Information cascade

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An information (or informational) cascade occurs when people observe the actions of others and then make the same choice that the others have made, independently of their own private information signals. Because it is usually sensible to do what other people are doing, the phenomenon is assumed to be the result of rational choice. Nevertheless, information cascades can sometimes lead to arbitrary or even erroneous decisions. The concept of information cascades is based on observational learning theory and was formally introduced in a 1992 article by Sushil Bikhchandani, David Hirshleifer, and Ivo Welch.[1]. A less technical article was released by the authors in 1998.[2]

Information cascades are fragile because new information can overturn a long-standing behavioral trend. Because people are rational, they realize that their behavior is based on limited information and are willing to change. Thus, even though a thousand people may have chosen one action, a single new piece of information can induce people to subsequently choose a different action.

There are two key conditions in an information cascade model:

  1. Sequential decisions with subsequent actors observing decisions (not information) of previous actors.
  2. A limited action space (e.g. an adopt/reject decision).[3]

Contents

[edit] Examples and fields of application

Information cascades occur in situations where seeing many people make the same choice provides evidence that outweighs ones own judgment. That is, one thinks: "It's more likely that I'm wrong than that all those other people are wrong. Therefore, I will do as they do."

[edit] Market cascades

Information cascades have become one of the topics of behavioral economics, as they are often seen in financial markets where they can feed speculation and create cumulative and excessive price moves, either for the whole market (market bubble...) or a specific asset, for example a stock that becomes overly popular among investors.

Information cascades are usually considered by economists:

  • as products of rational expectations at their start,
  • as irrational herd behavior if they persist for too long, which signals that collective emotions come also into play to feed the cascade.

[edit] Historical examples

  • Small protests began in Leipzig, Germany in 1989 with just a handful of activists challenging the German Democratic Republic.[4] For almost a year, protesters met every Monday growing by a few people each time.[4] By the time the government attempted to address it in September 1989, it was too big to squash.[4] In October, the number of protesters reached 100,000 and by the first Monday in November, over 400,000 people marched the streets of Leipzig. Two days later the Berlin Wall was dismantled.[4]
  • The adoption rate of drought-resistant hybrid seed corn during the Great Depression and Dust Bowl was slow despite its significant improvement over the previously available seed corn. Researchers at Iowa State University were interested in understanding the public's hesitation to the adoption of this significantly improved technology. After conducting 259 interviews with farmers[5] it was observed that the slow rate of adoption was due to how the farmers valued the opinion of their friends and neighbors instead of the word of a salesman. See [6] for the original report.

[edit] Legal aspects

The negative effects of informational cascades sometimes become a legal concern and laws have been enacted to neutralize them. Ward Farnsworth, a law professor, analyzed the legal aspects of informational cascades and gave several examples in his book The Legal Analyst: in many military courts, the officers voting to decide a case vote in reverse rank order (the officer of the lowest rank votes first), and he suggested it may be done so the lower-ranked officers would not be tempted by the cascade to vote with the more senior officers, who are believed to have more accurate judgement; another example is that countries such as Israel and France have laws that prohibit polling days or weeks before elections to prevent the effect of informational cascade that may influence the election results.[7]

[edit] See also

[edit] References

  1. ^ Bikhchandani, S., Hirshleifer, D., and Welch, I. (1992), "A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades," Journal of Political Economy, Volume 100, Issue 5, pp. 992-1026.
  2. ^ Bikhchandani, S., Hirshleifer, D., and Welch, I. (1998), "Learning from the Behavior of Others: Conformity, Fads, and Informational Cascades," Journal of Economic Perspectives, Volume 12, Issue 3, pp. 151-170.
  3. ^ Information Cascades and Rational Herding: An Annotated Bibliography and Resource Reference
  4. ^ a b c d Shirky, Clay (2008). Here Comes Everybody: The Power of Organizing Without Organizations. New York: Penguin Press. pp. 161–164. ISBN 1594201536. 
  5. ^ Carboneau, Clark. "Using Diffusion of Innovations and Academic Detailing to Spread Evidence-based Practices". Journal for Healthcare Quality. http://www.nahq.org/journal/online/0305/toolbox.html. Retrieved on 2008-11-11. 
  6. ^ Beal, George M.; Bohlen, Joe M. (PDF). The Diffusion Process. Iowa State University of Science and Technology of Ames, Iowa. http://www.soc.iastate.edu/extension/presentations/publications/comm/Diffusion%20Process.pdf. Retrieved on 2008-11-11. 
  7. ^ Farnsworth, Ward (2007). The Legal Analyst: A Toolkit for Thinking about the Law. Chicago: University of Chicago Press. ISBN 0226238350

[edit] External links

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