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Deliberating Groups versus Prediction Markets (or Hayek's Challenge to Habermas)

Published online by Cambridge University Press:  03 January 2012

Cass R. Sunstein
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Abstract

For multiple reasons, deliberating groups often converge on falsehood rather than truth. Individual errors may be amplified rather than cured. Group members may fall victim to a bad cascade, either informational or reputational. Deliberators may emphasize shared information at the expense of uniquely held information. Finally, group polarization may lead even rational people to unjustified extremism. By contrast, prediction markets often produce accurate results, because they create strong incentives for revelation of privately held knowledge and succeed in aggregating widely dispersed information. The success of prediction markets offers a set of lessons for increasing the likelihood that groups can obtain the information that their members have.

Type
Research Article
Information
Episteme , Volume 3 , Issue 3 , October 2006 , pp. 192 - 213
Copyright
Copyright © Cambridge University Press 2006

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References

Notes

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2 I explore these mechanisms from different directions in Sunstein, Cass R., Why Societies Need Dissent (Cambridge, Mass.: Harvard University Press, 2003)Google Scholar, and Sunstein, Cass R., Infotopia: How Many Minds Aggregate Knowledge (New York: Oxford University Press, 2006)Google Scholar; I have borrowed from those accounts here.

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39 Stasser and Titus, “Pooling of Unshared Information,” 1473.

40 Ibid., 1476.

41 Ibid.

42 Ibid.

43 Stasser and Titus, “Hidden Profiles,” 305.

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56 See Willinger and Ziegelmeyet, “Are More Informed Agents,” 291.

57 See Anderson and Holt, “Information Cascades in the Laboratory,” 847.

58 See Hung and Plott, “Information Cascades,” 1515–17.

59 Ibid., 1516.

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61 Ibid., 204.

62 Ibid., 224.

63 Ibid.

64 Sunstein, Schkade, and Ellman, “Ideological Voting,” 301.

65 See Schkade, David et al. , “Deliberating about Dollars: The Severity Shift,” Columbia Law Review 100 (2000): 101CrossRefGoogle Scholar.

66 Brown, Social Psychology, 200–45.

67 Ibid. It has similarly been suggested that majorities are especially potent because people do not want to incur the wrath, or lose the favor, of large numbers of others, and that when minorities have influence, it is because they produce genuine attitudinal change. See Baron, Robert et al. , “Social Corroboration and Opinion Extremity,” Journal of Experimental and Social Psychology, 32 (1996), 537CrossRefGoogle ScholarPubMed.

68 Baron et al., “Social Corroboration,” 537.

69 Ibid.

70 I am grateful to Christian List for pressing this point; he should not be held responsible for my restatement of it here.

71 For valuable overviews, see Wolfers, Justin and Zitzewitz, Eric, “Prediction Markets,” Journal of Economic Perspectives 18 (2004): 107;CrossRefGoogle ScholarAbramowicz, Michael, “Information Markets, Administrative Decisionmaking, and Predictive Cost-Benefit Analysis,” University of Chicago Law Review 71 (2004): 933;Google ScholarLevmore, Saul, “Simply Efficient Markets and the Role of Regulation,” Journal of Corporation Law 28 (2003): 589Google Scholar.

72 For early overviews, see Pennock, David M. et al. , The Real Power of Artificial Markets, 291 Science 987 (2001);Google Scholar David M. Pennock et al., The Power of Play: Efficiency and Forecast Accuracy in Web Market Games, NEC Research Institute Technical Report 1000-168 (2001). A wealth of valuable information can be found at http://www.chrisfmasse.com/

73 See Robert W. Hahn & Paul C. Tetlock, Harnessing the Power of Information: A New Approach to Economic Development 4 (AEI-Brookings Joint Ctr. For Regulatory Studies, Working Paper No. 04-21, 2004), available at http://www.aei-brookings.org/publications/abstract.php?pid=846.

74 See Robin Hanson, “Designing Real Terrorism Futures” (August 2005), available at http://hanson.gmu.edu.

75 Roll, Richard, Orange Juice and Weather, 74 Am. Econ. Rev. 861, 871 (1984)Google Scholar.

76 See Wolfers & Zitzewitz, supra note 207, at 113–14.

77 See Chen, Kay-Yut & Plott, Charles R., Information Aggregation Mechanisms: Concept, Design, and Implementation for a Sales Forecasting Problem 3 (Div. of the Humanities & Soc. Sci., Cal. Inst. of Tech., Social Science Working Paper No. 113, March 2002)Google Scholar (describing variation of this model employed by Hewlett-Packard), available at http://www.hss.caltech.edu/SSPapers/wp1131.pdf.

78 See Putting Crowd Wisdom to Work, available at http://googleblog.blogspot.com/2005/09/putting-crowd-wisdom-to-work.html

79 See Hahn, Robert W. and Tetlock, Paul C., “Using Information Markets to Improve Decision Making,” Harvard Journal of Law and Public Policy 29, No. 1 (Fall 2005): 213–289Google Scholar.

80 Abramowicz, Michael, “Prediction Markets, Administrative Decisionmaking, and Predictive Cost-Benefit Analysis,” University of Chicago Law Review 71 (2004), 933Google Scholar.

81 See Jeffrey A. Sonnenfeld, What Makes Great Boards Great, Harvard Business Review (Sept. 2002).

82 See Brooke Harrington, Pop Finance: Investment Clubs and the New Ownership Society (Princeton: Princeton University Press, 2006, forthcoming).