Models of Science and Policy

To diagnose and remedy fundamental problems in the current practices of interfacing science and policy of complex issues, Funtowicz (2006) introduced the “models of science” and policy that I discuss below. On the one hand he distinguishes the modern model of “speaking truth to power” along with three modifications that emerged in response to limitations of that model. On the other hand he presents the alternative – post normal - model of “working deliberatively within imperfections”. Funtowicz initial work was taken further and slightly modified by Funtowicz and Strand (2007a,b) and Van der Sluijs and Funtowicz (2008).

  • Perfection/perfectibility: The initial 'modern' model. Scientific facts (unproblematic), employed in rigorous demonstrations, would determine correct policy. In classical terms, the true entails the good; in modern terms, truth speaks to power. Being based on scientific facts, the power that is exercised is effective. There are no limits to the progress of man's control over his environment, and no limits to the material and moral progress of mankind. This is the classic 'technocratic' vision, dependent on an assumed perfection/perfectibility of science in theory and also (progressively) in practice.[1]
  • The Precautionary model (uncertain and inconclusive information). It is discovered that the scientific facts are neither fully certain in themselves, nor conclusive for policy. Progress cannot be assumed to be automatic. Attempts at control over social processes, economic systems, and the environment can fail, leading sometimes to pathological situations. Tenants of this model still pay homage to the truth/validity of science in general, but they contest particular unwelcome items of information. Because of “imperfection” in the science, there is proposed an extra element in policy decisions, precaution, which both protects and legitimises decisions.[2]
  • The Model of Framing (arbitrariness of choice and possible misuse). In the absence of conclusive facts, scientific information becomes one among many inputs to a policy process, functioning as evidence in the arguments. Debate is known to be necessary, as each stakeholder has their own perspective and values which shape their arguments. Moreover, all such processes involve complex issues, where the situation has a plurality of phases (causes, effects, prevention, remediation, etc.), each phase being treated with its own theoretical constructions of reality (which may not be fully reconciled). There are no simple 'facts' that resolve issues in all these phases and aspects. Hence the framing of the relevant scientific problem to be investigated, even the choice of the scientific discipline to which it belongs, becomes a prior policy decision. This can become part of the debate among stakeholders. Different scientific disciplines may become competing stakeholders; whoever 'owns' the research problem will make the greatest contribution and will enjoy the greatest benefits. However, an incorrect framing of the problem (e.g., due to error, ignorance, poor judgement, and not necessarily wilful) amounts to a misuse of the tool of scientific investigation. However, because there is no conclusive scientific basis for the choice of framework, it has to be admitted that, to some extent the choice is arbitrary (or social). Acceptance of the principle of framing entails an acceptance of the arbitrariness of choice, hence of the possible misuse of science in the policy context and, moreover, of the difficulty of deciding whether or not a misuse has occurred (the judgement will itself be influenced by framing).[3]
  • The Model of Science/Policy Demarcation (possibility of abuse of science). The scientific information and advice that are used in the policy process is created by people working in institutions with their own agendas. Experience shows that this context can affect the contents of what is offered, through the selection and shaping of data and conclusions. Although they are expressed in scientific terms, the information and advice cannot be guaranteed to be objective and neutral. Moreover, science practitioners and their funders have their own interests and values. In this view, science can (and probably will) be abused when used as evidence in the policy process. As a response to this problem, a clear demarcation between the institutions (and individuals) who provide the science, and those where it is used, is advocated as a means of protecting science from the ‘political’ interference that would threaten its integrity. This demarcation is meant to ensure that political accountability rests with policy makers and is not shifted, inappropriately, to the scientists. Designing the right form of demarcation of science and policy is therefore one of the urgent tasks of governance. This is not easy. Too great a separation can result in the scientific institutions pursuing their own, internal goals, and the work becoming irrelevant to the needs of the policy process. Too little a separation can aggravate rather than resolve the risks of ‘political interference’ in science.[4]
  • The Model of Extended Participation (working deliberatively within imperfection). Given these acknowledged complications, and ‘imperfections’ in the deployment of science in the policy process, it becomes ever more difficult to defend a monopoly of accredited expertise for the provision of scientific information and advice. 'Science' (understood as the activity of technical experts) is henceforth to be included as (only) one part of the 'relevant knowledge' that is (or may be) brought in as evidence to a decision or policy process. The ideal of rigorous scientific demonstration is replaced by that of open public dialogue. Citizens become both critics and creators in the knowledge production process. Their contribution is not to be patronized by using, in a pejorative way, labels such as 'local', 'practical', 'ethical' or 'spiritual' knowledge. A plurality of co-ordinated legitimate perspectives (each with their own value-commitments and framings) is accepted. The strength and relevance of scientific evidence is amenable to assessment by citizens. All sides come to the dialogue ready to learn, or else the process is a sham. Through this co-production of knowledge, the extended peer community creates a (deliberative) democracy of expertise.

[1]               It took centuries for this view to emerge clearly and, even during the period of its prominence, there were mavericks within (as well as outside of) the science profession who expressed dissent with this model.
[2]               Precaution has always been present as a practical, informal basis of policy formation not involving science. What changes, in this model, is the way that precaution is elevated to the status of a principle even when science is involved. This makes a decisive break with the technocratic vision. But, implementing 'precaution' is still not reduced to an unproblematic routine.
[3]               This can lead towards ‘post-modern’ and ‘relativist’ positions. Accusations about the easy or inevitable misuse of scientific forms and discourses (through ignorance, false consciousness or ‘interests’ of various sorts) can even motivate a rejection of science as a reference point for establishing quality and legitimacy in decisions (see also the ‘Demarcation’ model).
[4]               One contemporary solution — the current standard ‘economic model’ — is to propose the mobilisation of the interests of scientists as ‘suppliers’ of knowledge, responding explicitly to the ‘demands’ of the funding marketplace (private sector, public programmes, NGO research, etc.). This reinforces the role of context in framing interests and purposes, and introduces the problem of possible ‘market failure’ (viz., the coordination by the marketplace might not correspond with social demand, and, the ‘social demand’ (whatever this is) might depend in a dynamic way on the ways that knowledge is produced and used in society, etc.) The discipline of economics has attempted to institute the science/policy demarcation model via the distinction, explicit since the first part of the 20th century, between ‘positive’ and ‘normative’ analysis. However, this demarcation has always rebounded because it turns out to be difficult — both theoretically and in practice — to keep separate the two domains while still carrying out ‘policy relevant’ economic analysis.
 
References
Funtowicz, S. (2006). Models of Science and Policy, in: Guimarães Pereira Â, Guedes Vaz S, Tognetti, S, (eds), Interfaces between Science and Society. Sheffield: Greenleaf Publishers.
 
Funtowicz S. and R. Strand (2007b). “Models of Science and Policy”, in Traavik, T. and Lim, L.C. (eds.) Biosafety First: Holistic Approaches to Risk and Uncertainty in Genetic Engineering and Genetically Modified Organisms, Trondheim, Norway: Tapir, pp 263-278.
 
J.P. van der Sluijs (2010). Uncertainty and complexity: the need for new ways of interfacing climate science and climate policy. In: P. Driessen P. Leroy, and W. van Vierssen, From Climate Change to Social Change: Perspectives on Science – Policy Interactions, International Books, Utrecht, 31-49.
 

J.P. van der Sluijs, Uncertainty and complexity: the need for new ways of interfacing climate science and climate policy. Essay for the Workshop “Science Policy Interface and Climate Change”, 29 September 2008 organized by the Netherlands Knowledge for Climate program. 14pp.