• جزئیات بیشتر مقاله
    • تاریخ ارائه: 1392/07/24
    • تاریخ انتشار در تی پی بین: 1392/07/24
    • تعداد بازدید: 904
    • تعداد پرسش و پاسخ ها: 0
    • شماره تماس دبیرخانه رویداد: -
     it is known that the average of many forecasts about a future event tends to outperform the individual assessments. with the goal of further improving forecast performance, this paper develops and compares a number of models for calibrating and aggregating forecasts that exploit the well-known fact that individuals exhibit systematic biases during judgment and elicitation. all of the models recalibrate judgments or mean judgments via a two-parameter calibration function, and differ in terms of whether (1) the calibration function is applied before or after the averaging, (2) averaging is done in probability or log-odds space, and (3) individual differences are captured via hierarchical modeling. of the non-hierarchical models, the one that first recalibrates the individual judgments and then averages them in log-odds is the best relative to simple averaging, with 26.7 % improvement in brier score and better performance on 86 % of the individual problems. the hierarchical version of this model does slightly better in terms of mean brier score (28.2 %) and slightly worse in terms of individual problems (85 %).

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