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How Information Cascades Caused the 2008 Housing Bubble

One topic we spent a lot of time covering in lecture was the idea of information cascades. As explained in the lecture, information cascades are when people start to act based upon the information they gained from the actions of others (in the same situation) rather than act based upon the information they gain.  This can eventually lead to many people making the same wrong decision because they act based on the information from the actions of those before them (which was wrong) and make the same wrong decision (rather than make the right one based on the information in front of them). One specific time this caused massive damage was in the housing bubble of the late 2000’s. In an article written in the New York Times in 2008 titled “How a Bubble Stayed Under the Radar,” Robert J. Shiller explains how an information cascade caused many experts to miss obvious signs that could’ve hinted at the resulting crash. (

Shiller first explains the idea behind information cascades and gives a general example. After the reader has an understanding of information cascades, Shiller writes

“Let’s update the example to apply it to the recent bubble: The individuals in the group must each decide whether real estate is a terrific investment and whether to buy some property. Suppose that there is a 60 percent probability that any one person’s information will lead to the right decision.

In other words, that person’s information is useful but not definitive — and not clear enough to make a firm judgment about something as momentous as a market bubble. Perhaps that is how Mr. Greenspan assessed the probability that he could make an accurate judgment about the stock market bubble.

The theory helps explain why he — or anyone trying to verify the existence of a market bubble — may have squelched his own judgment.”

This is interesting to look at as a networks student. Prior to reading this, I was skeptical as to how information cascades actually worked (meaning I understood the mathematics that went into the calculations of an information cascade but was unsure how these probabilities actually changed the human mind). However, this article is a very good example of how information cascades can influence a person’s decision more than the information sitting in front of them.

Furthermore, this is just one example of the damage caused by information cascades. I am very curious to see other real-world examples where information cascades caused millions to make a wrong decision.


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