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

Aggregatingmultiple non-expert opinions into a collective estimate can improve accuracy acrossmany 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 arithmeticmean or the median, are influenced by these sources of error.We showthat themean tends to overestimate, and the median underestimate, the true value for awide range of numerosities. Quantifying estimation bias, and mapping individual bias to collective bias, allows us to develop and validate three newaggregationmeasures that effectively counter sources of collective estimation error. In addition, we present results from a further experiment that quantifies the social influence rules that individuals employ when incorporating personal estimates with social information. We showthat the corrected mean is remarkably robust to social influence, retaining high accuracy in the presence or absence of social influence, across numerosities andacross differentmethods foraveraging social information.Using knowledge of estimation biases and social influence rules may therefore be an inexpensive and general strategy to improve the wisdom of crowds.