论文标题
预测陷阱
The Forecast Trap
论文作者
论文摘要
受决策者对从选举到大流行的主题信息的需求的鼓励，并受到数据和计算方法的爆炸式爆炸，基于模型的预测增强了人们对现代社会决策广度的影响。使用渔业管理中的几个经典示例，我证明，选择产生最准确，最精确的预测的模型（通过统计得分来衡量）有时会导致较差的结果（通过现实世界目标衡量）。这可以创建一个预测陷阱，在这种预测陷阱中，诸如鱼类生物量或经济产量之类的结果下降，而经理越来越确信这些行动与可用的最佳模型和数据一致。预测陷阱不是此示例独有的，而是模型非唯一性的基本结果。促进更广泛模型的现有实践是避免陷阱的最佳方法。
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.
