Latest outputs

January 18, 2024

Paper out in Technometrics

Arnald Puy, Pamphile Roy and Andrea Saltelli propose a simple, easy to understand and computationally efficient approach for global sensitivity analysis based on the concept of discrepancy (the deviation of the distribution of points in a multi-dimensional space from the uniform distribution). The contribution provides modellers with a straightforward tool to conduct global sensitivity analysis.

September 11, 2023

Book chapter on model hubris in Oxford University Press

Arnald Puy and Andrea Saltelli reflect on the philosophical foundations that ground the quest towards ever more detailed models, and identify four practical dangers derived from this pursuit: an explosion of the model’s uncertainty space, model black-boxing, computational exhaustion, and model attachment. The book is edited by Andrea Saltelli and Monica Di Fiore and is published by Oxford University Press.

June 29, 2023

Paper out in Nature Reviews Earth & Environment

In this Comment, Arnald Puy, Michela Massimi, Bruce Lankford and Andrea Saltelli discuss three epistemological obstacles that prevent irrigation models from providing accurate and "objective" estimates: the models’ elusive tie to reality, model plurality and indeterminacy of the target system.

June 8, 2022

DAWN's proof of concept: the delusive accuracy of irrigation water withdrawal estimates

Several modellers, irrigation scientists and experts in uncertainty show in Nature Communications that previous studies on global irrigation water withdrawals might have missed uncertainties spanning two orders of magnitude already at the grid cell level. Current estimates thus present us with a mirage of accuracy.