Quantitative storytelling (QST) is a systematic approach used to explore the multiplicity of frames potentially legitimate in a scientific study or controversy.[1] QST assumes that in an interconnected society multiple frameworks and worldviews are legitimately upheld by different entities and social actors. QST looks critically on models used in evidence-based policy (EBP. Such models are often reductionist, in the sense discussed by,[2] in that tractability is achieved at the expenses of suppressing relevant available evidence.[3] QST suggests corrective approaches to this practice.

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; [4] Rayner, 2012).[5] 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;[6] Lakoff, 2010[7]).

For Rayner,[5] 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.[5] Once exposed, the strategic use of hypo-cognition erodes the trust in the involved actors and institutions.[5]

Approach

QST suggests acknowledging ignorance, as to work out ‘clumsy solutions’ (Rayner, 2012[5]), which may permit negotiation to be had among parties with different normative orientations. QST is also sensitive to power and knowledge asymmetries (Boden and Epstein, 2006;[8] Strassheim and Kettunen, 2014[9]), as interest groups have more scope to capture regulators than the average citizen ad consumer.[10][11]

QST does not eschew the use quantitative tools altogether. It suggests instead to explore quantitatively multiple narratives, avoiding spurious accuracy and focusing on some salient features of the selected stories. Rather than attempting to amass evidence in support of a given reading or policy, or to optimise it with modelling, QST tries to test whether a given policy option or framing conflicts with existing social or biophysical constraints. These are (Giampietro et al., 2014[1]):

  1. feasibility (is the policy permissible given the existing resources?)
  2. viability (is it compatible with existing social arrangements or rules?)
  3. desirability (will society subscribe to it?).

Applications

A recent application of QST exploring the transition to intermittent electrical energy supply in Germany and Spain is due to Renner and Giampietro.[12] Cabello et al. use QST to explore a case of water and agricultural governance in the Canary Islands.[13]

References

  1. ^ a b [Giampietro, M., Aspinall, R. J., Ramos-Martin, J. and Bukkens, S. G. F. (2014) Resource Accounting for Sustainability Assessment: The Nexus between Energy, Food, Water and Land Use. Taylor & Francis (Routledge Explorations in Sustainability and Governance).](https://books.google.es/books?id=Vb6uAwAAQBAJ)
  2. ^ van Zvanenberg, P., 2020. The unravelling of technocratic ortodoxy, in: Scoones, I., Stirling, A. (Eds.), The Politics of Uncertainty. Routledge, pp. 58–72.
  3. ^ Scoones, I., Stirling, A., 2020. The Politics of Uncertainty. Routledge, Abingdon, Oxon; New York, NY: Routledge, 2020. | Series: Pathways to sustainability.
  4. ^ Ravetz, Jerome R (2016). "Usable Knowledge, Usable Ignorance". Knowledge. 9: 87–116. doi:10.1177/107554708700900104. S2CID 146551904.
  5. ^ a b c d e Rayner, Steve (2012). "Uncomfortable knowledge: The social construction of ignorance in science and environmental policy discourses". Economy and Society. 41: 107–25. doi:10.1080/03085147.2011.637335. S2CID 144194568.
  6. ^ [Lakoff, 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)
  7. ^ Lakoff, George (2010). "Why it Matters How We Frame the Environment" (PDF). Environmental Communication. 4: 70–81. doi:10.1080/17524030903529749. S2CID 7254556.
  8. ^ Boden, Rebecca; Epstein, Debbie (2006). "Managing the research imagination? Globalisation and research in higher education". Globalisation, Societies and Education. 4 (2): 223–36. doi:10.1080/14767720600752619. S2CID 144077070.
  9. ^ Strassheim, Holger; Kettunen, Pekka (2014). "When does evidence-based policy turn into policy-based evidence? Configurations, contexts and mechanisms". Evidence & Policy: A Journal of Research, Debate and Practice. 10 (2): 259–77. doi:10.1332/174426514X13990433991320.
  10. ^ Drutman, L., 2015. The business of America is lobbying : how corporations became politicized and politics became more corporate. Oxford University Press.
  11. ^ Laurens, S., 2017. Lobbyists and bureaucrats in Brussels : capitalism’s brokers. Routledge.
  12. ^ 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.
  13. ^ 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.
Categories