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 continue the tour.
Blog post II: Using complexity to explain, name and label the policy process
A second contribution of complexity to the study of the science-policy interface is to offer concepts that better describe the messy process of policy making. Complexity theory is here mobilized to deconstruct the “policy process” model. Quoting Colebatch (2005), “The dominant paradigm presents government as a process of authoritative problem solving: there are actors called governments, they confront problems and make choices, which are then enforced with the coercive power of the state.” There are a number of idealizations in this view of the policy process that are criticized.
- Firstly, the idea of government as a coherent agent is problematic, both because it fails to describe the multiple agencies, agendas, actors, institutions, levels, etc. that compose government, and because it holds government up to a standard which then classifies fragmentation as a failure. The first criticism may be addressed by referring to governance instead, including governmental and non-governmental actors (Bache & Flinders, 2004), and to multi-level governance, questioning the boundaries of governing and sovereignty at something that belongs to nation states. The second criticism leads to a more nuanced understanding of governing, which resonates with the criticism of the ideals of prediction and control.
- Secondly, in Colebatch’s formulation problems are things that exist out there in the world. Conversely, representing something as a problem is a political choice (Bacchi, 2009; Colebatch, 2005) – see also my previous post on informal settlements. Policy making does not start in response to a problem that can be known through scientific research, but rather it starts by framing something as a problem, as in need of a solution, and in need of governing. The construction of a problem justifies government intervention. Construction in this case does not imply that governing entails the material creation of a problem, but that problem framing has a performative role. For example, immigration can be constructed both as something to be encouraged (because it increases the labor force) and as a problem (because it increases competition for jobs).
- Thirdly, I argue that hidden in the passage from “confronting problems” to “making choices” is the role of science, which is supposed to provide information upon which government can act, and is the image of the policy-maker as a rational agent, better described as (and reduced to) a decision-maker. The policy process model is based on instrumental rationality (Geyer & Cairney, 2015; Scott, 1998), in which useful knowledge is rationally used by agents. Decision-makers become abstract agents: be they economic agents or policy-makers, they can be described as making rational decisions, whatever the nature of that decision may be (maximizing utility, maximizing profit, maximizing public welfare).
Reductionism in this case is applied to the policy process, rather than to the objects of governance as in the discussion in part I. Similarly to the previous case, complexity theory has been used as an alternative description of the policy process (Cairney & Geyer, 2017; Geyer & Cairney, 2015). Cairney & Geyer refer to the concepts of: (i) positive and negative feedback to discuss the limited ability of policy makers to gather information, which means that “They receive the same amount of information over time, ignoring most for long periods (negative feedback) and paying disproportionate attention to some (positive feedback);” (ii) attractors to describe the tendency of policy-making patterns to persist, even when their limits are acknowledged (think of burocratization); (iii) path dependency to explain how early decisions restrict the option space of later decisions; (iv) emergence to emphasize that local behavior takes place despite central policies rules and beyond the control of the central government.
In this case too, the concepts of complexity theory can be used to describe the policy process. In this case too, the fragmented, messy, nature of policy making has been widely studied without need for the vocabulary of complexity to name it. The contribution of complexity theory is to translate the insights of policy and governance studies to natural scientists. This is an important role, which may help nuance the expectations of scientists that aim to provide policy relevant evidence. A better understanding of the policy process has led to a reconceptualization of the interface between science and policy as a matter of identifying “windows of opportunity” in the policy process, when policy-makers may be open to considering information from scientists.
What this use of complexity theory may miss, though, is a deeper understanding of why messy governance practices are portrayed as a policy process of problem “identification” and solution. Both simplification and instrumental rationality justify the existence of a central government (Scott, 1998). If local processes cannot be controlled by the center, if the outcomes of policies cannot be predicted, if path dependency means that policy has little room for maneuver, then why have a government at all? Colebatch (2005) explains that the narrative of policy process is used by policy-makers themselves as a means to make sense of the many activities in which they are engaged, to justify intervention and to provide a basis for accountability. Accountability is the key word here, for unless the understanding of governance as a messy assembly of multiple processes is taken as an argument for anarchy, this perspective may be used to eschew the responsibility of governing bodies for their actions or may be conflated with neoliberalism. The narrative of a linear policy process is thus important to accountability.
Finally, the more nuanced understanding of governance invites also a deeper study of the role of scientific advice in governance. Scientific evidence has an instrumental role, when used to justify a decision “based on evidence”, a conceptual role, when used to influence or change mindsets, a strategic role, when used to support of challenge existing positions (Waylen & Young, 2014). The corollary is that providing more evidence, counterfactual examples, alternative problem framings, and new narratives for governance may not lead to the desired changes, because different types of evidence are put to different uses. Research that supports the policies in place may be used instrumentally, and critical, nuanced, even “complex” evidence may be used strategically. This analysis leads to somewhat of a stalemate, since both governance is decoupled from control, and science’s ability to influence governance is questioned.
As long as these arguments do not lead to the conclusion that nothing can be done, but rather to a more nuanced understanding that interventions (both scientific and of governing) have effects that may or may not coincide with the intended effects, that may be indirect and not immediately observable, I think that this line of work provides a reality-check. This leads us to the next point: the use of complexity to argue for the need of modesty and reflexivity.
References
Bacchi, C. (2009). Analysing policy: What’s the problem represented to be? Frenchs Forest: Pearson Australia.
Bache, I., & Flinders, M. (2004). Multi-Level Governance and the Study of the British State. Public Policy and Administration, 19(1), 31–51. https://doi.org/10.1177/095207670401900103
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.
Colebatch, H. K. (2005). Policy analysis, policy practice and political science. Australian Journal of Public Administration, 64(3), 14–23. https://doi.org/10.1111/j.1467-8500.2005.00448.x
Geyer, R., & Cairney, P. (2015). Handbook on complexity and public policy. Cheltenham: Edward Elgar.
Scott, J. (1998). Seeing like a state: How certain scheme to improve the human condition have failed. New Haven: Yale University Press.
Waylen, K. A., & Young, J. (2014). Expectations and experiences of diverse forms of knowledge use: The case of the UK national ecosystem assessment. Environment and Planning C: Government and Policy, 32(2), 229–246. https://doi.org/10.1068/c1327j
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