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Eliminating Public Knowledge Biases in Information-Aggregation Mechanisms

Author

Listed:
  • Kay-Yut Chen

    (Hewlett-Packard Laboratories, Palo Alto, California 94304)

  • Leslie R. Fine

    (Hewlett-Packard Laboratories, Palo Alto, California 94304)

  • Bernardo A. Huberman

    (Hewlett-Packard Laboratories, Palo Alto, California 94304)

Abstract

We present a novel methodology for identifying public knowledge and eliminating the biases it creates when aggregating information in small group settings. A two-stage mechanism consisting of an information market and a coordination game is used to reveal and adjust for individuals' public information. A nonlinear aggregation of their decisions then allows for the calculation of the probability of the future outcome of an uncertain event, which can then be compared to both the objective probability of its occurrence and the performance of the market as a whole. Experiments show that this nonlinear aggregation mechanism outperforms both the imperfect market and the best of the participants.

Suggested Citation

  • Kay-Yut Chen & Leslie R. Fine & Bernardo A. Huberman, 2004. "Eliminating Public Knowledge Biases in Information-Aggregation Mechanisms," Management Science, INFORMS, vol. 50(7), pages 983-994, July.
    • Handle: RePEc:inm:ormnsc:v:50:y:2004:i:7:p:983-994
    DOI: 10.1287/mnsc.1040.0247
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    References listed on IDEAS

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    Cited by:

    1. van Bruggen, G.H. & Spann, M. & Lilien, G.L. & Skiera, B., 2006. "Institutional Forecasting: The Performance of Thin Virtual Stock Markets," ERIM Report Series Research in Management ERS-2006-028-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. Keser, Claudia & Markstädter, Andreas, 2014. "Informational asymmetries in laboratory asset markets with state-dependent fundamentals," University of Göttingen Working Papers in Economics 207 [rev.], University of Goettingen, Department of Economics.
    3. Cipriano Santos & Tere Gonzalez & Haitao Li & Kay-Yut Chen & Dirk Beyer & Sundaresh Biligi & Qi Feng & Ravindra Kumar & Shelen Jain & Ranga Ramanujam & Alex Zhang, 2013. "HP Enterprise Services Uses Optimization for Resource Planning," Interfaces, INFORMS, vol. 43(2), pages 152-169, April.
    4. Dai, Min & Jia, Yanwei & Kou, Steven, 2021. "The wisdom of the crowd and prediction markets," Journal of Econometrics, Elsevier, vol. 222(1), pages 561-578.
    5. Sascha Kurz, 2018. "Importance In Systems With Interval Decisions," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-23, September.
    6. Keser, Claudia & Markstädter, Andreas, 2014. "Informational asymmetries in laboratory asset markets with state-dependent fundamentals," University of Göttingen Working Papers in Economics 207, University of Goettingen, Department of Economics.
    7. Phillip E. Pfeifer & Yael Grushka-Cockayne & Kenneth C. Lichtendahl, 2014. "The Promise of Prediction Contests," The American Statistician, Taylor & Francis Journals, vol. 68(4), pages 264-270, November.
    8. 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.
    9. Asa B. Palley & Jack B. Soll, 2019. "Extracting the Wisdom of Crowds When Information Is Shared," Management Science, INFORMS, vol. 67(5), pages 2291-2309, May.
    10. Thomas Görzen & Florian Laux, 2019. "Extracting the Wisdom from the Crowd: A Comparison of Approaches to Aggregating Collective Intelligence," Working Papers Dissertations 56, Paderborn University, Faculty of Business Administration and Economics.
    11. Phillip E. Pfeifer, 2016. "The promise of pick-the-winners contests for producing crowd probability forecasts," Theory and Decision, Springer, vol. 81(2), pages 255-278, August.
    12. John P. Lightle & John H. Kagel & Hal R. Arkes, 2009. "Information Exchange in Group Decision Making: The Hidden Profile Problem Reconsidered," Management Science, INFORMS, vol. 55(4), pages 568-581, April.
    13. Cem Peker, 2023. "Extracting the collective wisdom in probabilistic judgments," Theory and Decision, Springer, vol. 94(3), pages 467-501, April.
    14. 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.
    15. Kenneth C. Lichtendahl & Yael Grushka-Cockayne & Phillip E. Pfeifer, 2013. "The Wisdom of Competitive Crowds," Operations Research, INFORMS, vol. 61(6), pages 1383-1398, December.
    16. Claudia Keser & Andreas Markstädter, 2014. "Informational Asymmetries in Laboratory Asset Markets with State-Dependent Fundamentals," CIRANO Working Papers 2014s-30, CIRANO.

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