The Strategy Aggregation Effect in Group Judgment

Predicting an outcome based on a few relevant pieces of information, or cues, is a paradigmatic task used to study individual and group judgment. I combine these two lines of research to understand performance differences between constrained and unconstrained strategies used individually or aggregated in groups. I show that constrained linear strategies (e.g., equal weighting of cues) are more accurate for individual judgments, but when these judgments are averaged, an unconstrained linear strategy (i.e., linear regression) is more accurate. This strategy aggregation effect can be understood by analyzing a decomposition of the mean squared error into bias, variance, and covariance. Because of their lower bias but higher variance, unconstrained linear strategies perform worse for individual judgments, but better for averaged judgments where aggregation minimizes variance. In simulations with artificial and real environments, I further show that this aggregation effect does not occur if there are correlations between individual judgments. Here, constrained linear strategies always outperform an unconstrained linear strategy, because the larger covariance component of the unconstrained linear strategy outweighs its lower bias. I end with real-world implications of the results for cognitive strategies and decision environments in group and organizational settings.