I think complexity has a lot to offer, and I will try to show that this potential is yet unexplored. I will start by referring to some of the uses complexity has been put to, namely, (i) using complexity to criticize the linear get-the-facts-then-act model of science for policy; (ii) using complexity to explain, name and label the policy process; (iii) using complexity to give advice on how to use evidence and make decisions. These roles can, and have been combined, and as usual, by speaking of three distinct roles, I am simplifying and creating pure categories that do not reflect complex practice. But the point I want to make is that complexity needs to be taken further: complexity can help us re-think the science-policy interface, or as I will put it, the interface between multiple forms of knowledge and policy. Let’s start the tour.
Blog post I: Complexity as a critical response to reductionism and the linear model
Complexity arose as a criticism to reductionism, with important contributions from the fields of ecology, biology and thermodynamics (Holling, 1973; Nicolis & Prigogine, 1977; Rosen, 1991). Scholars like Rosen were worried with the inability of mechanistic biology to explain life, others studied issues including evolution, far-from-equilibrium systems, more than 2 bodies problems in physics, etc. In my work, I am interested in the science-policy interface. In this case, the criticism of reductionism focuses on (Geyer & Rihani, 2010; Strand, 2002):
- The presumption of complete knowledge: reductionist science reduces the world to a mechanism. “The external world has an objective, human-independent existence and a structure that can be fully known by humans” (Strand, 2002). This mechanism can be described through distinct and distinguishable parts, and the relationships between parts are knowable and linear: there is a one-to-one mapping between causes and effects, which do not change in time and space (Geyer & Rihani, 2010). This means that, if/when given complete knowledge, policy can act on the causes and induce the desired results. The study of uncertainty (see post!) is one way through which criticisms have been articulated, highlighting the limits of knowledge.
- The commitment to the analysis of constitutive components: the mechanic view of the world implies that the behavior of the system can be understood from the observation of the parts and their linear interactions (this idea can be expressed as “the whole is equal to the sum of the parts”); that the parts and their functions can be observed in isolation. This leads to a governing style that views parts as substitutable and the objects of governing as clockworks that can be fixed, and even assembled, through functional equivalents. Scott (1998) uses planned cities such as Brasilia, as an example of governing that aims at reproducing the functions of a city (work space, residential space, recreational space) as independent categories to be realized in independent places in the city – a failed project, which has created city taxidermy: something frozen in time, with no life.
- Prediction and control: all that can be known about the world can be observed, or reduced to observable mechanisms. Reductionist science thus speaks of “reality” and produces matters of fact. These facts can be used to predict the behavior of a system. Policies can use scientific knowledge to increase control over the system to be governed, policies can establish new paths (e.g. away from unsustainable towards sustainable development) and leap-frog (e.g. achieve industrialization through modern technology and skip the part in which colonies provide cheap resources and labor). Prediction and control are at the basis of the get-the-facts-then-act model (Pielke, 2004), which assumes that facts give control and can guide decision-making.
Complexity is used in this case to argue that: there are limits to knowledge, the whole is greater than sum of the parts; components may have multiple functions; effects may have multiple causes; systems are self-organizing and adaptive, so that if a component is removed, the same functions may be performed by other components and/or through their interactions; complex systems are not predictable; and many et cetera.
The main contribution of the criticism of reductionism is, in my view, its diagnostic of the limits of the linear model. Complexity theory here serves as a compare and contrast exercise: where reductionist science sees linear causality, complexity sees non-linearity; where reductionist science uses mechanistic thinking, complexity offers emergence; and so on. When it comes to envisioning an alternative interface between science and policy, however, the critical approach makes thin contributions. As Geyer (2012) argues, the social sciences have long recognized the complexities of governing, policy-making, decision-making, they have long spoken about context, situatedness, contingency. Granted, the vocabulary of complexity theory can be used to describe some of these phenomena (Byrne, 1998). But complexity does not reveal anything new. The concepts of complexity theory, when applied to the social sciences, are at best newish (Cairney & Geyer, 2017).
The role of complexity theory can be that of translator between the social sciences and the natural sciences. Complexity theory provides a natural sciences based vocabulary to describe concepts and issues long studied and recognized by the social sciences. Translation could be highly important in transdisciplinary research, contributing to the creation of an overall theory that transcends individual disciplines. Translation can also play an important role in communication between science and policy, by using terms, or analogical images (such as the mechanism, the clockwork, the metabolism) that make it easier to communicate with policy makers. But I think there is more to the story.
References
Byrne, D. (1998). Complexity theory and the social sciences: An introduction. London and New York: Routledge.
Cairney, P., & Geyer, R. (2017). A critical discussion of complexity theory: how does “complexity thinking” improve our understanding of politics and policymaking? Complexity, Governance & Networks, 3(2), 1–11.
Geyer, R. (2012). Can Complexity Move UK Policy beyond “Evidence-Based Policy Making” and the “Audit Culture”? Applying a “Complexity Cascade” to Education and Health Policy. Political Studies, 60(1), 20–43. https://doi.org/10.1111/j.1467-9248.2011.00903.x
Geyer, R., & Rihani, S. (2010). Complexity and public policy: A new approach to 21st Century politics, policy and society. Oxon and New York: Routledge.
Holling. (1973). Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 4, 1–24.
Nicolis, G., & Prigogine, I. (1977). Self-organization in nonequilibrium systems. New York: Wiley-Iterscience.
Pielke, R. A. (2004). When scientists politicize science: Making sense of controversy over The Skeptical Environmentalist. Environmental Science and Policy, 7, 405–417. https://doi.org/10.1016/j.envsci.2004.06.004
Rosen, R. (1991). Life itself: A comprehensive inquiry into the nature, origin, and fabrication of life. New York: Columbia University Press.
Scott, J. (1998). Seeing like a state: How certain scheme to improve the human condition have failed. New Haven: Yale University Press.
Strand, R. (2002). Complexity, ideology and governance. Emergence, 4(1–2), 164–183.
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