The Wisdom of Crowds with Informative Priors


In some eyewitness situations, a group of individuals might have witnessed the same sequence of events. We consider the problem of aggregating eyewitness testimony, trying to reconstruct the true sequence of events as best as possible. We introduce a Bayesian model which incorporates individual differences in memory ability, as well as informative prior knowledge about event sequences, as measured in a separate experiment. We show how adding prior knowledge leads to improved model reconstructions, especially in small groups of error-prone individuals. This Bayesian aggregation model also leads to a “wisdom of crowds” effect, where the model's reconstruction is as good as some of the best individuals in the group.

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