The Pick-the-Winner-Picker Heuristic: Preference for Categorically Correct Forecasts
研究发现普通人评价预测时更看重分类正确性（如猜对赢家）而非连续误差最小化，这种“选赢家预测者启发式”在分类维度重要时尤为明显，但可能构成规范性错误。
People routinely make decisions based on predictions made by others (e.g., political pundits, market analysts), so it is in their best interest to identify high-quality forecasts. Experts characterize good forecasting as minimization of continuous error (i.e., predictions close to the eventual outcome). By contrast, the present work reveals that laypeople typically see good forecasts as those that correctly predict an event's categorical outcome (e.g., the winning team). Using within-subjects, between-subjects, and incentive-compatible designs, 15 studies demonstrate this “pick-the-winner-picker heuristic” as well as its psychological mechanism: People evaluate forecasts by assigning separate weights to (1) categorical correctness and (2) continuous error minimization, depending on the overall importance of the categorical and continuous dimensions for that situation. Thus, in the common case when the categorical dimension matters most (e.g., sports contests), people prize forecasts that accurately predicted the categorical outcome (e.g., the winner, not the margin of victory). However, when the categorical dimension's stakes are experimentally reduced, an attenuation is observed. Although this describes how people typically evaluate forecasts, crucially, a dimension's importance is not necessarily related to its diagnosticity of forecaster skill or reliability. Accordingly, the pick-the-winner-picker heuristic may constitute a normative mistake, while framing manipulations help debias judgments.