Quantitative storytelling

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.[2]

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.[2]

Alternative frames (Ravetz, 1987;[3] Rayner, 2012[4]) may represent ‘uncomfortable knowledge’, which is removed from the policy discourse. Thus, extensive mathematical modelling in EBP to support a given policy may lead to a simplification of the available perceptions and generate—rather than resolve—controversies. The word ‘hypo-cognition’ has been used in the context of these instrumental uses of frames (Lakoff et al., 2008;[5] Lakoff, 2010[6]).

Under this critical viewpoint, mathematical models can be seen as a tool for ‘displacement’. Displacement occurs where a model becomes the end 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.[4] Once exposed, the strategic use of hypo-cognition erodes the trust in the involved actors and institutions.[4]

QST suggests acknowledging ignorance, as to work out ‘clumsy solutions’ (Rayner, 2012[4]), which may accommodate unshared epistemological or ethical principles. This is in turn close to the PNS suggested style of inquiry known as ‘working deliberatively within imperfections’ (van der Sluijs and Petersen, 2008[7]), and to the exigence for a ‘rediscovery of ignorance’ (see preface to Pereira and Funtowicz, 2015[8]). QST also calls attention to the power relationships at play in the use of evidence. Saltelli and Giampietro (2017)[2] suggest that our present approach to evidence-based policy, even in the more nuanced formulation of evidence-informed policy (Gluckman, 2014[9]), requires our urgent attention. Unavoidable asymmetries are generated by the fact that stronger players have access to better evidence, and can use it strategically (Boden and Epstein, 2006;[10] Strassheim and Kettunen, 2014[11]). The decline of pollinators challenge (Insectageddon, Monbiot, 2017[12]) show that 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 rules sensitivity auditing by asking the question of ‘what to do’ in order to avoid that an issue is framed unilaterally. Obviously, the medicine for a diseased evidence-based policy is not a prejudice- or superstition-based policy, but a more democratic and dialogic access to the provision of evidence—even in terms of agenda setting. For this a new institutional setting is needed.[2]

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 operates ‘via negativa’, i.e. it tries to test whether the said framing runs afoul of a quantitative or qualitative analytical check. Here QST borrows from system ecology and attempts to refute whether or not the frames violate constraints of (Giampietro et al., 2014[1]):

1. feasibility (can we afford a given policy in terms of external constraints, e.g. existing biophysical resources?)
2. viability (can we afford it in the context of our internal constraints, governance, socioeconomic and technological arrangements?)
3. desirability (will the relevant constituency accept it?).

Perhaps the best application of the concept of QST is an old study of GMO-related perceptions (Marris, 2001[13]), which has lost very little of its actuality since the ongoing GMO and pesticide debate. By direct interview and measurements of stakeholders’ expectation and worldviews, Marris and co-authors showed that the prevailing narrative of the reaction to GMO as a ‘food scare’—i.e. as an issue of safety to consume GMO food—did not show up among the concerns raised by the interviewed citizens, which worried instead about who would benefit from these technologies, why were they introduced in the first place and whether existing regulatory authorities would be up to the task of resisting regulatory capture from powerful industrial incumbents. A more recent instructive application of QST exploring the transition to intermittent electrical energy supply in Germany and Spain is due to Renner and Giampietro.[14]

Other applications of approaches which can be referred to QST are to the analyses for the cost of climate change,[15][16] to the controversy surrounding the OECD-PISA study[17][18]), to food security,[19][20] to the controversy surrounding the use of Golden Rice, a GMO crop,[21] and to the ecological footprint of the Ecological Footprint Network.[22][23]