society, vol. 56(5), pages 1085-1118, September.
    8. Justin Wolfers & Eric Zitzewitz, 2006. "Interpreting prediction market prices as probabilities," Working Paper Series 2006-11, Federal Reserve Bank of San Francisco.
    9. Ali, Mukhtar M, 1977. "Probability and Utility Estimates for Racetrack Bettors," Journal of Political Economy, University of Chicago Press, vol. 85(4), pages 803-815, August.
    10. Kay-Yut Chen & Leslie R. Fine & Bernardo A. Huberman, 2001. "Forecasting Uncertain Events with Small Groups," Papers cond-mat/0108028, arXiv.org.
    11. Camerer, Colin & Weigelt, Keith, 1991. "Information Mirages in Experimental Asset Markets," The Journal of Business, University of Chicago Press, vol. 64(4), pages 463-493, October.
    12. Forsythe, Robert & Lundholm, Russell, 1990. "Information Aggregation in an Experimental Market," Econometrica, Econometric Society, vol. 58(2), pages 309-347, March.
    13. Robin Hanson & Ryan Oprea, 2009. "A Manipulator Can Aid Prediction Market Accuracy," Economica, London School of Economics and Political Science, vol. 76(302), pages 304-314, April.
    14. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring.
    15. McKelvey, Richard D & Page, Talbot, 1990. "Public and Private Information: An Experimental Study of Information Pooling," Econometrica, Econometric Society, vol. 58(6), pages 1321-1339, November.
    16. repec:kap:expeco:v:1:y:1998:i:1:p:43-62 is not listed on IDEAS
    17. Charles R. Plott & Jorgen Wit & Winston C. Yang, 2003. "Parimutuel betting markets as information aggregation devices: experimental results," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 22(2), pages 311-351, September.
    18. Charles R. Plott & Vernon L. Smith (ed.), 2008. "Handbook of Experimental Economics Results," Handbook of Experimental Economics Results, Elsevier, edition 1, volume 1, number 3.
    19. Charles R. Plott & Vernon L. Smith (ed.), 2008. "Handbook of Experimental Economics Results," Handbook of Experimental Economics Results, Elsevier, edition 1, volume 1, number 7.
    20. Milgrom, Paul & Stokey, Nancy, 1982. "Information, trade and common knowledge," Journal of Economic Theory, Elsevier, vol. 26(1), pages 17-27, February.
    21. Hanson, Robin & Oprea, Ryan & Porter, David, 2006. "Information aggregation and manipulation in an experimental market," Journal of Economic Behavior & Organization, Elsevier, vol. 60(4), pages 449-459, August.
    22. Robin Hanson, 2003. "Combinatorial Information Market Design," Information Systems Frontiers, Springer, vol. 5(1), pages 107-119, January.
    23. O'Brien, John & Srivastava, Sanjay, 1991. "Dynamic Stock Markets with Multiple Assets: An Experimental Analysis," Journal of Finance, American Finance Association, vol. 46(5), pages 1811-1838, December.
    24. Robin Hanson, 2007. "The Policy Analysis Market (A Thwarted Experiment in the Use of Prediction Markets for Public Policy)," Innovations: Technology, Governance, Globalization, MIT Press, vol. 2(3), pages 73-88, July.
    25. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    26. 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.
    27. Reinhard Selten, 1998. "Axiomatic Characterization of the Quadratic Scoring Rule," Experimental Economics, Springer;Economic Science Association, vol. 1(1), pages 43-61, June.
    28. Charles R. Plott & Vernon L. Smith (ed.), 2008. "Handbook of Experimental Economics Results," Handbook of Experimental Economics Results, Elsevier, edition 1, volume 1, number 4.
    29. Charles R. Plott & Vernon L. Smith (ed.), 2008. "Handbook of Experimental Economics Results," Handbook of Experimental Economics Results, Elsevier, edition 1, volume 1, number 2.
    30. Charles R. Plott & Vernon L. Smith (ed.), 2008. "Handbook of Experimental Economics Results," Handbook of Experimental Economics Results, Elsevier, edition 1, volume 1, number 8.
    31. Charles R. Plott & Vernon L. Smith (ed.), 2008. "Handbook of Experimental Economics Results," Handbook of Experimental Economics Results, Elsevier, edition 1, volume 1, number 5.
    32. Thaler, Richard H & Ziemba, William T, 1988. "Parimutuel Betting Markets: Racetracks and Lotteries," Journal of Economic Perspectives, American Economic Association, vol. 2(2), pages 161-174, Spring.
    33. Charles R. Plott & Vernon L. Smith (ed.), 2008. "Handbook of Experimental Economics Results," Handbook of Experimental Economics Results, Elsevier, edition 1, volume 1, number 1.
    34. Charles R. Plott & Vernon L. Smith (ed.), 2008. "Handbook of Experimental Economics Results," Handbook of Experimental Economics Results, Elsevier, edition 1, volume 1, number 6.
    35. Paul W. Rhode & Koleman S. Strumpf, 2004. "Historical Presidential Betting Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 127-141, Spring.
    36. Colin Camerer, 1998. "Can asset markets be manipulated? A field experiment with racetrack betting," Natural Field Experiments 00222, The Field Experiments Website.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Florian Teschner & David Rothschild & Henner Gimpel, 2017. "Manipulation in Conditional Decision Markets," Group Decision and Negotiation, Springer, vol. 26(5), pages 953-971, September.
    2. Page, Lionel & Siemroth, Christoph, 2017. "An experimental analysis of information acquisition in prediction markets," Games and Economic Behavior, Elsevier, vol. 101(C), pages 354-378.
    3. Karimi, Majid & Zaerpour, Nima, 2022. "Put your money where your forecast is: Supply chain collaborative forecasting with cost-function-based prediction markets," European Journal of Operational Research, Elsevier, vol. 300(3), pages 1035-1049.
    4. Abraham Othman & Tuomas Sandholm, 2013. "The Gates Hillman prediction market," Review of Economic Design, Springer;Society for Economic Design, vol. 17(2), pages 95-128, June.
    5. Edward Halim & Yohanes E. Riyanto & Nilanjan Roy, 2019. "Costly Information Acquisition, Social Networks, and Asset Prices: Experimental Evidence," Journal of Finance, American Finance Association, vol. 74(4), pages 1975-2010, August.
    6. John J. Nay & Martin Van der Linden & Jonathan M. Gilligan, 2016. "Betting and Belief: Prediction Markets and Attribution of Climate Change," Papers 1603.08961, arXiv.org, revised Jul 2016.
    7. 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.
    8. 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.
    9. Lian Jian & Rahul Sami, 2012. "Aggregation and Manipulation in Prediction Markets: Effects of Trading Mechanism and Information Distribution," Management Science, INFORMS, vol. 58(1), pages 123-140, January.
    10. Yusufcan Masatlioglu & Sarah Taylor & Neslihan Uler, 2012. "Behavioral mechanism design: evidence from the modified first-price auctions," Review of Economic Design, Springer;Society for Economic Design, vol. 16(2), pages 159-173, September.
    11. Peeters, R.J.A.P. & Wolk, K.L., 2014. "Eliciting and aggregating individual expectations: An experimental study," Research Memorandum 029, Maastricht University, Graduate School of Business and Economics (GSBE).
    12. Zhao, Yang & Yu, Min-Teh, 2020. "Predicting catastrophe risk: Evidence from catastrophe bond markets," Journal of Banking & Finance, Elsevier, vol. 121(C).
    13. Florian Teschner & Henner Gimpel, 2018. "Crowd Labor Markets as Platform for Group Decision and Negotiation Research: A Comparison to Laboratory Experiments," Group Decision and Negotiation, Springer, vol. 27(2), pages 197-214, April.
    14. Cary Deck & David Porter, 2013. "Prediction Markets In The Laboratory," Journal of Economic Surveys, Wiley Blackwell, vol. 27(3), pages 589-603, July.
    15. Ruiz-Buforn, Alba & Alfarano, Simone & Camacho-Cuena, Eva & Morone, Andrea, 2020. "Single vs. multiple disclosures in an experimental asset market with information acquisition," MPRA Paper 101035, University Library of Munich, Germany.
    16. Majid Karimi & Stanko Dimitrov, 2018. "On the Road to Making Science of “Art”: Risk Bias in Market Scoring Rules," Decision Analysis, INFORMS, vol. 15(2), pages 72-89, June.
    17. Krishnamurthy Iyer & Ramesh Johari & Ciamac C. Moallemi, 2014. "Information Aggregation and Allocative Efficiency in Smooth Markets," Management Science, INFORMS, vol. 60(10), pages 2509-2524, October.
    18. Galanis, S. & Ioannou, C. & Kotronis, S., 2019. "Information Aggregation Under Ambiguity: Theory and Experimental Evidence," Working Papers 20/05, Department of Economics, City University London.
    19. Frank M. A. Klingert & Matthias Meyer, 2018. "Comparing Prediction Market Mechanisms: An Experiment-Based and Micro Validated Multi-Agent Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(1), pages 1-7.
    20. Linardi, Sera, 2017. "Accounting for noise in the microfoundations of information aggregation," Games and Economic Behavior, Elsevier, vol. 101(C), pages 334-353.
    21. Sperb, Luis Felipe Costa & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2019. "Keeping a weather eye on prediction markets: The influence of environmental conditions on forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 35(1), pages 321-335.
    22. Liangfei Qiu & Subodha Kumar, 2017. "Understanding Voluntary Knowledge Provision and Content Contribution Through a Social-Media-Based Prediction Market: A Field Experiment," Information Systems Research, INFORMS, vol. 28(3), pages 529-546, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrea Albertazzi & Friederike Mengel & Ronald Peeters, 2021. "Benchmarking information aggregation in experimental markets," Economic Inquiry, Western Economic Association International, vol. 59(4), pages 1500-1516, October.
    2. Jason Shachat & Anand Srinivasan, 2011. "Informational Price Cascades and Non-aggregation of Asymmetric Information in Experimental Asset Markets," Working Papers 1102, Xiamen Unversity, The Wang Yanan Institute for Studies in Economics, Finance and Economics Experimental Laboratory, revised 14 Apr 2011.
    3. Snowberg, Erik & Wolfers, Justin & Zitzewitz, Eric, 2013. "Prediction Markets for Economic Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 657-687, Elsevier.
    4. Eric M. Aldrich & Kristian López Vargas, 2020. "Experiments in high-frequency trading: comparing two market institutions," Experimental Economics, Springer;Economic Science Association, vol. 23(2), pages 322-352, June.
    5. Joyce E. Berg & John Geweke & Thomas A. Rietz, 2010. "Memoirs of an indifferent trader: Estimating forecast distributions from prediction markets," Quantitative Economics, Econometric Society, vol. 1(1), pages 163-186, July.
    6. Johan Almenberg & Ken Kittlitz & Thomas Pfeiffer, 2009. "An Experiment on Prediction Markets in Science," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-7, December.
    7. Anthony Newell & Lionel Page, 2017. "Countercyclical risk aversion and self-reinforcing feedback loops in experimental asset markets," QuBE Working Papers 050, QUT Business School.
    8. John Dickhaut & Shengle Lin & David Porter & Vernon L. Smith, 2010. "Durability, Re-trading and Market Performance," Working Papers 10-01, Chapman University, Economic Science Institute.
    9. Markstädter, Andreas & Keser, Claudia, 2014. "Informational Asymmetries in Laboratory Asset Markets with State Dependent Fundamentals," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100359, Verein für Socialpolitik / German Economic Association.
    10. Galanis, S. & Ioannou, C. & Kotronis, S., 2019. "Information Aggregation Under Ambiguity: Theory and Experimental Evidence," Working Papers 20/05, Department of Economics, City University London.
    11. Wolfers, Justin & Zitzewitz, Eric, 2006. "Prediction Markets in Theory and Practice," CEPR Discussion Papers 5578, C.E.P.R. Discussion Papers.
    12. Bergemann, Dirk & Ottaviani, Marco, 2021. "Information Markets and Nonmarkets," CEPR Discussion Papers 16459, C.E.P.R. Discussion Papers.
    13. Lerner, Peter, 2010. "Theoretical analysis of the bid-ask bounce and Related Phenomena," MPRA Paper 35929, University Library of Munich, Germany.
    14. Veiga, Helena & Vorsatz, Marc, 2008. "Aggregation and dissemination of information in experimental asset markets in the presence of a manipulator," DES - Working Papers. Statistics and Econometrics. WS ws084110, Universidad Carlos III de Madrid. Departamento de Estadística.
    15. Dean Johnson & Patrick Joyce, 2012. "Bubbles and Crashes Revisited," Review of Economics & Finance, Better Advances Press, Canada, vol. 2, pages 29-42, August.
    16. RYan Oprea & David Porter & Chris Hibbert & Robin Hanson & Dorina Tila, 2008. "Can Manipulators Mislead Prediction Market Observers?," Working Papers 08-01, Chapman University, Economic Science Institute.
    17. Dorina Tila & David Porter, 2008. "Group Prediction in Information Markets With and Without Trading Information and Price Manipulation Incentives," Working Papers 08-06, Chapman University, Economic Science Institute.
    18. Lambert, Nicolas S. & Langford, John & Wortman Vaughan, Jennifer & Chen, Yiling & Reeves, Daniel M. & Shoham, Yoav & Pennock, David M., 2015. "An axiomatic characterization of wagering mechanisms," Journal of Economic Theory, Elsevier, vol. 156(C), pages 389-416.
    19. Marco Cipriani & Roberta De Filippis & Antonio Guarino & Ryan Kendall, 2020. "Trading by Professional Traders: An Experiment," Staff Reports 939, Federal Reserve Bank of New York.
    20. Tobias Salz & Emanuel Vespa, 2020. "Estimating dynamic games of oligopolistic competition: an experimental investigation," RAND Journal of Economics, RAND Corporation, vol. 51(2), pages 447-469, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:56:y:2010:i:11:p:1977-1996. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/inforea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Matthew Walls (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.