What is Quantitative Story-Telling?

Quantitative Story-Telling (QST) involves a participative and deliberative analysis of the quality of proposed or available policies and narratives on governance.

QST proceeds at first and foremost ‘via negativa’, using a method of falsification of the available options with respect to:

  • feasibility (compatibility with external constraints);
  • viability (compatibility with internal constraints); and
  • desirability (compatibility with normative values adopted in the given society).

This analysis – to be performed participatively with parsimonious use of mathematical modelling and quantification – will test whether any ‘impossibility’ or bottleneck can be identified which allows a framing or option to be falsified (in the sense of proven false). A key step in the identification of the feasibility, viability and desirability domains entails looking through different lens – i.e. dimensions and scales of analysis.

In these analyses the required tools and representations are continuously adapting to the task, as the quantification strategy useful to study feasibility is not the same as the one to test viability. In turn desirability demands direct interaction with the social actors carrying legitimate but contrasting normative values (Giampietro et al. 2006). An additional analytic steps might then combine these findings in a multi-criteria setting (Munda, 2008), or using so called ethical matrices (Mepham, 1996).

A desirable feature of QST is indeed that instead of searching for an optimal solution in a given problem space (what economics offers) it strives to enlarge the problem space itself and then map its attribute in terms of feasibility, viability and desirability. This implies that more time is spent on defining the problem and relatively less in populating this with data, indicators and models.

The style of quantification adopted in QST has some characteristic features:

  • The first is a commitment to a responsible use of quantitative information. The list of don’ts include for example refrain from using models that demands as an input data which need to be made up out of thin air, and to refrain from producing digits which do not correspond to the accuracy of the estimate.
  • The second feature – which is inter alia the one needed to test the salience and relevance of model generated numbers – is to use data and model appraisal strategies developed in the tradition of Post Normal Science (PNS, Funtowicz and Ravetz, 1991, 1992, 1993). Both practices are useful for taming scientific hubris, and for not treating genuine uncertainty or ignorance as if it were amenable to a computable risk, following the key distinction introduced by Knight (1921).
  • A third key aspect of quantification as advocated in QST is the use of tools from system ecology. For example Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM) is a method of accounting based on maintaining coherence a cross scales and dimensions (e.g. economic, demographic, energetic) of quantitative assessments generated using different metrics (Giampietro and Mayumi, 2000a, 2000b, Giampietro et al. 2012, 2013, 2014).

References:
Funtowicz, S.O. and Ravetz, J. R. 1991. "A New Scientific Methodology for Global Environmental Issues." In Ecological Economics: The Science and Management of Sustainability. Ed. Robert Costanza. New York: Columbia University Press: 137–152.
Funtowicz, S. O. and Ravetz, J. R. 1993. Science for the post-normal age. Futures, 257, 739–755.
Funtowicz, S. O., and Ravetz, J. R. 1992. Three types of risk assessment and the emergence of postnormal science. In S. Krimsky & D. Golding Eds., Social theories of risk pp. 251–273. Westport, CT: Greenwood.
Giampietro, M., Allen, T.F.H. and Mayumi, K. 2006. The epistemological predicament associated with purposive quantitative analysis Ecological Complexity, 3 4: 307-327.
Giampietro, M., Aspinall, R.J., Ramos-Martin, J. and Bukkens, S.G.F. Eds. 2014. Resource Accounting for Sustainability Assessment: The Nexus between Energy, Food, Water and Land use. Routledge.
Giampietro, M., and Mayumi, K. 2000a. Multiple-scale integrated assessment of societal metabolism: Introducing the approach. Population and the Environment, 222, 109–153.
Giampietro, M., and Mayumi, K. 2000b. Multiple-scale integrated assessments of societal metabolism: Integrating biophysical and economic representations across scales. Population and the Environment, 222, 155–210.
Giampietro, M., Mayumi, K. and Sorman, A.H. 2012. The Metabolic Pattern of Societies: Where Economists Fall Short. Routledge.
Giampietro, M., Mayumi, K. and Sorman, A.H. 2013. Energy Analysis for a Sustainable Future: Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism. Routledge.
Knight, F. H. 1921. Risk, Uncertainty, and Profit, New York: Hart, Schaffner & Marx.
Mepham, B. 1996. Ethical analysis of food biotechnologies: An evaluative framework. In B. Mepham Ed., Food ethics pp. 101–119. London: Routledge.
Munda, G. 2008. Social multi-criteria evaluation for a sustainable economy. Berlin: Springer.


For more information: M. Giampietro (2017): Structuring the perception (qualitative) and representation (quantitative) of the nexus with new concepts and narratives. In: Report on Nexus Security using Quantitative Story-Telling. MAGIC (H2020–GA 689669) Project Deliverable 4.1.

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