Early in 2015 I was invited, together with co-authors from the International Risk Governance Council, to write a review on the above topic for a special issue of the journal Ecological Economics. The referees liked the writing, but wanted us to add more economics before they would recommend it for publication. This we could not readily do, since we are not economists and would have had to add an economist to the author list and rewrite the whole thing, possibly losing much of its original flavour in the process.
We still believe, however, that we had some very important points to make, some of which are vital to our very future. So we decided to put the article as is on this website as a substantive resource for others who are working in this area, and hopefully as a catalyst for fresh ideas.
NOTE: It is yet another example of the ways in which scientists think about the problems of the world, which is why I have put it with my series of Mini Stories from Science.
IMAGE: Michael Mittag (email@example.com); reproduced with permission.
UPDATE October 2016 Now published as a feature article in the Newsletter of the Society for Chaos Theory in Psychology and the Life Sciences (Fisher, L., Florin, M.-V., & Nursimulu, A. (2016). Governing or slow-developing risks in socio-ecological systems. Society for Chaos Theory in Psychology & Life Sciences Newsletter, 24(1))
HERE IS THE FULL ARTICLE, FREE TO REPRODUCE WITH ACKNOWLEDGMENT
Governance of Slow-Developing Catastrophic Risks in Socio-Ecological Systems
Len Fisher1, Marie-Valentine Florin2 & Anjali Nursimulu2
1Corresponding author; School of Physics, University of Bristol, Tyndall Ave, Bristol BS8 1TL, UK. email: firstname.lastname@example.org
ABSTRACT Complex socio-economic-ecological systems contain many feedback loops, whose dynamic interaction can bring a slowly changing system to a critical point where rapid change to a very different state is imminent. Existing financial and administrative structures are often not fit for purpose in handling such slowly-developing catastrophic risks (SDCRs), being insufficiently flexible to adapt to the changing timescales characteristic of SDCRs, and unable to develop and implement effective investment and other strategies over these timescales.
We argue that the major issue for effective governance is an awareness of the nature and inevitability of regime shifts in complex adaptive systems as an essential foundation for the development of strategies, policies and institutions capable of dealing with them. Since these strategies and policies will usually involve “resilience” (of which there are more than seventy definitions in the published literature), we further argue that it is essential to specify whether strategies and policies are intended to help the system “bounce back,” to adapt to new circumstances, or to invest in both possibilities according to (changing) hopes, predictions, and expectations.
With respect to the institutions responsible for developing effective policies and strategies, key enablers are:
- An awareness of the possibility and nature of regime change
- Mechanisms for recognizing and responding to warning signs
- Ability to collect and update relevant information, and to modify policies in the light of new information
- Speed and flexibility of making and implementing decisions
- Cooperation across multiple levels
To achieve these objectives, we suggest a three-pronged approach, with policy-makers, modelers , and scientists from relevant disciplines all playing their part in an ongoing, interactive dialogue. We recognize how difficult this may be in practice but, however difficult it may be, we believe that it is essential for the effective governance of SDCRs.
KEYWORDS: SDCR; Resilience; tipping point; regime shift; socio-economic-ecological system
Slowly developing catastrophic risks (SDCRs) are situations where a system such as a society, an economy or an ecosystem is changing and evolving slowly (often so slowly as to be unnoticed and even unnoticeable) until it reaches a point where rapid change is imminent, and often irreversible. Such changes can be catastrophic, in the sense that they can be very abrupt; they can also be catastrophic from the point of view of many of the actors concerned. They can also occur at multiple scales, and often involve cross scale linkages or dynamics. Examples from economics include market and bank crashes, while examples from ecology include the Allee effect, which can result in a population level going into free fall below a critical density, in some cases leading to extinction (Hanski, 1998); shifts from coral to algal dominance in reef systems following climate-change-induced coral bleaching (Graham et al., 2015); and the well-studied clear/turbid transition in shallow lakes (Scheffer et al, 1993, 2001).
Such risks can be viewed – in fact, most of them can only be viewed – as intrinsic properties of complex adaptive systems (CASs), which includes all complex ecosystems, as well as the even more complex socio-economic-ecological systems of which they are often a part. The risks arise because the dynamic interaction between the many feedback loops in the system will almost inevitably bring it to a critical point at some stage where an abrupt transition to a different state is imminent. Such transitions are known as regime shifts. The possibility of their happening is ever-present, but often unsuspected and unprepared for because the changes that precede them are so slow. Their occurrence has been documented in oceans, freshwaters, forests, woodlands, drylands rangelands and agroecosystems (Biggs et al., 2102). The risk is always there, although timescales and severity vary greatly.
The remainder of this paper is divided into three sections:
- Resilience to SDCRs in complex adaptive systems. 2
- What strategies for SDCR governance need to achieve. 6
- Key enablers for effective SDCR governance. 11
Our observations apply to SDCRs of all types, but we confine our examples to those involving the governance of socio-economic-ecological systems. Our discussion focuses mainly on the ecological and administrative issues, but we emphasise that effective policies and strategies must inevitably involve appropriate choices and timescales for economic investment.
1.1 Resilience to SCDRs
We are concerned here with the governance of slow-moving risks with potentially catastrophic consequences because of dangerous regime shift. We argue that a major issue for effective governance is to match the strategy with the threat of regime change so as to produce the most desirable outcome.
Risk governance strategies can be divided into two categories: those that are designed to avert a potential threat, and those that are designed to cope with the consequences of its occurrence. Within the latter category, the concept of resilience comes into play, but this concept needs to be carefully defined and understood, since it is ambiguous, and liable to misinterpretation (Fisher, 2015).
In fact, there are more than 70 definitions of resilience (Comfort et al., 2010). These vary between two extremes. At the one end, resilience is defined as the ability of a system to “bounce back” after stress i.e. to return to something close to its original state, as a spring under tension does when the tension is released. This is the definition explicitly adopted by the World Economic Forum (Global Risks Report, 2013). It is also the definition implicitly adopted by many politicians, whose major objective may be the maintenance of the status quo, either for political ends, for maintaining stability and the provision of essential services, or both.
At the other extreme, resilience is seen as “the capacity of social-ecological systems to adapt or transform in response to unfamiliar, unexpected and extreme shocks.” This is the definition suggested by a distinguished group of scientists led by ecologist Steve Carpenter and including the economics Nobel laureate Kenneth Arrow (Carpenter et al., 2012). It is a definition that is particularly appropriate, and increasingly used, in the context of complex adaptive systems such as ecosystems and social-economic-ecological systems.
According to Carpenter (personal communication, 2015) “the key distinction is that you can build in bounce-back to known (if random) risks, but you can’t afford to build in bounce-back to every type of risk, nor do you know all of the possible risks. The [definition by Carpenter et al] … emphasizes flexibility to deal with unexpected events when they occur. Flexibility is affordable, whereas specific bounce-back mechanisms for millions of kinds of risks are not affordable.”
We would add that an external “shock” may not be necessary in order to induce a regime shift; an SDCR will do the job just as well, and with more subtlety. The concept of antifragility suggested by Taleb (2012) suggests the development of agility to adapt and “get better” when exposed to stress and shocks. We see this as a component of resilience, as defined by Carpenter.
Most definitions of resilience aim to achieve a balance between these two extreme possibilities, incorporating a measure of “bouncing back,” but also allowing for adaptation to new circumstances. The U.S. National Academy of Sciences, for example, in a report on ‘disaster resilience’, defines resilience as the ability of a system to perform four functions with respect to adverse events: (i) planning and preparation (ii) absorption (iii) recovery and (iv) adaptation (NAS, 2012).
We are not concerned here with the choice of definition so much as its appropriateness to the situation, and we would emphasize that a clear, unambiguous, appropriate definition is needed for the development of effective strategy. The appropriate choice can depend very much on the nature of the situation and on the hopes and expectations of the participants.
We are also not particularly concerned here with planning for disasters that arise from unexpected external shocks, although much of what we have to say will be relevant to such scenarios. We focus rather on the development of policies for governance in the event of regime shifts, especially those that are a consequence of slow, sometimes imperceptible changes in an ecological or social-ecological system.
It is necessary at the outset to distinguish between strategies that are simply concerned with assessing and coping with the risk of change in a system as it stands, and those that are concerned with developing resilience. Here we are principally concerned with the latter. As Igor Linkov et al. (2013) point out in their article “Measurable Resilience for Actionable Policy,” “resilience has a broader purview than risk and is essential when risk is incomputable … . Therefore, resilience measurement must be advanced with novel analytic approaches that are complementary to, but readily distinguishable from, those already identified with risk analysis.”
Strategies for governance in the event of regime change are likely to depend very much on just what balance of resilience is aimed for. Such balance is between bouncing back to a previous state or regime -which may no longer exist-, on the one hand, and adapting to a new state or regime -which is not yet well defined and was certaintly difficult to imagine before the shift occurs-, on the other hand. Strategies are also likely to be dramatically at variance with strategies designed to avert regime change altogether. We believe that the key issue for effective governance in either case is to foster complex adaptive systems thinking as a basis for the development of strategies and policies (see Biggs et al, 2014).
1.2 SCDRs in complex adaptive systms
Slowly Developing Risks in Complex Adaptive Systems (CASs): An Overview
An SDCR is an emergent property of the system as a whole, and not readily understandable or analyzable in terms of the behaviour and direct relationship between its components. A prime example is life itself, where the internal balance of our bodies slowly changes with time, and always ends up with death, often due to runaway collapse of the system as a whole with no particular single “cause”.
· Since complex systems have multiple stable states, it is not always possible to predict of visualize the nature of the new state that the system will ‘flip’ to.
· Regime shifts can be contagious by several different mechanisms, including several systems having a driver of change in common, and change in one system triggering change in another (the “domino effect”).
· Human activity can lead slowly to regime shifts, as with anthropogenic effects on climate change (IPCC, 2014), or can rapidly catalyze them, as happened when the expansion of irrigation networks in Kazakhakstan and Uzbekistan led to the sudden shrinkage and breakup of the Aral Sea (Precoda, 1991).
· Above all, changes near a critical transition can be highly non-linear, and can spiral out of control.
Books such as The Tipping Point (Gladwell, 2002) and The Black Swan (Taleb, 2010) have drawn public attention to the possibility of sudden change in social, economic and ecological systems, but have focused on the effect of unexpected developments or external shocks, and hardly mention regime shifts following slow change. Yet the fact that complex systems have multiple stable regimes, and are liable to flip with little or no warning from one regime to another after a prolonged period of slow, imperceptible change, has been known for decades (May,1977), especially in the field of ecology (Holling, 1973), where there is now a massive amount of empirical evidence, encompassing trophic level decline in food webs, lake eutrophication, desertification, and sudden transitions in coral reefs, kelp beds, and soils, among many others (Scheffer et al., 2001; Scheffer, 2009). We should also emphasize that some regime shifts, such as those from a forest to a grassland, or the unfolding consequences of ocean acidification, can themselves be slow and imperceptible. In such cases, we are indeed “living dangerously on borrowed time” (Hughes et al., 2013).
An awareness of the nature of regime shifts in CASs is an essential foundation for the development of strategies, policies and institutions capable of dealing with them. Here we describe goals and features of effective SCDR governance strategies.
2.1 Strategies for averting the occurrence of undesired regime shifts
Reading the warning signs and acting on them
Early-warning signs for impending regime shifts include a decreased ability to recover from small perturbations, an increasing frequency in the occurrence of extreme states, and an increasing rate of fluctuation between extreme states (Scheffer et al., 2009). These indicators are derived from theory, and their existence has been confirmed experimentally in ecosystems ranging from cyanobacteria (Veraart et al., 2012) to food webs in lakes (Carpenter et al., 2011), although much work remains to be done in this area.
The usefulness of such indicators has been analyzed and debated extensively in the scientific literature (e.g. Scheffer et al., 2012), especially for the case of socio-ecological systems (Biggs et al., 2009). As the latter authors point out, a critical question is whether such indicators provide sufficient warning to adapt management to avert regime shifts.
Unfortunately, as spelled out by Carl Folke et al. in “The Problem of Fit Between Ecosystems and Institutions…” (2007), and many others, the timescale over which warning signs become obvious is often the same or shorter than the timescale over which most human institutions are able to act. This is not always the case, however. As Hughes et al. (2013) point out, there are quite a few cases where transitions between ecological regimes unfold over decades, centuries or longer. The effects of global warming certainly fit into this category, and it may be, for example, that the current rapid increase in macroalgal cover on many coral reefs is merely “the tail-end of a much longer transition” with a history that goes back a century or more.
Whether the changes that lead to a regime shift are slow or fast, however, the problem remains the same: to create institutional structures that can make decisions and act over a timescale that is commensurate with the threat.
Business people have long faced this problem, and it has been suggested that managers need to develop a “weak signal mentality” (Ansoff, 1975) and/or build the capacity for improvisation into organizations in advance (Mendonça et al., 2004, 2012) in order to avoid nasty surprises. There is emphasis in businesses such as the insurance business on the critical power of early warning (e.g. Tanner, 2015).
With respect to monitoring and responding to warning signs so as to avert regime change, the preconditions are formidable, especially with threats that can transcend national and international boundaries and cultures. We can do little more than list those preconditions here (see references for more detailed analysis):
- Matching scales of law with social-ecological contexts across space and time to promote resilience (Ebbesson & Folke, 2014).
- Recognition that traditional policy instruments like taxes, trading schemes and quotas, derived from assumptions about linear dynamics and marginal change, are quite inadequate to account for or deal with the non-convexities, multiple scales and evolution of complex adaptive systems (Levin et al., 2013).
- Development of new policy instruments based on modeling, large-scale data gathering, improved communication between scientists, modelers and policy-makers, and responsiveness to new information as it becomes available, including that from local sources with detailed knowledge of the ecosystem in question (Berkes & Folke, 2002), possibly via “adaptive governance” (Folke et al., 2005).
Promoting a desired regime shift
A second approach to avoiding an undesirable regime shift is to take positive action to promote a more desirable one. It is a technique that is as old as history – indigenous Australians have used it for thousands of years by burning areas to which they planned to return in order to promote the growth of grasses on which native animals could feed (Bowman, 1998).
A more recent example is that of Ascension Island, where (at the request of the British Admiralty in 1843) the botanist Joseph Hooker was asked to make recommendations to ‘improve’ the bare and arid environment. His main idea was to plant trees on the highest mountains so as to promote rainfall. The result was striking – an artificial cloud forest created over the next few decades by a process of ‘ecological fitting’ that did indeed fulfill its hoped-for function, if not necessarily by the mechanism suggested (Wilkinson, 2004), and also at the expense of the previous fern cover.
Such deliberate modification, and even replacement, of ecosystems is, of course, controversial. But perhaps it is time to consider it seriously when it comes to dealing with socio-ecological SDCRs and the problems of ecosystem stewardship that these entail – acceptance and even embracing of “novel ecosystems” (Hobbs et al. 2009); accepting “the reality of human-dominated ecosystems, rather than the separation of humanity and nature underlying the modern conservation movement” (Western, 2001); and especially “transforming from undesirable trajectories when the opportunity arises” (Chapin et al., 2009).
A hint of the latter approach is contained in the summary of the Millennium Ecosystem Assessment (Carpenter et al., 2006), which suggests that “certain kinds of proactive policies may … even promote regime shifts to create more desirable conditions.” A case in point is the recreation of wetlands (USEPA, 2013) to avoid loss/collapse of ecosystem services. Of course, we must tread carefully in adopting such measures – but we suggest that now is the time to take the first steps.
2.2 Strategies for “bouncing back”
“Bouncing back” is a difficult concept when it comes to socio-ecological systems. To some, it means restoration to the virgin state of the system before disruption but, as Bradshaw (1997) points out, this can be a moving target, and is usually impossible of achievement. It is also important to identify whether we actually mean rehabilitation, remediation, reclamation, or some combination of these subtly different concepts.
To take one example, the plant community in some 250,000 km2 of the Australian states of New South Wales and Queensland was increasingly dominated by the introduced Opuntia stricta (prickly pear) from about 1870, but the introduction in 1925 of a South American moth Cactoblastis cactorum, whose larvae eat the plant, “restored” the plant ecosystem approximately to the state that it was before the introduction of the pear. But this is not complete restoration, nor can it be. The pear is still there in small numbers, in equilibrium with the moth, and the native ecosystem that it displaced will never be the same.
Nevertheless, the idea of “bouncing back” has its value, so long as we do not take it too literally, and focus on maintaining / restoring function in the face of disturbance – in other words, “sustainability”. This is the focus of programmes such as Natura 2000 (EU, 2015), the UNESCO Biosphere reserves (UNESCO, 2014), and the recently established Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) (IUCN, 2015).
Much has been written on sustainability, and especially on the maintenance of biodiversity and its restoration (where possible) after damage. We believe that a central issue, and a major key to “bouncing back” is speed: speed of recognition, speed of agreement, and speed of response.
Rajendra Pachauri, Chairman of the Intergovernmental Panel on Climate Change, addressed these issues in a speech delivered at a major conference on biodiversity in Bonn, Germany (see Masood, 2015), with special regard to the IPBES, and made the following points:
- For speed of recognition it is often vital to use indigenous knowledge (this is in fact built in to the constitution of the IPBES).
- For speed of agreement it is vital to have the cooperation of all participants – not an easy task, as the history of the IPCC has shown.
- For speed of action it is important to have a secure financial base – again, not an easy task, given (for example) that the U.S. has still to ratify the UN Convention on Biological Diversity (UNCBD).
- We would add that for speed of action the bureaucratic structures characteristic of the IPCC (and many governments) could benefit from revolutionary modifications, perhaps modeled on the idea of the Rapid Reflection Force (Béroux et al., 2008); Lagadec, 2009).
2.3 Strategies for adapting to new circumstances after regime change
Change and regime shifts always happen. The most common strategy to prepare for it is scenario planning – in other words, attempting to imagine different possible scenarios, assessing the relative odds of their occurrence, and making preparations in advance for their eventuality (Schoemaker, 1995); Peterson et al., 2003). But even with a maximum of information and imagination, the scenarios that are planned for can only be painted with a broad brush at best.
This is still better than nothing when it comes to preparing for disasters, natural or otherwise, and scenario planning is undoubtedly a useful tool in such cases. But when it comes to SDCRs, and the multiple new stable states to which they might lead, something more is required.
That something – especially if human activity/interests are involved – is for human institutions to follow ecological changes, and to be ready to adapt to them, whatever they may be. This approach goes under the name of adaptive governance, and has been extensively analyzed by Carl Folke and his colleagues (2005). We can give no more than a flavour here, but some of the principal points that adaptive governance requires:
- The necessity for connection of individuals, organizations, agencies and institutions at multiple organizational levels.
- Combining local knowledge systems with scientific knowledge to cope with change in resource and ecosystem management
- Using diversification as a primary strategy
- Taking advantage of the fact that crisis, perceived or real, seems to trigger learning and knowledge generation and open up space for new management trajectories
- Continuously updating and adjusting understanding so that each management action is an opportunity to further learn how to adapt to changing circumstances
They also need to be considered in conjunction with the position of such organizations within today’s highly interdependent global network – itself a complex adaptive system that is susceptible to sudden regime shifts. As Helbing (2013) rightly points out, a fundamental redesign is needed, and paradigm shifts in thinking must be the order of the day, to trigger the “right kinds of interactions, adaptive feedback mechanisms and institutional settings”. We also note the possibility of “adhocracies” – organizations that emerge in response to a surprise or a crisis (Olsson, 2007), but which cannot necessarily be planned for.
In summary, the key requirements for effective governance of SDCRs in socio-ecological systems are:
- Recognition of the nature of regime change in complex adaptive systems
- Continuous updating of information, and modification of policies in the light of new information
- Speed and flexibility of making and implementing decisions
- Cooperation of organizations across multiple levels to make the above possible
- Improving institutional design for better speed, flexibility and responsiveness to warning signs.
We believe that these objectives are best achieved by using a three-pronged approach, with policy-makers, modelers and scientists from relevant disciplines all playing their part in an ongoing, interactive dialogue.
2.1 The role of policymakers
Policy-makers rely on advice from experts. But sometimes, and given the existence of scientific uncertainties, they waver between argument and counter-argument until it becomes too late to act, often making decisions only when the actual or approaching crisis is evident.
This problem is often exacerbated by the fact that policy-makers have an obligation to support economic activity, and such support can involve a cost to fragile ecosystems. This is not to criticize such support, but to make the point that economic incentives for the protection of ecosystems need to be given higher priority than they sometimes are.
The management of SDCRs requires a far-sighted approach, but we believe that this is only likely to happen if policy-makers themselves become aware of the unique properties of complex systems that lead to SDCRs. This can be a very difficult message to get over, given our innate human tendency to look for short-term cause-and-effect relationships, even when the evidence is tenuous at best, and to manage situations on that basis (Keiningham et al., 2006). But SDCRs rarely behave in this conceptually conventional way, and policy-makers need especially to be aware of the facts outlined in Box 1 (above).
As seen above, the management of SDCRs also requires an adaptive approach. Improving the adaptive capacity among institutions to deal with fast-moving global markets (e.g. the market in seafood (Berkes et al., 2006) is certainly a necessary goal for policies.
So policy-makers need better support from scientists. The question of how policy-makers can be helped to be aware of these basic facts and able to act is a difficult one, and we are under no illusions as to the difficulty (or, sometimes, the impossibility). Our best suggestion is that these are such important issues that they should be introduced at an early stage into all courses on decision-making, as well as being publicly disseminated as widely as possible, and of course emphasized by scientists interacting with policy-makers.
This is only a first step, of course. Speaking truth to power seldom gets results, because the reality is that there is often little room for manouvre, even if the challenges are understood. But understanding those challenges is an essential first step, and the one with which we are principally concerned here.
2.2 The role of modelers and specialist scientists
We lump these two together because the development of models useful for policy development and action requires a team effort.
On the modeling side, we are in a new era. As recently as 2002, Stephen Wolfram was able to claim in his book A New Kind of Science that many complex systems (especially those in the natural world) are ‘computationally irreducible.’ That is, any realistic model would be as complex as the system itself, so that predictive modeling is impossible, and the only option is to sit back and watch the evolution of the real system, which effectively acts as its own computer model.
Fortunately, Wolfram’s pessimistic predictions have been overtaken by events, most notably by the development of computers sufficiently powerful to analyze realistic models of social-ecological systems on the basis of realistic assumptions Schlüter et al., 2012). We believe that such models can be an effective guide to strategy, provided that models are developed with appropriate input from ecologists, physical scientists, sociologists, human actors, and others with expert knowledge of the system under consideration. In particular, such input should contribute to:
- Establishment of the system boundaries. Models cannot be infinite, but the boundaries of the system that they cover must include all major factors, and must be adaptable as new information comes to light.
- Determination of the spatiotemporal characteristics of drivers of change.
- Assessment of the role of human actors in the drama.
One particularly important aspect of the input of modelers and other scientists to the governance of SDCRs is expanding the evidence base. On the side of the specialist scientists, this means an ongoing effort to uncover information relevant to the model, especially in areas where there are significant gaps. On the side of the modelers, it means the ongoing incorporation of new information as it becomes available, and also collaborative modeling, where models based on different assumptions or using different approaches are developed and their predictions cross-checked against each other. The process is cyclic, with specialist scientists in their turn checking the predictions of models and using the new data for fresh inputs.
This is not to say that understanding and predictions based on such modeling are as certain as understanding and predictions based on, say, Newton’s Three Laws of Motion. Nor should we expect them to be, and hubris in this regard must be avoided at all costs. We are, rather, in the region that Funtowicz and Ravetz (1991) describe as “post-normal” science where “facts are uncertain, values in dispute, stakes high, and decisions urgent.” We are, in other words, in the real world – a world where science must take its place, with suitable modesty, if these urgent decisions are to be guided in the most effective directions.
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 For the purposes of this article we take Scheffer’s (2009) definition of a regime shift as “a relatively sharp change from one regime to a contrasting one, where a regime is a dynamic ‘state’ of a system with its characteristic stochastic fluctuations and/or cycles”. We also note that regime shifts can be smooth, abrupt or even discontinuous, depending on the nature and time-scales of the interaction between the different feedback loops. (Collie et al., 2004)
 We note that such measures may be financed by schemes such as the Payment for Ecosystem Services Scheme (URS, 2013)