Unpacking the modeling process for energy policy making.

This article explores how the modeling of energy systems may lead to an undue closure of alternatives by generating an excess of certainty around some of the possible policy options. We retrospectively exemplify the problem with the case of the International Institute for Applied Systems Analysis (IIASA) global modeling in the 1980s. We discuss different methodologies for quality assessment that may help mitigate this issue, which include Numeral Unit Spread Assessment Pedigree (NUSAP), diagnostic diagrams, and sensitivity auditing (SAUD). We illustrate the potential of these reflexive modeling practices in energy policy-making with three additional cases: (i) the case of the energy system modeling environment (ESME) for the creation of UK energy policy; (ii) the negative emission technologies (NETs) uptake in integrated assessment models (IAMs); and (iii) the ecological footprint indicator. We encourage modelers to adopt these approaches to achieve more robust, defensible, and inclusive modeling activities in the field of energy research.

What can mathematical modelling contribute to a sociology of quantification?

Sociology of quantification has spent relatively less energies investigating mathematical modelling than it has on other forms of quantification such as statistics, metrics, or algorithms based on artificial intelligence. Here we investigate whether concepts and approaches from mathematical modelling can provide sociology of quantification with nuanced tools to ensure the methodological soundness, normative adequacy and fairness of numbers. We suggest that methodological adequacy can be upheld by techniques in the field of sensitivity analysis, while normative adequacy and fairness are targeted by the different dimensions of sensitivity auditing. We also investigate in which ways modelling can inform other instances of quantification as to promote political agency.

The Challenge of Quantification: An Interdisciplinary Reading.

The present work looks at what we call "the multiverse of quantification", where visible and invisible numbers permeate all aspects and venues of life. We review the contributions of different authors who focus on the roles of quantification in society, with the aim of capturing different and sometimes separate voices. Several scholars, including economists, jurists, philosophers, sociologists, communication and data scientists, express concerns or identify critical areas of our relationship with new technologies of 'numericization'. While mindful of the important specificities of the different families of quantification, we use our broad and holistic canvas to explore possible spaces for a more systematic investigation of incumbent and novel quantifications, as to increase communication among disciplinary communities, and among these and society, in the pursuit a democratic agency and self-defence.

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 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.

Improving the reliability of cohesion policy databases.

In this contribution, we present an innovative data-driven model to reconstruct a reliable temporal pattern for time-lagged statistical monetary figures. Our research cuts across several domains regarding the production of robust economic inferences and the bridging of top-down aggregated information from central databases with disaggregated information obtained from local sources or national statistical offices. Our test bed case study is the European Regional Development Fund (ERDF). The application we discuss deals with the reported time lag between the local expenditures of ERDF by beneficiaries in Italian regions and the corresponding payments reported in the European Commission database. Our model reconstructs the timing of these local expenditures by back-dating the observed European Commission reimbursements. The inferred estimates are then validated against the expenditures reported from the Italian National Managing Authorities (NMAs) in terms of cumulative monetary difference. The lower cumulative yearly distance of our modelled expenditures compared to the official European Commission payments confirms the robustness of our model. Using sensitivity analysis, we also analyse the relative importance of the modelling parameters on the cumulative distance between the modelled and reported expenditures. The parameters with the greatest influence on the uncertainty of this distance are the following: first, how the non-clearly regionalised expenditures are attributed to individual regions; and second, the number of backward years that the residuals of the yearly payments are spread onto. In general, the distance between the modelled and reported expenditures can be further reduced by fixing these parameters. However, the gain is only marginal for some regions. The present study paves the way for modelling exercises that are aimed at more reliable estimates of the expenditures on the ground by the ultimate beneficiaries of European funds. Additionally, the output databases can contribute to enhancing the reliability of econometric studies on the effectiveness of European Union (EU) funds.

Irrigated areas drive irrigation water withdrawals.

A sustainable management of global freshwater resources requires reliable estimates of the water demanded by irrigated agriculture. This has been attempted by the Food and Agriculture Organization (FAO) through country surveys and censuses, or through Global Models, which compute irrigation water withdrawals with sub-models on crop types and calendars, evapotranspiration, irrigation efficiencies, weather data and irrigated areas, among others. Here we demonstrate that these strategies err on the side of excess complexity, as the values reported by FAO and outputted by Global Models are largely conditioned by irrigated areas and their uncertainty. Modelling irrigation water withdrawals as a function of irrigated areas yields almost the same results in a much parsimonious way, while permitting the exploration of all model uncertainties. Our work offers a robust and more transparent approach to estimate one of the most important indicators guiding our policies on water security worldwide.

[Post-Normal Pandemics: why CoViD-19 requires a new approach to science.] / Pandemie post-normali. Perché CoViD-19 richiede un nuovo approccio alla scienza.

In addressing pandemics, science has never seemed more needed and useful, while at the same time limited and powerless. The existing contract between science and society is falling apart. A new covenant is urgently needed to navigate the days ahead.

Post-normal institutional identities: Quality assurance, reflexivity and ethos of care.

This paper suggests adopting a 'post-normal science' (PNS) style and practice in scientific advice, and motivate the urgency of this methodological stance with the increasing complexity, and polarisation affecting the use of science-based evidence for policy. We reflect on challenges and opportunities faced by a 'boundary organisation' that interfaces between science and policy, taking as example the European Commission's Directorate General Joint Research Centre, whose mission is stated as that to be the "in-house science service". We suggest that such an institution can be exemplary as to what could be changed to improve the quality of evidence feeding into the policy processes in the European Union. This paper suggests how an in-house culture of reflexivity and humility could trigger changes in the existing styles and methods of scientific governance; at the JRC, taken as example, this would mean opening up to the existing plurality of norms and styles of scientific inquiry, and adopting more participatory approaches of knowledge production, assessment and governance. We submit that the institutional changes advocated here are desirable and urgent in order to confront the ongoing erosion of trust in 'evidence based policy', anticipating controversies before they become evident in the institutional setting in which institutions operate.

[Statistics at the time of the crisis]. / EpiChange. Statistiche al tempo della crisi.

Science lies nowadays in the centre of several storms. The better known is the finding of non-reproducibility of many scientific results, which stretches from the medical field (clinic and pre-clinic tests) to study on behaviour (priming research). Although the bad use of statistics is reported to be a patent cause of the reproducibility crisis, its deep reasons are to be sought elsewhere; particularly, in the passage from a regimen of little science - regulated by small communities of researchers - to the current big science - identified by a hypertrophic production of millions of research papers and by the imperative "publish or perish", in a setting dominated by market. While spirited debates (on vaccines, climate change, GMO) unfold in society, scientific articles which are bought or withdrawn are the signal of a deep crisis not only of science, but also of the expert thought. In this background, statistics is the main defendant, charged with using methods which experts themselves are not able to explain in an understandable way (p-test). Is there an escape? Yes, there is. Researchers can either court the power and defend the status quo, or contribute to a deep process of reformation, refusing both a vision of science as a religion and the idea that the problem is the poor scientific knowledge of the lay public.

Trends in sensitivity analysis practice in the last decade.

The majority of published sensitivity analyses (SAs) are either local or one factor-at-a-time (OAT) analyses, relying on unjustified assumptions of model linearity and additivity. Global approaches to sensitivity analyses (GSA) which would obviate these shortcomings, are applied by a minority of researchers. By reviewing the academic literature on SA, we here present a bibliometric analysis of the trends of different SA practices in last decade. The review has been conducted both on some top ranking journals (Nature and Science) and through an extended analysis in the Elsevier's Scopus database of scientific publications. After correcting for the global growth in publications, the amount of papers performing a generic SA has notably increased over the last decade. Even if OAT is still the most largely used technique in SA, there is a clear increase in the use of GSA with preference respectively for regression and variance-based techniques. Even after adjusting for the growth of publications in the sole modelling field, to which SA and GSA normally apply, the trend is confirmed. Data about regions of origin and discipline are also briefly discussed. The results above are confirmed when zooming on the sole articles published in chemical modelling, a field historically proficient in the use of SA methods.