Representationalism—the view that scientific modeling is best understood in representational terms—is the received view in contemporary philosophy of science. Contributions to this literature have focused on a number of puzzles concerning the nature of representation and the epistemic role of misrepresentation, without considering whether these puzzles are the product of an inadequate analytical framework. The goal of this paper is to suggest that this possibility should be taken seriously. The argument has two parts, employing the “can’t have” and “don’t need” (...) tactics drawn from philosophy of mind. On the one hand, I propose that representationalism doesn’t work: different ways to flesh out representationalism create a tension between its ontological and epistemological components and thereby undermine the view. On the other hand, I propose that representationalism is not needed in the first place—a position I articulate based on a pragmatic stance on the success of scientific research and on the feasibility of alternative philosophical frameworks. I conclude that representationalism is untenable and unnecessary, a philosophical dead end. A new way of thinking is called for if we are to make progress in our understanding of scientific modeling. (shrink)

Is there progress in philosophy? If so, how much? Philosophers have recently argued for a wide range of answers to these questions, from the view that there is no progress whatsoever to the view that philosophy has provided answers to all the big philosophical questions. However, these views are difficult to compare and evaluate, because they rest on very different assumptions about the conditions under which philosophy would make progress. This paper looks to the comparatively mature debate about scientific progress (...) for inspiration on how to formulate four distinct accounts of philosophical progress, in terms of truthlikeness, problem-solving, knowledge, and understanding. Equally importantly, the paper outlines a common framework for how to understand and evaluate these accounts. We distill a series of lessons from this exercise, to help pave the way for a more fruitful discussion about philosophical progress in the future. (shrink)

This paper presents and argues for an account of objectual understanding that aims to do justice to the full range of cases of scientific understanding, including cases in which one does not have an explanation of the understood phenomenon. According to the proposed account, one understands a phenomenon just in case one grasps a sufficiently accurate and comprehensive model of the ways in which it or its features are situated within a network of dependence relations; one’s degree of understanding is (...) proportional to the comprehensiveness and accuracy of such a model. I compare this account with accounts of scientific understanding that explicate understanding in terms of having an explanation of the understood phenomenon. I discuss three distinct types of cases in which scientific understanding does not amount to possessing an explanation of any kind, and argue that the proposed model-based account can accommodate these cases while still retaining a strong link between understanding and explanation. (shrink)

This contribution sheds light on the role of infinite idealization in structural analysis, by exploring how infinite elements and finite element methods are combined in civil engineering models. This combination, I claim, should be read in terms of a ‘complementarity function’ through which the representational ideal of completeness is reached in engineering model-building. Taking a cue from Weisberg’s definition of multiple-model idealization, I highlight how infinite idealizations are primarily meant to contribute to the prediction of structural behavior in Multiphysics approaches.

Several prominent debates in biology, such as those surrounding adaptationism, group selection, and punctuated equilibrium, have focused on disagreements about the relative importance of a cause in producing a phenomenon of interest. Some philosophers, such as John Beatty have expressed scepticism about the scientific value of engaging in these controversies, and Karen Kovaka has suggested that their value might be limited. In this paper, I challenge that scepticism by giving a novel analysis of relative significance controversies, showing that there are (...) three forms they can take. I argue that these controversies can have significant epistemic upshots, in that they help scientists form predictions about new instances of the phenomenon of interest. Finally, using two historical examples, I show how engaging in these controversies can improve our understanding of causal relationships. (shrink)

The advancement of computing technology makes it possible to build extremely accurate digital reconstructions of brain circuits. Are such unprecedented levels of biological accuracy essential for brain simulations to play the roles they are expected to play in neuroscientific research? The main goal of this paper is to clarify this question by distinguishing between various roles played by large-scale simulations in contemporary neuroscience, and by reflecting about what makes a simulation biologically accurate. It is argued that large-scale simulations may play (...) model-oriented and prediction-oriented roles in brain research, and that the concept of biological accuracy can be interpreted as related to the plausibility of the theoretical model implemented in the simulation system, to the accuracy of the computer implementation, and to the level of details of the implemented model. Building on these observations and distinctions, it is argued that biological accuracy is not essential for a computer simulation to play the epistemic roles it is expected to play in brain research. (shrink)

A skeptical worry known as ‘the indeterminacy of computation’ animates much recent philosophical reflection on the computational identity of physical systems. On the one hand, computational explanation seems to require that physical computing systems fall under a single, unique computational description at a time. On the other, if a physical system falls under any computational description, it seems to fall under many simultaneously. Absent some principled reason to take just one of these descriptions in particular as relevant for computational explanation, (...) widespread failure of computational explanation would appear to follow. This paper advances a new solution to the indeterminacy of computation. Very roughly, I argue that the computational identity of a physical system is determinate relative to a contextually specified way of regarding that system computationally—known as a labelling scheme. When a system simultaneously implements multiple computations, it does so relative to different labelling schemes. But relative to a fixed labelling scheme, a physical system has a unique computational identity. I argue that this relativistic conception of computational identity vindicates computational explanation in the face of simultaneous implementation. (shrink)

Paleobiologists often provide simple narratives to explain complex, contingent episodes. These narratives are sometimes ‘one-shot hypotheses’ which are treated as being mutually exclusive with other possible explanations of the target episode, and are thus extended to accommodate as much about the episode as possible. I argue that a provisional preference for such hypotheses provides two kinds of productive scaffolding. First, they generate ‘hypothetical difference-makers’: one-shot hypotheses highlight and isolate empirically tractable dependencies between variables. Second, investigations of hypothetical difference-makers provision explanatory (...) resources, the ‘raw materials’ for constructing more complex—and likely more adequate—explanations. Provisional preferences for simple, one-shot hypotheses in historical science, then, is defeasibly justified on indirect—strategic—grounds. My argument is made in reference to recent developments regarding the K–Pg extinction. (shrink)

The past can be a stubborn subject: it is complex, heterogeneous and opaque. To understand it, one must decide which aspects of the past to emphasise and which to minimise. Enter frameworks. Frameworks foreground certain aspects of the historical record while backgrounding others. As such, they are both necessary for, and conducive to, good history as well as good philosophy. We examine the role of frameworks in the history and philosophy of science and argue that they are necessary for both (...) forms of enquiry. We then suggest that the right attitude towards frameworks is pluralism rather than monism: there is no single correct framework to be applied to a given scientific episode. Rather, a multitude of different frameworks are more or less appropriate given various contexts and aims. From this perspective, good frameworks generate and further, rather than frustrate, historical and philosophical enquiry. Our view sheds light on historical disagreement, and on the relationship between philosophy and history of science. (shrink)

It is often thought that the vindication of experimental work lies in its capacity to be revelatory of natural systems. I challenge this idea by examining laboratory experiments in ecology. A central task of community ecology involves combining mathematical models and observational data to identify trophic interactions in natural systems. But many ecologists are also lab scientists: constructing microcosm or ‘bottle’ experiments, physically realizing the idealized circumstances described in mathematical models. What vindicates such ecological experiments? I argue that ‘extrapolationism’, the (...) view that ecological lab work is valuable because it generates truths about natural systems, does not exhaust the epistemic value of such practices. Instead, bottle experiments also generate ‘understanding’ of both ecological dynamics and empirical tools. Some lab work, then, aids theoretical understanding, as well as targeting hypotheses about nature. 1Introduction2Trophic Interactions and Observational Techniques3Cryptic Dynamics in Bottle Experiments4Extrapolationism 4.1Ecological possibility and actuality4.2Ecological heterogeneity5Understanding 5.1The epistemic good of understanding5.2Bottle experiments as understanding-generators5.3How understanding travels6Conclusion. (shrink)

Angela Potochnik’s Idealization and the Aims of Science defends an ambitious and systematic account of scientific knowledge: ultimately science pursues human understanding rather than truth. Potochnik argues that idealization is rampant and unchecked in science. Further, given that idealizations involve departures from truth, this suggests science is not primarily about truth. I explore the relationship between truths about causal patterns and scientific understanding in light of this, and suggest that Potochnik underestimates the importance and power of highly particular narrative explanations.

Understanding natural phenomena is an important aim of science. Since the turn of the millennium the notion of scientific understanding has been a hot topic of debate in the philosophy of science. A bone of contention in this debate is the role of truth and representational accuracy in scientific understanding. So-called factivists and non-factivists disagree about the extent to which the theories and models that are used to achieve understanding must be true or accurate. In this paper we address this (...) issue by examining a case from the practice of synthetic chemistry. We investigate how understanding is obtained in this field by means of an in-depth analysis of the famous synthesis of periplanone B by W. Clark Still. It turns out that highly idealized models—that are representationally inaccurate and sometimes even inconsistent—and qualitative concepts are essential for understanding the synthetic pathway and accordingly for achieving the synthesis. We compare the results of our case study to various factivist and non-factivist accounts of how idealizations may contribute to scientific understanding and conclude that non-factivism offers a more plausible interpretation of the practice of synthetic chemistry. Moreover, our case study supports a central thesis of the non-factivist theory of scientific understanding developed by De Regt, namely that scientific understanding requires intelligibility rather than representational accuracy, and that idealization is one way to enhance intelligibility. (shrink)

According to the free energy principle, life is an “inevitable and emergent property of any random dynamical system at non-equilibrium steady state that possesses a Markov blanket” :20130475, 2013). Formulating a principle for the life sciences in terms of concepts from statistical physics, such as random dynamical system, non-equilibrium steady state and ergodicity, places substantial constraints on the theoretical and empirical study of biological systems. Thus far, however, the physics foundations of the free energy principle have received hardly any attention. (...) Here, we start to fill this gap and analyse some of the challenges raised by applications of statistical physics for modelling biological targets. Based on our analysis, we conclude that model-building grounded in the free energy principle exacerbates a trade-off between generality and realism, because of a fundamental mismatch between its physics assumptions and the properties of actual biological targets. (shrink)

The free-energy principle states that all systems that minimize their free energy resist a tendency to physical disintegration. Originally proposed to account for perception, learning, and action, the free-energy principle has been applied to the evolution, development, morphology, anatomy and function of the brain, and has been called a postulate, an unfalsifiable principle, a natural law, and an imperative. While it might afford a theoretical foundation for understanding the relationship between environment, life, and mind, its epistemic status is unclear. Also (...) unclear is how the free-energy principle relates to prominent theoretical approaches to life science phenomena, such as organicism and mechanism. This paper clarifies both issues, and identifies limits and prospects for the free-energy principle as a first principle in the life sciences. (shrink)

The use of machine learning instead of traditional models in neuroscience raises significant questions about the epistemic benefits of the newer methods. I draw on the literature on model intelligibility in the philosophy of science to offer some benchmarks for the interpretability of artificial neural networks used as a predictive tool in neuroscience. Following two case studies on the use of ANN’s to model motor cortex and the visual system, I argue that the benefit of providing the scientist with understanding (...) of the brain trades off against the predictive accuracy of the models. This trade-off between prediction and understanding is better explained by a non-factivist account of scientific understanding. (shrink)

This paper defends the viability of de-idealization strategies in economic modeling against recent criticism. De-idealization occurs when an idealized assumption of a theoretical model is replaced with a more realistic one. Recently, some scholars have raised objections against the possibility or fruitfulness of de-idealizing economic models, suggesting that economists do not employ this kind of strategy. We present a detailed case study from the theory of industrial organization, discussing three different models, two of which can be construed as de-idealized versions (...) of the first (the so-called Bertrand model of oligopoly). We conclude that recent pessimism about de-idealization in economics is largely unfounded, and that de-idealization strategies are not only possible but also widely employed in economics. (shrink)

In this paper, I offer an analysis of the radical disagreement over the adequacy of string theory. The prominence of string theory despite its notorious lack of empirical support is sometimes explained as a troubling case of science gone awry, driven largely by sociological mechanisms such as groupthink (e.g. Smolin 2006). Others, such as Dawid (2013), explain the controversy by positing a methodological revolution of sorts, according to which string theorists have quietly turned to nonempirical methods of theory assessment given (...) the technological inability to directly test the theory. The appropriate response, according to Dawid, is to acknowledge this development and widen the canons of acceptable scientific methods. As I’ll argue, however, the current situation in fundamental physics does not require either of these responses. Rather, as I’ll suggest, much of the controversy stems from a failure to properly distinguish the “context of justification” from the “context of pursuit”. Both those who accuse string theorists of betraying the scientific method and those who advocate an enlarged conception of scientific methodology objectionably conflate epistemic justification with judgements of pursuit-worthiness. Once we get clear about this distinction and about the different norms governing the two contexts, the current situation in fundamental physics becomes much less puzzling. After defending this diagnosis of the controversy, I’ll show how the argument patterns that have been posited by Dawid as constituting an emergent methodological revolution in science are better off if reworked as arguments belonging to the context of pursuit. (shrink)

Recent rival attempts in the philosophy of science to put forward a general theory of the properties that all (and only) natural kinds across the sciences possess may have proven to be futile. Instead, I develop a general methodological framework for how to philosophically study kinds. Any kind has to be investigated and articulated together with the human aims that motivate referring to this kind, where different kinds in the same scientific domain can answer to different concrete aims. My core (...) contention is that non-epistemic aims, including environmental, ethical, and political aims, matter as well. This is defended and illustrated based on several examples of kinds, with particular attention to the role of social-political aims: species, race, gender, as well as personality disorders and oppositional defiant disorder as psychiatric kinds. Such non-epistemic aims and values need not always be those personally favoured by scientists, but may have to reflect values that matter to relevant societal stakeholders. Despite the general agenda to study ‘kinds,’ I argue that philosophers should stop using the term ‘natural kinds,’ as this label obscures the relevance of humans interests and the way in which many kinds are based on contingent social processes subject to human responsibility. (shrink)

Deep neural networks have become increasingly successful in applications from biology to cosmology to social science. Trained DNNs, moreover, correspond to models that ideally allow the prediction of new phenomena. Building in part on the literature on ‘eXplainable AI’, I here argue that these models are instrumental in a sense that makes them non-explanatory, and that their automated generation is opaque in a unique way. This combination implies the possibility of an unprecedented gap between discovery and explanation: When unsupervised models (...) are successfully used in exploratory contexts, scientists face a whole new challenge in forming the concepts required for understanding underlying mechanisms. (shrink)

Why do we value higher-level scientific explanations if, ultimately, the world is physical? An attractive answer is that physical explanations often cite facts that don’t make a difference to the event in question. I claim that to properly develop this view we need to commit to a type of deterministic chance. And in doing so, we see the theoretical utility of deterministic chance, giving us reason to accept a package of views including deterministic chance.

Teaching false theories goes against the general pedagogical and philosophical belief that we must only teach and learn what is true. In general, the goal of pedagogy is taken to be epistemic: to gain knowledge and avoid ignorance. In this article, I argue that for realists and antirealists alike, epistemological and pedagogical goals have to come apart. I argue that the falsity of a theory does not automatically make it unfit for being taught. There are several good reasons for teaching (...) false theories in school science. These are false theories can bring about genuine understanding of the world; teaching some false theories from the history of science that line up with children’s ideas can provide students “intellectual empathy” and also aid in better grasp of concepts; teaching false theories from the history of science can sharpen students’ understanding of the nature of science; scientists routinely use false theories and models in their practice and it is good sense for science education to mirror scientific practice; and learning about patently non-scientific and antiscientific ideas will prepare students to face and respond to them. In making arguments for the foregoing five points, I draw upon the work of a variety of philosophers and historians of science, cognitive scientists, science education scholars, and scientists. My goal here is not only to justify theories considered false already being taught, but also to actively endorse the teaching of some theories considered false that are by and large not currently taught. (shrink)

In this paper, we explore an under-investigated question concerning the class of formal models that aim at providing normative guidance. We call such models normative models. In particular, we examine the question of how normative models can successfully exert normative guidance. First, we highlight the absence of a discussion of this question – which is surprising given the extensive debate about the success conditions of descriptive models – and motivate its importance. Second, we introduce and discuss two potential accounts of (...) the success conditions of normative models. Our tentative conclusion is that the second account is more promising. (shrink)

Functionalism about kinds is still the dominant style of thought in the special sciences, like economics, psychology, and biology. Generally construed, functionalism is the view that states or processes can be individuated based on what role they play rather than what they are constituted of or realized by. Recently, Weiskopf has posited a reformulation of functionalism on the model-based approach to explanation. We refer to this reformulation as ‘new functionalism’. In this paper, we seek to defend new functionalism and to (...) recast it in light of the concrete explanatory aims of the special sciences. In particular, we argue that the assessment of the explanatory legitimacy of a functional kind needs to take into account the explanatory purpose of the model in which the functional kind is employed. We aim at demonstrating this by appealing to model-based explanations from the social and behavioral sciences. Specifically, we focus on preferences and signals as functional kinds. Our argument is intended to have the double impact of deflecting criticisms against new functionalism from the perspective of mechanistic decomposition while also expanding the scope of new functionalism to encompass the social and behavioral sciences. (shrink)

From a scientific perspective, hunger can be regarded as an identifiable conscious sensation which can be distinguished from other conscious states. The hunger state can be measured and is a marker of the existence of underlying biological processes. Measured hunger is functional and is normally associated with the act of eating. However, the conscious state of hunger, although driven physiologically, is not exclusively determined by biology; there is an environmental influence that can modulate its intensity and periodicity, and cultural factors (...) that shape the appropriateness of the expression of hunger. Within a psychobiological framework, hunger can be considered as the expression of a ‘need state’ which mediates between biological requirements and environmental satisfaction. Our empirical studies show that hunger is clearly associated with biological signals and is central to the relationship between energy expenditure and energy intake. (shrink)

Biologists and philosophers of science have recently called for an extension of evolutionary theory. This so-called ‘extended evolutionary synthesis’ seeks to integrate developmental processes, extra-genetic forms of inheritance, and niche construction into evolutionary theory in a central way. While there is often agreement in evolutionary biology over the existence of these phenomena, their explanatory relevance is questioned. Advocates of EES posit that their perspective offers better explanations than those provided by ‘standard evolutionary theory’. Still, why this would be the case (...) is unclear. Usually, such claims assume that EES’s superior explanatory status arises from the pluralist structure of EES, its different problem agenda, and a growing body of evidence for the evolutionary relevance of developmental phenomena. However, what is usually neglected in this debate is a discussion of what the explanatory standards of EES actually are, and how they differ from prevailing standards in SET. In other words, what is considered to be a good explanation in EES versus SET? To answer this question, we present a theoretical framework that evaluates the explanatory power of different evolutionary explanations of the same phenomena. This account is able to identify criteria for why and when evolutionary explanations of EES are better than those of SET. Such evaluations will enable evolutionary biology to find potential grounds for theoretical integration. (shrink)

The Galilean explanatory style is characterized by the search for the underlying structure of phenomena, the positing of "deep" explanatory principles, and a view of the relation between theory and data, on which the search for "crucial data" is of primary importance. In this paper, I trace the dynamics of adopting the Galilean style, focusing on the science of episodic memory. I argue that memory systems, such as episodic and semantic memory, were posited as underlying competences producing the observable phenomena (...) of memory. Considered in idealized isolation from other systems, episodic memory was taken to underlay the ability of individuals to remember events from their personal past. Yet, in reality, memory systems regularly interact, standing in many-to-many relations to actual memory tasks and experiences. Upon this backdrop, I explore a puzzle about the increasing prominence of the notion of autonoetic consciousness in Tulving's theory of episodic memory. I argue that, contrary to widespread belief, the prominence is not best explained by the purported essential link between autonoetic consciousness and episodic memory. Rather, it is explained by the fact that autonoetic consciousness, hypothesized to uniquely accompany episodic retrieval, was considered a source of crucial data, predictable only from theories positing a functionally distinct episodic memory system. However, with the emergence of a new generation of theories, positing wider memory systems for remembering and imagination, the question of the relation between episodic memory and autonoetic consciousness has been reopened. This creates a pressing need for de-idealization, triggering a new search for crucial data. (shrink)

Change is the fundamental idea of evolution. Explaining the extraordinary biological change we see written in the history of genomes and fossil beds is the primary occupation of the evolutionary biologist. Yet it is a surprising fact that for the majority of evolutionary research, we have rarely studied how evolution typically unfolds in nature, in changing ecological environments, over space and time. While ecology played a major role in the eventual acceptance of the population genetic viewpoint of evolution in the (...) synthetic era (circa 1918-1956), it held a lesser role in the development of evolutionary theory until the 1980s, when we began to systematically study the evolutionary dynamics of natural populations in space and time. As a result, early evolutionary theory was initially constructed in an abstract vacuum that was unrepresentative of evolution in nature. The subtle synthesis between ecology with evolutionary biology (eco-evo synthesis) over the past 40 years has progressed our knowledge of natural selection dynamics as they are found in nature, thus revealing how natural selection varies in strength, direction, form, and, more surprisingly, level of biological organization. Natural selection can no longer be reduced to lower levels of biological organization (i.e., individuals, selfish genes) over shorter timescales but should be expanded to include adaptation at higher levels and over longer timescales. Long-term and/or emergent evolutionary phenomena, such as multilevel selection or evolvability, have thus become tenable concepts within an evolutionary biology that embraces ecology and spatiotemporal change. Evolutionary biology is currently suspended at an intermediate stage of scientific progress that calls for the organization of all the recent knowledge revealed by the eco-evo synthesis into a coherent and unified theoretical framework. This is where philosophers of biology can be of particular use, acting as a bridge between the subdisciplines of biology and inventing new theoretical strategies to organize and accommodate the recent knowledge. Philosophers have recommended transitioning away from outdated philosophies that were originally derived from physics within the philosophical zeitgeist of logical positivism (i.e., monism, reductionism, and monocausation) and toward a distinct philosophy of biology that can capture the natural complexity of multifaceted biological systems within diverse ecosystems—one that embraces the emerging philosophies of pluralism, emergence, and multicausality. Therefore, I see recent advances in ecology, evolutionary biology, and the philosophy of biology as laying the groundwork for another major biological synthesis, what I refer to as the Second Synthesis because, in many respects, it is analogous to the aims and outcomes of the first major biological synthesis (but is notably distinct from the inorganic and contrived progressive movement known as the extended evolutionary synthesis). With the general development of a distinctive philosophy of science, biology has rightfully emerged as an autonomous science. Thus, while the first synthesis legitimized biology, the Second Synthesis autonomized biology and afforded biology its own philosophy, allowing biology to finally realize its full scientific potential. (shrink)

This paper addresses the question of what constitutes an apt interpreted model for the purpose of analyzing causation. I first collect universally adopted aptness principles into a basic account, flagging open questions and choice points along the way. I then explore various additional aptness principles that have been proposed in the literature but have not been widely adopted, the motivations behind their proposals, and the concerns with each that stand in the way of universal adoption. I conclude that the remaining (...) work of articulating aptness for a SEM analysis of causation is tied up with issue to do with modality, ontology, and mereology. Continuing this work is therefore likely to shed light on the relationship between these areas and causation more generally. (shrink)

In his chapter in this volume, Christopher Pincock develops an argument for scientific realism based on scientific understanding, and he argues that Giere’s (2006) and my (2017, 2020) commitment to the context-dependence of scientific understanding or knowledge renders our views unable to account for an essential step in how scientists come to know. Meanwhile, in my chapter in this volume, I motivate a view that I call "causal pattern realism." In this response to Pincock's chapter, I will sketch a revised (...) version of Pincock’s argument for realism that is consistent with causal pattern realism. Then I will respond to Pincock’s concern that the context-dependence of understanding on my view would interfere with the scientific community’s ability to extrapolate from specific experimental and observational contexts as needed to develop knowledge. My goal is not to convince anyone to be a causal pattern realist but rather to create the space for such a view, taking into account the concerns motivating Pincock. (shrink)

Hájek (Erkenntnis 70(2):211–235, 2009) argues that probabilities cannot be the limits of relative frequencies in counterfactual infinite sequences. I argue for a different understanding of these limits, drawing on Norton’s (Philos Sci 79(2):207–232, 2012) distinction between approximations (inexact descriptions of a target) and idealizations (separate models that bear analogies to the target). Then, I adapt Hájek’s arguments to this new context. These arguments provide excellent reasons not to use hypothetical frequencies as idealizations, but no reason not to use them as (...) approximations. (shrink)

The widely accepted view states that an intention to deceive is not necessary for lying. Proponents of this view, the so-called non-deceptionists, argue that lies are simply insincere assertions. We conducted three experimental studies with false explanations, the results of which put some pressure on non-deceptionist analyses. We present cases of explanations that one knows are false and compare them with analogical explanations that differ only in having a deceptive intention. The results show that lay people distinguish between such false (...) explanations and to a higher degree classify as lies those explanations that are made with the intention to deceive. Non-deceptionists fail to distinguish between such cases and wrongly classify both as lies. This novel empirical finding indicates the need for supplementing non-deceptionist definitions of lying, at least in some cases, with an additional condition, such as an intention to deceive. (shrink)

Grace A. de Laguna was an American philosopher of exceptional originality. Many of the arguments and positions she developed during the early decades of the twentieth century later came to be central to analytic philosophy. These arguments and positions included, even before 1930, a critique of the analytic-synthetic distinction, a private language argument, a critique of type physicalism, a functionalist theory of mind, a critique of scientific reductionism, a methodology of research programs in science and more. Nevertheless, de Laguna identified (...) herself as a defender of the speculative vision of philosophy, a vision which, in her words, “analytic philosophy condemns.” I outline her speculative vision of philosophy as well as what is, in effect, an argument she offers against analytic philosophy. This is an argument against the view that key parts of established opinion, e.g., our best theoretical physics or most certain common sense, should be assumed to be true in order to answer philosophical questions. I go on to bring out the implications of her argument for the approaches to philosophy of Bertrand Russell, Willard V. Quine and David Lewis, and I also compare the argument to recent, related arguments against analytic philosophy. I will suggest that de Laguna offers a viable critique of analytic philosophy and an alternative approach to philosophy that meets this critique. (shrink)

In a recent paper, Erasmus et al. (2021) defend the idea that the ambiguity of the term “explanation” in explainable AI (XAI) can be solved by adopting any of four different extant accounts of explanation in the philosophy of science: the Deductive Nomological, Inductive Statistical, Causal Mechanical, and New Mechanist models. In this chapter, I show that the authors’ claim that these accounts can be applied to deep neural networks as they would to any natural phenomenon is mistaken. I also (...) provide a more general argument as to why the notion of explainability as it is currently used in the XAI literature bears little resemblance to the traditional concept of scientific explanation. It would be more fruitful to use the label “understandable AI” to avoid the confusion that surrounds the goal and purposes of XAI. In the second half of the chapter, I argue for a pragmatic conception of understanding that is better suited to play the central role attributed to explanation in XAI. Following De Regt (2017) and Kuorikoski and Ylikoski (2015), the conditions of satisfaction for understanding an ML system are fleshed out in terms of an agent’s success in using the system. (shrink)

Commitment to sparseness amounts to the idea that there is an objective, worldly privileging of certain properties over others that makes the privileged properties suited to play certain roles, and is responsible for their playing such roles. In this chapter I offer a brief, opinionated overview of sparseness. I begin by examining a set of problems that I call “problems of abundance”, which generate canonical motivations for sparseness. I then survey some influential approaches to sparseness and the roles that they (...) attribute to sparse properties, noting that on most approaches sparse properties are deeply connected to inquiry. Finally I consider some problems for sparseness, focusing on the purported connections between sparseness and inquiry, particularly in philosophy of science and inquiry into the social world. (shrink)

The ontic conception of explanation is predicated on the proposition that “explanation is a relation between real objects in the world” and hence, according to this approach, scientific explanation cannot take place absent such a premise. Despite the fact that critics have emphasized several drawbacks of the ontic conception, as for example its inability to address the so-called “abstract explanations”, the debate is not settled and the ontic view can claim to capture cases of explanation that are non-abstract, such as (...) causal relations between events. However, by eliminating the distinction between abstract and non-abstract explanations, it follows that ontic and epistemic proposals can no longer contend to capture different cases of explanation and either all are captured by the ontic view or all are captured by the epistemic view. On closer inspection, it turns out that the ontic view deals with events that fall outside the scientists’ scope of observation and that it does not accommodate common instances of explanation such as explanations from false propositions and hence it cannot establish itself as the dominant philosophical stance with respect to explanation. On the contrary, the epistemic conception does account for almost all episodes of explanation and can be described as a relation between representations, whereby the explanans transmit information to the explanandum and that this information can come, dependent on context, in the form of any of the available theories of explanation (law-like, unificatory, causal and non-causal). The range of application of the ontic view thus is severely restricted to trivial cases of explanation that come through direct observation of the events involved in an explanation and explanation is to be mostly conceived epistemically. (shrink)

Theology used to be the discipline that arbitrated and ‘said’ the truth. Some argued that its methodical engagement had to make it a search-driven experimentation with an inductive outlook intended at tracking truth through practice and praise, in short conversion. The empirico-formal sciences have sought canonical norms of knowledge away from any regimentation. Neopositivism had for a time entertained a fact-derived language and, as such, it banned metaphor as mingling problems by perpetuating the belief in entirely theoretical knowledge-terms. Here we (...) aim at putting together an examination of some of the reasons why a pluralistic epistemology has come to be adopted, and an attempt at finding pluralistic responses to that very idea through interdisciplinary comparison. This is the editors' (M. Fuller & A. Runehov) introduction: Philippe Gagnon asks the interesting question: Does pluralism itself need to be plural? He argues that neo-positivism has for some time now focused on using a fact-derived language, banning metaphoricity. He explores the reasons pluralistic epistemology came to be adopted, and leads us through some important philosophical problems and how they were tackled by different philosophers and in various times. Such philosophical problems or questions concern theories, truth and knowledge. If a theory is considered to be the best one, on which criteria is this judgement made? Might it be better to have several theories that are ex æquo? Gagnon argues that even though ‘pluralism’ is a better term compared to ‘the many’, it does not entirely escape relativism. Hence, he proposes to use ‘plurality’ instead, which does not threaten the unity of truth. After this philosophical exploration, he turns to theology, specifically Trinitarian theology, to implement his findings there. (shrink)

Detailed examinations of scientific practice have revealed that the use of idealized models in the sciences is pervasive. These models play a central role in not only the investigation and prediction of phenomena, but in their received scientific explanations as well. This has led philosophers of science to begin revising the traditional philosophical accounts of scientific explanation in order to make sense of this practice. These new model-based accounts of scientific explanation, however, raise a number of key questions: Can the (...) fictions and falsehoods inherent in the modeling practice do real explanatory work? Do some highly abstract and mathematical models exhibit a noncausal form of scientific explanation? How can one distinguish an exploratory "how-possibly" model explanation from a genuine "how-actually" model explanation? Do modelers face tradeoffs such that a model that is optimized for yielding explanatory insight, for example, might fail to be the most predictively accurate, and vice versa? This chapter explores the various answers that have been given to these questions. (shrink)

The history of developmental biology is interwoven with debates as to whether mechanistic explanations of development are possible or whether alternative explanatory principles or even vital forces need to be assumed. In particular, the demonstrated ability of embryonic cells to tune their developmental fate precisely to their relative position and the overall size of the embryo was once thought to be inexplicable in mechanistic terms. Taking a causal perspective, this Element examines to what extent and how developmental biology, having turned (...) molecular about four decades ago, has been able to meet the vitalist challenge. It focuses not only on the nature of explanations but also on the usefulness of causal knowledge – including the knowledge of classical experimental embryology – for further scientific discovery. It also shows how this causal perspective allows us to understand the nature and significance of some key concepts, including organizer, signal and morphogen. This title is also available as Open Access on Cambridge Core. (shrink)

Abstraction and idealization are the two notions that are most often discussed in the context of assumptions employed in the process of model building. These notions are also routinely used in philosophical debates such as that on the mechanistic account of explanation. Indeed, an objection to the mechanistic account has recently been formulated precisely on these grounds: mechanists cannot account for the common practice of idealizing difference-making factors in models in molecular biology. In this paper I revisit the debate and (...) I argue that the objection does not stand up to scrutiny. This is because it is riddled with a number of conceptual inconsistencies. By attempting to resolve the tensions, I also draw several general lessons regarding the difficulties of applying abstraction and idealization in scientific practice. Finally, I argue that more care is needed only when speaking of abstraction and idealization in a context in which these concepts play an important role in an argument, such as that on mechanistic explanation. (shrink)

What features will something have if it counts as an explanation? And will something count as an explanation if it has those features? In the second half of the 20th century, philosophers of science set for themselves the task of answering such questions, just as a priori conceptual analysis was generally falling out of favor. And as it did, most philosophers of science just moved on to more manageable questions about the varieties of explanation and discipline-specific scientific explanation. Often, such (...) shifts are sound strategies for problem-solving. But leaving fallow certain basic conceptual issues can also result in foundational debates. (shrink)

We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal (...) patterns of variable granularity and scope. We characterise the conditions under which such a game is almost surely guaranteed to converge on a optimal explanation surface in polynomial time, and highlight obstacles that will tend to prevent the players from advancing beyond certain explanatory thresholds. The game serves a descriptive and a normative function, establishing a conceptual space in which to analyse and compare existing proposals, as well as design new and improved solutions. (shrink)

Phylogenetic models traditionally represent the history of life as having a strictly-branching tree structure. However, it is becoming increasingly clear that the history of life is often not strictly-branching; lateral gene transfer, endosymbiosis, and hybridization, for example, can all produce lateral branching events. There is thus motivation to allow phylogenetic models to have a reticulate structure. One proposal involves the reconciliation of genealogical discordance. Briefly, this method uses patterns of disagreement – discordance – between trees of different genes to add (...) lateral branching events to phylogenetic trees of taxa, and to estimate the most likely cause of these events. I use this practice to argue for: (1) a need for expanded accounts of multiple-models idealization, (2) a distinction between automatic and manual de-idealization, and (3) recognition that idealization may serve the meso-level aims of science in a different way than hitherto acknowledged. (shrink)

The purpose of this essay is to present and analyse the basic assumptions of Leszek Nowak’s conception of the unity of science. According to Nowak, the unity of science is manifested in the common application of the method of idealisation in scientific research. In accordance with his conception, regardless of the discipline they represent, researchers go through the same stages in building a theory. Two key ones among them are: introducing idealising assumptions into the representation and then their concretisation. In (...) this view, idealisation is the basis of the scientific method, while other cognitive procedures complement it. Nowak’s conception has particular relevance in the context of the dispute between naturalism and anti-naturalism and in the context of the continuing rift between social scientists and natural scientists. It calls into question the anti-naturalist thesis of the ontological uniqueness of social sciences and the resulting methodological consequences. I argue that Nowak’s conception is a cognitively valuable contribution to the contemporary epistemology of science, but it also has weaknesses, mainly due to the limitations of applying the idealisation-concretisation scheme in research practice. For it turns out, as I point out in this essay, that many idealising assumptions are not subject to concretisation and that concretisations do not always condition an increase in the explanatory and/or predictive power of the representations. (shrink)

This paper explores a neglected normative dimension of algorithmic opacity in the workplace and the labor market. It argues that explanations of algorithms and algorithmic decisions are of noninstrumental value. That is because explanations of the structure and function of parts of the social world form the basis for reflective clarification of our practical orientation toward the institutions that play a central role in our life. Using this account of the noninstrumental value of explanations, the paper diagnoses distinctive normative defects (...) in the workplace and economic institutions which a reliance on AI can encourage, and which lead to alienation. (shrink)

Highly idealized models may serve various epistemic functions, notably explanation, in virtue of representing the world. Inferentialism provides a prima facie compelling characterization of what constitutes the representation relation. In this paper, I argue that what I call factive inferentialism does not provide a satisfactory solution to the puzzle of model-based—factive—explanation. In particular, I show that making explanatory counterfactual inferences is not a sufficient guide for accurate representation, factivity, or realism. I conclude by calling for a more explicit specification of (...) model-world mismatches and properties imputation. (shrink)

In a recent special issue dedicated to Dani Rodrik’s (2015) influential monograph Economics Rules, Grüne-Yanoff and Marchionni (2018) raise a potentially damning problem for Rodrik’s suggestion that progress in economics should be understood and measured laterally, by a continuous expansion of new models. They argue that this could lead to an “embarrassment of riches”, i.e. the rapid expansion of our model library to such an extent that we become unable to choose between the available models, and thus needs to be (...) solved to make ‘model pluralism’ viable. Drawing on Veit’s (2019a) ‘model pluralism’ account, this paper argues that model pluralism as a thesis about the relationship between science and nature undermines the very idea of a general model selection framework for policy making. (shrink)