Scientific origins are information sources that transmit encoded information signals to receivers. Originary sciences identify information preserving receivers and decode the signals to infer their origins. Paradigmatic cases of scientific origination such as the Big Bang, the origins of species, horizontal gene transfer, the origin of the Polynesian potato, and ideational origins in the history of ideas are analyzed to discover what is common to them ontologically and epistemically. Some causes are not origins. Origination supervenes on causation, but has different (...) properties. The unique properties of origins that causes do not share shield theories of origination from the kind of counterexamples and counterintuitive results that challenge comparable theories of causation. The epistemology of origination may serve as a basis for founding a novel epistemic and methodological division of the sciences into originary historical sciences and causal theoretical sciences. (shrink)

I highlight a metaphysical concern that stands in the way of more widespread adoption of causal modeling techniques such as causal Bayes nets. Researchers in some fields may resist adoption due to concerns that they don't 'really' understand what they are saying about a system when they apply such techniques. Students in these fields are repeated exhorted to be cautious about application of statistical techniques to their data without a clear understanding of the conditions required for those techniques to yield (...) genuine insight into the data. They are acutely aware that anyone can chuck some data into a software package and get what looks like an answer, even though these tests may not be well-defined for the data on which they can apparently be run. This is thus a healthy skepticism for uptake of causal modeling methods, which points directly to the need for a metaphysical understanding of causation in order to successfully use the modeling methods. Without a clear understanding of what the methods are committing to, including what it means to say that there exists a causal relationship, researchers have limited ability to identify potentially bad output, or to independently verify results. (shrink)

This paper shows how a niche account of the metaphysics of laws of nature and physical properties—the Powers-BSA—can underpin both a sense in which the laws are metaphysically necessary and a sense in which it is true that the laws could have been different. The ability to reconcile entrenched disagreement should count in favour of a philosophical theory, so this paper constitutes a novel argument for the Powers-BSA by showing how it can reconcile disagreement about the laws’ modal status. This (...) paper also constitutes a defence of modal necessitarianism, the interesting and controversial view according to which all worlds are nomologically identical, because it shows how the modal necessitarian can appease the orthodox contingentist about laws. (shrink)

It is generally acknowledged by proponents of ‘new mechanism’ that mechanistic explanation involves adopting a perspective, but there is less agreement on how we should understand this perspective-taking or what its implications are for practising science. This paper examines the perspectival nature of mechanistic explanation through the lens of the ‘mechanistic stance’, which falls somewhere between Dennett’s more familiar physical and design stance. We argue this approach implies three distinct and significant ways in which mechanistic explanation can be interpreted as (...) perspectival: ‘phenomenon perspectivism’, ‘pattern perspectivism’ and ‘hierarchy perspectivism’. We evaluate the strength of the perspective-dependency implied by each of these, and along the way, discuss their significance for wider debates within the new mechanism literature, such as the nature of function attribution and an ontic vs epistemic understanding of explanation. (shrink)

Our approach aims at accounting for causal claims in terms of how the physical states of the underlying dynamical system evolve with time. Causal claims assert connections between two sets of physicals states—their truth depends on whether the two sets in question are genuinely connected by time evolution such that physical states from one set evolve with time into the states of the other set. We demonstrate the virtues of our approach by showing how it is able to account for (...) typical causes, causally relevant factors, being ‘the’ cause, and cases of overdetermination and causation by absences. (shrink)

In this dissertation, I develop and defend a model of causation that allows for dualist mental causation in worlds where the physical domain is physically complete. In Part I, I present the dualist ontology that will be assumed throughout the thesis and identify two challenges for models of mental causation within such an ontology: the exclusion worry and the common cause worry. I also argue that a proper response to these challenges requires a thoroughly lightweight account of causation, i.e. an (...) account that allows for causes to be metaphysically distinct from the phenomena that produce or physically necessitate their effects. In Part II, I critically evaluate contemporary responses to these challenges from the philosophical literature. In particular, I discuss List and Stoljar’s criticism of exclusion worries, Kroedel’s alternative dualist ontology, concerns about the notion of causal sufficiency, and Lowe’s models of dualist mental causation. I argue that none of these proposals provide independent motivation for a thoroughly lightweight account of causation and therefore leave room for improvement. In the first four chapters of Part III, I develop a thoroughly lightweight model of causation, which builds on interventionist approaches to causation. First, I explain how so-called ‘holding fixed’-requirements in standard interventionist accounts stand in the way of dualist mental causation. I then argue that interventionist accounts should impose a robustness condition on causal correlations and that, with this condition in place, the ‘holding fixed’-requirements can be weakened such that they do allow for dualist mental causation. I dub the interventionist model with such weakened ‘holding fixed’-requirements ‘insensitive interventionism’, argue that it can counter the exclusion worry as well as the common cause worry, and explain under which circumstances it would predict there to be dualist mental causation. Importantly, these circumstances might, for all we know, hold in the actual world. In the final three chapters of Part III, I defend insensitive interventionism against some objections. I consider the objection that causation must be productive, the objection that causes must physically necessitate their effects, and the objection that insensitive interventionism is too permissive. I respond by drawing from the literature on causation by absences and on the relation between causation and fundamental physics. Overall, insensitive interventionism performs as well as standard interventionist accounts. I conclude that insensitive interventionism is a credible model of causation. (shrink)

While scientific inquiry crucially relies on the extraction of patterns from data, we still have a far from perfect understanding of the metaphysics of patterns—and, in particular, of what makes a pattern real. In this paper we derive a criterion of real-patternhood from the notion of conditional Kolmogorov complexity. The resulting account belongs to the philosophical tradition, initiated by Dennett :27–51, 1991), that links real-patternhood to data compressibility, but is simpler and formally more perspicuous than other proposals previously defended in (...) the literature. It also successfully enforces a non-redundancy principle, suggested by Ladyman and Ross, that aims to exclude from real-patternhood those patterns that can be ignored without loss of information about the target dataset, and which their own account fails to enforce. (shrink)

This paper sketches a causal account of scientific explanation designed to sustain the judgment that high-level, detail-sparse explanations—particularly those offered in biology—can be at least as explanatorily valuable as lower-level counterparts. The motivating idea is that complete explanations maximize causal economy: they cite those aspects of an event’s causal run-up that offer the biggest-bang-for-your-buck, by costing less (in virtue of being abstract) and delivering more (in virtue making the event stable or robust).

This article considers the popular thesis that a more proportional relationship between a cause and its effect yields a more abstract causal explanation of that effect, which in turn produces a deeper explanation. This thesis is taken to have important implications for choosing the optimal granularity of explanation for a given explanandum. In this article, I argue that this thesis is not generally true of probabilistic causal relationships. In light of this finding, I propose a pragmatic, interest-relative measure of explanatory (...) depth. This measure uses a decision-theoretic model of information pricing to determine the optimal granularity of explanation for a given explanandum, agent, and decision problem. (shrink)

Dennett [1991] proposes a novel ontological account of the propositional attitudes—real patterns. Despite its name, the degree to which this account is committed to realism remains unclear. In this paper, I propose an alternative criterion of pattern instantiation, one that assesses the difficultly of faithfully interpreting a physical system as instantiating a particular pattern. Drawing on formal measures of simplicity and similarity, I argue that, for well-instantiated patterns, our interpretation will be computable by using a short program. This approach preserves (...) the flexibility of Dennett’s original, while offering substantially stronger realist commitments. (shrink)

This dissertation aims to determine the optimal level of granularity for the variables used in probabilistic causal models. These causal models are useful for generating explanations in a number of scientific contexts. In Chapter 1, I argue that there is rarely a unique level of granularity at which a given phenomenon can be causally explained, thereby rejecting various causal exclusion arguments. In Chapter 2, I consider several recent proposals for measuring the explanatory power of causal explanations, and show that these (...) measures fail to track the comparative depth of explanations given at different levels of granularity. In Chapter 3, I offer a pragmatic account of how to partition the measure space of a causal variable so as to optimally explain its effect. My account uses the decision-theoretic notion of value of information, and indexes the relative depth of an explanation to a particular agent faced with a particular decision problem. Chapter 4 applies this same decisiontheoretic framework to answer the epistemic question of how to discover constitutive relationships in nature. In Chapter 5, I describe the formal details of the relationship between random variables that are meant to be coarse-grained and fine-grained representations of the same type of phenomenon. I use this formal framework to rebut a popular argument for the view that special science probabilities can be objective chances. Chapter 6 discusses challenges related to the causal interpretation of Bayes nets that use imprecise rather than precise probabilities. (shrink)

The predominance of machine learning based techniques in cognitive neuroscience raises a host of philosophical and methodological concerns. Given the messiness of neural activity, modellers must make choices about how to structure their raw data to make inferences about encoded representations. This leads to a set of standard methodological assumptions about when abstraction is appropriate in neuroscientific practice. Yet, when made uncritically these choices threaten to bias conclusions about phenomena drawn from data. Contact between the practices of multivariate pattern analysis (...) and philosophy of science can help to illuminate the conditions under which we can use artificial neural networks to better understand neural mechanisms. This paper considers a specific technique for MVPA called representational similarity analysis. I develop a theoretically-informed account of RSA that draws on early connectionist research and work on idealization in the philosophy of science. By bringing a philosophical account of cognitive modelling in conversation with RSA, this paper clarifies the practices of neuroscientists and provides a generalizable framework for using artificial neural networks to study neural mechanisms in the brain. (shrink)

This manuscript focuses on a problem for Millian Russellianism raised by Fine : “[Assuming] that we are in possession of the information that a Fs and the information that a Gs, it appears that we are sometimes justified in putting this information ‘together’ and inferring that a both Fs and Gs. But how?” It will be my goal to determine a Millian-Russellian solution to this problem. I will first examine Nathan Salmon’s Millian-Russellian solution, which appeals to a non-semantic and subjective (...) notion of coordination defined in terms of guises. I will object that in order to convincingly solve a specific version of Fine’s problem, identity conditions for guises must be provided. On the other hand, the most plausible way to individuate guises is by means of the equivalence classes of coordination itself. But, if so, the guise-based strategy to solve Fine’s problem risks being circular; in addition, there are serious doubts that coordination is transitive. An alternative Millian-Russellian solution to Fine’s problem will then be explored, which gives up guises and employs, instead, a non-semantic and subjective relation of coordination not defined in terms of guises, along with occurrences of Russellian propositions of a special sort, for which identity conditions will be provided and via which token attitude states intuitively more fine-grained than guises will be individuated. (shrink)

Objects appear to causally interact with one another in virtual worlds, such as video games, virtual reality, and training simulations. Is this causation real or is it illusory? In this paper I argue that virtual causation is as real as physical causation. I achieve this in two steps: firstly, I show how virtual causation has all the important hallmarks of relations that are causal, as opposed to merely accidental, and secondly, I show how virtual causation is genuine according to one (...) influential metaphysical theory of causation: the mechanistic approach. (shrink)

There is a vast literature within philosophy of mind that focuses on artificial intelligence, but hardly mentions methodological questions. There is also a growing body of work in philosophy of science about modeling methodology that hardly mentions examples from cognitive science. Here these discussions are connected. Insights developed in the philosophy of science literature about the importance of idealization provide a way of understanding the neural implausibility of connectionist networks. Insights from neurocognitive science illuminate how relevant similarities between models and (...) targets are picked out, how modeling inferences are justified, and the metaphysical status of models. (shrink)

The past few years have seen several novel information-theoretic measures of causal emergence developed within the scientific community. In this paper I will introduce one such measure, called ‘effective information’, and describe how it is used to argue for causal emergence. In brief, the idea is that certain kinds of complex system are structured such that an intervention characterised at the macro-level will be more informative than one characterised at the micro-level, and that this constitutes a form of causal emergence. (...) Having introduced this proposal, I will then assess the extent to which it is genuinely ‘causal’ and/or ‘emergent’, and argue that it supports only an epistemic form of causal emergence that is not as exciting as it first seems. (shrink)