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

  • Cass R. Sunstein

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.

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Notes

1 Janis, Irving L., Groupthink, 2d ed. (Boston: Houghton Mifflin, 1982), 79.

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

3 Sunstein, Cass R., Schkade, David, and Ellman, Lisa, “Ideological Voting on Federal Courts of Appeals: A Preliminary Investigation,” Virginia Law Review 90 (2004): 301.

4 See the comparison of democratic and nondemocratic regimes in Johnson, Dominic, Overconfidence and War: The Havoc and Glory of Positive Illusions (Cambridge, Mass.: Harvard University Press, 2004): 180–83.

5 Brown, Roger, Social Psychology, 2d ed. (New York: Free Press, 1986): 206–7.

7 Heath, Chip and Gonzalez, Rich, “Interaction with Others Increases Decision Confidence but Not Decision Quality: Evidence against Information Collection Views of Interactive Decision Making,” Organizational Behavior and Human Decision Processes 61 (1995): 305.

8 See Baron, Robert et al. , “Social Corroboration and Opinion Extremity, Journal of Experimental Social Psychology 32 (1996): 537.

9 Gigone, Daniel and Hastie, Reid, “Proper Analysis of the Accuracy of Group Judgments,” Psychological Bulletin 121 (1997): 149, 161;Hastie, Reid, “Experimental Evidence of Group Accuracy,” in Information Pooling and Group Decision Making, ed. Grofman, Bernard, Owen, Guillermo et al. (Greenwich,Conn.: JAI Press, 1986), 129.

10 MacCoun, Robert J., “Comparing Micro and Macro Rationality,” in Judgments, Decisions, and Public Policy, ed. Gowda, Rajeev and Fox, Jeffrey (Cambridge: Cambridge University Press, 2002), 116, 121.

12 Armstrong, J. Scott, “Combining Forecasts,” in Principles of Forecasting, ed. Armstrong, J. Scott (Boston: Kluwer Academic, 2001), 433.

13 See Sunstein, Why Societies Need Dissent.

14 Christenson, Caryn and Abbott, Ann, “Team Medical Decision Making,” in Decision Making in Health Care, ed. Chapman, Gretchen and Sonnenberg, Frank (New York: Cambridge University Press, 2000), 267, 273–76.

15 Thorndike, Robert L., “The Effect of Discussion upon the Correctness of Group Decisions: When the Factor of Majority Influence Is Allowed For,” Journal of Social Psychology 9 (1938): 343.

16 See Habermas, Jürgen, Between Facts and Norms: An Author's Reflections, 76 DENV. U. L. REV. 937, 940 (1999).

17 See Habermas, Jürgen, ‘What is Universal Pragmatics?’, in Communication and the Evolution of Society 1, 2–4, 32 (McCarthy, Thomas trans., 1979) (discussing preconditions for communication).

18 See Gutmann, Amy and Thompson, Dennis, Democracy and Disagreements (Cambridge, Mass: Harvard University Press, 1997), 78 (outlining foundations of authors' vision of deliberative democracy).

19 See generally Hayek, F.A., ‘The Use of Knowledge in Society’, 35 Am. Econ. Rev. 519 (1945) (discussing dispersal of knowledge and its aggregation through markets).

20 For an overview, see Gilovich, Thomas, Griffin, Dale, and Kahneman, Daniel, Heuristics and Biases: The Psychology of Intuitive Judgment (New York: Cambridge University Press, 2002).

21 Tversky, Amos and Kahneman, Daniel, “Judgment under Uncertainty: Heuristics and Biases,” in Judgment under Uncertainty: Heuristics and Biases, ed. Kahneman, Daniel, Slovic, Paul, and Tversky, Amos (Cambridge: Cambridge University Press), 1982, 3.

22 Paul Rozin and Carol Nemeroff, “Sympathetic Magical Thinking: The Contagion and Similarity ‘Heuristics,’” in Gilovich, Griffin, and Kahneman, Heuristics and Biases p. 201

23 Tversky and Kahneman, “Judgment under Uncertainty,” 3.

24 MacCoun, “Comparing Micro and Macro Rationality,” 116, 121.

25 Stasson, Mark F. et al. , “Group Consensus Approaches in Cognitive Bias Tasks,” Japanese Psychological Research 30 (1988): 68.

26 See Kerr, Norbert L. et al. , “Bias in Judgment: Comparing Individuals and Groups,” Psychology Review 103 (1996): 687, 689, 691–93.

27 Sniezek, Janet A. and Henry, Rebecca A., “Accuracy and Confidence in Group Judgment,” Organizational Behavior and Human Decision Processes 43 (1989): 1. This finding very much bears on excessive risk-taking, including in the context of making war. See Dominic Johnson, Overconfidence and War, 180–83.

28 Schumann, Edward L. and Thompson, W. C., “Effects of Attorney's Arguments on Jurors’ Use of Statistical Evidence” (unpublished manuscript, 1989).

29 Whyte, Glen, “Escalating Commitment in Individual and Group Decision Making,” Organizational Behavior and Human Decision Processes 54 (1993): 430.

30 Gentry, James W. and Mowen, John C., “Investigation of the Preference Reversal Phenomenon in a New Product Introduction Task,” Journal of Applied Psychology 65 (1980): 715;Irwin, Julie R. and Davis, James H., “Choice/Matching Preference Reversals in Groups,” Organizational Behavior and Human Decision Processes 64 (1995): 325.

31 Whyte, “Escalating Commitment,” 430.

32 Stasson et al., “Group Consensus Approaches,” 68.

33 Stasser, Garold and Titus, William, “Hidden Profiles: A Brief History,” Psychological Inquiry 14 (2003): 304.

34 Gigone, Daniel and Hastie, Reid, “The Common Knowledge Effect: Information Sharing and Group Judgments,” Journal of Personality and Social Psychology 65 (1993): 959.

35 See Hightower, Ross and Sayeed, Lutfus, “The Impact of Computer-Mediated Communication Systems on Biased Group Discussion,” Computers in Human Behavior 11 (1995): 33.

36 Wallace, Patricia, The Psychology of the Internet (Cambridge: Cambridge University Press, 1999), 82.

37 See Stasser, Garold and Titus, William, “Pooling of Unshared Information in Group Decision Making: Biased Information Sampling during Discussion,” Journal of Personality and Social Psychology 48 (1985): 1467.

38 Ibid., 1473; see also Stasser and Titus, “Hidden Profiles,” 304.

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

40 Ibid., 1476.

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

44 See Gigone, Daniel and Hastie, Reid, “The Common Knowledge Effect: Information Sharing and Group Judgments,” Journal of Personality and Social Psychology 65 (1993): 959.

45 Ibid., 960.

46 Ibid., 973.

47 See Stasser, Garold et al. , “Information Sampling in Structured and Unstructured Discussions of Three and Six-Person Groups,” Journal of Personality and Social Psychology 57 (1989): 67.

48 Ibid., 78.

49 Ibid., 72.

50 I draw here on Hirschleifer, David, “The Blind Leading the Blind,” in The New Economics of Human Behavior, ed. Tommasi, Marianno and Ierulli, Kathryn (Cambridge: Cambridge University Press, 1995), 188, 193–94.

51 Ibid., 195.

52 See ibid; also see Sunstein, Why Societies Need Dissent.

53 See Anderson, Lisa and Holt, Charles, “Information Cascades in the Laboratory,” American Economic Review 87 (1997): 847.

54 See Hung, Angela and Plott, Charles, “Information Cascades: Replication and an Extension to Majority Rule and Conformity-Rewarding Institutions,” American Economic Review 91 (2001): 1508, 1515.

55 Thus, 72 percent of subjects followed Bayes's rule in Anderson, Lisa and Holt, Charles, “Information Cascades in the Laboratory,” American Economic Review 87 (1997): 847, and 64 percent in Willinger, Marc and Ziegelmeyet, Anthony, “Are More Informed Agents Able to Shatter Information Cascades in the Lab?” in The Economics of Networks: Interaction and Behaviours, ed. Cohendet, Patrick et al. (New York: Springer, 1998), 291, 304.

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.

60 Brown, Roger, Social Psychology: The Second Edition (New York, N.Y.: Free Press, 1986), 206–7.

61 Ibid., 204.

62 Ibid., 224.

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): 101.

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), 537.

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

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;Abramowicz, Michael, “Information Markets, Administrative Decisionmaking, and Predictive Cost-Benefit Analysis,” University of Chicago Law Review 71 (2004): 933;Levmore, Saul, “Simply Efficient Markets and the Role of Regulation,” Journal of Corporation Law 28 (2003): 589.

72 For early overviews, see Pennock, David M. et al. , The Real Power of Artificial Markets, 291 Science 987 (2001); 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).

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) (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): 213289.

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

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).

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

  • Cass R. Sunstein

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