Technical problem (science)
5.2 WICKED PROBLEMS, POST-NORMAL SCIENCE AND KNOWLEDGE QUALITY ASSESSMENT
Where social scientists long ago coined the term ‘wicked problems’ (Rittel & Webber, 1973) for issues characterised by perpetual controversy (in contrast to ‘tame’, soluble problems, as in puzzle-solving), natural scientists tend to formulate new research projects based on the implicit assumption (and explicit promise) that uncertainty will be reduced and controversy will be resolved (van der Sluijs, 2005). Often, by the end of such projects, the uncertainties have actually increased (van der Sluijs, 2005, 2010, 2012), a paradox described by climate scientist and IPCC lead author Kevin Trenberth (2010) as ‘more knowledge, less certainty’. Sarewitz’s (2004) seminal paper How science makes environmental controversies worse further shows that more and better science will not close the societal controversies (cf. Collingridge & Reeve, 1986). This paradox stems from a mismatch between the theory of knowledge implicitly assumed in scientific assessments of such risks, and the wicked characteristics of this class of problems, which Bremer (2013) summarised as:
1. Differing degrees of uncertainty (or indeed complete ignorance) associated with the issue;
2. A lack of consensus on the definition of the issue and its most appropriate
‘solution’, owing to a plurality of legitimate yet intractable perspectives within society;
3. The governing system as a complex network of political interactions between stakeholders, pressured by urgency and high stakes.
A similar analysis is made by the EU COST action Expert Judgement Network: Bridging the Gap between Scientific Uncertainty and Evidence-Based Decision Making (COST Action IS1304, 2019). The action responds to the major gap between the science models relevant for cutting-edge research and those required for policy analysis and advice. For instance, science-based models often involve substantial uncertainty which require explicit and timely characterisation. However, the shortage and cost
of timely empirical data inevitably require expert judgement. How such judgement is best elicited is critical to a decision process, as differences in the robustness of elicitation methods can be substantial and require careful consideration of a range of well-known biases and pitfalls, as we discussed in Section 4.3.
This mismatch highlights the urgent need for a new approach to issues that are characterised by deep uncertainty, high systems complexity and a highly polarised societal context. For the governance of complex risks, the ‘modern model’ of scientific knowledge as perfection, determinism, and predictability (speaking truth to power) (Collingridge & Reeve, 1986; Wildavsky, 1979) is increasingly untenable and unfit.
This mismatch promotes paralysis, an infinite loop of demand for more research and sustained doubt on whether the limited state of knowledge can justify any interventions (Harremoës, Gee, & Macgarvin, 2001). In view of complexity and deep uncertainty, the modern model needs to be replaced by a model of pluralistic knowledge production, aiming at enhancement of the quality and relevance of knowledge for policy, while fully acknowledging pluralism of relevant views of reality, complexity, and incompleteness of our understanding (working deliberatively within imperfections) (Funtowicz & Ravetz, 1993; van der Sluijs, 2012; Verweij & Thompson, 2006).
As has been stated in Chapter 3, this call for pluralistic knowledge input should not be seen as an invitation for arbitrariness in creating and presenting evidence. In addition, society’s capacity to tolerate uncertainty can be enhanced by increasing resilience (Resilience Alliance, 2010). The major mechanisms of rigorous review and methodological soundness also apply to the analysis of wicked problems, but, due to complexity, uncertainty and ambiguity, there will be more than one adequate and scientifically-substantiated interpretation of what the evidence is and what it means for society (Funtowicz & Ravetz, 1993).
Funtowicz and Ravetz (1993) have called this class of problems post-normal, where
‘normal’ refers to Kuhn’s (1962) concept of normal science. Kuhn describes normal science both as ‘a strenuous and devoted attempt to force nature into the conceptual boxes supplied by professional education’ (Kuhn, 1962, p. 5) and as the practice of uncritical puzzle-solving within an unquestioned framework or ‘paradigm’. Funtowicz and Ravetz (1993) signalled that such a normal science approach runs into serious limitations when addressing societal issues (at that time, nuclear reactor safety), where scientific evidence is highly contested and plagued by uncertainties while decision stakes are high and values are in dispute. The available knowledge bases are typically characterised by imperfect understanding of the complex systems involved (Ravetz, 1987). Models, scenarios, and assumptions dominate assessment of these problems and many (hidden) value loadings reside in problem frames, indicators chosen and assumptions made (Kloprogge, van der Sluijs, & Petersen, 2011). Scientific assessments of complex risks are thus unavoidably based on a mixture of knowledge, assumptions, models, scenarios, extrapolations, and known and unknown unknowns.
Consequently, scientific assessments will unavoidably use expert judgements (van der Sluijs et al., 2005). They comprise bits and pieces of knowledge that differ in status, covering the entire spectrum from well-established knowledge to judgements,
educated guesses, tentative assumptions and even crude speculations (Funtowicz &
Ravetz, 1990; van der Sluijs, 2012; van der Sluijs et al., 2005; van der Sluijs et al., 2008).
Knowledge utilisation for governance of complex issues requires a full and public awareness of the various sorts of uncertainty and underlying assumptions.
Post-normal science (PNS) is both a critical concept and an inspiration for a new style of research practice. Its dichotomous nature can be described as both descriptive (describing urgent decision problems — post-normal issues — characterised by incomplete, uncertain or contested knowledge and high decision stakes and how these characteristics change the relationship between science and governance) and normative (proposing a style of scientific enquiry and practice that is reflexive, inclusive and transparent regarding scientific uncertainty, and that moves towards democratisation of expertise) (Strand, 2017). It is based on three defining features (Funtowicz & Ravetz, 1993; A. C. Petersen, Cath, Hage, Kunseler, & van der Sluijs, 2011):
• The management of uncertainty. Post-normal science acknowledges that uncertainty is more than a number-range. Ambiguous knowledge assumptions and ignorance give rise to deep uncertainties;
• The acknowledgement of a plurality of legitimate perspectives — both cognitive and social. Complex problem-solving requires interdisciplinary teamwork, including expertise from outside science (NGOs, stakeholders, citizens). Scientists from different backgrounds often have irreconcilable and conflicting, yet tenable and legitimate scientific interpretations of the same body of evidence;
• The management of quality. An extended peer community includes representatives from social, political and economic domains who openly discuss various dimensions of uncertainties, strengths, weaknesses and ambiguities in the available body of scientific evidence and its implications for all stakeholders with respect to the issue at hand.
In a post-normal approach, the normal task in science of fact-finding is still regarded as necessary, but no longer as fully feasible nor as sufficient for the interface between science and policy (van der Sluijs, 2012). It needs to be complemented by the task of exploring the relevance of deep uncertainty and ignorance that limit our ability to establish objective, reliable and valid facts. To perform this task, Knowledge Quality Assessment (KQA) tools are central in post-normal science (Clark & Majone, 1985;
Funtowicz & Ravetz, 1990; Kloprogge et al., 2011; Maxim & van der Sluijs, 2011, 2014;
Refsgaard, van der Sluijs, Brown, & van Der Keur, 2006; Refsgaard, van der Sluijs, Højberg, & Vanrolleghem, 2007; Saltelli et al., 2008; van der Sluijs et al., 2005; van der Sluijs et al., 2008; Walker et al., 2003). These tools seek to systematically reflect on the limits of knowledge in relation to its fitness for function. It comprises systematic analysis of, and critical reflection on, uncertainty, assumptions and dissent in scientific assessments in their societal and institutional contexts (Haque, Bremer, Aziz, & van der Sluijs, 2017; van der Sluijs et al., 2008).
A similar notion comes from Gibbons et al. (1994), who coined the notion of Mode 2 knowledge production that is socially distributed (see also Section 3.3). While Mode 1 knowledge production used to be located primarily at scientific institutions
(universities, government institutes and industrial research labs) and structured by scientific disciplines, its new locations, practices and principles are much more heterogeneous. Mode 2 yields ‘socially robust knowledge’, which has a different epistemological status from Mode 1 science (Nowotny et al., 2001). As summarised by Hessels and Van Lente (2008), Mode 2 knowledge production differs from Mode 1 in five characteristics:
• Mode 2 knowledge is generated in an applied context. Mode 1 knowledge can also result in practical applications, but these are always separated in space and time from the actual knowledge production. This gap requires a so-called knowledge transfer. In Mode 2, such a distinction does not exist.
• Mode 2 is transdisciplinary, which refers to the mobilisation of a range of theoretical perspectives and practical methodologies to solve problems. Transdisciplinarity goes beyond interdisciplinarity, in the sense that the interaction of scientific disciplines is much more dynamic and research results are more diffuse (to problem contexts and practitioners) during the process of knowledge production.
• Mode 2 knowledge is produced in a diverse variety of organisations, resulting in a very heterogeneous practice. The range of potential sites for knowledge generation includes not only traditional universities, institutes and industrial labs, but also research centres, government agencies, think-tanks, high-tech spin-off companies and consultancies. These sites are linked through networks of communication and research is conducted in mutual interaction.
• Mode 2 knowledge is based on reflexivity; it is primarily a dialogic process and has the capacity to incorporate multiple views. This relates to researchers becoming more aware of the societal consequences of their work (‘social accountability’).
Sensitivity to the impact of the research is built in from the start.
• Mode 2 comes with novel forms of quality control. Traditional discipline-based peer review systems are supplemented by additional criteria of an economic, political, social or cultural nature. Due to the wider set of quality criteria, it becomes more difficult to determine ‘good science’, since this no longer is limited to the judgement of disciplinary peers. However, this does not imply that Mode 2 research is generally of a lower standard.