Bumps on the Road to Here (from Eternity)

In his recent book, _From Eternity to Here_, and in other more technical papers, Sean Carroll (partly in collaboration with Jennifer Chen) has put forward an intriguing new way to think about the origin of the Universe. His approach, in a nutshell, is to raise certain worries about a standard Boltzmannian picture of statistical mechanics, and to present certain commitments that he thinks we ought to hold—commitments that the standard picture doesn’t share. He then proposes a cosmological model—one that purports to give us insight into what sort of process brought about the “initial state” of the universe—that can uniquely accommodate those commitments. The conclusion of Carroll’s argument is that statistical mechanical reasoning provides grounds for provisionally accepting that cosmological model. My goal in this paper is to reconstruct and critically assess this proposal. I argue that “statistical cosmology” requires a careful balance of philosophical intuitions and commitments against technical, scientific considerations; how much stock we ought to place in these intuitions and commitments should depend on where they lead us—those that lead us astray scientifically might well be in need of philosophical re examination.

Values and uncertainties in the predictions of global climate models

Over the last several years, there has been an explosion of interest and attention devoted to the problem of Uncertainty Quantification (UQ) in climate science – that is, to giving quantitative estimates of the degree of uncertainty associated with the predictions of global and regional climate models. The technical challenges associated with this project are formidable, and so the statistical community has understandably devoted itself primarily to overcoming them. But as these technical challenges are being met, a number of persistent
conceptual difficulties remain. So why is UQ so important in climate science? UQ, I would like to argue, is first and foremost a tool for communicating knowledge from experts to policymakers in a way that is meant to be free from the influence of social and ethical values. But the standard ways of using probabilities to separate ethical and social values from scientific practice cannot be applied in much of climate modeling, because the roles of values in creating the models cannot be discerned after the fact—the models are too complex and the result of too much distributed epistemic labor. I argue, therefore, that typical approaches for handling ethical/social values in science do not work
well here.

The metaphysical foundations of statistical mechanics: on the status of PROB and PH

One of the central aims of Time and Chance (2000) was to bring all of our experience of the macroscopic world under the umbrella of a small package of fundamental physical postulates. For many of the central features of our experience--particularly the especially puzzling temporally irreversible ones: like the thermodynamic behavior of isolated systems, the asymmetry of knowledge of the past and future, and the temporal asymmetry of causal interventions--the goal of Time and Chance was to show how these features of our experience follow from a package of laws consisting of a set of time-reversible, deterministic, dynamical microlaws; a hypothesis about the macroscopic state of the early universe (the “past hypothesis”; PH); and a statistical postulate about the distribution of microstates compatible with that macrostate (PROB).

Other than the claim that they are to be understood as laws, however, Time and Chance did not have very much to say about the metaphysical underpinnings of PH and PROB. This was despite the fact that PH and PROB appear to be somewhat unusual from the point of view of most ordinary understandings of laws of nature. As for the probabilities postulated by PROB, the book contains some brief arguments against construing them as degrees of belief, but for the most part, it is relatively quiet about a potentially puzzling question: how are we to understand the claim that the initial physical state of the universe involves objective chances? There is, after all, only one universe, and it began only once.

Through Tarski, Darkly: A typology of Contemporary concepts of truth

co-authored with Paul Cudney

In this paper, we argue that much of the contemporary landscape in the philosophy of truth can be viewed through the lens of four different readings of Alfred Tarski’s famous work. These four readings arise in response to two central interpretative questions in reading that
text: how to understand the role of recursion, and how to understand the relationship between the object-langauge and the meta-language as they occur in the T-biconditionals. We argue that these two interpretive “axes” carve out what ought to be regarded as four of the central contenders for a proper philosophical understanding of truth: correspondence theories, disquotationalism, quasi-disquotationalism, and the Davidsonian “anti-theory” account of truth. We argue, moreover, that viewing these possible theories of truth through the eyes of the four readings of Tarski helps clarify how to think about the various commitments each of these theories entail, and to bring to light some of the advantages and costs of the various positions—particularly with respect to our understanding of the sentential connectives.

Laws and Statistical Mechanics

This is the paper where I articulate and defend a version of the "branch systems" approach to Boltzmannian statistical mechanics.

Holism, entrenchment, and the future of climate model pluralism

co-authored with Johannes Lenhard

In this paper, we explore the extent to which issues of simulation model validation take on novel characteristics when the models in question become particularly complex. Our central claim is that
complex simulation models in general, and global models of climate in particular, face a form of confirmation holism. This holism, moreover, makes analytic understanding of complex models of climate either extremely difficult or even impossible. We argue that this supports a position we call convergence skepticism: the belief that the existence of a plurality of different models making a plurality of different forecasts of future climate is likely to be a persistent feature of global climate science.

Value Judgments and the Estimation of Uncertainty in Climate Modeling

co-authored with Justin Biddle

Probabilities in Statistical Mechanics

In the Boltzmannian approach to statistical mechanics, one postulates a probability distribution over a set of possible initial states for the universe. But there is only one universe, and it began only one time. How should we interpret these probabilities? This paper argues that the most promising way to understand the probabilistic posit is as a convenient shorthand for expressing non-probabilistic facts. On this view, there are no genuine probabilities in a classical Boltzmannian world. It also argues that what is required of us to obtain a clear view of the foundations of statistical mechanics puts pressure on certain non-Humean views about laws and induction.

Computer simulation and the Philosophy of Science

There are a variety of topics in the philosophy of science that need to be rethought, in varying degrees, after one pays careful attention to the ways in which computer simulations are used in
the sciences. There are a number of conceptual issues internal to the practice of computer simulation that can benefit from the attention of philosophers. This essay surveys some of the recent literature on simulation from the perspective of the philosophy of science and argues that philosophers have a lot to learn by paying closer attention to the practice of simulation.

A Tale of two methods

Simulations (both digital and analog) and experiments share many features. But what essential features distinguish them? I discuss two proposals in the literature. On one proposal, experiments investigate nature directly, while simulations
merely investigate models. On another proposal, simulations differ from experiments in that simulationists manipulate objects that bear only a formal (rather than material) similarity to the targets of their investigations. Both of these proposals are
rejected. I argue that simulations fundamentally differ from experiments with regard to the background knowledge that is invoked to argue for the “external validity” of the investigation.

Can Conditioning on the Past Hypothesis Militate against the reversibility objections?

In his recent book, Time and Chance, David Albert claims that by positing that there is a uniform probability distribution defined, on the standard measure, over the space of microscopic states that are compatible with both the current macrocondition of the
world, and with what he calls the “past hypothesis”, we can explain the time asymmetry of all of the thermodynamic behavior in the world. The principal purpose of this paper
is to dispute this claim. I argue that Albert’s proposal fails in his stated goal—to show how to use the time-reversible dynamics of Newtonian physics to “underwrite the actual content of our thermodynamic experience” (Albert 2000, 159). Albert’s proposal can satisfactorily explain why the overall entropy of the universe as a whole is increasing, but it does not and cannot explain the increasing entropy of relatively small, relatively short-lived systems in energetic isolation without making use of a principle that leads to reversibility objections.

Models of Success and the Success of Models

In computer simulations of physical systems, the construction of models is guided, but not determined, by theory. At the same time simulations models are often constructed precisely because data are sparse. They are meant to replace experiments and observations as sources of data about the world; hence they
cannot be evaluated simply by being compared to the world. So what can be the source of credibility for simulation models? I argue that the credibility of a simulation model comes not only from the credentials supplied to it by the governing theory, but also from the antecedently established credentials of the model building techniques employed by the simulationists. In other words, there are certain sorts of model building techniques which are taken, in and of themselves, to be reliable. Some of these model building techniques, moreover, incorporate what are sometimes called ‘‘falsifications.’’ These are contrary-to-fact principles that are included in a simulation model and whose inclusion is taken to increase the reliability of the results. The
example of a falsification that I consider, called artificial viscosity, is in widespread use in computational fluid dynamics. Artificial viscosity, I argue, is a principle that is successfully and reliably used across a wide domain of fluid dynamical applications,
but it does not offer even an approximately ‘‘realistic’’ or true account of fluids. Artificial viscosity, therefore, is a counter-example to the principle that success implies truth – a principle at the foundation of scientific realism. It is an example of reliability without truth.

Simulated Experiments: Methodology for a Virtual World

This paper examines the relationship between simulation and experiment. Many discussions of simulation, and indeed the term “numerical experiments,” invoke a strong metaphor of experimentation. On the other hand, many simulations begin as attempts to apply scientific theories. This has lead many to characterize simulation as lying between theory and experiment. The aim of the paper is to try to reconcile these two points of view—to understand what methodological and epistemological features simulation has in common with experimentation, while at the same time keeping a keen eye on simulation’s ancestry as a form of scientific theorizing. In so doing, it seeks to apply some of the insights of recent work on the philosophy of experiment to an aspect of theorizing that is of growing philosophical interest: the construction of local models.

Quantum Life: interaction, entanglement, and separation.

co-authored with Arthur Fine.

Simulations, Models and Theories, Complex Physical Systems and their representations.

Sanctioning Models: the epistemology of simulation

In its reconstruction of scientific practice, philosophy of science has traditionally placed scientific theories in a central role, and  has reduced the problem of mediating between theories and the world to formal considerations. Many applications of scientific theories, however, involve complex mathematical models whose constitutive equations are analytically unsolvable. The study of these applications often consists in developing representations of the underlying physics on a computer, and using the techniques of computer simulation in order to learn about the behavior of these systems. In many instances, these computer simulations are not simple numbercrunching techniques. They involve a complex chain of inferences that serve to transform theoretical structures into specific concrete knowledge of physical systems. In this paper I argue that this process of transformation has its own epistemology. I also argue that this kind of epistemology is unfamiliar to most philosophy of science, which has traditionally concerned itself with the justification of theories, not in their application. Finally, I urge that the nature of this epistemology suggests that the end results of some simulations do not bear a
simple, straightforward relation to the theories from which they stem.