A short comment on statistical versus mathematical modelling

By Andrea Saltelli. 2019

90 citations recorded by the source corpus.

While the crisis of statistics has made it to the headlines, that of mathematical modelling hasn’t. Something can be learned comparing the two, and looking at other instances of production of numbers.Sociology of quantification and post-normal science can help.

Subfields: Management Science and Operations Research

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In Management Science and Operations Research

1. S. Lo Piano
2. Siddharth Sareen
3. J. Bergh
4. Andrea Saltelli

Citations in the corpus

• Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward (2020, background): “Decision-making-based algorithms rest inevitably on assumptions, even silent ones, such as the quality of data the algorithm is trained on (Saltelli and Funtowicz, 2014), or the actual modelling relations adopted (Hoerl, 2019), with all the implied consequences (Saltelli, 2019).”
• Ethics of quantification: illumination, obfuscation and performative legitimation (2020, background): “For instance, the convulsions of signi fi cance testing in statistics have received wide attention (Amrhein et al., 2019), whereas mathematical modelling is a fi eld with severe uncharted problems (Saltelli, 2019).”
• European energy poverty metrics: Scales, prospects and limits (2020, unknown): “Policy uptakemerits express attention to the legitimacy of metrics: who operationalises and uses emerging metrics in what precise manner [59], through what formal and informal power relations these metrics are oriented at energy poverty alleviation, and whether they risk catering to narrow interests”
• A review of agent‐based modeling of climate‐energy policy (2020, background): “This requires making a trade-off between model inadequacy, due to too many simplifying assumptions, and model propagation errors, due to too much model complexity (Saltelli, 2019).”
• The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “A nalogous to under-and over-fitting issues in statistical models, mechanistic model development suffers from a trade-off between ‘model completeness ’ and ‘propagation error ’ (Saltelli, 2019) - see Fig.”