Abstract Forecasts are generated by both human experts and statistical models, and their forecast accuracy can be understood using error decompositions. However, the assumptions that underlie decompositions used in the analysis of human error differ substantially from those used in the analysis of models. The lens model, one of the most popular error decompositions for human errors, treats the beliefs of the human forecaster as fixed parameters to be estimated. Modern decompositions of model error treat the model as a random result from the process of fitting to noisy data. We highlight how these different approaches can be combined, expanding the application of the lens model to groups and opening up new perspectives on the study of human forecasting. We argue that treating human beliefs as the result of a process of learning from noisy data (even without specifying that process) can help to explain many documented phenomena in the world of forecasting such as: what kinds of environments human judgment will have difficulty with and what kinds they will be successful in; what conditions underlie the success of bootstrapping and aggregation of independent forecasts. Just as understanding statistical models as random variables has helped to improve the understanding of error in statistics and machines learning, we believe this framework will be able to help guide the literature on human judgment to a better understanding of error, its determinants and the mechanisms capable of improving forecasting accuracy.

中文翻译：

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偏差-方差分解在人类预测中的应用

摘要 预测是由人类专家和统计模型共同生成的，其预测准确性可以使用误差分解来理解。然而，人为错误分析中使用的分解的基础假设与模型分析中使用的假设大不相同。镜头模型是最流行的人为错误分解之一，它将人类预测者的信念视为要估计的固定参数。模型误差的现代分解将模型视为拟合噪声数据过程的随机结果。我们强调如何将这些不同的方法结合起来，将镜头模型的应用扩展到群体，并为人类预测研究开辟新的视角。我们认为，将人类信念视为从嘈杂数据中学习的过程的结果（即使没有指定该过程）有助于解释预测领域中的许多记录现象，例如：人类判断将遇到哪些类型的环境以及他们会在哪些方面取得成功；独立预测的引导和聚合成功的基础是什么条件。正如将统计模型理解为随机变量有助于提高对统计和机器学习中的错误的理解一样，我们相信这个框架将能够帮助指导有关人类判断的文献更好地理解错误、其决定因素和机制提高预测准确性。