Three understandings of "uncertainty"

At the interface of science and policy one can look at scientific uncertainties in three different ways (Van der Sluijs 2006). Each way leads to a different approach to uncertainties and each has its own drawbacks.

Approach 1: Uncertainty as lack of knowledge
One can first see uncertainty as a shortcoming in knowledge, where uncertainty is experienced as a temporary problem. The approach is to push back the uncertainty, among other things by creating increasingly complex models. As long as this is unsuccessful, the uncertainty is expressed numerically, for example a distribution around an average. This approach runs into the limitation that by far 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. The drawback of this approach is that there is a semblance of certainty because the numbers coming from the increasingly complex models suggest that there is more knowledge than is actually the case.

Approach 2: Uncertainty as lack of unequivocalness
The second vision sees uncertainty as a problematic lack of unequivocalness. One scientist says this, the other says that. It is unclear who is right. 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. The drawback of this paradigm is that issues over which there is no consensus remain underexposed, whereas it is precisely this dissent which tends to be extremely relevant to policymaking.

Approach 3: Uncertainty as a fact of life
One can see uncertainty as a mere fact of life, something which unavoidably plays a role in complex and politically sensitive topics. We accept the fact that uncertainty is not temporary but permanent, and recognise that not all uncertainties can be expressed quantitatively. Such an approach demands a culture that is open to uncertainty and that recognises that there are many things that science cannot yet provide an answer for. Ignorance and the influence of values are focused on here. Techniques applied to deal with it are knowledge quality assessment and risk management, including knowledge production, as deliberative or participative social processes.

Robustness is sought here primarily in policy strategy and not in the knowledge base: which policy 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 forget how much we do know about these risks and which items actually enjoy broad consensus.

References

Jeroen P. van der Sluijs (2006), Uncertainty, assumptions, and value commitments in the knowledge-base of complex environmental problems, in: Ângela Guimarães Pereira, Sofia Guedes Vaz and Sylvia Tognetti, Interfaces between Science and Society, Green Leaf Publishing, pp. 67-84.

Arthur C. Petersen, Maria Hage, Albert Cath, and Jeroen P. van der Sluijs (2011), Post-Normal Science in Practice at the Netherlands Environmental Assessment Agency,Science Technology & Human Values