Mathias Frisch

How could the initial, drastic decisions to implement “lockdowns” to control the spread of Covid-19 infections be justifiable, when they were made on the basis of such uncertain evidence? We defend the imposition of lockdowns in some countries by, first, looking at the evidence that undergirded the decision (focusing particularly on the decision-making process in the United Kingdom); second, arguing that this provided sufficient grounds to restrict liberty, given the circumstances; and third, defending the use of poorly empirically constrained epidemiological models as tools that can legitimately guide public policy.

David Albert and Barry Loewer have recently argued that all non-fundamental laws can be derived from the fundamental laws in conjunction with the Past Hypothesis and the proposition that the probability that a given macroscopic state is realized by a given microscopic state is provided by the canonical statistical mechanical probability distribution for that macroscopic state, conditional on the Past Hypothesis. A reconstruction of the Albert and Loewer argument is provided and argued to be unsuccessful. First, the fact that a well-confirmed theory entails a probability distribution over all physically possible propositions does not entail that all probabilities assigned to those propositions are well confirmed. Second, the laws cannot be identified with all and only the propositions that are likely. Third, even if the non-fundamental laws could be derived in the way that Albert and Loewer suggest, the explanations they ground would remain irreducible in one important respect.

How can scientists and science communicators effectively engage the public in times of crisis and in the face of pervasive uncertainty? Drawing on the experiences of the Covid-19 pandemic and the escalating climate crisis, the three essays in this volume examine key dimensions of this challenge. What roles should scientists play in policy-making? How can the spread of fake news in science communication be curtailed? And how can we represent uncertainties in a way that upholds the credibility of scientific knowledge?

In this dissertation I address the general question, "What is the role of mathematical models in the way in which scientific theories represent the world?" The specific questions I address are these: What is the relation between the laws of a theory and its models? Do scientific theories give us coherent accounts of physical possibility? Must a theory represent a phenomenon truthfully in order to explain it? ;In the first chapter I distinguish and examine some central uses to which the term "model" has been put in philosophical discussions of scientific theories. According to the current "semantic view," theories are to be understood as consisting of families of models. I argue that standard presentations of the semantic view are guilty of confounding two distinct notions of model: that of a structure which represents the world and that of a structure which satisfies a set of sentences. ;In the second chapter I examine the relation between the general principles or "laws" of a theory and the mathematical models that scientists use to represent individual phenomena within the theory's purview. According to an intuitively plausible view, individual models satisfy the laws of the theory and can, at least in principle, be integrated into coherent 'global' representations of the phenomena in the domain of the theory. Scientific theories, according to this view, delineate coherent physical possibilities. Against this, I show in a detailed case study of classical electrodynamics that there are no possible worlds of which all the basic equations of the theory are simultaneously true under their standard interpretation. Instead of presenting us with a conceptually coherent picture of a range of possibilities, classical electrodynamics allows for the construction of a variety of models which locally represent different phenomena, but which cannot be integrated into a single unified and coherent account. ;In the third and fourth chapters, I take up the question of whether a theory must represent the phenomena within its purview truthfully in order to explain them. Classical electrodynamics could not possibly be a true theory, yet, perhaps surprisingly, physicists take the theory to provide adequate explanations. After surveying many of the major current accounts of explanation, most of which hold that truth is a necessary condition of explanatory adequacy, I propose an account of scientific explanation that does two things: it tries to do justice to the intuition embodied in these accounts that there is a close connection between explanation and truth, but at the same time it allows good scientific explanations to be at least partially untrue. To explain a phenomenon, I argue, is to exhibit it as part of a systematic pattern of alternative possibilities. I elaborate this idea and show how it can be made precise by proposing a technical framework which appeals to Tarski's notion of a relational system. I argue that we use three criteria to evaluate putative explanations: degrees of truth, richness of explanatory pattern, and simplicity; and I claim that something like the weighted average of the three criteria determines the goodness of an explanation. I show how my proposal can account for the structure of paradigmatic instances of scientific explanations, and I defend the proposal from challenges posed by causal theories of explanation

One of the central characteristics of the causal relation is that it is asymmetric. This chapter looks at possible relations between the direction of time and the causal asymmetry. It first presents a discussion on a causal theory of the temporal asymmetry that takes the causal asymmetry to be basic. It then examines two kinds of accounts that take asymmetric causal relations to be further reducible. The first kind is a subjectivist account of causation that argues that the fact that we describe the world in asymmetric causal terms has its origins in a more fundamental asymmetry of deliberation. The author here focuses on the more recent defense of such an account in Price and Weslake. The second kind is an entropy account of the causal asymmetry. A more recent version of such an account, the account developed by David Albert and Barry Loewer, is discussed in the chapter.

Models not only represent but may also influence their targets in important ways. While models’ abilities to influence outcomes has been studied in the context of economic models, often under the label ‘performativity’, we argue that this phenomenon also pertains to epidemiological models, such as those used for forecasting the trajectory of the Covid-19 pandemic. After identifying three ways in which a model by the Covid-19 Response Team at Imperial College London may have influenced scientific advice, policy, and individual responses, we consider the implications of epidemiological models’ performative capacities. We argue, first, that performativity may impair models’ ability to successfully predict the course of an epidemic; but second, that it may provide an additional sense in which these models can be successful, namely by changing the course of an epidemic.

How could the initial, drastic decisions to implement “lockdowns” to control the spread of COVID-19 infections be justifiable, when they were made on the basis of such uncertain evidence? We defend the imposition of lockdowns in some countries by first, and focusing on the UK, looking at the evidence that undergirded the decision, second, arguing that this provided us with sufficient grounds to restrict liberty given the circumstances, and third, defending the use of poorly-empirically-constrained epidemiological models as tools that can legitimately guide public policy.

When do probability distribution functions (PDFs) about future climate misrepresent uncertainty? How can we recognise when such misrepresentation occurs and thus avoid it in reasoning about or communicating our uncertainty? And when we should not use a PDF, what should we do instead? In this paper we address these three questions. We start by providing a classification of types of uncertainty and using this classification to illustrate when PDFs misrepresent our uncertainty in a way that may adversely affect decisions. We then discuss when it is reasonable and appropriate to use a PDF to reason about or communicate uncertainty about climate. We consider two perspectives on this issue. On one, which we argue is preferable, available theory and evidence in climate science basically excludes using PDFs to represent our uncertainty. On the other, PDFs can legitimately be provided when resting on appropriate expert judgement and recognition of associated risks. Once we have specified the border between appropriate and inappropriate uses of PDFs, we explore alternatives to their use. We briefly describe two formal alternatives, namely imprecise probabilities and possibilistic distribution functions, as well as informal possibilistic alternatives. We suggest that the possibilistic alternatives are preferable.

This chapter examines two approaches to climate policy: expected utility calculations and a precautionary approach. The former provides the framework for attempts to calculate the social cost of carbon. The latter approach has provided the guiding principle for the United Nations Conference of Parties from the 1992 Rio Declaration to the Paris Agreement. The chapter argues that the deep uncertainties concerning the climate system and climate damages make the exercise of trying to calculate a well-supported value for the SCC impossible. Moreover, cost-benefit analyses are blind to important moral dimensions of the climate problem. Yet it is an open question to what extent an alternative, precautionary approach can result in specific policy recommendations such as the temperature targets of the Paris agreement.

This chapter examines the role of parameterParametercalibrationCalibration in the confirmation and validation of complex computer simulation models. I examine the question to what extent calibration data can confirm or validate the calibrated model, focusing in particular on Bayesian approaches to confirmation. I distinguish several different Bayesian approaches to confirmation and argue that complex simulation models exhibit a predictivist effect: Complex computer simulation models constitute a case in which predictive success, as opposed to the mere accommodation of evidence, provides a more stringent test of the model. DataData used in tuning do not validate or confirm a model to the same extent as data successfully predicted by the model do.

According to a view widely held among philosophers of science, the notion of cause has no legitimate role to play in mature theories of physics. In this paper I investigate the role of what physicists themselves identify as causal principles in the derivation of dispersion relations. I argue that this case study constitutes a counterexample to the popular view and that causal principles can function as genuine factual constraints. 1. Introduction2. Causality and Dispersion Relations3. Norton's Skepticism4. Conclusion.

As the record-breaking heat of 2016 continues into 2017, making it likely that 2017 will be the second hottest year on record just behind the El Niño year 2016, and as Arctic heat waves pushing the sea ice extent to record lows are mirrored by large scale sheets of meltwater and even rain in Antarctica—the Trump administration is taking dramatic steps to undo the Obama administration’s climate legacy.In its final years, the Obama administration pursued two principal strategies toward climate policy. First, by signing the Paris Accord it committed the U.S. to contribute to global efforts to hold “the increase in the global average temperature to well below 2°C above pre-industrial levels and to pursue efforts to...

Classical dispersion relations are derived from a time-asymmetric constraint. I argue that the standard causal interpretation of this constraint plays a scientifically legitimate role in dispersion theory, and hence provides a counterexample to the causal skepticism advanced by John Norton and others. Norton ([2009]) argues that the causal interpretation of the time-asymmetric constraint is an empty honorific and that the constraint can be motivated by purely non-causal considerations. In this paper I respond to Norton's criticisms and argue that Norton's skepticism derives its force partly by holding causal principles to a standard too high to be met by other scientifically legitimate constraints

In Frisch 2004 and 2005 I showed that the standard ways of modeling particle-field interactions in classical electrodynamics, which exclude the interactions of a particle with its own field, results in a formal inconsistency, and I argued that attempts to include the self-field lead to numerous conceptual problems. In this paper I respond to criticism of my account in Belot 2007 and Muller 2007. I concede that this inconsistency in itself is less telling than I suggested earlier but argue that existing solutions to the theory's foundational problems do not support the kind of traditional philosophical conception of scientific theorizing defended by Muller and Belot. *Received January 2007; revised October 2007. †To contact the author, please write to: Department of Philosophy, University of Maryland, Skinner Building, College Park, MD 20742; e-mail: [email protected].