Book on the politics of modelling
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| Editors | Andrea Saltelli Monica Di Fiore |
|---|---|
| Author | |
| Illustrator | |
| Language | English |
| Subjects | Mathematical modelling Social statistics Politics |
| Publisher | Oxford University press |
Publication date | August 2023 |
| Pages | 272 |
| ISBN | 978-0198872412 Search this book on  . |
The Politics of Modelling, Numbers Between Science and Policy is a multi-authors book edited by Andrea Saltelli and Monica Di Fiore and published in August 2023 by Oxford University Press.
Synopsis[edit]
The Politics of Modelling elaborates and expands on themes of responsible modelling from a manifesto published in the journal Nature in 2020.[1]. The text is structured into three main sections: Meeting Models, The Rules, and The Rules in Practice.[2][3] The combination of theory with policy relevant examples makes the book accessible to modellers, researchers of modelling, and policy makers.[3]
The volume comes with a foreword of Wendy Nelson Espeland] and a preface of Daniel Sarewitz], with chapters from Andy Stirling, Wolfgang Drechsler, Philip B. Stark, Ting Xu, Paolo Vineis, Andrea Saltelli, and other scholars (Table).
| Chapter | Essay title | Contributor(s) |
|---|---|---|
| Preliminary | Foreword. Mathematical modelling as a critical cultural enterprise | Wendy Nelson Espeland |
| Preface. The sciences of modelling through | Daniel Sarewitz | |
| Meeting the Models | Introduction | Monica Di Fiore and Andrea Saltelli |
| Pay no attention to the model behind the curtain | Philip B. Stark | |
| The Rules | Mind the framings: Match purpose and context | Monica Di Fiore, Marta Kuc-Czarnecka, Samuele Lo Piano, Arnald Puy, and Andrea Saltelli |
| Mind the hubris: Complexity can misfire | Arnald Puy and Andrea Saltelli | |
| Mind the assumptions: Quantify uncertainty and assess sensitivity | Emanuele Borgonovo | |
| Mind the consequences: Quantification in economic and public policy | Wolfgang Drechsler and Lukas Fuchs | |
| Mind the unknowns: Exploring the politics of ignorance in mathematical models | Andy Stirling | |
| The Rules in Practice | Sensitivity auditing: A practical checklist for auditing decision-relevant models | Samuele Lo Piano, Razi Sheikholeslami, Arnald Puy, and Andrea Saltelli |
| Mathematical modelling: Lessons from composite indicators | Marta Kuc-Czarnecka and Andrea Saltelli | |
| Mathematical modelling, rule-making, and the COVID-19 pandemic | Ting Xu | |
| In the twilight of probability: COVID-19 and the dilemma of the decision maker | Paolo Vineis and Luca Savarino | |
| Models as metaphors | Jerome R. Ravetz | |
| Epilogue: Those special models: A political economy of mathematical modelling | Andrea Saltelli and Monica Di Fiore |
Reception[edit]
The book argues that models live in a “state of exception” provided by their access to a wealth of methodology of analysis, and by their epistemic authority borrowed from mathematics. This state allows models to better defend an appearance of neutrality that is appreciated by policymakers in search of a justification.[4] A review published in the journal Science (journal) notes that the volume incorporated insights from science and technology studies to explore modeling beyond its technical aspects[2] A second review[3] in the journal Minerva (Springer journal) notes the book’s reference to the works of historians Margaret Morrison and Mary S. Morgan in considering models as mediators whereby models are simultaneously a tool, an interpretation, and a representation of the system.[5] :205. Does this encourage cynicism as to the utility of models?
Underexplored and overinterpreted as they are, models indeed have ’a state of exception’ (Epilogue). The cynical question would be: Is there any use in models at all? Or, to cite Stirling in Ch. 7. ”does the modelling baby need to be thrown out with the justificatory bathwater?”[3]
The book contrasts the danger of cynicism with suggestions to make models serve society, based on theory, examples, and a call for participatory modelling linked to Post-normal science, sensitivity auditing and the concept of extended peer community.[3] On the critical side,[3] the book ignores other ongoing efforts in enhancing or formalizing modelling practices such as the framework proposed van Voorn[6], and the Good Modelling Practice handbook developed for water management purposes in the Netherlands.[7] Also not treated is the ‘fit-for purpose’[8] movement in modelling.[2] The book could say more on the challenge of participatory modelling, related to gaming and power relations – for example, Arnsteinds’ ladder of participation is not treated in the volume.[3]
If politicians on your news throw the ‘our modelling work shows’ line, then you are an audience who will benefit from this book.[9]
A review in Mathematics_Magazine[10] notes the book’s attention to sensitivity analysis:
They [the authors] stress the importance of sensitivity analysis, with a highly-illustrative and illuminating example that analyzes the EOQ (economic order quantity) formula.
Chapters[edit]
- Monica Di Fiore and Andrea Saltelli open the book tracing its origin to a manifesto on responsible modelling published in the journal Nature[1].
- Philip Stark[11] writing in the first section of the book describes various cases of poor modelling in statistics and mathematical modelling proper.[2] Open access
- Samuele Lo Piano and co-authors[12] tackle sensitivity auditing, an approach to modelling expanding the sensitivity analysis to the entire model generating process, inquisitive of the motivations and bias of developers and to the context of use for policy.[13]:569
- A chapter titled Mind the framings - Match purpose and context by Monica Di Fiore and co-authors discuss the importance to precise the framing of a modelling activity. The chapter makes reference to the orders of worth proposed by the French sociologists Luc Boltanski and Laurent Thévenot.
- Arnald Puy and co-authors discusses possible modelling hubris, the trade-off between the usefulness of a model and the breadth it tries to capture, the conjecture of O’Neil[14] (a principle of diminishing return for the complexity of mathematical models) and why model accuracy should be measured prospectively – e.g. in relation to pre-specified target and not adjusted ad hoc or on the flight.[15] Open access
- Emanuele Borgonovo explores the importance of retracing and mapping the assumptions made in the construction of the model, and indicates how sensitivity analysis can assist in this task.
- Wolfgang Drechsler and Lukas Fuchs explore Quantification in economic and public policy in a chapter entitled Mind the consequences. The chapter reviews some texts of sociology of quantification and zooms in on the Hermeneutic approach of Hans-Georg Gadamer.
- Andy Stirling Explores in detail the politics of uncertainty and ignorance in policy studies (see also.[16])
- Samuele Lo Piano and co-workers offer example of application of Sensitivity auditing for decision-relevant models. Open access
- Marta Kuc-Czarnecka and Andrea Saltelli discuss application to a popular class of models: composite indicators.
- Ting Xu explores how mathematical modelling was used for political rule-making during the COVID-19 pandemic.
- Also focused on the COVID-19 pandemic the chapter of Paolo Vineis and Luca Savarino, entitled ‘’In the twilight of probability’’, explores the dilemma of decision maker faced with uncertain probabilistic knowledge.
- ‘’Models as metaphors’’ is the title of a 2003 work from Jerry R. Ravetz, reprinted courtesy of Cambridge University Press
- Andrea Saltelli and Monica Di Fiore in their ‘’Epilogue’’ wrap up the various strands of reflection on mathematical modelling presented in the volume by discussing the peculiarity of the mathematical modelling practice, where a strong epistemic authority is associated with a scarce disciplinary control. They name this a ‘state of exception’ and identify elements of a Political Economy of Mathematical Modelling (PEMM).
References[edit]
- ↑ 1.0 1.1 Saltelli, Andrea; et al. (June 2020). "Five ways to ensure that models serve society: a manifesto". Nature. 582 (7813): 482–484. Bibcode:2020Natur.582..482S. doi:10.1038/d41586-020-01812-9. hdl:1885/219031. PMID 32581374 Check
|pmid=value (help). - ↑ 2.0 2.1 2.2 2.3 Nabavi, E., Razavi, S. (17 November 2023). "The responsibility turnThe Politics of Modelling: Numbers Between Science and Policy Andrea Saltelli and Monica Di Fiore, Eds. Oxford University Press, 2023. 272 pp". Science. 382 (6672): 775. doi:10.1126/science.adl3473. ISSN 1095-9203. PMID 37972171 Check
|pmid=value (help). Unknown parameter|s2cid=ignored (help) - ↑ 3.0 3.1 3.2 3.3 3.4 3.5 3.6 Melsen, L. A. (17 February 2024). "The Politics Behind Overinterpreted and Underexplored Models: A Review of Andrea Saltelli and Monica Di Fiore (eds.), The Politics of Modelling - Numbers between Science and Policy". Minerva. 62: 141–144. doi:10.1007/s11024-024-09524-4. ISSN 1573-1871. Retrieved 17 February 2024. Unknown parameter
|s2cid=ignored (help) - ↑ Tarran, B. (2023), 'I would like modellers to be less ambitious in developing monster models that are impossible to inspect', retrieved 25 November 2023
- ↑ Saltelli, A.; Di Fiore, M. (2023), Saltelli, Andrea; Di Fiore, Monica, eds., The politics of modelling. Numbers between science and policy, Oxford: Oxford University Press, doi:10.1093/oso/9780198872412.001.0001, ISBN 978-0-19-887241-2
- ↑ Voorn, G. A. K. van, Verburg, R. W., Kunseler, E.-M., Vader, J., Janssen, P. H. M. (1 September 2016). "A checklist for model credibility, salience, and legitimacy to improve information transfer in environmental policy assessments". Environmental Modelling & Software. 83: 224–236. Bibcode:2016EnvMS..83..224V. doi:10.1016/j.envsoft.2016.06.003. hdl:1874/334178. ISSN 1364-8152. Unknown parameter
|s2cid=ignored (help) - ↑ Waveren, H., Groot, S., Scholten, H., Geer, F., Wösten, H., Koeze, R., Noort, J. (1 January 1999). Good Modelling Practice Handbook. STOWA. ISBN 978-90-5773-056-6. Search this book on
- ↑ Hamilton, S. H., Pollino, C. A., Stratford, D. S., Fu, B., Jakeman, A. J. (1 February 2022). "Fit-for-purpose environmental modeling: Targeting the intersection of usability, reliability and feasibility". Environmental Modelling & Software. 148: 105278. Bibcode:2022EnvMS.14805278H. doi:10.1016/j.envsoft.2021.105278. ISSN 1364-8152. Unknown parameter
|s2cid=ignored (help) - ↑ Elsawah, S. (2024). "A Review of Andrea Saltelli and Monica Di Fiore (eds.), The Politics of Modelling - Numbers between Science and Policy". Environmental Modelling and Software. doi:10.1016/j.envsoft.2024.106023.
- ↑ Campbell, P. J. (14 March 2024). "Reviews: Saltelli, Andrea, and Monica Di Fiore (eds.), The Politics of Modelling: Numbers Between Science and Policy, Oxford University Press". Mathematics Magazine. Taylor & Francis. 97 (2): 234–235. doi:10.1080/0025570X.2024.2313390. ISSN 0025-570X.
- ↑ Stark, P. B. (2023). "The politics of modelling. Numbers between science and policy". In Saltelli, A., Di Fiore, M. Pay No Attention to the Model Behind the Curtain. Oxford University Press. pp. 15–34. Search this book on
- ↑ Lo Piano, S., Puy, A., Sheikholeslami, R., Saltelli, A. (2023). "The politics of modelling. Numbers between science and policy". In Saltelli, A., Di Fiore, M. Sensitivity auditing: A practical checklist for auditing decision-relevant models. Oxford University Press. pp. 121–136. Search this book on
- ↑ European Commission (2023), Better Regulation Toolbox
- ↑ O’Neill, R. V. (1971), Nelson, D. J., ed., Error analysis of ecological models
- ↑ Puy, A., Beneventano, P., Levin, S. A., Lo Piano, S., Portaluri, T., Saltelli, A. (2022). "Models with higher effective dimensions tend to produce more uncertain estimates". Science Advances. 8 (eabn9450): eabn9450. Bibcode:2022SciA....8N9450P. doi:10.1126/sciadv.abn9450. PMC 9581491 Check
|pmc=value (help). PMID 36260678 Check|pmid=value (help). - ↑ Stirling, A. (2023). "Against misleading technocratic precision in research evaluation and wider policy – A response to Franzoni and Stephan (2023), 'uncertainty and risk-taking in science'". Research Policy. 52 (3): 104709. doi:10.1016/j.respol.2022.104709. ISSN 0048-7333. Unknown parameter
|s2cid=ignored (help)
Links[edit]
- Saltelli, A. and Di Fiore, M. (eds) (2023) The politics of modelling. Numbers between science and policy. Oxford: Oxford University Press, at OUP website.
- Chapters available in open access.
- Models: a state of exception, Oxford Martin School, November 20 2023, Launch of The Politics of Modelling – video.
- Podcast - interview for ABC NET RADIO, AUS August 26, 2023: Assumptions and consequences: the politics of modelling, Guests: Ehsan Nabavi and Andrea Saltelli, Producer - Chris Bullock.
See also[edit]
Related readings[edit]
- Desrosières, Alain. (1998). The Politics of Large Numbers: a history of statistical reasoning, Harvard University Press.
- Scoones, I., & Stirling, A. (2020). The Politics of Uncertainty. (I. Scoones & A. Stirling, Eds.), Abingdon, Oxon; New York, NY: Routledge, 2020. Series: Pathways to sustainability: Routledge. doi:10.4324/9781003023845
- Mennicken, A., & Salais, R. (Eds.). (2022b). The New Politics of Numbers: Utopia, Evidence and Democracy, Palgrave Macmillan.
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