Computational aspects increasingly shape environmental sciences. Actually, transdisciplinary modelling of complex and uncertain environmental systems is challenging computational science (CS) and also the science-policy interface. Large spatial-scale problems falling within this category - i.e. wide-scale transdisciplinary modelling for environment (WSTMe) - often deal with factors (a) for which deep-uncertainty may prevent usual statistical analysis of modelled quantities and need different ways for providing policy-making with science-based support. Here, practical recommendations are proposed for tempering a peculiar - not infrequently underestimated - source of uncertainty. Software errors in complex WSTMe may subtly affect the outcomes with possible consequences even on collective environmental decision-making. Semantic transparency in CS and free software are discussed as possible mitigations.

Software Uncertainty in Integrated Environmental Modelling: the role of Semantics and Open Science

DE RIGO, DANIELE
2013-01-01

Abstract

Computational aspects increasingly shape environmental sciences. Actually, transdisciplinary modelling of complex and uncertain environmental systems is challenging computational science (CS) and also the science-policy interface. Large spatial-scale problems falling within this category - i.e. wide-scale transdisciplinary modelling for environment (WSTMe) - often deal with factors (a) for which deep-uncertainty may prevent usual statistical analysis of modelled quantities and need different ways for providing policy-making with science-based support. Here, practical recommendations are proposed for tempering a peculiar - not infrequently underestimated - source of uncertainty. Software errors in complex WSTMe may subtly affect the outcomes with possible consequences even on collective environmental decision-making. Semantic transparency in CS and free software are discussed as possible mitigations.
2013
software uncertainty; software errors; open science; free software; free scientific software; semantic array programming; data-transformation modelling; reproducible research; environmental modelling; complexity; uncertainty
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/716748
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