Arnon Levy

The aim of this article is to paint a big picture of several approaches to explanation and to motivate one of them. The approaches differ in the kind of answer they provide to the question “what is an explanation?” and, derivatively, to questions about what explanations are grounded in and how to assess explanatory quality. The syntactic approach defines explanations as arguments with a particular form. It has well-known problems, dating back to discussions of the covering law model. More recently, semantic approaches have become mainstream, primarily in a causal guise. These approaches treat explanations as defined by their content, particularly causal content. A concise statement of the semantic approach says that to explain a phenomenon is to provide a description of its causal basis. But explanations do not behave like descriptions—as seen by the importance of idealization in explanation. Moreover, recent philosophy has discussed multiple cases of non-causal explanation. Arguably, the semantic approach also provides a scant basis for assessing explanatory power. These problems motivate an alternative, pragmatic approach. I outline a version of the pragmatic approach, which grounds explanation in understanding, and note some of its precedents and inspirations, both in William Bechtel’s work and beyond. I then suggest that the pragmatic approach can readily solve the issues facing the semantic approach. I close by remarking on the considerable work that remains to be done to turn the pragmatic approach into a well-articulated account of scientific explanation.

Much of the literature on values in science is framed around the idea of a shift in the status of the so-called value-free ideal (VFI) for science: having been widely accepted in the past it has come to be thoroughly rejected. In turn, with the rejection of the VFI nearly status quo, a widely asserted view is that there is now a new problem for philosophy of science to address, namely, to distinguish appropriate value-influence on science from inappropriate value-influence. While we agree that understanding the many intricate ways that values can influence science is an important task for philosophy, we argue that this framing—that there is a single phenomenon to be understood, or a single problem to be solved—is misleading, and liable to distort inquiry. This framing creates a chimera.

The idea that metaphors can serve an explanatory function, or that they can carry any theoretical weight, has raised the hackles of philosophers of science from Hempel onward. But there seem to be bona fide cases of explanatory metaphors in science. This chapter sketches an account of explanation, grounded in the connection between explanation and understanding. It discusses some potential arguments for this account and shows how it allows us to make sense of the idea that metaphors can explain. The chapter illustrates these ideas by looking at the case of informational metaphors in cell biology.

Approximations have come to occupy a central role within Bayesian cognitive science. Many cognitive scientists view current approximation-based models as showing that the mind approximates Bayesian inference. In this paper I interrogate this idea, asking what it means to claim that the mind approximates one computation by executing another. I look at three possible interpretations of such claims, finding problems with each. I argue that this poses challenges to one central rationale for the Bayesian approach, and to its top-down methodology.

Science has multiple goals: to describe the world, as it is now; to predict the future and to make inferences about the past. But science also aims to understand the world - to explain why it is the way it is. But what does it take to explain a phenomenon? What philosophical questions are at stake? In this thorough and clearly written introduction to scientific explanation, Arnon Levy explores the following problems and questions: the background to the topic of scientific explanation, particularly the questions of what an explanation is, what makes a good explanation and why seek them in the first place empiricism about explanation: Hempel's Deductive-Nomological model and criticisms of Hempel's theory unificationism about explanation: are good scientific explanations those fit into an overall 'explanatory store'? causation and explanation: Salmon's 'statistical relevance' and 'mark transmission' arguments causation and explanation: Lewis's description-based model, Woodward's interventionist view and Strevens's criterion of selection theory connections between explanation and understanding non-scientific explanation: metaphysics. Additional features, such as chapter summaries, suggestions for further reading and a glossary make this an excellent resource for students of philosophy of science, metaphysics and philosophy of psychology.

The argument from inductive risk (AIR) states that scientists should consider the consequences of hypotheses and methodological choices in the course of ongoing research. It has played a central role in the widespread retreat from the ideal of value-free science. The argument is motivated, to a significant extent, by the laudable concern to use science to better society. I argue that this concern, when taken seriously, tells against the idea that individual working scientists should consider social consequences. First, I show that the AIR assigns scientists a striking task: to frame social decisions and assess their full spectrum of consequences. Then, I argue that scientists should not be assigned such a task. For one thing, if both scientists and policy makers take social consequences into account, we are liable to end up with skewed and biased decisions. For another, scientists are not well-placed to carry out social decision making. I then discuss and diffuse some objections and potential misapprehensions. The paper concludes by noting contexts in which the inductive risk line has more force, and by highlighting the importance, in discussing values in science, of attending to units and levels of analysis.

Science and fiction seem to lie at opposite ends of the cognitive-epistemic spectrum. The former is typically seen as the study of hard, real-world facts in a rigorous manner. The latter is treated as an instrument of play and recreation, dealing in figments of the imagination. Initial appearances notwithstanding, several central features of scientific modeling in fact suggest a close connection with the imagination and recent philosophers have developed detailed accounts of models that treat them, in one way or another, as akin to fictions. This chapter will critically discuss the fictions approach as an epistemology of scientific modeling.

Thought experiments play a role in science and in some central parts of contemporary philosophy. They used to play a larger role in philosophy of science, but have been largely abandoned as part of the field’s “practice turn”. This chapter discusses possible roles for thought experimentation within a practice-oriented philosophy of science. Some of these roles are uncontroversial, such as exemplification and aiding discovery. A more controversial role is the reliance on thought experiments to justify philosophical claims. It is proposed that if we adopt an underlying empiricist view of concepts, then thought experiments can be seen as affording us contact with scientific practice, despite their seemingly a priori character. The advantages and drawbacks of thought experiments are discussed via comparison with case studies, on the one, and simulations on the other hand. The chapter closes with some remarks on how to combine thought experiments with other methods.

To a large extent, the evidential base of claims in the philosophy of science has switched from thought experiments to case studies. We argue that abandoning thought experiments was a wrong turn, since they can effectively complement case studies. We make our argument via an analogy with the relationship between experiments and observations within science. Just as experiments and ‘natural’ observations can together evidence claims in science, each mitigating the downsides of the other, so too can thought experiments and case studies be mutually supporting. After presenting the main argument, we look at potential concerns about thought experiments, suggesting that a judiciously applied mixed-methods approach can overcome them.

Recently, various philosophers have argued that we can obtain knowledge via the imagination. In particular, it has been suggested that we can come to know concrete, empirical matters of everyday significance by appropriately imagining relevant scenarios. Arguments for this thesis come in two main varieties: black box reliability arguments and constraints-based arguments. We suggest that both strategies are unsuccessful. Against black-box arguments, we point to evidence from empirical psychology, question a central case-study, and raise concerns about a (claimed) evolutionary rationale for the imagination's reliability. Against the constraints-based account, we argue that to the extent that it works, this does not give rise to knowledge that is distinctively from the imagination. We conclude by suggesting that the imagination's role in raising possibilities, traditionally seen as part of the context of discovery, can in fact play a role in justification, including as a bulwark against certain sorts of skepticism.

I offer a defense, albeit a qualified one, of machine analogies in biology, focusing on molecular contexts. The defense is rooted in my prior work (Levy in Philosopher’s Imprint 14(6), 2014), which construes the machine machine-likeness of a system as a matter of the extent to which it exhibits an internal division of labor. A concrete aim is to shore up the notion of molecular biological machines, paying special attention to processive molecular motors, such as Kinesin. But I will also try to show how the division of labor account gives us guidance more broadly, both about where and why machine analogies can be expected to prove helpful and about their limitations.

I offer a defense, albeit a qualified one, of machine analogies in biology, focusing on molecular contexts. The defense is rooted in prior work (Levy 2014), which construes the machine machine-likeness of a system as a matter of the extent to which it exhibits an internal division of labor. A concrete aim is to shore up the notion of molecular biological machines, and I’ll pay special attention to processive molecular motors, such as Kinesin. But I will also try to show how the division of labor account gives us guidance more broadly, both about where and why machine analogies can be expected to prove helpful and about their limitations.

Moral realism is often taken to have common sense and initial appearances on its side. Indeed, by some lights, common sense and initial appearances underlie all the central positive arguments for moral realism. We offer a kind of debunking argument, taking aim at realism’s common sense standing. Our argument differs from familiar debunking moves both in its empirical assumptions and in how it targets the realist position. We argue that if natural selection explains the objective phenomenology of moral deliberation and judgement, then this undermines arguments from that phenomenology. This results in a simpler, and in some ways more direct, challenge to realism. It is also less vulnerable to the main objections that have been leveled against such arguments. If accepted, our conclusion should make a real difference to the dialectic in this area. It means that neither realism nor its denial is the default, to-be-refuted, position.

According to [Bayesian] models” in cognitive neuroscience, says a recent textbook, “the human mind behaves like a capable data scientist”. Do they? That is to say, do such model show we are rational? I argue that Bayesian models of cognition, perhaps surprisingly, do not and indeed cannot, show that we are Bayesian-rational. The key reason is that such models appeal to approximations, a fact that carries significant implications. After outlining the argument, I critique two responses, seen in recent cognitive neuroscience. One says that the mind can be seen as approximately Bayes-rational, while the other reconceives norms of rationality.

The study of biological altruism is a cornerstone of modern evolutionary biology. Associated with foundational issues about natural selection, it is often supposed that explaining altruism is key to understanding social behavior more generally. Typically, biological altruism is defined in purely effects-based, behavioral terms – as an interaction in which one organism contributes fitness to another, at its own expense. Crucially, such a definition isn’t meant to rest on psychological or intentional assumptions. We show that, appearances and official definitions notwithstanding, the notion biological altruism carries a vestige of the psychological, intentional concept familiar to us from the human domain. In particular, definitions of altruism from Hamilton onwards presuppose an actor\recipient distinction – a distinction, so we argue, that has questionable biological grounding. We arrive at this conclusion step-by-step, first looking at several simple, “austere” definitions and their problems, and then critiquing the actor\recipient distinction directly. If successful, our arguments suggest that the category of biological altruism requires a significant rethink.

Experimentation is traditionally considered a privileged means of confirmation. However, why and how experiments form a better confirmatory source relative to other strategies is unclear, and recent discussions have identified experiments with various modeling strategies on the one hand, and with ‘natural’ experiments on the other hand. We argue that experiments aiming to test theories are best understood as controlled investigations of specimens. ‘Control’ involves repeated, fine-grained causal manipulation of focal properties. This capacity generates rich knowledge of the object investigated. ‘Specimenhood’ involves possessing relevant properties given the investigative target and the hypothesis in question. Specimens are thus representative members of a class of systems, to which a hypothesis refers. It is in virtue of both control and specimenhood that experiments provide powerful confirmatory evidence. This explains the distinctive power of experiments: although modelers exert extensive control, they do not exert this control over specimens; although natural experiments utilize specimens, control is diminished.

Evolutionary debunking arguments appeal to selective etiologies of human morality in an attempt to undermine moral realism. But is morality actually the product of evolution by natural selection? Although debunking arguments have attracted considerable attention in recent years, little of it has been devoted to whether the underlying evolutionary assumptions are credible. In this paper, we take a closer look at the evolutionary hypotheses put forward by two leading debunkers, namely Sharon Street and Richard Joyce. We raise a battery of considerations, both empirical and theoretical, that combine to cast doubt on the plausibility of both hypotheses. We also suggest that it is unlikely that there is in the vicinity a plausible alternative hypothesis suitable for the debunker’s cause.

Many have expected that understanding the evolution of norms should, in some way, bear on our first-order normative outlook: How norms evolve should shape which norms we accept. But recent philosophy has not done much to shore up this expectation. Most existing discussions of evolution and norms either jump headlong into the is/ought gap or else target meta-ethical issues, such as the objectivity of norms. My aim in this paper is to sketch a different way in which evolutionary considerations can feed into normative thinking—focusing on stability. I will discuss two forms of argument that utilize information about social stability drawn from evolutionary models, and employs it to assess claims in political philosophy. One such argument treats stability as feature of social states that may be taken into account alongside other features. The other uses stability as a constraint on the realization of social ideals, via a version of the ought-implies-can maxim. These forms of argument are not new; indeed they have a history going back at least to early modern philosophy. But their marriage with evolutionary information is relatively recent, has a significantly novel character, and has received little attention in recent moral and political philosophy.

Accounts of mechanistic explanation, especially as applied to biology and sometimes going under the heading of “new mechanism,” provided an attractive alternative to nomological accounts that preceded them. These accounts were motivated by selected examples, drawn primarily from cell and molecular biology and neuroscience. However, the range of examples that scientists take to be mechanistic explanations is far broader. We focus on examples that differ from those traditionally recruited by Mechanists. Our contention is that attention to additional examples will lead to a richer conception of mechanistic explanation, prompting a shift from what we refer to as Mechanism 1.0 to Mechanism 2.0.

Idealization and abstraction are central concepts in the philosophy of science and in science itself. My goal in this paper is suggest an account of these concepts, building on and refining an existing view due to Jones Idealization XII: correcting the model. Idealization and abstraction in the sciences, vol 86. Rodopi, Amsterdam, pp 173–217, 2005) and Godfrey-Smith Mapping the future of biology: evolving concepts and theories. Springer, Berlin, 2009). On this line of thought, abstraction—which I call, for reasons to be explained, abstractness—involves the omission of detail, whereas idealization consists in a deliberate mismatch between a description and the world. I will suggest that while the core idea underlying these authors’ view is correct, they make several assumptions and stipulations that are best avoided. For one thing, they tie abstractness too close to truth. For another, they do not allow sufficient room to the difference between idealization and error. Taking these points into account leads to a refined account of the distinction, in which abstractness is seen in terms of relative richness of detail, and idealization is seen as closely connected with the knowledge and intentions of idealizers. I lay out these accounts in turn, and then discuss the relationship between the two concepts, and several other upshots of the present way of construing the distinction.

Evolutionary debunking arguments (EDAs) have attracted extensive attention in meta-ethics, as they pose an important challenge to moral realism. Mogensen (2015) suggests that EDAs contain a fallacy, by confusing two distinct forms of biological explanation – ultimate and proximate. If correct, the point is of considerable importance: evolutionary genealogies of human morality are simply irrelevant for debunking. But we argue that the actual situation is subtler: while ultimate claims do not strictly entail proximate ones, there are important evidential connections between the two. Attending to these connections clears ground for a new and improved EDA. However, it also brings into view some possible problems with EDAs that have been largely neglected so far.

Many biological investigations are organized around a small group of species, often referred to as ‘model organisms’, such as the fruit fly Drosophila melanogaster. The terms ‘model’ and ‘modelling’ also occur in biology in association with mathematical and mechanistic theorizing, as in the Lotka–Volterra model of predator-prey dynamics. What is the relation between theoretical models and model organisms? Are these models in the same sense? We offer an account on which the two practices are shown to have different epistemic characters. Theoretical modelling is grounded in explicit and known analogies between model and target. By contrast, inferences from model organisms are empirical extrapolations. Often such extrapolation is based on shared ancestry, sometimes in conjunction with other empirical information. One implication is that such inferences are unique to biology, whereas theoretical models are common across many disciplines. We close by discussing the diversity of uses to which model organisms are put, suggesting how these relate to our overall account. 1 Introduction2 Volterra and Theoretical Modelling3 Drosophila as a Model Organism4 Generalizing from Work on Model Organisms5 Phylogenetic Inference and Model Organisms6 Further Roles of Model Organisms6.1 Preparative experimentation6.2 Model organisms as paradigms6.3 Model organisms as theoretical models6.4 Inspiration for engineers6.5 Anchoring a research community7 Conclusion

Design thinking in general, and optimality modeling in particular, have traditionally been associated with adaptationism—a research agenda that gives pride of place to natural selection in shaping biological characters. Our goal is to evaluate the role of design thinking in non-evolutionary analyses. Specifically, we focus on research into abstract design principles that underpin the functional organization of extant organisms. Drawing on case studies from engineering-inspired approaches in biology we show how optimality analysis, and other design-related methods, play a specific methodological role that is tangential to the study of adaptation. To account for the role of these reasoning strategies in contemporary biology, we therefore suggest a reevaluation of the connection between design thinking and adaptationism

The Hodgkin–Huxley (HH) model of the action potential is a theoretical pillar of modern neurobiology. In a number of recent publications, Carl Craver ([2006], [2007], [2008]) has argued that the model is explanatorily deficient because it does not reveal enough about underlying molecular mechanisms. I offer an alternative picture of the HH model, according to which it deliberately abstracts from molecular specifics. By doing so, the model explains whole-cell behaviour as the product of a mass of underlying low-level events. The issue goes beyond cellular neurobiology, for the strategy of abstraction exhibited in the HH case is found in a range of biological contexts. I discuss why it has been largely neglected by advocates of the mechanist approach to explanation. 1 Introduction2 A Primer on the HH Model2.1 The basic qualitative picture2.2 The quantitative model3 Interlude: What Did Hodgkin and Huxley Think?4 Craver’s View4.1 Mechanistic explanation4.2 Sketches4.3 Craver's view: The HH model as a mechanism sketch5 An Alternative View of the HH Model5.1 Another look at the equations5.2 The discrete-gating picture5.3 The road paved by Hodgkin and Huxley5.4 Summary and comparison to Craver6 Conclusion: The HH Model and Mechanistic Explanation6.1 Sketches and abstractions6.2 Why has aggregative abstraction been overlooked?

Some philosophers of science – the present author included – appeal to fiction as an interpretation of the practice of modeling. This raises the specter of an incompatibility with realism, since fiction-making is essentially non-truth-regulated. I argue that the prima facie conflict can be resolved in two ways, each involving a distinct notion of fiction and a corresponding formulation of realism. The main goal of the paper is to describe these two packages. Toward the end I comment on how to choose among them.