4 item(s) found.

What is Quantitative Story-Telling?

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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How is the usefulness of narratives judged?

How is the usefulness of narratives judged?

The usefulness of narratives can only be judged considering three criteria:

  1. How fair is the choice of the given purpose – when dealing with sustainability - What do we want to sustain, For whom? For how long? At which cost? – here we are in the realm of politics and moral choices;
  2. How reasonable is the choice of the given narrative when contrasting the insights provided by it with the insights that can be obtained by the consideration of alternative narratives about the issue? – here we are dealing with the quality and robustness of the knowledge associated with the adoption of the narrative;
  3. How useful is the choice of: (i) relevant agents, (ii) time scale (a before and an after in the chosen events described at a given time scale), (iii) the identification of the causality associated with the chosen explanation, – here we are in the realm of the practical problems faced when trying to generate a reliable input to be used for governance.

Looking at these quality criteria, the usefulness of the narrative depends on the “wisdom” of those that endorse it. That is, a check on the robustness of the choice of narratives requires a check on the wisdom of the policy determined by the endorsement of that narrative. The models and data to be used as “evidence” for policy are only by-products of the pre-analytical choice of narratives. The quality check on models and data is only a part of the story.

For this reason a quality check on a specific policy can only be obtained by: (i) comparing the effects of different policies suggested according to different narratives – e.g. by considering steady-state versus evolutionary changes, and (ii) analyzing the implications of the robustness of the representation of the different pre-analytical choices associated with different potential endorsements of a narrative – e.g. by considering what happens to winners and losers. This open approach implies that a given issue can be perceived in terms of either a problem or an opportunity by different actors. This heterogeneity of views can provide a variety of insights about the pros and cons of potential policies. Put it in another way, the “falsification” of narratives cannot be done looking at their “validity” in absolute term. All narratives are valid depending on the purpose of those proposing them and on their perception of what should be considered a wise thing to do.

In conclusion, the quality control on the policy implications of a given narrative can only be done by adopting view-points provided by other narratives. If the evidence generated according to a given narrative suggests to adopt a given policy, it is always wise before endorsing that narrative to consider other non-equivalent narratives capable of checking whether the policy is: (i) feasible – according to external constraints, (ii) viable – according to internal constraints, (iii) desirable according to existing institutions and normative values.

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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What is a story-teller?

What is a story-teller?

The story-teller is the person (or the institution) that select and use a narrative relevant for guiding action. By doing so a story-teller endorses both the relevance of the story (in terms of framing of the issue) and usefulness (in terms of guiding action).


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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What is a Narrative?

What is a Narrative?

According to T.F.H. Allen a narrative is the result of a series of scaling operations over the perception of a given event used for identifying: (i) relevant agents, (ii) a given scale of analysis, and (iii) a direction of causality, a combination providing an explanation for the event. Narratives are essential for humans because:

1. Narratives provide anticipation, but a tricky anticipation

The representation of relevant events based on the choice of a given narrative makes it possible to describe dynamic relations – i.e. the scaling makes it possible to define a “before” and an “after” and a causal link in the plot of perceived events. This explains why models need (and depend on) a pre-analytical choice of a narrative about the events to be modeled.

2. Narratives make it possible to make sense of our perception of the reality

Narratives represent a heuristic solution adopted by humans to avoid impredicativity. When dealing with complex systems it is unavoidable to find contrasting perceptions in which: A -> B, but also B -> A (i.e. chicken-egg paradox).

Impredicativity generates a bifurcation in the definition of:

  • what should be considered a relevant agent (dependent or independent variable in the model). This choice depends on the point of view used for the perception. Governments rule on citizens over a time duration of a year, whereas citizens rule over the government at election time (over a time duration of 10 years);
  • what should be considered the right scale to be used for analysis (it blurs the definition of “before” and “after”) – the famous chicken-egg dilemma!; and
  • what should be considered the right direction of causality used to provide an explanation over the relations agents/phenomena perceived in the external world – Are farmers needed to reproduce rural communities or rural communities needed to reproduce farmers? The same impredicative relation applies to the relation between urban and rural communities.
3. Narratives cannot handle the trade-off between efficiency and adaptability

The use of narratives does not solve the impasse faced when trying to deal with the well-known and non-quantifiable trade-off between “efficiency” and “adaptability”. When optimizing efficiency (what is more efficient right now, under the ceteris paribus assumption) we systematically reduce the diversity of possible solutions: those described as “not optimal” by the chosen formalization, which is based on a given set of relevant criteria and the existing perception of external boundary conditions. Because of this forced trade-offs, more efficient systems become less capable of adapting (they lose their potentiality of expressing different functions) when the definition of relevant criteria of performance and/or the constraints determined by boundary conditions will change. When addressing the trade-offs between efficiency and adaptability (resilience!!!!) we cannot focus on the detailed indications given by models or data. Looking only at the results of models carries the risk of missing the presence of elephant(s) in the room(s). This is why, the basic goal of MAGIC is to develop an approach that will allow us to check the usefulness of the narratives that are behind the choice of models and data used as “evidence” for policy.

The usefulness of a narrative depends on the purpose and the value judgment of the agent endorsing and using the narrative (see What is a story-teller?).

References:
Allen, T.F.H. and T.W. Hoekstra 1992. Toward a Unified Ecology. New York: Columbia University Press.
Allen, T.F.H., Starr, T., 1982. Hierarchy: Perspectives for Ecological Complexity. University of Chicago Press, Chicago.


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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