Holly Andersen

Humean accounts of laws are often contrasted with governing accounts, and recent developments have added pragmatic versions of Humeanism. This paper offers Mitchell's pragmatist, perspectival account of laws as a third option. The differences between these accounts come down to the role of modality. Mitchell's bottom-up account allows for subtle gradations of modal content to be conveyed by laws. The perspectival character of laws is not an accident or something to be eventually eliminated - it is part of how this modal content is conveyed. I conclude with a discussion of the metaphysical commitments in Humeanism as requiring a perspectiveless view of the manifold from outside, and how Mitchell's situated account is better able to account for the substantive notion of possibility involved in scientific laws.

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.

Given the technological constraints of long-duration space travel and planetary settlement, off-Earth humans will likely need to employ food systems very different from their terrestrial counterparts, and newly emerging food technologies are being developed that will shape novel food systems in these off-Earth contexts. Projected off-Earth food systems may therefore potentially “alienate” their users in new ways compared to Earth-based food systems. They will be susceptible to alienation in ways that are similar to such potential on Earth, where there are points of overlap between off-Earth food systems and any of the multitudes of ways in which food systems on Earth are structured. They will also be susceptible to new forms of alienation, as we encounter scenarios that are genuinely structurally novel to humanity. These are especially important to consider since there are comparatively fewer analyses of these food systems where they differ from existing ones. We propose five non-exhaustive sources of value beyond nutrition our individual relationships with a food may possess: gustatory, social, cultural, epistemic, and authorial value. Using these, we offer examples of ways in which an off-Earth food system may exacerbate or alleviate alienation for humans in long-term off-Earth food systems.

The reduction of grounding to causation, or each to a more general relation of which they are species, has sometimes been justified by the impressive inferential capacity of structural equation modelling, causal Bayes nets, and interventionist causal modelling. Many criticisms of this assimilation focus on how causation is inadequate for grounding. Here, I examine the other direction: how treating grounding in the image of causation makes the resulting view worse for causation. The distinctive features of causal modelling that make this connection appealing are distorted beyond use by forcing them to fit onto grounding. The very inferential strength that makes causation attractive is only possible because of a narrow construal of what counts as a causal relation; as soon as that broadens, the inferential capacity markedly diminishes. Making causation suitable for application to grounding spoils what was appealing about causation for this task in the first place. However, grounding need not appeal to causation: causal modelling does not have exclusive claim to structural equation modeling or other formal techniques of modelling structure. I offer a case in favour of a different kind of metaphysical frugality, which tend towards narrow, more restrictive construals of relations like causation or grounding, because then each relation behaves more homogenously. This more homogenous behavior delivers stronger inferential power per relation even though there may be more relations to which one is committed.

People's doings as agents in the world are irreducibly temporally extended, involving both time itself as well as various representations of temporality. There are three distinct elements this chapter disentangles in order to draw out the connections between them: temporal experience, agency, and representation. It outlines some of the key issues that arise when agency and temporality are considered jointly. The chapter traces out some intriguing paths for future work from the tangle of issues involved in the representation of time in agency. The issue of time and agency in psychology and the cognitive sciences are also covered. The chapter explores the implication of the dissociation between agency and the sense of agency. It explores options for developing a parallel project to that of explaining the Edmund Husserl's tripartite structure of time consciousness, a project capable of grounding the intentional content of temporal representations involved in agency in neurophysiological processes.

Even in areas of philosophy of science that don’t involve formal treatments of truth, one’s background view of truth still centrally shapes views on other issues. I offer an informal way to think about truth as trueing, like trueing a bicycle wheel. This holist approach to truth provides a way to discuss knowledge products like models in terms of how well-trued they are to their target. Trueing emphasizes: the process by which models are brought into true; how the idealizations in models are not false but rather like spokes in appropriate tension to achieve a better-trued fit to target; and that this process is not accomplished once and done forever, but instead requires upkeep and ongoing fine-tuning. I conclude by emphasizing the social importance of being a pragmatist about truth in order to accurately answer questions about science such as, “but do we really know that…”

Shadworth Hodgson offers an account of how philosophy relates to science - both physical and psychological - in which three different conceptions of time can be identified. He distinguishes the methods of philosophy, involving analysis of the contents of immediate consciousness, and of science, which presumes the existence of the world of common sense. Hodgson holds that philosophical analysis of immediate consciousness, or the analysis of a present moment in the experience, provides the ultimate justification for knowledge in science. Time as an object of study in science must be distinguished from the temporal structure of immediate consciousness. Time as a target of scientific study is thus differentiable into time in physical science, and time in psychology, where the temporal characteristics of consciousness can be studied, but only from a perspective external to that consciousness. Each of those scientific conceptions of time still presupposes and are evidentially dependent on the analysis of immediate consciousness, itself already temporal. The result is that time as a fundamental unit of experience could not, even in principle, conflict with time as studied in science, because it is presupposed by and the evidential base for claims about time in science.

This paper brings together Thompson's naive action explanation with interventionist modeling of causal structure to show how they work together to produce causal models that go beyond current modeling capabilities, when applied to specifically selected systems. By deploying well-justified assumptions about rationalization, we can strengthen existing causal modeling techniques' inferential power in cases where we take ourselves to be modeling causal systems that also involve actions. The internal connection between means and end exhibited in naive action explanation has a modal strength like that of distinctively mathematical explanation, rather than that of causal explanation. Because it is stronger than causation, it can be treated as if it were merely causal in a causal model without thereby overextending the justification it can provide for inferences. This chapter introduces and demonstrate the usage of the Rationalization condition in causal modeling, where it is apt for the system(s) being modeled, and to provide the basics for incorporating R variables into systems of variables and R arrows into DAGs. Use of the Rationalization condition supplements causal analysis with action analysis where it is apt.

A finer-grained delineation of a given explanandum reveals a nexus of closely related causal and non-causal explanations, complementing one another in ways that yield further explanatory traction on the phenomenon in question. By taking a narrower construal of what counts as a causal explanation, a new class of distinctively mathematical explanations pops into focus; Lange’s characterization of distinctively mathematical explanations can be extended to cover these. This new class of distinctively mathematical explanations is illustrated with the Lotka–Volterra equations. There are at least two distinct ways those equations might hold of a system, one of which yields straightforwardly causal explanations, and another that yields explanations that are distinctively mathematical in terms of nomological strength. In the first case, one first picks out a system or class of systems, and finds that the equations hold in a causal–explanatory way. In the second case, one starts with the equations and explanations that must apply to any system of which the equations hold, and only then turns to the world to see of what, if any, systems it does in fact hold. Using this new way in which a model might hold of a system, I highlight four specific avenues by which causal and non-causal explanations can complement one another. _1_. Introduction _2._ Delineating the Boundaries of Causal Explanation _2.1._ Why construe causal explanation narrowly? The land of explanation versus grain-focusing _2.2._ Reasons to narrow the scope of causal explanation _3._ Broadening the Scope of Mathematical Explanation _4._ Lotka–Volterra: Same Model, Different Explanation Types _4.1._ General biocide in the Lotka–Volterra model _4.2._ Two ways a model can hold, yielding causal versus mathematical explanations _5._ Four Complementary Relationships between Mathematical and Causal Explanation _5.1._ Slight reformulations of explananda _5.2._ Causal distortion of idealized mathematical models _5.3._ Partial explanations requiring supplementation _5.4._ Explanatory dimensionality _6._ Conclusion

Meta-laws, including conservation laws, are laws about the form of more specific, phenomenological, laws. Lange distinguishes between meta-laws as coincidences, where the meta-law happens to hold because the more specific laws hold, and meta-laws as constraints to which subsumed laws must conform. He defends this distinction as a genuine metaphysical possibility, such that metaphysics alone ought not to rule one way or another, leaving it an open question for physics. Lange’s distinction marks a genuine difference in how a given meta-law can be used in explanations. Yet, I argue, it is not simply an empirical matter as to whether a given conservation law is a constraint or a coincidence. There is no set matter of fact about the world that determines this, and physics alone will not be able to return a determinate verdict on a law-by-law basis, even while there is a genuine difference between any given law as constraint and as coincidence. Rather, the difference marks different ways of treating the same law in a theoretical setting: by shifting the explanatory context, treating the same law as part of a different mathematical structure, it can be a genuine constraint and a genuine coincidence. The difference between constraint and coincidence relates to the way in which we use a law in specific theoretical and explanatory settings. Because the same law can appear in multiple contexts, it can be used in these genuinely different ways, without itself ‘‘really’’ being either one or the other as some atomistic empirical fact. Conservation laws as constraints and conservation laws as coincidences are both genuine theoretical roles that the same law can play. I conclude by considering how this pragmatist construal of constraints versus coincidences reveals how two parts of Lange’s work in this section of the book are unexpectedly independent of one another.

This paper articulates an account of causation as a collection of information-theoretic relationships between patterns instantiated in the causal nexus. I draw on Dennett’s account of real patterns to characterize potential causal relata as patterns with specific identification criteria and noise tolerance levels, and actual causal relata as those patterns instantiated at some spatiotemporal location in the rich causal nexus as originally developed by Salmon. I develop a representation framework using phase space to precisely characterize causal relata, including their degree of counterfactual robustness, causal profiles, causal connectivity, and privileged grain size. By doing so, I show how the philosophical notion of causation can be rendered in a format that is amenable for direct application of mathematical techniques from information theory such that the resulting informational measures are causal informational measures. This account provides a metaphysics of causation that supports interventionist semantics and causal modeling and discovery techniques.

I criticize the tendency to address the causal role of awareness in agency in terms of the awareness of agency, and argue that this distorts the causal import of experimental results in significant ways. I illustrate, using the work of Shaun Gallagher, how the tendency to focus on the awareness of agency obscures the role of extrospective awareness by considering it only in terms of what it contributes to the awareness of agency. Focus on awareness of agency separates awareness from agency itself, and then turns it inwards to introspect distinct agentive processes. If we then assume that the causal influence of awareness is directed at the same object as awareness itself, then the only avenue for conscious causal involvement in action is to somehow interfere with the separate, even neuronal, processes leading to action. I label this the Micromanagement Model of conscious agency, because it forces awareness to micromanage other, nonconscious, processes in order to be causally efficacious. Implicit adherence to the Micromanagement Model prejudices us towards the mistaken conclusion that awareness has limited to no causal role in action.

This paper outlines some key issues that arise when agency and temporality are considered jointly, from the perspective of psychology, cognitive neuroscience, phenomenology, and action theory. I address the difference between time simpliciter and time as represented as it figures in phenomena like intentional binding, goal-oriented action plans, emulation systems, and ‘temporal agency’. An examination of Husserl’s account of time consciousness highlights difficulties in generalizing his account to include a substantive notion of agency, a weakness inherited by explanatory projects like neurophenomenology. I conclude by sketching a project analogous to the projects in neurophenomenology, based on Thompson’s naïve action theory.

I present three reasons why philosophers of science should be more concerned about violations of causal faithfulness (CF). In complex evolved systems, mechanisms for maintaining various equilibrium states are highly likely to violate CF. Even when such systems do not precisely violate CF, they may nevertheless generate precisely the same problems for inferring causal structure from probabilistic relationships in data as do genuine CF-violations. Thus, potential CF-violations are particularly germane to experimental science when we rely on probabilistic information to uncover the DAG, rather than already knowing the DAG from which we could predict the right experiments to ‘catch out’ the hidden causal relationships.

The problem of mental causation in contemporary philosophy of mind concerns the possibility of holding two different views that are in apparent tension. The first is physicalism, the view that there is nothing more to the world than the physical. The second is that the mental has genuine causal efficacy in a way that does not reduce to pure physical particle-bumping. This article provides a historical background to this question, with focus on Davidson’s anomalous monism and Kim’s causal exclusion problem. Responses to causal exclusion are categorized in terms of six different argumentative strategies. In conclusion, caution is advised regarding the inclination to reduce the mental to the physical and sketch a positive direction for substantively characterizing mental causation by recourse to well-confirmed accounts of causation coupled with empirical research.

In this field guide, I distinguish five separate senses with which the term ‘mechanism’ is used in contemporary philosophy of science. Many of these senses have overlapping areas of application but involve distinct philosophical claims and characterize the target mechanisms in relevantly different ways. This field guide will clarify the key features of each sense and introduce some main debates, distinguishing those that transpire within a given sense from those that are best understood as concerning distinct senses. The ‘new mechanisms’ sense is at the center of most of these contemporary debates and will be treated at greater length; subsequent senses of mechanism will be primarily distinguished from this one. In part I of this paper, I distinguish two senses of the term ‘mechanism’, both of which are explicitly hierarchical and nested in character, such that any given mechanism is comprised of smaller sub-mechanisms, in turn comprised of yet smaller sub-sub-mechanisms and so on. While both of the senses discussed here are anti-reductive, they differ in their focus on scientific practice versus metaphysics, in the degree of regularity they attribute to mechanisms, and in terms of their relationships to the discussions of mechanisms in the history of philosophy and science

This chapter examines the relationship between laws and mechanisms as approaches to characterising generalizations and explanations in science. I give an overview of recent historical discussions where laws failed to satisfy stringent logical criteria, opening the way for mechanisms to be investigated as a way to explain regularities in nature. This followed by a critical discussion of contemporary debates about the role of laws versus mechanisms in describing versus explaining regularities. I conclude by offering new arguments for two roles for laws that mechanisms cannot subsume, one epistemically optimistic and one pessimistic, both broadly Humean. Do note that this piece is not primarily Hume exegesis; it is more of a riff in the key of Hume.

Leuridan (2010) argued that mechanisms cannot provide a genuine alternative to laws of nature as a model of explanation in the sciences, and advocates Mitchell’s (1997) pragmatic account of laws. I first demonstrate that Leuridan gets the order of priority wrong between mechanisms, regularity, and laws, and then make some clarifying remarks about how laws and mechanisms relate to regularities. Mechanisms are not an explanatory alternative to regularities; they are an alternative to laws. The existence of stable regularities in nature is necessary for either model of explanation: regularities are what laws describe and what mechanisms explain.

Daniel Wegner argues that our feelings of conscious will are illusory: these feelings are not causally involved in the production of action, which is rather governed by unconscious neural processes. I argue that Wegner's interpretation of neuroscientific results rests on two fallacious causal assumptions, neither of which are supported by the evidence. Each assumption involves a Cartesian disembodiment of conscious will, and it is this disembodiment that results in the appearance of causal inefficacy, rather than any interesting features of conscious will. Wegner's fallacies illustrate two take-away points to heed if making claims about the causal structure of agency

How regular do mechanisms need to be, in order to count as mechanisms? This paper addresses two arguments for dropping the requirement of regularity from the definition of a mechanism, one motivated by examples from the sciences and the other motivated by metaphysical considerations regarding causation. I defend a broadened regularity requirement on mechanisms that takes the form of a taxonomy of kinds of regularity that mechanisms may exhibit. This taxonomy allows precise explication of the degree and location of regular operation within a mechanism, and highlights the role that various kinds of regularity play in scientific explanation. I defend this regularity requirement in terms of regularity’s role in individuating mechanisms against a background of other causal processes, and by prioritizing mechanisms’ ability to serve as a model of scientific explanation, rather than as a metaphysical account of causation. It is because mechanisms are regular, in the expanded sense described here, that they are capable of supporting the kinds of generalizations that figure prominently in scientific explanations.