We consider the question of whether thermodynamic macrostates are objective consequences of dynamics, or subjective reflections of our ignorance of a physical system. We argue that they are both; more specifically, that the set of macrostates forms the unique maximal partition of phase space which 1) is consistent with our observations (a subjective fact about our ability to observe the system) and 2) obeys a Markov process (an objective fact about the system's dynamics). We review the ideas of computational mechanics, (...) an information-theoretic method for finding optimal causal models of stochastic processes, and argue that macrostates coincide with the ``causal states'' of computational mechanics. Defining a set of macrostates thus consists of an inductive process where we start with a given set of observables, and then refine our partition of phase space until we reach a set of states which predict their own future, i.e. which are Markovian. Macrostates arrived at in this way are provably optimal statistical predictors of the future values of our observables. (shrink)

Opinionated state of the art paper on scientific explanation. Analysis and discussion of the most relevant models and theories in the contemporary literature, namely, the deductive-nomological model, the models of inductive-statistical and statistical relevance, the pragmatic theory of why questions, the unifying theory of standard arguments, and the causal/non-causal counterfactual theory.

In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we argue that topological predictions (...) can and do guide interventions in science, both inside and outside of neuroscience. Topological models allow researchers to predict many phenomena, including diseases, treatment outcomes, aging, and cognition, among others. Moreover, we argue that these predictions also offer strategies for useful interventions. Topology-based predictions play this role regardless of whether they do or can receive a mechanistic interpretation. We conclude by making a case for philosophers to focus on prediction in neuroscience in addition to explanation alone. (shrink)

The theory of concepts advanced in the dissertation aims at accounting for a) how a concept makes successful practice possible, and b) how a scientific concept can be subject to rational change in the course of history. Traditional accounts in the philosophy of science have usually studied concepts in terms only of their reference; their concern is to establish a stability of reference in order to address the incommensurability problem. My discussion, in contrast, suggests that each scientific concept consists of (...) three components of content: 1) reference, 2) inferential role, and 3) the epistemic goal pursued with the concept's use. I argue that in the course of history a concept can change in any of these three components, and that change in one component—including change of reference—can be accounted for as being rational relative to other components, in particular a concept's epistemic goal.This semantic framework is applied to two cases from the history of biology: the homology concept as used in 19th and 20th century biology, and the gene concept as used in different parts of the 20th century. The homology case study argues that the advent of Darwinian evolutionary theory, despite introducing a new definition of homology, did not bring about a new homology concept (distinct from the pre-Darwinian concept) in the 19th century. Nowadays, however, distinct homology concepts are used in systematics/evolutionary biology, in evolutionary developmental biology, and in molecular biology. The emergence of these different homology concepts is explained as occurring in a rational fashion. The gene case study argues that conceptual progress occurred with the transition from the classical to the molecular gene concept, despite a change in reference. In the last two decades, change occurred internal to the molecular gene concept, so that nowadays this concept's usage and reference varies from context to context. I argue that this situation emerged rationally and that the current variation in usage and reference is conducive to biological practice.The dissertation uses ideas and methodological tools from the philosophy of mind and language, the philosophy of science, the history of science, and the psychology of concepts. (shrink)

According to one large family of views, scientific explanations explain a phenomenon (such as an event or a regularity) by subsuming it under a general representation, model, prototype, or schema (see Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441; Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge: MIT Press; Darden (2006); Hempel, C. G. (1965). Aspects of scientific (...) explanation. In C. G. Hempel (Ed.), Aspects of scientific explanation (pp. 331–496). New York: Free Press; Kitcher (1989); Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25). My concern is with the minimal suggestion that an adequate philosophical theory of scientific explanation can limit its attention to the format or structure with which theories are represented. The representational subsumption view is a plausible hypothesis about the psychology of understanding. It is also a plausible claim about how scientists present their knowledge to the world. However, one cannot address the central questions for a philosophical theory of scientific explanation without turning one’s attention from the structure of representations to the basic commitments about the worldly structures that plausibly count as explanatory. A philosophical theory of scientific explanation should achieve two goals. The first is explanatory demarcation. It should show how explanation relates with other scientific achievements, such as control, description, measurement, prediction, and taxonomy. The second is explanatory normativity. It should say when putative explanations succeed and fail. One cannot achieve these goals without undertaking commitments about the kinds of ontic structures that plausibly count as explanatory. Representations convey explanatory information about a phenomenon when and only when they describe the ontic explanations for those phenomena. (shrink)

In this thesis I investigate causal inquiry in the social sciences, drawing on examples from various disciplines and in particular from conflict studies. In a backlash against the pervasiveness of statistical methods, in the last decade certain social scientists have focused on finding the causal mechanisms behind observed correlations. To provide evidence for such mechanisms, researchers increasingly rely on ‘process tracing’, a method which attempts to give evidence for causal relations by specifying the chain of events connecting a putative cause (...) and effect of interest. I will ask whether the causal claims process tracers make are defensible, and where they are not defensible I will ask how we can improve the method. Throughout these investigations, I show that the conclusions of process tracing are constrained both by the causal structure ofthe social world and by social scientists’ aims and values. My central argument is this: all instances of social phenomena have causally relevant differences, which implies that any research design that requires some comparison between cases is limited by how we systematize these phenomena. Moreover, such research cannot rely on stable regularities. Nevertheless, to forego causal conclusions altogether is not the right response to these limitations; by carefully outlining our epistemic assumptions we can make progress in causal inquiry. While I use philosophical theories of causation to comment on the feasibility of a social scientific method, I also do the reverse: by investigating a popular contemporary method in the social sciences, I show to what extent our philosophical theories of causation are workable in practice. Thus, this thesis is both a methodological and a philosophical work. Every chapter discusses both a fundamental philosophical position on the social sciences and a relevant case study from the social sciences. (shrink)

In this dissertation I develop a pluralist theory of actual causation. I argue that we need to distinguish between total, path-changing, and contributing actual causation. The pluralist theory accounts for a set of example cases that have raised problems for extant unified theories and it is supported by considerations about the various functions of causal concepts. The dissertation also analyses the context-sensitivity of actual causation. I show that principled accounts of causal reasoning in legal inquiry face limitations and I argue (...) that the context-sensitivity of actual causation is best represented by a distinction between default and deviant states in causal models. (shrink)

Elliott Sober has suggested his criterion of contrastive testability as an improvement over previous criteria of empirical significance like falsifiability. I argue that his criterion renders almost any theory empirically significant because its restrictions on auxiliary assumptions are to weak. Even when the criterion is modified to avoid this trivialization, it fails to meet other conditions of adequacy for a criterion of empirical significance that follow from Sober's position. I suggest to define empirical significance as empirical non-equivalence to a tautology, (...) because this definition does meet the conditions of adequacy. Specifically, it is equivalent to the standard Bayesian criterion of empirical significance whenever all probabilities are defined and contains falsifiability as a special case. This latter feature is important because those conditions of adequacy that apply to criteria of deductive empirical significance single out falsifiability. (shrink)

This dissertation consists of three parts. Part I is a defense of an artificial language methodology in philosophy and a historical and systematic defense of the logical empiricists' application of an artificial language methodology to scientific theories. These defenses provide a justification for the presumptions of a host of criteria of empirical significance, which I analyze, compare, and develop in part II. On the basis of this analysis, in part III I use a variety of criteria to evaluate the scientific (...) status of intelligent design, and further discuss confirmation, reduction, and concept formation. (shrink)

In this thesis, I give a metascientific account of causality in medicine. I begin with two historical cases of causal discovery. These are the discovery of the causation of Burkitt’s lymphoma by the Epstein-Barr virus, and of the various viral causes suggested for cervical cancer. These historical cases then support a philosophical discussion of causality in medicine. This begins with an introduction to the Russo- Williamson thesis (RWT), and discussion of a range of counter-arguments against it. Despite these, I argue (...) that the RWT is historically workable, given a small number of modifications. I then expand Russo and Williamson’s account. I first develop their suggestion that causal relationships in medicine require some kind of evidence of mechanism. I begin with a number of accounts of mechanisms and produce a range of consensus features of them. I then develop this consensus position by reference to the two historical case studies with an eye to their operational competence. In particular, I suggest that it is mechanistic models and their representations which we are concerned with in medicine, rather than the mechanism as it exists in the world. -/- I then employ these mechanistic models to give an account of the sorts of evidence used in formulating and evaluating causal claims. Again, I use the two human viral oncogenesis cases to give this account. I characterise and distinguish evidence of mechanism from evidence of difference-making, and relate this to mechanistic models. I then suggest the relationship between types of evidence presents us with a means of tackling the reference-class problem. This sets the scene for the final chapter. Here, I suggest the manner in which these two different classes of evidence become integrated is also reflected in the way that developing research programmes change as their associated causal claims develop. (shrink)

In this paper, I present a general theory of topological explanations, and illustrate its fruitfulness by showing how it accounts for explanatory asymmetry. My argument is developed in three steps. In the first step, I show what it is for some topological property A to explain some physical or dynamical property B. Based on that, I derive three key criteria of successful topological explanations: a criterion concerning the facticity of topological explanations, i.e. what makes it true of a particular system; (...) a criterion for describing counterfactual dependencies in two explanatory modes, i.e. the vertical and the horizontal; and, finally, a third perspectival one that tells us when to use the vertical and when to use the horizontal mode. In the second step, I show how this general theory of topological explanations accounts for explanatory asymmetry in both the vertical and horizontal explanatory modes. Finally, in the third step, I argue that this theory is universally applicable across biological sciences, which helps to unify essential concepts of biological networks. (shrink)

Scientific antirealists run the argument from underconsideration against scientific realism. I argue that the argument from underconsideration backfires on antirealists’ positive philosophical theories, such as the contextual theory of explanation (van Fraassen, 1980), the English model of rationality (van Fraassen, 1989), the evolutionary explanation of the success of science (Wray, 2008; 2012), and explanatory idealism (Khalifa, 2013). Antirealists strengthen the argument from underconsideration with the pessimistic induction against current scientific theories. In response, I construct a pessimistic induction against antirealists that (...) since antirealists generated problematic philosophical theories in the past, they must be generating problematic philosophical theories now. (shrink)

Scientific realists and antirealists believe that a successful scientific theory is true and merely empirically adequate, respectively. In contrast, epistemic reciprocalists believe that realists’ positive theories are true, and that antirealists’ positive theories are merely empirically adequate, treating their target agents as their target agents treat other epistemic agents. Antirealists cannot convince reciprocalists that their positive theories are true, no matter how confident they might be that they are true. In addition, reciprocalists criticize antirealists’ positive theories exactly in the way (...) that antirealists criticize their epistemic colleagues’ theories. Reciprocalism is a better epistemic policy than realism and antirealism in the epistemic battleground in which we strive to be epistemically safe vis-à-vis our epistemic colleagues’ theories and strive to convince our epistemic colleagues that our theories are true. (shrink)