Resilient Natures

Today, in the midst of the COVID-19 pandemic, the term new normal circulates ad nauseum throughout news outlets and social networks. This new normal is largely defined by a naturalization of precarity for some and the dramatic elevation of profit for others. Endless curves and data visualizations show us these “truths.”

It is hard to gaze upon these curves and not be reminded of a history of actuarial practices involving populations. It is also surprising how tenacious the ideology of the normal is and how reluctant we are to cease using it. The idea of the normal curve was an invention of nineteenth-century human sciences underpinning contemporary understandings of economies, populations, and “race.” Our adherence to the language of the normal is, therefore, also about nature. Despite years of arguing that nature and culture have recombined and we live in a modulatory post-normal, anthropocenic, and post-human society, it appears that many of us very much continue to adhere to ideals of nature. But what form of nature is this? My intent is to briefly historically situate this “new” nature.

 

Figure 1: Centers for Disease Control, “Interim Pre-Pandemic Planning Guidance: Community Strategy for Pandemic Influenza Mitigation in the United States—Early, Targeted, Layered Use of Nonphamrmaceutical Interventions, ” 18.
Figure 2. Image of Dow Rally, https://www.cnbc.com/2020/03/02/stock-market-today-live.html. March 2, 2020.

Populations

Few concepts are more prevalent right now than “flattening the curve.” The current instantiation emerged from a 2007 article on community-based mitigation of influenza pandemics published by the CDC. (See figure 1.)  Apparently no one can remember exactly who wrote it. There are precedents, of course. In the 1918 flu pandemic different urban areas were compared. During World War II, in the United States similar charts demonstrated how rationing would save materials and energy for the military. I am sure there are many others as well.

For our purposes, however, the “flatten the curve” discourse has some curious features. Among them is the assumption that pandemics are inevitable and that there is uncertainty as to when and where they will start. Public health officials have long warned of coming pandemics—there is a large literature to this effect—and such calls have become ever more visible and popular in the past few decades since the re-emergence of infectious diseases in the Global North with HIV/AIDS. Books with titles like The Hot Zone and The Coming Plague and movies like Contagion  and Outbreak, not to mention a slew of zombie apocalypses, all virus-induced, have filled our imaginaries. Virtually no one in public health doubted the possibility of another zoonotically transferred epidemic; the only question was when (not if). There is, however, no agreement as to the exact moment of the outbreak. Pandemics are clouded in uncertainty but still demand to be managed. The only certainty is that they will happen, but we do not know when or where. Pandemics, in short, are “known unknowns.”

Furthermore, the discourse assumes that the emergence of new diseases is difficult to entirely mitigate. While public health professionals and many others fully understand that better urban planning, better social equity and public health infrastructures, the transformation of agricultural systems, improvement of environmental management, and many other factors might change the inevitability of future pandemics, almost none of us actually believe that the necessary infrastructural changes that would save so many lives will happen. As a result, we must manage this uncertain event, ergo “flattening the curve.” In that COVID-19 spreads through breath and life, we have to slow the metabolism of the system to accelerate the demise of the virus. This is the management of temporalities strategy: a strategy that assumes catastrophe will occur, but that there are ways to treat this trauma.

“Ecos”

Volatility and uncertainty were not always considered the norms of nature.

Figure 3. Hutchinson image of biogeochemical processes from Circular Causal Systems in Ecology, 1946.

Since World War II, cybernetically informed ecologists have built models that understood the world in terms of homeostatically organized networked systems. Initial models grounded in communication sciences and tested on the landscape of nuclear blast sites valorized stability. Ecosystems were supposed to be made of feedback loops that aspired to balance, much like the early models of a homeostat coming from the sciences of communication and control.

 

Figure 4. The World Model, Limits to Growth, 1972, p.24.

 Imbalance was to be avoided, and systems should be managed for stability. The most extensive efforts at computing the future of the planet and its populations, The Limits to Growth, a report published in 1972, modeled, to cite Paul Edwards, such a “closed” world with limited resources that had to be kept in balance. The clarion call to an emergent environmental movement, this computerized report saw a world in need of balance, one where change was an anomaly, not a norm. The computer scientists modeled human behavior and populations as aberrations producing terminal traumas on the environment that would lead to catastrophe. The answer was to restore the balance of the planet through the careful management of feedback loops and return it to a sustainable state.

But many ecologists, environmentalists, and economists did not agree with the report. Ecosystems, they argued, did not appear to stabilize after suffering disruption. There could be no going back historically to a less “damaged” planet. DDT had demonstrated destructive results impacting systems far outside the immediate locus of intended insect elimination in agriculture and for purposes of public health. Agent Orange, heavily used in the Vietnam War as a defoliant, and related dioxins were demonstrated to produce long-ranging impacts in humans and ecosystems. And the list goes on. Just ceasing the use of a toxin or attempting to reseed an environment did not return systems to their pasts. Even seemingly environmentally friendly actions, such as lowering fishing quotas or replanting trees, would be found to have little effect once certain levels of disruption to the ecosystem were surpassed (Hamblin). Nature appeared to be constantly evolving.

The economist Friedrich August von Hayek, attacking The Limits to Growth, pleaded for the global community to refute such certainties and imaginaries of control over the future. Hayek and many other economists and engineers questioned the assumption that the world could just evolve without change. They asked, do humans not learn? And what about technology? Hayek stated that “[a man must] guard…against becoming an accomplice in men’s fatal striving to control society—a striving which makes him not only a tyrant over his fellows, but which may well make him the destroyer of a civilization which no brain has designed but which has grown from the free efforts of millions of individuals.”

Hayek posited a world full of uncertainty and chance. Unable to predict the future, we should relinquish attempts at management, assume that societies emerge, almost ecologically, from decentralized networks of coordinated information through markets, and refute the possibility of regulating the economy. Systems would self-organize from the “free efforts of millions,” not from the conscious decision-making of the few. And control, understood as the prediction of future events, was impossible.

These new ideas of nature arose, then, within a context where older models of political economy were also in flux. The end of Bretton Woods, decolonization, post-Fordism, and the OPEC oil crisis, to name a few of the transformations at the time, induced extreme volatility in politics, currency, and commodity markets. New financial technologies and institutions, such as derivative-pricing equations and hedge funds emerged in order to “hedge” bets. These technologies literally produced ways to short bets and ensure that risks were reallocated, decentralized, and networked. Dangerous bets would be combined with safer ones and dispersed across multiple territories and temporalities—consider short bets, credit swaps, and futures markets. Corporations, governments, and financiers flocked to these techniques of uncertainty management in the face of unnameable and unquantifiable risks. (It is worth noting that the Black Scholes Derivative pricing equation inaugurating the financialization of the global economy was introduced in 1973. For an excellent summary of these links and of the insurance and urban planning fields please see Kevin Grove’s Resilience.) Epistemologically, ecology and finance would come to share a model of a world of ceaseless volatility and uncertainty.

The question ecologists and economists turned to asking was: if prediction of the future was impossible, how were the models failing? And more importantly, how can these seemingly un-anticipatable events be dealt with? How does one manage for radical uncertainty? And change?

Resilience

 

Figure 5. Diagram from C. S. Holling” Resilience and Stability of Ecological Systems” demonstrating various theoretical population curves  (A,C,E) and their derivation dependent on different projections of  fecundity and morbidity (B,D,F).

In response, a new discourse began to emerge in ecology—resilience. In 1973, a year after Limits to Growth, the ecologist C. S. Holling introduced this new concept:

Individuals die, populations disappear, and species become extinct. That is one view of the world. But another view of the world concentrates not so much on presence or absence as upon the numbers of organisms and the degree of constancy of their numbers. These are two very different ways of viewing the behavior of systems and the usefulnessness of the view depends very much on the properties of the system concerned.

Essentially offering another rebuke to Limits, Holling posited an alternative world: not a world without change heading towards catastrophe, but a world where change, even catastrophic, is the norm and heralds not the end of systems, but evolution. Extinctions happen, but systems, “degrees,” and evolution continue.

Holling developed the concept of resilience to contest the premise that ecosystems were most healthy when they returned quickly to an equilibrium state after being disturbed. His argument was that over-emphasis on predator-prey relationships often ignored more complex interactions and over-valued equilibrium. Nitrogen, carbon, and other cycles, interactions of mutual aid, collaboration, or competition between many species not structured as predator-prey relations, and a myriad of other factors might permit ecosystems to persevere in their functions even if in mutated or varied forms. Extinction might not be the limit to the growth or change of a system, unless it fundamentally transformed a complex web of interactions. The seeming absolute limit to life—extinction—could be extended through complexity and a new value for biodiversity.

Figure 6. Topological models generated from historical data since 1951 of budworm population densities in space. It’s also worth noting that these new forms of dynamic maps and capacities to compare data sets came with the introduction of digital computation and new platforms such as the Canadian Geographic System (CGIS) considered the root of contemporary GIS systems in the early 1970s. From “The Spruce Budworm/Forest Management Problem.”

If sustainability was the language of stable systems in a cyclical economy, resilience is the language of volatility. In an early critique of industrial fishery and forestry management, Holling argued that the focus on using insecticides, re-seeding lakes with fish, or attempting to simply replant one type of tree would not work over extended periods of time. Managing ecosystems with a focus on stability was an error. Managers, he suggested, must cease counting and taxonomically placing populations in boxes and flow charts and needed to realize that positive feedback is dynamic and produces change. Populations are not static numbers but ongoing processes. The important thing is to maintain the process, not the steady state of the system.

For example, in the case of the boreal forest, the absolute number of spruces is not important. What is important is the ability for the forest to rejuvenate and continue growing trees, which depends on fluctuating numbers of populations and constant variations between spruce, fir, birch, and budworms. The system regularly changes. In general, this allows the forest as a forest to continue existing. Better ecological management might also apprehend the fact that systems ultimately change. For example, forests in Ontario are increasingly used for leisure and vacationing rather than for forestry, and their management must change accordingly. For other systems, one might imagine a different process or processes defining them. Today we deploy the term ecosystem services.

Resilience, by contrast, denoted for Holling the capacity of a system itself to change in periods of intense external perturbation as a mode of persistence. The concept of resilience enabled a management approach to ecosystems that “would emphasize the need to keep options open, the need to view events in a regional rather than a local context, and the need to emphasize heterogeneity.” Managers had to create multiple strategies for future actions, think “regionally,” which is to say in terms of networks and connections across different territories and times, and emphasize heterogeneity, or biodiversity, in order to secure more possible routes for adaptation in case of unanticipated shocks. He would later label this form of management “adaptive management,” arguing that it necessitated the constant feedback of data to respond to constant changes. (See here for a summary of strategies in adaptive management.)

Resilience is, in this sense, defined in relationship to crisis and states of exception; that is, it is a virtue when such states are assumed to be either quasi-constant or the most relevant for managerial actions. Holling also underscored that the movement from valuing stability to valuing resilience depended upon an epistemological shift: “Flowing from this would be not the presumption of sufficient knowledge, but the recognition of our ignorance: not the assumption that future events are expected, but that they will be unexpected.” In short, expect the unexpected. Plan for extreme events without any conception of an absolute prediction.

There are three summary points I want to emphasize. The first is that resilience within this genealogy assumes uncertainty and volatility as common, perhaps “normal” conditions. Stability and resilience are not correlated. As a corollary the life and death of individuals or even populations is secondary to the ongoing evolution of systems. Second, resilience was a new way to model systems and therefore measure them. Instead of taxonomy and organizing populations into stable categories, one must define systems in terms of processes and measure the relationships between populations and potentially other factors (nitrates, carbon, energy, etc.). A corollary of this new approach is that past data can be used to build concepts but can never actually predict the future. Probabilities have to intervene. Finally, ecologists emphasized “heterogeneity” and diversity as important to facilitating resilience. Systems without a surplus of functions and populations could not adapt. Perfectly optimized systems would collapse when change happened.

Resilience thus possesses some curious features. On one hand, the focus on processes and what are today labelled “ecosystem” services means that some lives and populations are acceptably sacrificed as long as the system continues to operate and trauma is a regularized and normalized event.  On the other hand, environmental managers recognized that only systems with robust diversity, redundancy, and supplemental capacities might survive abrupt and catastrophic events. Resilience fluctuates between the two poles of Darwinian evolutionary theory—survival of the fittest and the necessity for variety and diversity within and between populations to allow for adaptability. Perfect optimization might come at the cost of adaptation.

Managing for resilience also vacillates between other debates involved in evolution—nature or nurture. Except that has been reformulated to code and context. Do you focus on the singular genome, or the entire landscape of biodiversity? The term allows both understandings to advance.

Resilient Speculation

This brings us to the present and to our curves. Following 9/11, the 2008 financial crisis, and climate change, resilience has taken a central discursive place in fields ranging from business management and logistics to psychology. “Adaptive management,” “business continuity management,” “climate resiliency planning,” and many related terms are all the direct outgrowths from ecological resilience and largely shape our understanding of how changing climate and security conditions are to be dealt with.

An online search for “resilience” in the time of COVID-19 reveals a massive number of articles, websites, and consulting services dedicated to logistics, psychology, and community activism. For managers of supply chains and corporations such as SAP and IBM, resilience is what corporations must do to ensure business continuity. “Just in time” manufacturing is now “just in case” manufacturing, and corporations are urged to increase their options, to diversify supply chains geographically, to begin thinking about plasticity in manufacturing infrastructure (for example, being able to make alternative products), and to identify vital services and processes ahead of time. For the Trump administration and many of the world’s leaders, resilience is a call to expend populations they do not value—the elderly, people with underlying health conditions, people of color—in the name of saving the economy. Resilience thus becomes a mode of naturalizing violence for the Right.

This violence is naturalized through uncertainty. There is a crisis of evidence and objectivity that the Right has now utilized to attack regulation, the notion of planning, legislation against disease, and any defense of diversity. On the one hand, the uncertainty over the future of the pandemic becomes cause to do nothing. We don’t have enough data to make a decision, or our data, since it cannot perfectly predict the future, is flawed and invalid. In this case certain corporate and government institutions become, to use historian of science Naomi Oreske’s parlance, “merchants of doubt.” They profit off of the uncertainty inherent in complex systems and have made this uncertainty an economic and political strategy to legitimate their actions (or lack thereof as it may be).

On the other hand, as public health ethicist Nicholas King has noted, there is a politics of evidence at play in COVID-19 responses. In the US, President Trump has made a career of critiquing elitism and a general attack on scientific forms of evidence and evidence-based decision making. The uncertainty in this case within scientific forums only facilitates a legitimizing of his critique and allows the Right to transform the catastrophe into a war of ideologies to which Trump answers with authoritarian confidence as the best and most valid voice, while simultaneously invoking the concept that some people should be sacrificed for the economy.

However, resilience might have positive connotations or faint messianic capacities, to invoke Benjamin. Recent events have highlighted other comprehensions of resilience. For many people on Earth, trauma has long been a norm, but the future should not be the same. On the Black Lives Matter website, resilience is imagined as an alternative possibility: “We affirm our humanity, our contributions to this society, and our resilience in the face of deadly oppression.” The race theorist Kara Keeling has recently argued for resilience, or citing Nassim Taleb, “antifragility” as a figure of thought for Black liberation, the possibility of getting stronger through ongoing shock. For Taleb “anti-fragility” is opposed to economic ideas of resilience, but is strikingly commensurate with ecological resilience as explicated by Holling. While the Holling text is itself a neoliberal treatise, which points to another form of politics, Keeling seizes on Taleb’s concept to critique present economics and its effort to control the future through computational and calculative techniques of derivation and commensurability—the very techniques that descend from the slave trade and that naturalize neoliberal violence and “shocks” as natural. For Keeling, Black and queer times emerge from the present, refracting the contemporary call “enough is enough” that echoes through the streets in our present. This call for a present whose future is not yet decided makes the future unknowable, but also radically different and unrecognizable from the present. This is not a concept of shock that legitimates the sacrifice of lives, but one that recognizes that for Black people, and many others, trauma has been ongoing, has been continuous, and can be survived. At the center is an argument that shock may make one stronger, but resilience always admits to our ecological relations to others, to the necessity for diversity, and to the possibility that the future of a system will never be its past. Resilience, we might recall from ecology, demands change and diversity.

Here we must contend with how we understand evolution and genealogy. Financial and logistical comprehensions of resilience largely assume a world of plans for different scenarios and un-anticipatable futures divorced from historical legacy or context. Focusing on services eliminates the need to focus on environments or milieus. The conditions of possibility for life. But resilience could also operate differently. In the spirit of Black Lives Matter could we not understand resilience as being about historical consciousness and an actual redesigning of institutions and environments? The future does not need to replicate the past. Resilience can be a call for multiplicity and for futures not yet known; it could yet offer a model of ecological thinking that might defeat the optimizing demands of capital or conservatism. It might offer the possibility of not a “new normal” but a new nature.

 

 

This research was supported by a Sinergia Grant from the Swiss National Science Foundation. 

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Orit Halpern

Orit Halpern is an associate professor at Concordia University in Montréal. Her work bridges the histories of science, computing, and cybernetics with design and art practice. She especially focuses on histories and practices of big data, interactivity, and ubiquitous computing.