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Ecological forecasting models: Accuracy versus decisional quality

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  • Pierre, Jean-Sébastien

Abstract

We consider here forecasting models in ecology or in agronomy, aiming at decision making based upon exceeding a quantitative threshold. We address specifically how to link the intrinsic quality of the model (its accuracy) with its decisional quality, ie its capacity to avoid false decisions and their associated costs. The accuracy of the model can be evaluated by the ρ of the regression of observed values versus estimated ones or by the determination coefficient R2. We show that the decisional quality depends not only of this accuracy but also of the threshold retained to make the decision as well as on the state of nature. The two kinds of decisional errors consists either in deciding no action while an action is required (false negatives) or to act while it is useless (false positives). We also prove that the costs associated to those decisions depend also both of the accuracy of the model and of the value of the decision threshold.

Suggested Citation

  • Pierre, Jean-Sébastien, 2023. "Ecological forecasting models: Accuracy versus decisional quality," Ecological Modelling, Elsevier, vol. 482(C).
    • Handle: RePEc:eee:ecomod:v:482:y:2023:i:c:s0304380023001230
    DOI: 10.1016/j.ecolmodel.2023.110392
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    References listed on IDEAS

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    1. Jack W Scannell & Jim Bosley, 2016. "When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-21, February.
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    3. Biswas, Atanu & Hwang, Jing-Shiang, 2002. "A new bivariate binomial distribution," Statistics & Probability Letters, Elsevier, vol. 60(2), pages 231-240, November.
    4. Karlis, Dimitris & Ntzoufras, Ioannis, 2005. "Bivariate Poisson and Diagonal Inflated Bivariate Poisson Regression Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i10).
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