by Judith Curry
At the Workshop, there was an interesting presentation made by by Jeroen van der Sluijs, who also presented this at the public event. The talk addresses paradigms of uncertain risk, and how to act under conditions of uncertainty.
Paradigms of uncertain risk
‘deficit view’
• Uncertainty is provisional.
• Reduce uncertainty, make ever more complex models
• Tools: quantification, Monte Carlo, Bayesian belief networks
‘evidence evaluation view’
• Comparative evaluations of research results
• Tools: Scientific consensus building; multi disciplinary expert panels
• focus on robust findings
‘complex systems view’
• Uncertainty is intrinsic to complex systems
• Uncertainty can be result of production of knowledge
• Acknowledge that not all uncertainties can be quantified
• Openly deal with deeper dimensions of uncertainty (problem framing indeterminacy, ignorance, assumptions, value loadings, institutional dimensions)
• Tools: Knowledge Quality Assessment
• Deliberative negotiated management of risk
How to act upon such uncertainty?
As an example, a practical problem was provided, wheregy 5 scientific consultants addressed the same question: “Which parts of this area are most vulnerable to nitrate pollution and need to be protected?” Each of the 5 consultants gave a different answer. The following different approaches are outlined:
- Bayesian approach: 5 priors. Average and update likelihood of each grid-cell being red with data (but oooops, there is no data and we need decisions now)
- IPCC approach: Lock the 5 consultants up in a room and don’t release them before they have consensus
- Nihilist approach: Dump the science and decide on another basis
- Precautionary robustness approach: protect all grid-cells
- Academic bureaucrat approach: Weigh by citation index (or H-index) of consultant.
- Select the consultant that you trust most
- Real life approach: Select the consultant that best fits your policy agenda
- Explore the relevance of our ignorance: working deliberatively within imperfections
I left the meeting with a memory stick full of van der Sluijs’ papers and presentations. From van der Sluijs et al. paper “Beyond consensus: reflections from a democratic perspective on the interaction between climate politics and science:”
Three strategies to deal with scientific uncertainties in the science–policy interface
When the science–policy interface is confronted with complex issues that are characterized by many scientific uncertainties three coping strategies may be distinguished .
Interfacing strategy 1: quantify uncertainties
In the ‘Linear Model’ of interfacing science and policy, science informs policy by producing objective, valid, and reliable knowledge. To develop a policy is then a matter of scientists delivering the facts and then, in a second step, policy makers sorting out diverse values and preferences. In classical terms, the true entails the good; in modern terms, truth speaks to power. This interfacing model implicitly assumes that scientific facts linearly determine correct policy: good governance is getting the facts right and calculate the optimal policy. The belief is that being based on scientific facts, the power that is exercised is effective, legitimate, and based on unambiguous objectivity and indisputable rationality. This approach implicitly assumes that there are no limits to the progress of man’s control over his environment, no limits to the capacity of science to know and understand, and no limits to the material and moral progress of mankind. This is the classic ‘technocratic’ view of governance dependent on an assumed perfection/perfectibility of science in theory and also (progressively) in practice. Within this Linear Model, scientific uncertainty is seen as a temporary shortcoming in knowledge. The related interfacing strategy is to quantify and push back the uncertainty by more research, for example, creating increasingly complex climate models and through perturbed physics ensemble modelling . Calculation is seen as key to well-informed good governance. This approach is limited by the fact that not all uncertainties can be expressed quantitatively in a reliable way. What’s more, in practice uncertainties do not become reduced with more research: the problem appears to become ever more complex . It further assumes that there is only one correct scientific description of the system that is analyzed: in other words it assumes that the system and problem are not complex. It thereby ignores that multiple – often conflicting – scientific interpretations of the same available knowledge are tenable. The drawback of this approach is that there is a semblance of certainty, for example, because the numbers coming from the increasingly complex models suggest that there is more knowledge and more certainty than is actually the case.
Interfacing strategy 2: build scientific consensus
In response to the phenomenon that science does not speak with one voice to policy but tends to speak many, often conflicting truths, to power, the emergence of a Consensus Model can be observed in an attempt to ‘rescue’ the Linear Model from conflicting certainties and multiple framings. Within this interfacing model uncertainty is primarily perceived as a problematic lack of unequivocalness. One scientist says this, the other says that. It is unclear who is right and which scientific viewpoint should guide the decision making. The solution has been a comparative and independent evaluation of research results, aimed at building scientific consensus via multidisciplinary expert panels. This approach is geared towards generating robust findings representing ‘the best of our knowledge’ that is used as a proxy for the scientific truth that is needed in the Linear Model. The drawbacks of this approach are that it leads to anchoring towards previously established consensus positions, it hides diversity of perspectives thereby unduly constraining decision-makers options, it underexposes issues over which there is no consensus whereas it is precisely this dissent that tends to be extremely relevant to policymaking.
Interfacing strategy 3: openness about ignorance
In the Consensus Model, the core activity of the Linear Model, the experts’ (desire for) truth speaking to the politicians’ (need for) power, is left unquestioned and unchanged. Confronted with complex issues with high decision stakes, uncertain facts and values in dispute, scientists may still aim to deliver truth, but often there are many competing interpretations of the same problem (conflicting truths), none of which can be refuted given the state of knowledge—so that a consensus can only be an enforced reduction of complexity into single ‘best of our knowledge’ claim. In case of such complex issues, both the Linear Model and the Consensus Model are not fit for the characteristics of the issue addressed, because the truth cannot be known at the moment the decision needs to be made, and can thus not be a substantial aspect of the issue. Building on these notions, an alternative model of science and policy has been proposed: the Deliberative Model, in which the appreciation of a plurality of (often irreconcilable) perspectives is key. Within this interfacing model uncertainty is seen as something that unavoidably plays a permanent role in complex and politically sensitive topics. This approach recognizes that ignorance (lack of understanding of the complex climate system) and values play a central role. The search is for a robust policy, which is useful regardless of which of the diverging scientific interpretations of the knowledge is correct. The drawback of this approach is that uncertainty and minority interpretations are so much in the spotlight that we may forget the items that actually do enjoy broad scientific consensus .
Epilogue: towards a more democratic perspective
To move beyond consensus the deliberative model offers a promising complementary approach to interface climate science and policy, based on openness about uncertainty and ignorance, systematic reflection, and argued choice. This remedies the basic weakness of the Linear Model that underexposes the scientific as well as the political dissent. It can fruitfully broaden the option space for decision making and enhance societies’ capacity to deal with uncertainties surrounding knowledge production and knowledge use in the management of climate risks. To this end, both the scientific and the political climate debate need more space and attention for diversity and uncertainty in knowledge and views. Consequently, it is necessary to make climate science less political. This can be accomplished by offering room for dissent within climate science and communicating about it with policymakers. It should also be acknowledged that climate policies can be justified in moral terms without any need for recourse to abstract climate or economic models. An excessive mutual dependence between science and policy should also be prevented. The political climate debate would benefit from clarification of the political values and visions that are at play in climate change. The climate debate could be expanded by paying attention to socially attractive development perspectives. The growing focus on climate adaptation also has the power to highlight and expand the political climate debate.
Note: tell me how all this sounds, and whether you object to any of it. Think about this before I tell you that I carefully excised the words “postnormal” in the text (it was used about 4 times in the text i lifted from van der Sluijs’ work) and I also excised the name “Ravetz”. If you don’t see the word “postnormal,” I suspect that there is far less objection to the actual concepts.
Van der Sluijs’ web site is here.