First Chapter - While mainstream decision theory only allows for variations
in the severity of uncertainty, the plurality of labels with which uncertainty has been referred to in the literature and the variety of doubts that decision makers can have seem to suggest that there are different types of uncertainty. Given the importance that uncertainty has in almost any decision, understanding this plurality can be helpful to decide effectively. I propose an account of uncertainty as based on a disagreement between reasons supporting alternative mental attitudes. Under this account, dealing with uncertainty means dealing with disagreement; however, this disagreement can be radical, i.e. persistent under ideal cognitive and epistemic conditions. When this is the case, the disagreement and therefore the uncertainty cannot be resolved with an increase in evidence. I draw a typology of uncertainty reflecting the conditions that must obtain for the possibility of radical disagreement, and I trace the role that each of the types identified plays in decision making.Second Chapter - Decision theories have largely ignored the step of decision making in which the agent models the situation. Given that a decision can be represented with different models, and that these can lead to different recommendations, then without a principled way to assess them the agent’s choice is under-determined. As models require the agent to select the aspects that matter to the decision, an account of rational decision modelling must include a notion of relevance.
I propose that the most rational model is the one taking into account all and only the considerations relevant for the decision. I define relevance for a decision as a matter of providing reasons for some option, and I identify four functional types of reasons leading to four corresponding types of relevance. I focus on what I call “constitutive relevance”, which provides the content of the decision model, and propose a formal definition of this concept.Third Chapter - The increasing success of the evidence-based policy movement is raising the demand for 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 argue that the authority of scientific advisors is limited to cognitive uncertainty and cannot extend beyond it. While the appeal of evidence-based policy rests on a view of scientific advice as limited to cognitive 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-cognitive types of uncertainty.Fourth Chapter - The COVID-19 pandemic has presented the world 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 this uncertainty provides both democratic and epistemic reasons to include stakeholders in our collective decision making
