Melissa Leach: Dynamic Sustainabilities: Taking complexity and uncertainty seriously in environment and development


Published on

Published in: Technology, Business
1 Like
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • In our work we have drawn on and drawn together – and go beyond - a related set of ideas and approaches that draw in different ways on complexity science, and offer specific helpful concepts and modes of analysis for different fields of development. The last is particularly significant, and we would argue vital in a development context, drawing attention to the importance of both reflective traditions in analysis, and constructivist/reflexive ones drawn from other social science traditions.
  • The idea of pathways is useful in thinking about development, change, directions
  • But central to the pathways approach is the addition of a reflexive dimension, drawing on methodological constructivism in the social sciences. That is to recognise that there are multiple ways of understanding and representing a system; and that all analysis of a system involves framing. Framing involves not just choices about which elements to highlight, and how to bound the system, at what scale, but also subjective and value judgements. Such framings are produced by particular actors – whether different local people, scientific, policy or business actors, and co-constituted with their particular institutional, political and life settings.
  • Some applications of complexity science tools seek to apply these in a deterministic way; if we just understand the range of tipping points, we can plan for them But of course a fundamental implication of complexity and dynamics is that there are limits to knowability, and to control/planning. Taking complexity seriously means dealing with incomplete knowledge in situations where uncertainty and surprise are inevitable, and tailoring actions and strategies to situations where dynamics of change and their drivers are not always tractable to control. Will now go on to look at each of these and their implications in turn.
  • But what do we mean by incomplete knowledge? The top left hand quadrant defines risk in the strict sense of the term. This refers to a situation where there is confidence that probabilities can be calculated across a range of known outcomes. Under the strict definition of uncertainty (lower left quadrant), we can be confident in our characterisation of the different possible outcomes, but the available empirical information or analytical models do not present a definitive basis for assigning probabilities the rigorous approach is therefore to acknowledge the open nature of a variety of possible interpretations.   Under the condition of ambiguity (upper right quadrant), Disagreements may exist, for instance, over the selection, partitioning, bounding, measurement, prioritisation or interpretation of outcomes. For instance in decisions over the right questions to pose in regulation: ‘is this safe?’, ‘sustainable?’, ‘sustainable enough?’, ‘acceptable?’ or ‘the most sustainable option?’. Similar ambiguities emerge when we are forced to compare ‘apples and oranges’. These might be qualitatively different forms of damage; impacts on different people (e.g. workers or the public; children or adults); or consequences over different time-frames (e.g. present or future generations) – in effect, questions over ‘contradictory certainties’ (Thompson and Warburton, 1985.   Finally, there is the condition of ignorance (lower right quadrant) where ‘we don’t know what we don’t know’ (Wynne, 1992; 2002), we face the ever-present prospect of ‘surprise’ . where the parameters are not just contestable, but are acknowledged to be at least partly unknown, implying straightforward ignorance over the possibilities themselves.
  •   Let us pick up the epidemics example and look at how different dimensions of incomplete knowledge are invoked in different narratives about avian flu – in the diagram, we could envisage different narratives and sub-narratives ‘clustering’ in different corners Much of the debate has been dominated by quantitative probabilistic models of risk. In 2005, for example, two models were presented in Nature (Ferguson et al, 20050 and Science (Longini et al, 2005) which together had a huge influence in framing the response as one that needed to be focused on containment at the source of the outbreak. But of course a wide range of uncertainties exist – from the big uncertainty (will a pandemic happen at all, and if so when?) to more specific uncertainties (about the impacts of veterinary control measures, about vaccination and drug efficacy, about behaviour change in situations of crisis and so on). Thus, for example, the interplay between viral ecology and genetics, transmission mechanisms (e.g. the role of wild birds or poultry, back-yard chickens or large factory units) and impacts (e.g. the consequences in immuno-compromised individuals and populations) are highly complex and contingent. There are also ambiguities: how do we define an ‘outbreak’, and its impacts and distributional effects? Outcomes defined in terms of potential impacts on human mortality globally - up to 150 million deaths in a major global pandemic, according to some estimates – contrast with those defined in relation to particular groups at risk – for example, women or children handling poultry. Surprise renders it intrinsically difficult to substantiate possible examples of ignorance ex ante . Yet possible surprises may plausibly be anticipated around the emergence of radically new strains of the virus, unexpected transmission mechanisms or unanticipated health outcomes, including those arising in complex interactions with other health/social conditions. Of course, there is always the broader possibility of the emergence of entirely novel pathogens; indeed over 70% of new infectious diseases affecting humans that have emerged over the last 30 years have emerged unexpectedly from non-human animal populations (Jones et al, 2008; Woolhouse and Gaunt, 2007).
  • Frequently, and around many issues, one can observe a process of ‘closing down’ towards this top-left-hand corner. The implications of system dynamics come to be treated in terms of risk, neglecting or underplaying these other dimensions of incomplete knowledge. Put another way, it is often risk-based narratives and the pathways they justify that come to dominate, over and above narratives that appreciate uncertainty, ambiguity, ignorance and their implications. Thus in the case of avian flu, we often see narratives which cast the problem in terms of risk predominating; these are the ones which are articulated by the major international agencies, and which drive outbreak-focused policy responses. Other dimensions of incomplete knowledge – despite their relevance to the problem – receive far less attention   How does this happen? A number of political, procedural, and knowledge-related pressures appear to be at play., varying according to the issue and context, but with some general tendencies . Thus the institutional remits of organisations may encourage a move from ignorance to uncertainty: ruling out surprise. the institutional remits and organisational mandates of international agencies such as the WHO and FAO are simply not geared up to dealing with ignorance and surprise; the very existence and status of the agencies is interlocked with the idea that outbreaks and their effects can be known about and thus rendered amenable to management The use of particular indicators and definitions, and the use of particular legal frameworks, may assist this. Further techniques and strategies of governance in turn may lead uncertainty to be re-defined and treated as risk. These range from bureaucratic and planning procedures that rely on, and thus reinforce, an image of a calculable, manageable world, to particular techniques of modelling, reasoning and categorisation that render the world legible and calculable in risk-based terms. Moving up the right hand side of the diagram, further techniques and strategies of governance may be used to ‘tame’ ignorance and surprise into a more manageable range of possibilities, even if these end up as ‘apples and pears’, non-congruent possibilities. Practices such as drawing on (transdisciplinary) expertise, and setting agendas, and defining organisational mandates may be significant here. However, further pressures may act to narrow a range of possible outcomes (ambiguity) further, creating a set which can be clearly defined and dealt with as risk. Political closure, strategies of ordering (and exclusion), and processes of subjectification whereby people – whether supposed policy beneficiaries, or workers within an organistaion or agency, or others) come to internalise this possible range of outcomes as the appropriate set for consideration, are all relevant here. In the avian flu case, political closure was encouraged by political-economic interests in garnering support for a massive global response, and by aggregative forms of analysi. Claims about particular sorts of vulnerability, perhaps associated with the livelihoods or social positions of particular local groups, tend to disappear as mere ‘twitter’ amidst the dramatic figures about aggregate risk that garnered public, political and media attention. Thus, through a cluster of interlocking political, institutional and knowledge-power processes, the problem of avian flu tends to be treated in terms of risk, at the expense of uncertainty, ambiguity and ignorance.  
  • Moving from knowledge to action, here we are talking about directing and shaping pathways to sustainability Butt recognising that sustainability is not just one thing ... Different goals Different dynamic properties
  • Actions aiming to promote sustainability involve assumptions about the nature, or ‘temporalities’, of changes – are these seen as short-term shocks or long-term stresses? In complex dynamic systems there may be multiple, interacting shocks and stresses in different system elements operating simultaneously. And the styles of actions that are envisaged. Is the aim to control the causes or drivers of change, or to respond to them? In complex, dynamic systems, it is likely that many changes and their drivers will be intractable or uncontrollable, rendering more conventional control-oriented management inappropriate. Instead, need responsive, adaptive management, aimed either at resilience (to shocks) or robustness (to shifts). Thus we might ask, within any given narrative: are intervention strategies aimed at exercising control in order to resist shocks (stability )? Or is there an acknowledgement that there may be limits to control, and thus that interventions should resist shocks in a more responsive fashion ( resilience )? In other circumstances, the system may be subject to important stresses, driving long run-shifts. In this case, interventions might attempt to control the potential changes – aiming at durabilit y. Alternatively, embracing both the limits to control and an openness to enduring shifts would suggest strategies aimed at robustness. These are important practical distinctions that are often elided or ignored in existing analysis for policy-making on sustainability.
  • Melissa Leach: Dynamic Sustainabilities: Taking complexity and uncertainty seriously in environment and development

    1. 1. Dynamic Sustainabilities: Taking complexity and uncertainty seriously in environment and development Melissa Leach ESRC STEPS Centre, Institute of Development Studies, Sussex UKCDS Workshop May 12 2011
    2. 2. <ul><li>Complex dynamics and uncertainties in development challenges involving environment, food, health, water (epidemics, seeds and drought, water management, low carbon energy) </li></ul><ul><li>A heuristic - the STEPS Centre’s pathways approach to understanding and acting on ‘dynamic sustainabilities’ </li></ul><ul><li>Complexity sciences and beyond, blending natural and social science approaches </li></ul>
    3. 3. Contradictions <ul><li>Growing recognition (in research and everyday life) of complexity, dynamism and uncertainties </li></ul><ul><li>Growing recognition of diverse ways of knowing, values, perspectives, priorities </li></ul><ul><li>Growing search for technical-managerial solutions premised on a far more static, consensual view of the world – solvable problems, achievable stability, controllable risks </li></ul><ul><li>…… A mismatch - cycles of ‘failure’ as dynamics undermine assumptions of stability; emerging backlashes from nature, politics; mires of disagreement; those who are already vulnerable and marginal often lose out </li></ul>
    4. 4. Dynamic systems thinking in/for development – an extended family of concepts and approaches <ul><li>Complexity science (interdependence, co-evolution and inter-coupling; feedbacks: non-linear dynamics; context-dependence; emergent properties; self-organisation) </li></ul><ul><li>Resilience thinking and sustainability science (shocks and stresses, disturbance and response, phase shifts, attractors) </li></ul><ul><li>New perspectives in ecology (non-equilibrium dynamics, multiple stable states) </li></ul><ul><li>Dynamics of technological change (socio-technical regimes, lock-in, contingency, niches, transitions) </li></ul><ul><li>Organizations and management responses in dynamic settings (complexity as experienced and engaged in as well as described, soft systems, reflective practitioners, organizational learning) </li></ul>
    5. 5. Complex, dynamic system Interacting social, ecological, technical, political elements A dynamic systems heuristic Reflective scope: Environment Inchoate ‘reality’ <ul><li>Complexity science </li></ul><ul><li>seeks comprehensively </li></ul><ul><li>to reflect a full </li></ul><ul><li>range and diversity of </li></ul><ul><li>- elements, </li></ul><ul><li>linkages and </li></ul><ul><li>dynamics </li></ul><ul><li>in a system and its </li></ul><ul><li>environment </li></ul><ul><li>And might describe </li></ul><ul><li>pathways: </li></ul><ul><li>Particular directions in which </li></ul><ul><li>system elements co-evolve </li></ul><ul><li>over time (non-linear, </li></ul><ul><li>context-dependent, etc) </li></ul><ul><li>Change, development…. </li></ul>
    6. 6. Framings : Different ways of understanding/representing complex system dynamics and change Multiple possible pathways to different sustainabilities (which functions and values, for whom) Normative agendas (What is ‘good change’? which pathways, to where? ) Reflexive attention to framings/narratives of different actors/researchers in development Integrating a reflexive understanding: <ul><li>Dimensions of framing </li></ul><ul><li>Not just : </li></ul><ul><li>- Scale </li></ul><ul><li>- Boundaries </li></ul><ul><li>- Key elements and complex interrelationships </li></ul><ul><li>Dynamics in play </li></ul><ul><li>But also : </li></ul><ul><li>Perspectives </li></ul><ul><li>Interests </li></ul><ul><li>Goals </li></ul><ul><li>Values </li></ul><ul><li>- Narratives </li></ul>
    7. 7. Complexity and dynamism mean pathways cannot be expected to unfold in deterministic ways Dealing with incomplete knowledge : Uncertainty and surprise are inevitable Tailoring strategies and actions : Dynamics cannot always be controlled
    8. 8. unproblematic problematic unproblematic problematic knowledge about likelihoods knowledge about outcomes Dealing with incomplete knowledge Many contrasting aspects .... RISK AMBIGUITY UNCERTAINTY IGNORANCE
    9. 9. unproblematic problematic unproblematic problematic knowledge about likelihoods knowledge about outcomes Dealing with incomplete knowledge e.g. Avian influenza RISK AMBIGUITY UNCERTAINTY IGNORANCE ostensibly definitive quantitative probabilistic models of risk pandemic or not? impacts of veterinary controls? behaviour change in crisis? interplay in viral ecology / genetics immuno -compromisation ? define ‘outbreak’: distributional consequences? mortality / morbidity? vulnerable groups? economic costs? livelihoods impacts? new strains of the virus? unexpected transmission vectors? unanticipated health outcomes? complex social interactions? entirely novel pathogens?
    10. 10. unproblematic problematic unproblematic problematic knowledge about likelihoods knowledge about outcomes RISK UNCERTAINTY AMBIGUITY IGNORANCE decision rules aggregative analysis deliberative process political closure reductive modeling stochastic reasoning rules of thumb insurance ` evidence-basing agenda-setting horizon scanning transdisciplinarity liability law harm definitions indicators / metrics institutional remits Powerful pressures to ‘close down’ towards risk
    11. 11. unproblematic problematic unproblematic problematic knowledge about likelihoods knowledge about outcomes RISK UNCERTAINTY AMBIGUITY IGNORANCE uncertainty heuristics interval analysis sensitivity testing scenarios / backcasting interactive modeling mapping / Q-methods participatory deliberation reflexive research institutional learning adaptive management From closing-down to opening-up Various potential tools and methods... reductive aggregative models ALL INVOLVE INTERACTIVE MAPPING OF DIFFERENT UNDERSTANDINGS
    12. 12. STABILITY DURABILITY RESILIENCE ROBUSTNESS SUSTAINABILITY Shaping pathways to sustainability, doing development But sustainability is not one thing..... What is to be sustained (functions, values, services...) and who values these? From Knowledge to Ac tion Temporality of change – are changes seen as shocks or stresses? Potency of action – is the aim to control or respond to change?
    13. 13. shock (transient disruption) stress (enduring shift) control respond temporality of change style of action STABILITY Tailoring strategies and actions Multiple dynamics, often uncontrollable.... DURABILITY RESILIENCE ROBUSTNESS
    14. 14. shock (transient disruption) stress (enduring shift) control respond temporality of change style of action STABILITY Tailoring strategies and actions e.g. dealing with water resources in dryland India DURABILITY RESILIENCE ROBUSTNESS Control of short-term supply variability through dams, pumps and pipes Engineering solutions geared to long-term shifts in rainfall and hydrology (e.g. margins, reduced water levels) Adaptive responses and interventions geared to floods and droughts (e.g. crop mixes, mobility, water harvesting) ; local knowledge, culturally-embedded practices Response to long-term shifts in water supply and use (e.g. changes in land use, agricultural practices, livelihoods); variegated, flexible institutional and engineering arrangements
    15. 15. shock (against transient disruption) stress (agaInst enduring shift) control (change is internal to control system) response (change is external to control system) temporality of change potency of action STABILITY e.g. blueprint planning in development e.g. top-down engineering approaches in water management e.g. avian influenza: routine responses, institutionalised practices encoded in standard, global surveillance, early warning and rapid response routines Powerful pressures to ‘close down’ around planned equilibrium Need to be reflexive about the dynamics of power DURABILITY RESILIENCE ROBUSTNESS
    16. 16. shock (transient disruption) stress (enduring shift) temporality of change style of action control (tractable drivers ) respond (intractable drivers ) From closing-down to opening-up Some candidate styles of institution and intervention
    17. 17. shock (transient disruption) stress (enduring shift) control response temporality of change potency of action From closing-down to opening-up Broad reflection, reflexivity and humility are vital DURABILITY RESILIENCE ROBUSTNESS Reflection and Reflexivity engage stakeholders; address multiple goals and values; explore uncertainties; map ambiguities; maintain flexibility / diversity
    18. 18. Development and pathways to sustainability amidst/as complex dynamics: some pointers <ul><li>Broad Reflection </li></ul><ul><li>acknowledge quantification beyond reductive-aggregative modelling </li></ul>Open Reflexivity - beware powerful pressures to justify deterministic-, risk- and stability-based policies <ul><ul><li>Combine scientific rigour, democratic accountability and humility </li></ul></ul>- don’t be ashamed of ‘heuristics’ : examine sensitivities to uncertainties - disaggregate different framings : explore diverse scenarios / narratives, recognise multiple possible goals and values and their contestation - show humility about ignorance – admit “we don’t know what we don’t know” - challenge (‘ sound science ’) rhetorics in analysis of complex dynamic systems - don’t take parameters for granted – acknowledge and deliberate over subjectivity - acknowledge positionality and interactions of all involved actors, including different researchers, policy-makers and practitioners, citizens – engaged, participatory, transdiscipinary research - no ‘analytical fixes’ – highlight intrinsic politics in complexity and sustainability -