The forecast trap. (30th May 2022)

• Record Type: Journal Article
• Authors: Boettiger, Carl
• Editors: Munch, Stephan
• Is Part Of: Ecology letters. Volume 25:Number 7(2022)
• Page Start: 1655
• Page End: 1664
• Publication Date: 2022-05-30
• Subjects: adaptive management -- forecasting -- optimal control -- stochasticity -- uncertainty; Ecology -- Periodicals; 577
• DOI: 10.1111/ele.14024
• Languages: English
• ISSNs: 1461-023X

Abstract

Abstract: 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. Abstract : Selecting models for decision‐making based on forecast skill alone can lead to undesirable outcomes, even when predictive accuracy is very high. Existing practices promoting a broader set of models are the best way to avoid this forecast trap.