The thermal time hypothesis (TTH) is a proposed solution to the problem of time: a coarse-grained state determines a thermal dynamics according to which it is in equilibrium, and this defines the flow of time in generally covariant quantum theories. This article raises a series of objections to the TTH as developed by Alain Connes and Carlo Rovelli. Two technical challenges concern the relationship between thermal time and proper time and the possibility of implementing the TTH in classical theories. Three conceptual problems concern the flow of time in nonequilibrium states and the extent to which the TTH is background independent and gauge invariant.

I propose a dynamic causal approach to characterizing the notion of a mechanism. Levy and Bechtel, among others, have pointed out several critical limitations of the new mechanical philosophy and pointed in a new direction to extend this philosophy. Nevertheless, they have not fully fleshed out what that extended philosophy would look like. Given a closer look at neuroscientific practice, I propose that a mechanism is a dynamic causal system that involves various components interacting, typically nonlinearly, with one another to produce a phenomenon of interest.

This article examines the role of historical science in clarifying the causal structure of complex natural processes. I reject the pervasive view that historical science does not uncover natural regularities. To show why, I consider an important methodological distinction in geology between uniformitarianism and actualism; methodological actualism, the preferred method of geologists, often relies on historical reconstructions to test the stability of currently observed processes. I provide several case studies that illustrate this, including one that highlights how historical narratives can improve predictive models.

Cognitive scientists often describe the mind as constructing and using models of aspects of the environment, but it is not obvious what makes something a model as opposed to a mere representation. The leading proposal among philosophers is that models are structural representations and are therefore distinguished by their format. However, an alternative conception is suggested by recent work in artificial intelligence, on which models are distinguished by their content. This article outlines the two conceptions and argues for the content conception, against the standard philosophical view.

Econophysics is a branch of economics that applies concepts and methods from physics to the financial markets. This article focuses on the approaches to quantum finance developed by Kirill Ilinski and Belal E. Baaquie to deal with the uncertainty characterizing financial time series. Allegedly, their models rest on a formal analogy between quantum mechanics and finance. In order to evaluate them, we raise the question what is really quantum in quantum econophysics. We then argue that the supposed analogy breaks in an important manner, which is relevant to explain the empirical success of the proposed models.

This article offers a characterization of what I call multiple-models juxtaposition (MMJ), a strategy for managing trade-offs among modeling desiderata. MMJ displays models of distinct phenomena together and fulfills different desiderata both in the individual models and by a comparison of those models. I discuss a concrete case from developmental biology, where MMJ coordinates generality and detail. I also clarify the distinction between MMJ and multiple-models idealization (MMI), which also uses multiple models to manage trade-offs among desiderata. MMJ and MMI differ in several points, such as the ways they manage trade-offs and the purposes of using multiple models.

Mathematics is often taken to play one of two roles in the empirical sciences: either it represents empirical phenomena or it explains these phenomena by imposing constraints on them. This article identifies a third and distinct role that has not been fully appreciated in the literature on applicability of mathematics and may be pervasive in scientific practice. I call this the “bridging” role of mathematics, according to which mathematics acts as a connecting scheme in our explanatory reasoning about why and how two different descriptions of an empirical phenomenon relate to each other. I discuss two bridging roles appearing in biological and physical explanations.

In many ways, entropy and probability are two concepts that complement each other. But it has been argued that there is no ‘straightforward connection’ between them with a no-go thesis from Kevin Davey. However, this skeptical conclusion rests on counterexamples that fail to do justice to the entropy principle and the equivocality of the notion of probability. Proceeding from the disambiguation of probability, and acknowledging the explanatory goal of the entropy principle, it is argued that the Boltzmannian statistical mechanics account can be vindicated with a justification of the explanandum, the reference class, and the standard uniform probability measure.

I will argue that Kochen-Specker arguments do not provide an algebraic proof for quantum contextuality since, for the argument to be effective, (1) operators must be uniquely associated with measurements and (2) commuting operators must represent simultaneous measurements. However, in all Kochen-Specker arguments discussed in the literature either 1 or 2 is not met. Arguments meeting 1 contain commuting operators that do not represent simultaneous measurements and hence fail to physically justify the functional composition principle. Arguments meeting 2 associate some operators with more than one measurement and hence need to invoke an extra assumption different from noncontextuality.

Reformulating a scientific theory often leads to a significantly different way of understanding the world. Nevertheless, accounts of both theoretical equivalence and scientific understanding have neglected this important aspect of scientific theorizing. This essay provides a positive account of how reformulation changes our understanding. My account simultaneously addresses a serious challenge facing existing accounts of scientific understanding. These accounts have failed to characterize understanding in a way that goes beyond the epistemology of scientific explanation. By focusing on cases in which we have differences in understanding without differences in explanation, I show that understanding does not reduce to explanation.

Epidemiological explanation often has a “black box” character, meaning the intermediate steps between cause and effect are unknown. Filling in black boxes is thought to improve causal inferences by making them intelligible. I argue that adding information about intermediate causes to a black box explanation is an unreliable guide to pragmatic intelligibility because it may mislead us about the stability of a cause. I diagnose a problem that I call wishful intelligibility, which occurs when scientists misjudge the limitations of certain features of an explanation. Wishful intelligibility gives us a new reason to prefer black box explanations in some contexts.

I argue that explanations for time asymmetry in terms of a ‘Past Hypothesis’ face serious new difficulties. First I strengthen grounds for existing criticism by outlining three categories of criticism that put into question essential requirements of the proposal. Then I provide a new argument showing that any time-independent measure on the space of models of the universe must break a gauge symmetry. The Past Hypothesis then faces a new dilemma: reject a gauge symmetry and introduce a distinction without difference or reject the time independence of the measure and lose explanatory power.

Since the discovery of dark matter in the 1980s, multiple experiments have been set up to detect dark matter particles through some other mode than gravity. Particle physicists provide detailed justifications as to why their experiments should be able to detect dark matter. I show that these justifications take on a structure different from what is often the case in experimental practice, and I argue that this is because of the limited description of dark matter. By illuminating this ‘method-driven logic’, I shed new light on questions surrounding measurement robustness and methodological pluralism in the context of dark matter research.

The universal optimality theorem for metainduction works for epistemic agents faced with a choice among finitely many prediction methods. Eckhart Arnold and Tom Sterkenburg objected that it breaks down for infinite or unboundedly growing sets of methods. In this article the metainductive approach is defended against this challenge by extending the optimality theorem (i) to unboundedly growing sets of methods whose number grows less than exponentially in time, (ii) to sequences of methods with an application to Goodman's problem, and (iii) to infinite sets of methods whose number of predictive equivalence classes grows less than linearly in time.

Approximate coherentism suggests that imperfectly rational agents should hold approximately coherent credences. This norm is intended as a generalization of ordinary coherence. I argue that it may be unable to play this role by considering its application under learning experiences. While it is unclear how imperfect agents should revise their beliefs, I suggest a plausible route is through Bayesian updating. However, Bayesian updating can take an incoherent agent from relatively more coherent credences to relatively less coherent credences, depending on the data observed. Thus, comparative rationality judgments among incoherent agents are unduly sensitive to luck.

In this article, I present a serious problem for confirmation measure Z.

A new justification of probabilism is developed that pays close attention to the structure of the underlying space of possibilities. Its central assumption is that rational numerical degrees of belief ought to be partition invariant. By means of a representation theorem, one can prove that if graded belief satisfies the resulting set of postulates, rational degrees of belief may be identified with probabilities.

Conventional wisdom holds that the von Neumann entropy corresponds to thermodynamic entropy, but Meir Hemmo and Orly Shenker have recently argued against this view by attacking von Neumann’s argument. I argue that Hemmo and Shenker’s arguments fail because of several misunderstandings about statistical-mechanical and thermodynamic domains of applicability, about the nature of mixed states, and about the role of approximations in physics. As a result, their arguments fail in all cases: in the single-particle case, the finite-particles case, and the infinite-particles case.

Callender and Cohen have proposed to apply a “Gricean strategy” to the constitution problem of scientific representation. They suggest that scientific representation can be reduced to stipulation by epistemic agents. This account has been criticized for not making a distinction between symbolic and epistemic representation and not taking into account the communal aspects of representation. These criticisms would not apply if Grice’s actual strategy were properly employed. I transpose Grice’s strategy to epistemic representation. The main novelty of the resulting account is a distinction between contextual representational use and general representational status, which I address using the notion of indexicality.

The coherence of independent reports provides a strong reason to believe that the reports are true. This plausible claim has come under attack from recent work in Bayesian epistemology. This work shows that, under certain probabilistic conditions, coherence cannot increase the probability of the target claim. These theorems are taken to demonstrate that epistemic coherentism is untenable. To date no one has investigated how these results bear on different conceptions of coherence. I investigate this situation using Thagard’s ECHO model of explanatory coherence. Thagard’s ECHO model provides a natural representation of the evidential significance of multiple independent reports.