Modeling risk in complex bioeconomies

Abstract

Like all bioeconomies, markets in synthetic biology will reconfigure complex arrangements of coupled social, technological, and biogeophysical systems. These rearrangements will create novel pathways for risks to propagate across and through systems that will be difficult if not impossible to predict or analyze using conventional risk assessment paradigms. To anticipate potential risks, new approaches will be needed to model cross-system dynamics, using methods that successfully integrate and synthesize diverse forms of knowledge about quantifiable and non-quantifiable features of multiple systems and to ensure early phase innovations are sufficiently transparent to assess the broad system reconfigurations associated with new market development.

Keywords:

• risk
• bioeconomy
• synthetic biology
• sociotechnical system
• synthesis

Economic theories – and their applied variants in business practice – are almost entirely grounded in abstract concepts (e.g. supply, demand, labor, capital) that divorce market phenomena from the specific social, political, and technological contexts within which they occur. Social scientists have studied the processes and practices by which actors make market abstractions, noting that they do so simultaneously across epistemic, performativity, and materiality dimensions (e.g. Steinberg 1991; Cronon 1992; Mackenzie 2006; Rajan 2006; Busch 2011). Yet, as recent concerns about sustainability highlight, even the simplest of markets, such as commodity markets, can never fully be isolated from the complex webs that link key aspects of these markets to diverse social, ecological, technological, and geophysical systems (Vitousek et al. 1997; Vorley 2001). Complex market phenomena, such as those envisioned in novel synthetic bioeconomies that cut across biology, society, economy, engineering, and ecology, make even more challenging the identification and assessment of risks that arise from the ways in which markets act as sites for the reconfiguration of relationships between multiple complex systems.

Bioeconomies have a lengthy historical record that testifies to the problem of market abstraction. Agriculture has fed large populations for thousands of years; yet, great famine has also persisted as a systemic feature of the political economy of agricultural markets (Sen 1981). For most of agricultural history, slavery and indentured servitude were common (Jameson 1977; Lovejoy 2011). Today's agricultural system has perhaps escaped famine and slavery, although hunger and malnutrition persist at high levels, globally, as do mistreated farm workers (World Health Organization 2000; Villarejo 2003). At the same time, today's agro-food systems require massive resource inputs that have contributed to land degradation, disruptions of carbon and nitrogen cycles (and associated impacts, such as climate change, deforestation, ocean acidification, and ocean dead zones), widespread pollution from agrochemicals, the construction of massive water systems (with their own disruptive effects), and the rise of monocultures that are highly vulnerable to increasingly pesticide-resistant diseases and demand constant innovation in order to fend off rapid declines in yield and productivity (Tilman et al. 2002). Likewise, today, agro-food systems have transformed human nutritional patterns vis-à-vis prior eras, especially around the consumption of sugars and carbohydrates, contributing to the growing prevalence in the USA in childhood obesity, diabetes, and other diseases (Frazao 1999).

Other modern bioeconomies have also had their share of problems. Blood manufacturing systems, crucial to the practice of surgery and emergency medicine, as well as treatments for hemophilia and other diseases of the blood, have experienced numerous episodes of disease transmission, including hepatitis C and AIDS (Dubin and Francis 2013). Hospitals and feedlots have combined to produce super-resistant microbes that cannot be treated by any known anti-bacterial medicines (Levin, Baquero, and Johnsen 2014). Interwoven food and transportation markets combine with migratory bird ecologies to annually generate and spread deadly infectious diseases among people, pigs, and avians (Monto and Webster 2013). Other market interactions generate sporadic but seemingly inevitable outbreaks of food-borne pathogens – for example, periodic outbreaks of Ebola from bushmeat markets and Escherichia coli infections in hamburger (Tham and Danielsson-Tham 2013). Recently, complex interactions between food and fuel markets contributed to price spikes for staple foods in poor communities around the globe (de Gorter, Drabik, and Just 2013). These risks are compounded when we go beyond the consideration of health and biophysical harm to include the consideration of social and economic risk, as well as issues of power, identity, justice, and ethics.

The risks associated with complex bioeconomies arise both because: (1) markets transform complex relationships among social, technological, biological, organizational, economic, and ecological systems, and (2) existing forms of risk knowledge and systems of knowledge production about risk have a great deal of difficulty in capturing the complexities of cross-system dynamics. While new bioeconomies created through synthetic biology are hardly unique in regard to these factors, they complicate concerns about risks for several reasons. First, they create novel forms of life whose biological properties are not fully known or predictable when integrated into complex biological and ecological systems. Second, they unsettle taken-for-granted social, political, and cultural assumptions around which (sometimes only modestly) stable social orders have been established. For example, they may alter ideas about the kinds of interventions that are possible or desirable in human biology in ways that dissolve conventional boundaries between therapy and enhancement (see e.g. Hays, Miller, and Cobb 2013). Third, they create new demands for biological production that may stress already overwrought relationships among social, technological, and biological systems. The use of metabolic engineering to develop synthetic biofeedstocks that could replace petroleum as an input to fuel and chemicals production (see e.g. Lee et al. 2008; this is often touted as a valuable contribution of synthetic biology), for example, would likely further stress the sustainability of agricultural systems that are already under strain from continued growth in demand for food (Vitousek et al. 1997). Fourth, they potentially disrupt stable markets that provide critical services to large populations. Biofuels production not only means a new market for biofuels, but it potentially also means both a declining market for conventional fuels (with ancillary consequences for workers and communities dependent on those industries) and competition for land, fertilizer, water, labor, and other resources that serve as inputs to food markets (and thus potentially drives higher food prices). Bioeconomies do not simply add new markets; they disrupt existing ones. Peoples’ lives, livelihoods, identities, relationships, institutions, and communities are bound up with biomarkets – to transform such markets is inevitably to transform society.

To develop a capacity to address risks associated with synthetic bioeconomies in an anticipatory fashion – as opposed to simply reacting to surprises that occur – requires a capacity to model complex bioeconomic transformations that straddle biological (including medical and ecological), economic, engineering, and sociological disciplines. By model, I mean significantly more than computational modeling. While some risks may occur as a result of dynamics that are fully quantifiable, others may not. Social practices, meaning and identity formation, ethical norms, and organizational and institutional dynamics are frequently critical elements in the rise and propagation of risks. Often risks arise as a result of social practices, or of social responses to new possibilities or events, as the recent outbreak of Ebola virus in West Africa is revealing. Just as significantly, these challenges demand analyses that extend across supply chains and over the full life cycle of synthetic bio-products, and they demand a capacity to model both functioning systems/markets and the ways in which systems/market transformations come into being and take shape, sociologically.

Accomplishing these goals will require unprecedented interdisciplinary collaboration. It will require new forms of synthesis in systems modeling that provide meaningful insights across social, organizational, technological, biological, ecological, and economic models – models built according to quite distinct epistemological assumptions and based in very different disciplinary styles of reasoning (on styles of reasoning, see e.g. Hacking 2002). Approaches such as supply chain analysis, life cycle analysis, systems modeling, and others that have arisen in conjunction with sustainability analysis will provide crucial foundations for this work, but will need to be expanded to include: (a) more sophisticated treatment of social, ecological, and epidemiological dimensions of bioeconomies; (b) more cross-cutting analyses that get beyond the limits of individual products and supply chains to understand how multiple systems and product chains intersect with one another; and (c) new methods for studying complex multi-system dynamics that blend what can be measured quantitatively and modeled computationally with research strategies that examine social, institutional, and other dynamics that cannot. It will also require greater transparency in the early phases of synthetic bio-product and bioeconomy formation than is common in current intellectual property regimes or innovation policies. In general, except in certain circumstances (e.g. pharmaceuticals) existing approaches to innovation encourage secrecy at early phases of new product development that make it difficult if not impossible to analyze complex bioeconomies in an anticipatory fashion, prior to their initial construction (see e.g. Lyndon 2007). Yet, modeling risk in complex bioeconomies will require a robust capacity to anticipate the social dynamics of new bio-capabilities and to monitor evolving social dynamics to compare real-world developments to modeled and anticipated expectations. Finally, it will require new forms of inquiry and organization to feed the insights of these types of knowledge into practices of responsible innovation within the synthetic biology industry. Absent such an effort, synthetic biology will, as history suggests of previous bioeconomies, generate new risks that surprise us in not so happy ways.

Notes on contributor

Clark A. Miller is Associate Director of the Consortium for Science, Policy & Outcomes and Chair of the Ph.D. in Human and Social Dimensions of Science and Technology at Arizona State University. He has co-edited four books: Science and Democracy: Making Knowledge and Making Power in the Biosciences and Beyond (2015); Nanotechnology, the Brain, and the Future (2013); Arizona's Energy Future (2011); and Changing the Atmosphere: Expert Knowledge and Environmental Governance (2001).