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Models with higher effective dimensions tend to produce more uncertain estimates

Mathematical models are getting increasingly detailed to better predict phenomena or gain more accurate insights into the dynamics of a system of interest, even when there are no validation or training data available. Here, we show through ANOVA and statistical theory that this practice promotes fuz...

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Detalles Bibliográficos
Autores principales: Puy, Arnald, Beneventano, Pierfrancesco, Levin, Simon A., Lo Piano, Samuele, Portaluri, Tommaso, Saltelli, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581491/
https://www.ncbi.nlm.nih.gov/pubmed/36260678
http://dx.doi.org/10.1126/sciadv.abn9450
Descripción
Sumario:Mathematical models are getting increasingly detailed to better predict phenomena or gain more accurate insights into the dynamics of a system of interest, even when there are no validation or training data available. Here, we show through ANOVA and statistical theory that this practice promotes fuzzier estimates because it generally increases the model’s effective dimensions, i.e., the number of influential parameters and the weight of high-order interactions. By tracking the evolution of the effective dimensions and the output uncertainty at each model upgrade stage, modelers can better ponder whether the addition of detail truly matches the model’s purpose and the quality of the data fed into it.