Whether, and under what conditions, groups exhibit ``crowd wisdom'' has been a major focus of research across the social and computational sciences. Much of this work has focused on the role of social influence in promoting the wisdom of the crowd versus leading the crowd astray, resulting in conflicting conclusions about how the social network structure determines the impact of social influence. Here, we demonstrate that it is not enough to consider the network structure in isolation. Using theoretical analysis, numerical simulation, and reanalysis of four experimental datasets (totaling 4,002 human subjects), we find that the wisdom of crowds critically depends on the interaction between (i) the centralization of the social influence network and (ii) the distribution of the initial, individual estimates , i. e., the estimation context. Specifically, we propose a feature of the estimation context that measures the suitability of the crowd to benefit from influence centralization and show its significant predictive powers empirically. By adopting a framework that integrates both the structure of the social influence and the estimation context , we bring previously conflicting results under one theoretical framework and clarify the effects of social influence on the wisdom of crowds.
Whether, and under what conditions, groups exhibit "crowd wisdom" has been a major focus of research across the social and computational sciences. Much of this work has focused on the role of social influence in promoting the wisdom of the crowd versus leading the crowd astray, resulting in conflicting conclusions about how the social network structure determines the impact of social influence. Here, we demonstrate that it is not enough to consider the network structure in isolation. Using theoretical analysis, numerical simulation, and reanalysis of four experimental datasets (totaling 2,885 human subjects), we find that the wisdom of crowds critically depends on the interaction between (i) the centralization of the social influence network and (ii) the distribution of the initial, individual estimates . By adopting a framework that integrates both the structure of the social influence and the distribution of the initial estimates , we bring previously conflicting results under one theoretical framework and clarify the effects of social influence on the wisdom of crowds.
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``crowd wisdom''
"crowd wisdom"
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4,002
2,885
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, i. e., the estimation context. Specifically, we propose a feature of the estimation context that measures the suitability of the crowd to benefit from influence centralization and show its significant predictive powers empirically.
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estimation context
distribution of the initial estimates