Here we focus on the description of the mechanisms behind the process of
information aggregation and decision making, a basic step to understand
emergent phenomena in society, such as trends, information spreading or the
wisdom of crowds. In many situations, agents choose between discrete options.
We analyze experimental data on binary opinion choices in humans. The data
consists of two separate experiments in which humans answer questions with a
binary response, where one is correct and the other is incorrect. The questions
are answered without and with information on the answers of some previous
participants. We find that a Bayesian approach captures the probability of
choosing one of the answers. The influence of peers is uncorrelated with the
difficulty of the question. The data is inconsistent with Weber's law, which
states that the probability of choosing an option depends on the proportion of
previous answers choosing that option and not on the total number of those
answers. Last, the present Bayesian model fits reasonably well to the data as
compared to some other previously proposed functions although the latter
sometime perform slightly better than the Bayesian model. The asset of the
present model is the simplicity and mechanistic explanation of the behavior.Comment: 8 pages, 6 figures, 1 tabl
