The forecast trap
The forecast trap
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DOI：
10.1111/ele.14024
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发表时间：
2022-05-30
期刊：
影响因子：
8.8
通讯作者：
Boettiger, Carl
中科院分区：
文献类型：
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作者：
Boettiger, Carl
决策者对从选举到流行病等各种主题的未来信息的需求，以及数据和计算方法的爆炸性增长，促使基于模型的预测对现代社会的广泛决策产生了越来越大的影响。使用渔业管理的几个经典例子，我证明了选择模型或模型，产生最准确和精确的预测（统计得分衡量）有时会导致更糟糕的结果（现实世界的目标衡量）。这可能会造成一个预测陷阱，其中的结果，如鱼类生物量或经济产量下降，而管理人员越来越相信这些行动是符合最好的模型和数据。预测陷阱并不是这个例子所独有的，而是模型非唯一性的一个基本后果。推广一套更广泛的模式的现有做法是避免陷阱的最佳途径。
Encouraged by decision makers' appetite for future information on topics ranging from elections to pandemics, and enabled by the explosion of data and computational methods, model-based forecasts have garnered increasing influence on a breadth of decisions in modern society. Using several classic examples from fisheries management, I demonstrate that selecting the model or models that produce the most accurate and precise forecast (measured by statistical scores) can sometimes lead to worse outcomes (measured by real-world objectives). This can create a forecast trap, in which the outcomes such as fish biomass or economic yield decline while the manager becomes increasingly convinced that these actions are consistent with the best models and data available. The forecast trap is not unique to this example, but a fundamental consequence of non-uniqueness of models. Existing practices promoting a broader set of models are the best way to avoid the trap.