Informational Cascades in the Laboratory: Do They Occur for the Right Reasons?

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This is an older article about information cascades and how laboratory experiments can give results which coincide with theory, but perhaps may not be for the right reasons. There are more than a few sources which I have read which cite this article, so I thought it would be helpful to see where their ideas originated.

In this study, Bayesian probability was tested versus a subset of undergraduate students who had recently completed a study in Bayesian decision making. The question was administered as part of an exam. However, instead of previous studies, in which the subjects were shown the previous results of other subjects, in this study the information given to the subjects was purposefully prepared, in order to test how well Bayesian theory holds in a real-life setting.  Instead of seeing what others had answered, the experiments purposefully manipulated the subjects’ beliefs about the previous decision(s). Out of a sample of 63 subjects, approximately half responded in accordance to Bayesian theory, but even if they were in accordance, a scant few could respond correctly how they used it to obtain the answer.

From the results of the study, it can be said that Bayesian probability “works” – that is to say, it works when it possibly coincides with other heuristics, such as following one’s own signal, or following the majority. From there, cascades appear to form, but for different reasons than the probability would predict. As discussed in class, the “follow one’s own signal” approach is the resolution when trying to work through a cascade situation, when given conflicting data in equal amounts (i.e. 3 high signals, and 3 low signals).  But according to the paper, appearance of this phenomena appears much more readily than simply when signals are in conflict – approximately 66% of the results are in line with a “follow your signal” approach more than Bayesian logic (~49%). As stated in the paper, there are reasons why cascades form, but the reasons behind such may not be as predictable as the mathematical models might immediately suggest - the probabilities, for example, might be skewed more than the Bayesian conditional model would immediately predict.