Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Predicting the Next Big Thing: Success as a Signal of Poor Judgment
Management Science
Combining SKU-level sales forecasts from models and experts
Expert Systems with Applications: An International Journal
Demand Forecasting Behavior: System Neglect and Change Detection
Management Science
Environmental Modelling & Software
A decision support system for stock investment recommendations using collective wisdom
Decision Support Systems
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Averaging estimates is an effective way to improve accuracy when combining expert judgments, integrating group members judgments, or using advice to modify personal judgments. If the estimates of two judges ever fall on different sides of the truth, which we term bracketing, averaging must outperform the average judge for convex loss functions, such as mean absolute deviation (MAD). We hypothesized that people often hold incorrect beliefs about averaging, falsely concluding that the average of two judges estimates would be no more accurate than the average judge. The experiments confirmed that this misconception was common across a range of tasks that involved reasoning from summary data (Experiment 1), from specific instances (Experiment 2), and conceptually (Experiment 3). However, this misconception decreased as observed or assumed bracketing rate increased (all three studies) and when bracketing was made more transparent (Experiment 2). Experiment 4 showed that flawed inferential rules and poor extensional reasoning abilities contributed to the misconception. We conclude by describing how people may face few opportunities to learn the benefits of averaging and how misappreciating averaging contributes to poor intuitive strategies for combining estimates.