Meta-analysis demonstrates group judgment outperforms independent aggregations, suggesting synergy arises from learning and differential weighting.
Whereas recent research on the wisdom of the crowd effect questioned the importance of group interaction by showing that, in many cases, simple aggregations of independent judgments are already highly accurate, we present extensive meta-analytic evidence that interacting groups outperform such aggregations and, thereby, achieve synergy. In particular, we show that group judgments are more accurate than the average of a comparable number of individual judgments, and that group judgments can be as accurate as the judgments of the best individual member of the nominal groups (i.e., the person with the lowest judgment error). Additionally, our meta-analytic results substantiate two different mechanisms that lead to synergy in quantitative group judgment. On the one hand, group members can learn from each other and, thereby, increase their individual accuracy (G-I transfer). On the other hand, when controlling for G-I transfer, group members are also able to identify the more accurate judgments and assign more weight to them (differential weighting).
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Lippold et al. (2025) studied this question.