Forecast aggregation via recalibration
Forecast aggregation via recalibration
复制标题
通过重新校准进行预测聚合
DOI：
10.1007/s10994-013-5401-4
复制
发表时间：
2014
期刊：
影响因子：
7.5
通讯作者：
T. Wallsten
中科院分区：
文献类型：
--
作者：
Brandon M. Turner;M. Steyvers;Edgar C. Merkle;D. Budescu;T. Wallsten
众所周知，对未来事件的许多预测的平均值往往优于单独的评估。随着进一步提高预测性能的目标，本文开发和比较了一些模型，用于校准和汇总预测，利用众所周知的事实，即个人表现出系统性的偏见，在判断和启发。所有模型都通过双参数校准函数重新校准判断或平均判断，并且在以下方面有所不同：（1）校准函数是在平均之前还是之后应用，（2）平均是在概率或对数几率空间中完成的，以及（3）通过分层建模捕获个体差异。在非分层模型中，首先重新校准个人判断，然后将其平均为对数比值的模型相对于简单平均是最好的，Brier得分提高了26.7%，并且在86%的个人问题上表现更好。该模型的分层版本在平均Brier评分方面稍好（28.2%），在个别问题方面稍差（85%）。
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 %).
影响因子：
5.4
作者：
Pleskac, Timothy J.;Busemeyer, Jerome R.
通讯作者：
Busemeyer, Jerome R.