In this paper, I contend that the uncertainty faced by policy-makers in the COVID-19 pandemic goes beyond the one modelled in standard decision theory. A philosophical analysis of the nature of this uncertainty could suggest some principles to guide policy-making.

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In this paper, I propose an assessment of the interpretation of the mathematical notion of probability that Wittgenstein presents in TLP (1963: 5.15 – 5.156). I start by presenting his definition of probability as a relation between propositions. I claim that this definition qualifies as a logical interpretation of probability, of the kind defended in the same years by J. M. Keynes. However, Wittgenstein’s interpretation seems prima facie to be safe from two standard objections moved to logical probability, i. e. the mystic nature of the postulated relation and the reliance on Laplace’s principle of indifference. I then proceed to evaluate Wittgenstein’s idea against three criteria for the adequacy of an interpretation of probability: admissibility, ascertainability, and applicability. If the interpretation is admissible on Kolmogorov’s classical axiomatisation, the problem of ascertainability brings up a difficult dilemma. Finally, I test the interpretation in the application to three main contexts of use of probabilities. While the application to frequencies rests ungrounded, the application to induction requires some elaboration, and the application to rational belief depends on ascertainability.

The Covid-19 pandemic has shaken the world. It has presented us with a series of new challenges, but the policy response may be difficult due to the severe uncertainty of our circumstances. While pressure to take timely action may push towards less inclusive decision procedures, in this paper I argue that precisely our current uncertainty provides reasons to include stakeholders in collective decision-making. Decision-making during the pandemic faces uncertainty that goes beyond the standard, probabilistic one of Bayesian decision theory. Agents may be uncertain not just about factual properties of the world, but also about how to model their decision problems and about the values of the possible consequences of their options. As different stakeholders may have irreconcilable disagreement about how to resolve these uncertainties, decision-making procedures should take everybody’s perspectives into account. Moreover, those communities that are hit harder by the pandemic are also those that are typically excluded from knowledge production. Thus, in the face of Covid-19 uncertainty, both democratic and epistemic considerations highlight the importance of stakeholders’ inclusion in policy decision-making.

The increasing success of the evidence-based policy movement is raising the demand of empirically informed decision making. As arguably any policy decision happens under conditions of uncertainty, following our best available evidence to reduce the uncertainty seems a requirement of good decision making. However, not all the uncertainty faced by decision makers can be resolved by evidence. In this paper, we build on a philosophical analysis of uncertainty to identify the boundaries of scientific advice in policy decision making. We start by introducing a distinction between empirical and non-empirical types of uncertainty, and we explore the role of two non-empirical uncertainties in the context of policy making. We argue that the authority of scientific advisors is limited to empirical uncertainty and cannot extend beyond it. While the appeal of evidence-based policy rests on a view of scientific advice as limited to empirical uncertainty, in practice there is a risk of over-reliance on experts beyond the legitimate scope of their authority. We conclude by applying our framework to a real-world case of evidence-based policy, where experts have overstepped their boundaries by ignoring non-empirical types of uncertainty.