Context
Quantitative storytelling (QST) addresses evidence based policy and can be considered as a reaction to a style of quantification based on cost benefit or risk analysis which—in the opinion of QST proponents—may contain important implicit normative assumptions. In the logic of QST, a single quantification corresponding to a single view of what the problem is runs the risk of distracting from what could be alternative readings. The concept that some of the evidence needed for policy is removed from view is discussed by Ravetz, 1987; Rayner, 2012). They suggest that ‘uncomfortable knowledge’ is subtracted from the policy discourse with the objective to ease tractability or to advance a given agenda. The word ‘hypo-cognition’ has been used in the context of these instrumental uses of frames (Lakoff et al., 2008; akoff, G., Dean, H. and Hazen, D. (2008) Don’t Think of an Elephant!: Know Your Values and Frame the Debate. Chelsea Green Publishing.https://books.google.es/books?id=zbJ1oxHC9a0C) Lakoff, 2010). For Rayner, a phenomenon of ‘displacement’ takes place when a model becomes the objective instead of the tool, e.g. when an institution chooses to monitor and manage the outcome of a model rather than what happens in reality. Once exposed, the strategic use of hypo-cognition erodes the trust in the involved actors and institutions.Approach
QST suggests acknowledging ignorance, as to work out ‘clumsy solutions’ (Rayner, 2012), which may permit negotiation to be had among parties with different normative orientations. Saltelli and Giampietro (2017) suggest that our present approach to evidence-based policy, even in the more nuanced formulation of evidence-informed policy (Gluckman, 2014), is often based on an arbitrary restriction of the definition of the problem, which is then reinforced by an effort of quantification - via models and/or indicators, of the selected frame. QST is also sensitive to power and knowledge asymmetries (Boden and Epstein, 2006; Strassheim and Kettunen, 2014), as interest groups have more scope to capture regulators than the average citizen ad consumer. QST encourages an effort in the pre-analytic, pre-quantitative phase of the analysis to map a socially robust (i.e. inclusive of the interest of different stakeholders) universe of possible frames. QST expands on one of the rulesApplications
A recent application of QST exploring the transition to intermittent electrical energy supply in Germany and Spain is due to Renner and Giampietro.A. Renner and M. Giampietro, “Socio-technical discourses of European electricity decarbonization: Contesting narrative credibility and legitimacy with quantitative story-telling,” Energy Res. Soc. Sci., vol. 59, Jan. 2020. Cabello et al. use QST to explore a case of water and agricultural governance in the Canary Islands.Cabello, V., Romero, D., Musicki, A. et al. Co-creating narratives for WEF nexus governance: a Quantitative Story-Telling case study in the Canary Islands. Sustain Sci (2021). https://doi.org/10.1007/s11625-021-00933-y. Other applications of approaches which can be referred to QST are to the analyses for the cost of climate change, to the controversy surrounding the OECD-PISA study altelli, A., 2017, International PISA tests show how evidence-based policy can go wrong, The Conversation, June 12.https://theconversation.com/international-pisa-tests-show-how-evidence-based-policy-can-go-wrong-77847)), to food security, to the controversy surrounding the use of Golden Rice, a GMO crop, altelli, A., Giampietro, M. & Gomiero, T. Forcing consensus is bad for science and society. The Conversation (2017).https://theconversation.com/forcing-consensus-is-bad-for-science-and-society-77079) and to theReferences
{{reflist, 30em Scientific modelling Ecological economics Industrial ecology Environmental science