Sources of Wisdom and Madness

My previous post, Wisdom and Madness of the Yelp Crowd, was a bit on the long side so I didn’t want to clutter it up with references. Here’s two books and a list of relevant papers I’ve enjoyed reading, with a few comments.

Charles MacKay Extraordinary Popular Delusions and The Madness of Crowds, originally published in 1841
This antique is uneven – often dry and boring. Nevertheless, the spine of copy I bought 20 years ago is thoroughly creased. Despite being written in a stuffy archaic style that sometimes bring to mind the worst of Edward Bulwer-Lytton, quite a bit of it remains relevant.  

James Surowiecki. The Wisdom of Crowds 2004
Surowiecki is constantly cited by crowdsourcing fans, peer-based sites, and social activists, most of whom do not appear to have read it, based on their seeming ignorance of his criteria of independence, decentralization and diversity. This book is well-thought and well-written, and is essential for anyone interested in crowdsourcing, social software, and collective decisions. Quibble: I disagree with his conclusions about the sociological implications of crowd-based estimates and the promise of crowd wisdom in the area of coordination.

Mostapha Benhenda A Model of Deliberation Based on Rawls’s Political Liberalism, Social Choice and Welfare, Vol. 36, 2011 

Sushil Bikhchandani, David Hirshleifer, A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades, The Journal of Political Economy; Oct 1992 

B. Fischhoff. Hindsight ≠ foresight: the effect of outcome knowledge on judgment under uncertainty Journal of Experimental Psychology, 1975.

Benjamin Golub, Matthew O. Jackson. Naive Learning in Social Networks and the Wisdom of Crowds, American Economic Journal Microeconomics. February 2010. A really interesting study showing, among other things, that accuracy of crowd judgment is unrelated to the time elapsed before consensus is reached.

 Cars Hommes , Joep Sonnemans, et. al. Coordination of Expectations in Asset Pricing Experiments, Tinbergen Institute Discussion Papers, Mar 2004.

Vassilis Kostakos, Is the crowd’s wisdom biased? A quantitative assessment of three online communities, Proceedings of the 2009 International Conference on Computational Science and Engineering – Volume 04. Kostakos studied three voting/rating websites, Imdb, Amazon and BookCrossings, finding the aggregate voting behavior in them – particularly Amazon – to be very similar to what I saw in Yelp data. 

Jaron Lanier. Digital Maoism: The Hazards of the New Online Collectivism, Edge Essay, May 30, 2006

 Albert E. Mannes. “Are We Wise About the Wisdom of Crowds? The Use of Group Judgments in Belief Revision” Management Science, Aug 2009.
Mannes shows substantial evidence that in cases where members of organizations agree that group wisdom has yielded a better conclusion, most people still overvalued  their original beliefs and conclusions, while underweighting the more correct judgment reached by group wisdom. Is this merely managerial arrogance, or perhaps something more complex, related to pluralistic ignorance?

Jan Lorenz, Heiko Rauhut, et. al. How social influence can undermine the wisdom of crowd effect, Proceedings of the National Acadamy of Science, May 31 2011.
Excerpt from Abstract: “Social groups can be remarkably smart and knowledgeable when their averaged judgements are compared with the judgements of individuals. Already Galton [Galton F (1907) Nature 75:7] found evidence that the median estimate of a group can be more accurate than estimates of experts. This wisdom of crowd effect was recently supported by examples from stock markets, political elections, and quiz shows [Surowiecki J (2004) The Wisdom of Crowds]. In contrast, we demonstrate by experimental evidence (N = 144) that even mild social influence can undermine the wisdom of crowd effect in simple estimation tasks.”

 Ville Miettinen. Crowdsourced Design: It’s a Crowd, Not a Committee, blog post, Mar 26, 2012.

 Matthew J. Salganik, Peter Sheridan Dodds et. al. “Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market,” Science Feb 10 2006.
This is a fabulous piece of web-based experimental psychology of peer influence in cultural markets. They explore the phenomenon of “hits” in popular music, showing that increasing the strength of social influence increased both inequality and unpredictability of success. They make sense of the fact that while music superstars are orders of magnitude more popular than average selling bands, music experts invariably fail to predict superstars’ success. 

Joseph P. Simmons, Lief D. Nelson, et. al. Intuitive Biases in Choice vs. Estimation: Implications for the Wisdom of Crowds, Carnegie Mellon UniversityResearch Showcase, July 22, 2010.
The study looks at how different types of judgments show varying degrees of crowd wisdom. In an NFL football betting context, incorrect intuition led a crowd to predict favorites more than underdogs where betting on underdogs would have won the bets. Moreover, they showed that crowd bias toward this error increased over the season.

Amos Tversky, Daniel Kahneman , Judgment under Uncertainty: Heuristics and Biases, Science, Sep. 27, 1974


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