Are people in a cascade being irrational?

Not necessarily. Following others can be individually rational when their choices carry more information than one's own signal; the problem is that private information then stops being shared with society.

How do cascades break?

They can collapse when new public information appears, when someone with highly accurate information chooses differently, or when people can share their reasons and not only their choices.

Information Cascade

📈 Economics By CampusDown Wiki Editorial Team Last updated October 26, 2026

An information cascade occurs when people deciding in sequence observe others' earlier choices and find it rational to ignore their own private information and follow the majority, so the same choice propagates down the line.

Contents

1. The basic model
2. Properties: lock-in and fragility
3. Experiments and real-world cases
4. FAQ
5. References

The basic model

In the same year, Bikhchandani, Hirshleifer and Welch (1992) and Banerjee (1992) published models of informational cascades and herd behavior. Suppose people choose between two restaurants; each receives an imperfect private signal about which is better and can observe only the choices of those before them. If the first two both chose A, a third person whose signal points to B still does better, by Bayes' theorem, to choose A. Her choice then reveals nothing about her own information, and everyone after her picks A for the same reason, so a cascade begins. The key point is that information fails to accumulate in the group not because individuals are irrational but because they act rationally.

Properties: lock-in and fragility

Because cascades can start from the choices of only a few early movers, they can lock in the wrong option and produce outcomes much worse than the information society collectively holds would allow. At the same time, their thin foundations make them fragile: a single piece of public information or a confident minority can suddenly overturn them, which helps explain why fads and fashions shift abruptly (Bikhchandani et al., 1998). Smith and Sørensen (2000) analyzed conditions, such as unbounded signal strength, under which learning eventually converges to the truth, and Acemoglu et al. (2011) showed how social network structure determines whether learning succeeds. Cascades differ from Asch's (1951) conformity under group pressure because people follow for informational reasons, and they are a narrower, rational mechanism than the popularity-driven bandwagon effect.

Experiments and real-world cases

Anderson and Holt (1997) used urn-drawing experiments to show that cascades form frequently in the laboratory. Welch (1992) analyzed how early investors' reactions in initial public offerings ripple through later investors. Zhang (2010) showed that patients on the U.S. kidney transplant waiting list infer low quality from kidneys that earlier patients declined and decline them in turn. In Salganik, Dodds and Watts's (2006) artificial music market, showing download counts made popularity more unequal and less predictable, and Muchnik, Aral and Taylor (2013) found that randomly giving an online comment one up-vote substantially increased later positive ratings. Kuran and Sunstein (1999) warned that "availability cascades," in which repeated mention spreads belief, can distort risk regulation, linking the idea to the availability heuristic.

FAQ

Are people in a cascade being irrational?: Not necessarily. Following others can be individually rational when their choices carry more information than one's own signal; the problem is that private information then stops being shared with society.
How do cascades break?: They can collapse when new public information appears, when someone with highly accurate information chooses differently, or when people can share their reasons and not only their choices.

Sources reviewed: 2026-10-26

References

Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992–1026.
Banerjee, A. V. (1992). A simple model of herd behavior. Quarterly Journal of Economics, 107(3), 797–817.
Welch, I. (1992). Sequential sales, learning, and cascades. Journal of Finance, 47(2), 695–732.
Asch, S. E. (1951). Effects of group pressure upon the modification and distortion of judgments. In H. Guetzkow (Ed.), Groups, Leadership and Men (pp. 177–190). Pittsburgh: Carnegie Press.
Anderson, L. R., & Holt, C. A. (1997). Information cascades in the laboratory. American Economic Review, 87(5), 847–862.
Bikhchandani, S., Hirshleifer, D., & Welch, I. (1998). Learning from the behavior of others: Conformity, fads, and informational cascades. Journal of Economic Perspectives, 12(3), 151–170.
Smith, L., & Sørensen, P. (2000). Pathological outcomes of observational learning. Econometrica, 68(2), 371–398.
Kuran, T., & Sunstein, C. R. (1999). Availability cascades and risk regulation. Stanford Law Review, 51(4), 683–768.
Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762), 854–856.
Acemoglu, D., Dahleh, M. A., Lobel, I., & Ozdaglar, A. (2011). Bayesian learning in social networks. Review of Economic Studies, 78(4), 1201–1236.
Zhang, J. (2010). The sound of silence: Observational learning in the U.S. kidney market. Marketing Science, 29(2), 315–335.
Muchnik, L., Aral, S., & Taylor, S. J. (2013). Social influence bias: A randomized experiment. Science, 341(6146), 647–651.

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This article was last updated on October 26, 2026. It is based on widely recognized original works and textbooks; when citing it in a paper, please check the original sources listed in the references.