Joe Roussos

When an agent learns of an expert's credence in a proposition about which they are an expert, the agent should defer to the expert and adopt that credence as their own. This is a popular thought about how agents ought to respond to (ideal) experts. In a Bayesian framework, it is often modelled by endowing the agent with a set of priors that achieves this result. But this model faces a number of challenges, especially when applied to non-ideal agents (who nevertheless interact with ideal experts). I outline these problems, and use them as desiderata for the development of a new model. Taking inspiration from Richard Jeffrey's development of Jeffrey conditioning, I develop a model in which expert reports are taken as exogenous constraints on the agent's posterior probabilities. I show how this model can handle a much wider class of expert reports (for example reports of conditional probabilities), and can be naturally extended to cover propositions for which the agent has no prior.

I argue that normative formal epistemology (NFE) is best understood as modelling, in the sense that this is the reconstruction of its methodology on which NFE is doing best. I focus on Bayesianism and show that it has the characteristics of modelling. But modelling is a scientific enterprise, while NFE is normative. I thus develop an account of normative models on which they are idealised representations put to normative purposes. Normative assumptions, such as the transitivity of comparative credence, are characterised as modelling idealisations motivated normatively. I then survey the landscape of methodological options: what might formal epistemologists be up to? I argue the choice is essentially binary: modelling or theorising. If NFE is theorising it is doing very poorly: generating false claims with no clear methodology for separating out what is to be taken seriously. Modelling, by contrast, is a successful methodology precisely suited to the management of useful falsehoods. Regarding NFE as modelling is not costless, however. First, our normative inferences are less direct and are muddied by the presence of descriptive idealisations. Second, our models are purpose-specific and limited in their scope. I close with suggestions for how to adapt our practice.

This is a paper about the methodology of normative ethics. I claim that much work in normative ethics can be interpreted as modelling, the form of inquiry familiar from science, involving idealised representations. I begin with the anti-theory debate in ethics, and note that the debate utilises the vocabulary of scientific theories without recognising the role models play in science. I characterise modelling, and show that work with these characteristics is common in ethics. This establishes the plausibility of my interpretation. Taking methodological inspiration from modelling in science gives us new tools for managing idealisations, and a new perspective on pluralism. I demonstrate why this interpretation is a fruitful way of interpreting ethics, by looking at three case studies. First, I return to the anti-theory debate and argue that modelling opens up a new middle ground. Second, I argue that a modelling lens offers a new way of understanding impossibility theorems in population ethics, and their bearing on ethics as a whole. Finally, I show how viewing our work as modelling can be deployed in debates within ethics, using the debate over prioritarianism as an example. I close with further methodological suggestions for those who choose to see themselves as modellers. I discuss the role of counterexamples, our responses to moral disagreement, and the training of new ethicists.

The UK has been ‘following the science’ in response to the COVID-19 pandemic in line with the national framework for the use of scientific advice in assessment of risk. We argue that the way in which it does so is unsatisfactory in two important respects. Firstly, pandemic policy making is not based on a comprehensive assessment of policy impacts. And secondly, the focus on reasonable worst-case scenarios as a way of managing uncertainty results in a loss of decision-relevant information and does not provide a coherent basis for policy making.

Many policy decisions take input from collections of scientific models. Such decisions face significant and often poorly understood uncertainty. We rework the so-called confidence approach to tackle decision-making under severe uncertainty with multiple models, and we illustrate the approach with a case study: insurance pricing using hurricane models. The confidence approach has important consequences for this case and offers a powerful framework for a wide class of problems. We end by discussing different ways in which model ensembles can feed information into the approach, appropriate to different collections of models.

Policymakers who seek to make scientifically informed decisions are constantly confronted by scientific uncertainty and expert disagreement. This thesis asks: how can policymakers rationally respond to expert disagreement and scientific uncertainty? This is a work of non-ideal theory, which applies formal philosophical tools developed by ideal theorists to more realistic cases of policymaking under scientific uncertainty. I start with Bayesian approaches to expert testimony and the problem of expert disagreement, arguing that two popular approaches— supra-Bayesianism and the standard model of expert deference—are insufficient. I develop a novel model of expert deference and show how it can deal with many of these problems raised for them. I then turn to opinion pooling, a popular method for dealing with disagreement. I show that various theoretical motivations for pooling functions are irrelevant to realistic policymaking cases. This leads to a cautious recommendation of linear pooling. However, I then show that any pooling method relies on value judgements, that are hidden in the selection of the scoring rule. My focus then narrows to a more specific case of scientific uncertainty: multiple models of the same system. I introduce a particular case study involving hurricane models developed to support insurance decision-making. I recapitulate my analysis of opinion pooling in the context of model ensembles, confirming that my hesitations apply. This motivates a shift of perspective, to viewing the problem as a decision theoretic one. I rework a recently developed ambiguity theory, called the confidence approach, to take input from model ensembles. I show how it facilitates the resolution of the policymaker’s problem in a way that avoids the issues encountered in previous chapters. This concludes my main study of the problem of expert disagreement. In the final chapter, I turn to methodological reflection. I argue that philosophers who employ the mathematical methods of the prior chapters are modelling. Employing results from the philosophy of scientific models, I develop the theory of normative modelling. I argue that it has important methodological conclusions for the practice of formal epistemology, ruling out popular moves such as searching for counterexamples.

What is the proper role for scientists in policymaking? This paper explores various roles that scientists can play, with an eye to questions that these roles raise about value-neutrality and technocracy. Where much philosophical literature is concerned with the conduct of research or the transmission of research results to policymakers, I am interested in various non-research roles that scientists take on in policymaking. These include raising the alarm on issues, framing and conceptualising problems, formulating potential policies, assessing policy options for expected efficacy, and more. I consider examples from climate change and Covid-19 policymaking. My intention is to encourage philosophers to expand their interest in values in science out from the conduct of research to the wide array of roles that scientists play in policymaking. The paper is therefore an overview of the landscape of potential research questions, rather than a presentation of a single argument.

Climate science is expected to provide usable information to policy-makers, to support the resolution of climate change. The complex, multiply connected nature of climate change as a social problem is reviewed and contrasted with current modular and discipline-bounded approaches in climate science. We argue that climate science retains much of its initial “physics-first” orientation, and that it adheres to a problematic notion of objectivity as freedom from value judgments. Together, these undermine its ability to provide usable information. We develop the notion of usability using work from the literature on adaptation, but our argument applies to all of climate science. We illustrate the tension between usability and the objective, physics-first orientation of climate science with an example about model development practices in climate science. For solutions, we draw on two frameworks for science which responds to societal challenges: post-normal science and mandated science. We generate five recommendations for adapting the practice of climate science, to produce more usable information and thereby respond more directly to the social challenge of climate change. These are: (1) integrated cross-disciplinarity, (2) wider involvement of stakeholders throughout the lifecycle of a climate study, (3) a new framing of the role of values in climate science, (4) new approaches to uncertainty management, and (5) new approaches to uncertainty communication.

This note critiques the concept of “wicked problems” and its usefulness in crises such as Covid-19. There are two problems with the concept as defined by Rittel, Webber, and those who draw from them, which undermine its value in the analysis of social policy. First, their characterisation of wicked problems is founded on a crude and false picture of science (cf. Turnbull and Hoppe 2019). Second, it is so vague that on an expansive reading all social problems are wicked problems while on a restrictive reading almost none are. Having discussed these, we close with a reflection on managing complexity and uncertainty in complex policy problems like the pandemic.