FAQS

What is MAGIC?

General

15 December 2017

MAGIC (“Moving Towards Adaptive Governance in Complexity: Informing Nexus Security”) is a four-year project funded under the H2020-WATER-2015-two-stage programme; topic “Integrated approaches to food security, low-carbon energy, sustainable water management and climate change mitigation” (WATER-2b-2015).

MAGIC brings together, from multiple research centres across the EU, expertise in biophysical, computational, economic and social sciences underpinned by theories of transdisciplinary science-for-governance. The quantitative engine of MAGIC is MuSIASEM (Multi-ScaleIntegrated Analysis of Societal and Ecosystem Metabolism) an innovative method of accounting having the goal of keeping coherence across scales and dimensions of quantitative assessments generated using different metrics.

The goal of MAGIC is to transform Nexus from a shorthand to signify the complexity of the relationship between water, soils energy and climate into a set of relationship over identified factors which can be systematically used to explore this complexity. This implies integrating into the analysis social challenges and stakeholders perceptions related to the climate-water-food-energy nexus. Dialogue spaces will be opened, dissemination strategies enacted and mixed qualitative-quantitative tools developed in the context of a community building exercise transcending mechanistic scientist-policy maker separation but taking full advantage of the rich spectrum of actors and institutions active in the Nexus.


For more information:

What is the nexus?

General

14 December 2017

The three definitions of the nexus used in MAGIC

In the last few years the term nexus has become very popular in sustainability discussions. It is becoming a “leitmotif” in scientific papers, policy discussions, on internet and widely used in the social media. In this deluge of references to the nexus, it is possible to identify the co-existence of different frames in which the term is used. Put in another way the term nexus right now has different “meanings” when used in relation to different semantic contexts. Hence, it is important to understand the nature of the ambiguity associated with this term.

From our literature review (and from the results of our preliminary interviews) we can say that there are three distinct frames used to interpret the term nexus, depending on the context:

Nexus a: When the term nexus is used in relation to biophysical events taking place in the external world
The nexus refers to the entanglement over biophysical flows (water, energy and food) determined by the expected characteristics of the metabolic pattern of socialecological systems.
Nexus b: when the term nexus is used in relation to the process of governance and policy making
The nexus refers to the acknowledgment existing institutions should be capable of expressing an effective system of governance (policy coherence and integrations) in relation to the three securities (water, energy and food). At the moment this is not achieved and this is a reason of concern when considering existing trends of population growth, consumption per capita and the aggregate requirement of water, energy and food inputs against the deterioration of ecosystems’ health all over the planet.
Nexus c: when the term nexus is used in relation to the problem of scientific inquiry
The nexus refers to the acknowledgment of the existence of an elephant in the room – i.e. the Cartesian dream of prediction and control is smashing against the complexity of the nexus. At the moment, we do not have effective analytical tools capable of generating useful scientific information for dealing with it.

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.

What is a socio-ecological system?

General

14 December 2017

The concept of socio-ecological systems has evolved from the seminal work of Holling, Folke, Berkes (Holling, (1998, 2001), Berkes et al. (2001), Gunderson and Holling (2002), Berkes et al. (2003)). A socio-ecological system can be defined as:

“the complex of biophysical processes taking place in a geographical area, that is controlled in an integrated way by the activities expressed by a given set of ecosystems and a given set of social actors and institutions”.

The sustainability of the metabolic pattern of “socio-ecological systems” (SES) can be studied using the concept of metabolic networks. However, this study entails facing a major epistemological challenge: the study of the characteristics of “ecological fund elements” and the characteristics of “societal fund elements” (the nodes and the flows of the metabolic network) require the simultaneous adoption of different scales of analysis.

  1. Fund elements required for expressing the ecological metabolism: (i) terrestrial ecosystems, (ii) aquatic ecosystems, (iii) primary fresh water sources (aquifers, rivers, lakes and reservoirs), (iv) soils, (v) atmosphere.
  2. Fund elements required for expressing the societal metabolism: functional elements of the society requiring (i) human activity (people), (ii) land use, (iii) power capacity (technology), and (iv) infrastructures for expressing their functions.

References:
Berkes, F., Colding, J., and Folke, C. 2001. Linking Social-Ecological Systems. Cambridge: Cambridge University Press.
Berkes, F., Colding, J., and Folke, C. 2003. Navigating social–ecological systems: building resilience for complexity and change. Cambridge University Press, Cambridge, UK.
Gunderson, L. H., and Holling C. S. 2002. Panarchy: understanding transformations in human and natural systems. Island Press, Washington, D.C., USA.
Holling, C. S. 1998. Two cultures of ecology. Conservation Ecology 2/2: 4. www.consecol.org/ vol2/iss2/art4.
Holling, C. S. 2001. Understanding the complexity of economic, ecological, and social systems, Ecosystems, Vol.45, pp.390-405.


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.

What is a Narrative?

General

14 December 2017

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.

What is a story-teller?

General

14 December 2017

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.

How is the usefulness of narratives judged?

General

14 December 2017

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.

What is Quantitative Story-Telling?

General

14 December 2017

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.

What are the Policy Case Studies?

General

15 December 2017

MAGIC will undertake a critical appraisal of key narratives that underpin EU sustainability policies and strategies.  The quality check of policy narratives has started from EU directives and related statements in the policy areas of water, energy, CAP, environment, and circular economy, supplemented by interviews with DG and other EC staff.

 

Policy Case Studies

  • Common Agriculture Policy (CAP) 1
    • Narrative - The basis of EU farm competitiveness and its wider consequences.
  • Circular Economy (CE) 3
    • Narrative A - Imaginary of the Circular Economy.
    • Narrative B - Indicators for the Circular Economy.
    • Narrative C - Critiquing the Circular Economy.
  • Energy Policy 4
    • Narrative A - Transition to renewable energies.
    • Narrative B - Intermittency challenge.
    • Narrative C - Energy efficiency narrative.
    • Narrative D - Outsourcing challenge.
  • Environment 1
    • Narrative - Meeting environmental targets in the EU: is externalisation of food production a solution or a problem?
  • Water Framework Directive (WFD) 2
    • Narrative A - The WFD as a success story in environmental policy and integrated governance.
    • Narrative B - Europe needs to improve the aquatic environment in order to secure water for its citizens and economic benefits.

What are the Innovation Case Studies?

General

15 December 2017

The seven innovations, policy solutions or interventions that will be analysed in MAGIC are: