Counteracting estimation bias and social influence to improve the wisdom of crowds.

ABSTRACT: Aggregating multiple non-expert opinions into a collective estimate can improve accuracy across many contexts. However, two sources of error can diminish collective wisdom: individual estimation biases and information sharing between individuals. Here, we measure individual biases and social influence rules in multiple experiments involving hundreds of individuals performing a classic numerosity estimation task. We first investigate how existing aggregation methods, such as calculating the arithmetic mean or the median, are influenced by these sources of error. We show that the mean tends to overestimate, and the median underestimate, the true value for a wide range of numerosities. Quantifying estimation bias, and mapping individual bias to collective bias, allows us to develop and valida

SUBMITTER: Kao AB 

PROVIDER: S-EPMC5938592 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature