Malvina Ongaro

Landslide risk offers a particularly interesting field of study for philosophers of science working on causation. Landslides are the result of complex causal dynamics, and the object of study of a variety of disciplines which are ultimately aimed at effective risk mitigation. For these reasons, causation is central to many instruments developed to study landslides. We illustrate four such instruments, and we show how each of them assumes a different concept of causation. This observation provides the ground for the defence of a pluralist account of conceptual causation. However, in defending this position we do not aim at establishing a universally correct account of causation. Instead, we examine the adequacy of different concepts for a specific purpose and in a specific context. We therefore supplement the descriptive observation of the plurality of concepts of causation involved in landslide research with the normative claim that this plurality cannot be reduced under a monist account, because the different concepts are necessary for different epistemic purposes. While our conclusions only hold for the specific case we analyse, the instrumentalist approach we propose can be generalised to other contexts and to other concepts.

Uncertainty is a crucial concept in our life, and consequently it is pervasive in both the sciences and philosophy. And yet, while there are several frameworks to identify different types of uncertainty, to design instruments to represent them more or less quantitatively, and to explore their implications for scientific practice and for decision-making, there are almost no general accounts of what the concept of uncertainty in itself is. This work proposes three requirements that a philosophically sound concept of uncertainty should satisfy. First, it should allow for variability, i.e. for the possibility that there are different types of uncertainty with which uncertainty should be in a genus/species relation. Second, it should be agent-dependent, and therefore possibly change with the agent or with their situation. Third, it should include gradable elements that allow to account for the fact that uncertainty comes in degrees, and that sometimes it can be reduced. These are necessary requirements for a philosophical analysis of uncertainty to be acceptable, but they are neither sufficient nor exhaustive. I conclude by illustrating how these requirements play out with respect to three alternative possible definitions of uncertainty.

Given the importance that it has in almost any decision, understanding uncertainty and its possible variations is crucial in deciding effectively. I propose an account of uncertainty as based on a disagreement between reasons for and against 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. Thus, when this is the case, the disagreement and therefore the uncertainty cannot be resolved with an increase in evidence. I illustrate how this unitary notion can be used to derive different types of uncertainty proposing a possible typology that reflects the conditions that must obtain for radical disagreement, and I trace the role that each of the types identified plays in decision making. This application to decision making suggests that there are uncertainties that go beyond those modelled in mainstream decision theory.

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: the central idea is that the model should include all and only those considerations the inclusion of which changes the utility of at least one of the alternative options.

In this paper, I ask whether a referendum is more legitimate than parliamentary voting as a procedure to reach a collective decision on the question of mandatory vaccinations. Since I define both procedures as applications of binary majority rule, I start by exploring the political legitimacy of such rule. There are two main legitimising arguments for it: an instrumental one and a procedural one. After an illustration of their respective limitations, I defend a hybrid account of legitimacy that makes use of both. My main claim is that which of these should be used to ground the legitimacy of a procedure depends crucially on the empirical content of the question at stake. However, I contend that only a procedural argument could make a referendum more legitimate than parliamentary voting. Thus, the answer to my research question depends on the empirical status of the issue of mandatory vaccinations. I describe it as a complex issue, the answer to which depends on both empirical and non-empirical components. Under a premise-based approach, a referendum would be more legitimate only for the non-empirical components. Under a conclusion-based one, it would be more legitimate only if an overall assessment conferred more relevance to the non-empirical components.

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.

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.

Rationality is a cornerstone of economics. The properties defining rationality are embodied by the Rational Agent, whose actions are prescriptive for economic agents. However, the Rational Agent is a fictional character: so why should real agents act like it? The Rational Agent takes its normative force from the arguments in support of the properties it embodies. In this paper, I explore the grounds for the normative force of the Rational Agent by looking at one of them. I explain the compelling pull of the famous Dutch Book argument using tools from narratology. I contend that the argument presents a branching narrative structure that allows the comparison of outcomes. Thus, the agent can see that one option serves her economic desires better than the other, and this is the specific way in which it provides normative support to the rational agent. Since the comparison of outcomes requires the use of imagination, I conclude the paper drawing some implications of my analysis for a connection between imagination and action.