What is scientific progress? On Alexander Bird’s epistemic account of scientific progress, an episode in science is progressive precisely when there is more scientific knowledge at the end of the episode than at the beginning. Using Bird’s epistemic account as a foil, this paper develops an alternative understanding-based account on which an episode in science is progressive precisely when scientists grasp how to correctly explain or predict more aspects of the world at the end of the episode than at the (...) beginning. This account is shown to be superior to the epistemic account by examining cases in which knowledge and understanding come apart. In these cases, it is argued that scientific progress matches increases in scientific understanding rather than accumulations of knowledge. In addition, considerations having to do with minimalist idealizations, pragmatic virtues, and epistemic value all favor this understanding-based account over its epistemic counterpart. (shrink)

How can a model that stops short of representing the whole truth about the causal production of a phenomenon help us to understand the phenomenon? I answer this question from the perspective of what I call the simple view of understanding, on which to understand a phenomenon is to grasp a correct explanation of the phenomenon. Idealizations, I have argued in previous work, flag factors that are casually relevant but explanatorily irrelevant to the phenomena to be explained. Though useful to (...) the would-be understander, such flagging is only a first step. Are there any further and more advanced ways that idealized models aid understanding? Yes, I propose: the manipulation of idealized models can provide considerable insight into the reasons that some causal factors are difference-makers and others are not, which helps the understander to grasp the nature of explanatory connections and so to better grasp the explanation itself. (shrink)

I propose and defend the hypothesis that understanding is the norm of explanation. On this proposal, an explanation should express understanding. I call this the understanding account of explanation. The understanding account is supported by social and introspective observations. It is also supported by the relationship between knowledge and understanding, on the one hand, and assertion and explanation, on the other.

One sometimes believes a proposition without grasping it. For example, a complete achromat might believe that ripe tomatoes are red without grasping this proposition. My aim in this paper is to shed light on the difference between merely believing a proposition and grasping it. I focus on two possible theories of grasping: the inferential theory, which explains grasping in terms of inferential role, and the phenomenal theory, which explains grasping in terms of phenomenal consciousness. I argue that the phenomenal theory (...) is more plausible than the inferential theory. (shrink)

There is increasing attention to the centrality of idealization in science. One common view is that models and other idealized representations are important to science, but that they fall short in one or more ways. On this view, there must be an intermediary step between idealized representation and the traditional aims of science, including truth, explanation, and prediction. Here I develop an alternative interpretation of the relationship between idealized representation and the aims of science. In my view, continuing, widespread idealization (...) calls into question the idea that science aims for truth. I argue that understanding must replace truth as the ultimate epistemic aim of science. Additionally, science has a wide variety aims, epistemic and non-epistemic, and these aims motivate different kinds of scientific products. Finally, I show how these diverse aims---all rather distant from truth---result in the expanded influence of social values on science. (shrink)

Epistemic relativists often appeal to an epistemic incommensurability thesis. One notable example is the position advanced by Wittgenstein in On certainty (1969). However, Ian Hacking’s radical denial of the possibility of objective epistemic reasons for belief poses, we suggest, an even more forceful challenge to mainstream meta-epistemology. Our central objective will be to develop a novel strategy for defusing Hacking’s line of argument. Specifically, we show that the epistemic incommensurability thesis can be resisted even if we grant the very insights (...) that lead Hacking to claim that epistemic reasons are always relative to a style of reasoning. Surprisingly, the key to defusing the argument is to be found in recent mainstream work on the epistemic state of objectual understanding. (shrink)

The arguments for and against a truth-relativist semantics for propositional knowledge attributions (KTR) have been debated almost exclusively in the philosophy of language. But what implications would this semantic thesis have in epistemology? This question has been largely unexplored. The aim of this paper is to establish and critique several ramifications of KTR in mainstream epistemology. The first section of the paper develops, over a series of arguments, the claim that MacFarlane's (2005, 2010) core argument for KTR ultimately motivates (for (...) better or worse) the extension of a truth-relativist semantics to a subset of understanding attributions – attributions of understanding-why. I conclude by presenting some reasons to think that even if KTR were otherwise plausible, a truth-relativist semantics for understanding-why attributions is not. These claims, taken together, constitute a kind of epistemological argument against MacFarlane-style truth-relativism for knowledge attributions. (shrink)

Value capture occurs when an agent’s values are rich and subtle; they enter a social environment that presents simplified — typically quantified — versions of those values; and those simplified articulations come to dominate their practical reasoning. Examples include becoming motivated by FitBit’s step counts, Twitter Likes and Re-tweets, citation rates, ranked lists of best schools, and Grade Point Averages. We are vulnerable to value capture because of the competitive advantage that such crisp and clear expressions of value have in (...) our private reasoning and our public justification. There is, however, a price. In value capture, we take a central component of our autonomy — our ongoing deliberation over the exact articulation of our values — and we outsource it. And the metrics to which we outsource usually engineered for the interests of some external force, like a large-scale institution’s interest in cross-contextual comprehensibility and quick aggregability. That outsourcing cuts off one of the key benefits to personal deliberation. In value capture, we no longer adjust our values and their articulations in light of own rich experience of the world. Our values should often be carefully tailored to our particular selves or our small-scale communities, but in value capture, we buy our values off the rack. In some cases – like decreasing CO2 emissions – the costs of non-tailored values are outweighed by the benefit of precise collective coordination. In other cases, like in our aesthetic lives, they are not. This suggests that we should want different values suited to different scales. We should want value federalism. Some values are perhaps best pursued at the largest-scale level, others at smaller scales. The problem occurs when we exhibit an excess preference for the largest-scale values – when we consistently let the universal metrics swamp our quieter interests. (shrink)

The ‘great divide’ in the metaphysics of science is between Humean approaches—which reduce scientific laws (and related modalities) to patterns of occurrent facts—and anti-Humean approaches—where laws stand apart from the patterns of events, making those events hold. There is a vast literature on this debate, with many problems raised for the Humean. But a major problem comes right at the start—what’s the motivation for Humeanism in the first place? This is rather unclear. In fact Maudlin, and other anti-Humeans, claim that (...) there is no good motivation for Humeanism. I criticize a few influential approaches to motivating Humeanism—in particular those based on empiricism, pragmatism, and fidelity to science. In their place I suggest a different type of motivation, which has not received much attention in the literature, that rests on considerations of the role of unification in scientific understanding. (shrink)

Recent work in explainable artificial intelligence (XAI) attempts to render opaque AI systems understandable through a divide-and-conquer strategy. However, this fails to illuminate how trained AI systems work as a whole. Precisely this kind of functional understanding is needed, though, to satisfy important societal desiderata such as safety. To remedy this situation, we argue, AI researchers should seek mechanistic interpretability, viz. apply coordinated discovery strategies familiar from the life sciences to uncover the functional organisation of complex AI systems. Additionally, theorists (...) should accommodate for the unique costs and benefits of such strategies in their portrayals of XAI research. (shrink)

When does a model explain? When does it promote understanding? A dominant approach to scientific explanation is the interventionist view. According to this view, when X explains Y, intervening on X can produce, prevent or alter Y in some predictable way. In this paper, I argue for two claims. First, I reject a position that many interventionist theorists endorse. This position is that to explain some phenomenon by providing a model is also to understand that phenomenon. While endorsing the interventionist (...) view, I argue that explaining and understanding are distinct scientific achievements. Second, I defend a novel theory of scientific understanding. According to this view, when some model M promotes understanding, M makes available a distinctive mental state. This state is of the same psychological kind as when we grasp events in a narrative as bearing on some ultimate conclusion. To conclude, I show that, given this view, mechanistic explanations often provide a powerful source of understanding that many causal-historical models lack. This paper will be of interest to both philosophers of science and epistemologists engaged in the topics of explanation and understanding. (shrink)

Recent discourse in the philosophy of scientific explanation involves an account known as the Kairetic account. I proposed implementing a complementarity view involving a mapping scheme to the Kairetic account and similar models. There are two natural concerns related to this mapping. The first concern is the treatment of multiple mappings required for an explanation: phenomena may involve two complementarity features. The second concern is regarding the acquisition of understanding and whether context-dependence facilitates understanding. This article aims to address the (...) first thought through an example involving interference and photon detectors in a telescope. I claim that context-dependent mapping is on a particle basis, accommodating the wave-particle duality for every single particle without generalization. I further introduce the implications of mathematical developments of a complementarity relation grounded in the uncertainty principle. In addition, I will offer the stance that context-dependence facilitates understanding because it ensures that explanations are logically consistent, precise, relevant, and comprehensive. (shrink)

It is widely acknowledged in the literature in social epistemology that knowledge has a social dimension: we are epistemically dependent upon one another for most of what we know. Our knowledge can be, and very often is, grounded on the epistemic achievement of somebody else. But what about epistemic aims other than knowledge? What about understanding? Prominent authors argue that understanding is not social in the same way in which knowledge is. Others can put us in the position to understand, (...) but when we understand something, this accomplishment is to be credited mainly if not entirely to us, as it is due to the successful exercise of our own cognitive abilities. In this paper, I show that the social dimension of understanding closely resembles the social dimension of knowledge. I distinguish between three different ways in which a subject can depend upon another subject for (either the acquisition or the possession of) a certain epistemic good. I then argue that all these kinds of epistemic dependence apply to knowledge and understanding alike. If I am right, understanding is not (always) an achievement to be (mainly) credited to the single epistemic agent who understands. (shrink)

Our interpretive resources enable us to make sense of, navigate, and communicate about our shared world. These resources not only carve the world up into categories, but also guide how we, individually and collectively, are oriented towards it. In this thesis, I examine how these resources, and the dispositions they guide, may be harmful. A vital kind of interpretive resources are frames, which equip us with unified perspectives on the world. Perspectives are suites of open-ended interpretive (inquisitive, attentional, inferential, evaluative, (...) and affective) dispositions. Frames thereby guide how we interpret, respond to, and navigate the world. I show that these perspectives are epistemically powerful and indispensable. I argue that flawed perspectives, and the distorting frames that produce them, are deeply pernicious and I examine their relationship to oppression. (shrink)

Understanding is an ability manifested by grasping relations of a phenomenon and articulating new explanations. Hence, scientific understanding is inextricably intertwined with and not possible without explanation, and understanding is not a type of propositional knowledge. Anna Elisabeth Höhl provides a novel philosophical account of scientific understanding by developing and defending necessary and sufficient conditions for the understanding that scientists achieve of the phenomena they are researching. This account of scientific understanding is based on and supported by a detailed investigation (...) of an episode from scientific practice in biology. (shrink)

There is a difference between merely thinking that P and really grasping that P. For example, Jackson's (1982) black-and-white Mary cannot (before leaving her black-and-white room) fully grasp what it means to say that fire engines are red, but she can perfectly well entertain the thought that fire engines are red. The contrast between merely thinking and grasping is especially salient in the context of certain moral decisions. For example, an individual who grasps the plight of starving children thanks to (...) evocative images seems much more likely to donate for the children's benefit than someone who is merely told about their plight. This paper offers a phenomenal account of grasping according to which grasping a content (whether a proposition or concept) consists in one's thoughts about the content being constituted by phenomenal experiences of constituents of the content. This account improves on earlier phenomenal accounts of grasping (Bourget 2017a, 2018) by allowing for partial grasping and generalizing to concepts. The paper also addresses objections to phenomenal accounts of grasping, including the objection that recent developments in AI technology show that consciousness is irrelevant to grasping and understanding. (shrink)

Although understanding is the object of a growing literature in epistemology and the philosophy of science, only few studies have concerned understanding in mathematics. This essay offers an account of a fundamental form of mathematical understanding: proof understanding. The account builds on a simple idea, namely that understanding a proof amounts to rationally reconstructing its underlying plan. This characterization is fleshed out by specifying the relevant notion of plan and the associated process of rational reconstruction, building in part on Bratman's (...) theory of planning agency. It is argued that the proposed account can explain a significant range of distinctive phenomena commonly associated with proof understanding by mathematicians and philosophers. It is further argued, on the basis of a case study, that the account can yield precise diagnostics of understanding failures and can suggest ways to overcome them. Reflecting on the approach developed here, the essay concludes with some remarks on how to shape a general methodology common to the study of mathematical and scientific understanding and focused on human agency. (shrink)

In this article, I explore the compatibility of inference to the best explanation (IBE) with several influential models and accounts of scientific explanation. First, I explore the different conceptions of IBE and limit my discussion to two: the heuristic conception and the objective Bayesian conception. Next, I discuss five models of scientific explanation with regard to each model’s compatibility with IBE. I argue that Kitcher’s unificationist account supports IBE; Railton’s deductive–nomological–probabilistic model, Salmon’s statistical-relevance model, and van Fraassen’s erotetic account are (...) incompatible with IBE; and Salmon’s causal–mechanical model is merely consistent with IBE. In short, many influential models of scientific explanation do not support IBE. I end by outlining three possible conclusions to draw: (1) either philosophers of science or defenders of IBE have seriously misconstrued the concept of explanation, (2) philosophers of science and defenders of IBE do not use the term ‘explanation’ univocally, and (3) the ampliative conception of IBE, which is compatible with any model of scientific explanation, deserves a closer look. (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)

Artificial intelligent (AI) systems that perform image classification tasks are being used to great success in many application contexts. However, many of these systems are opaque, even to experts. This lack of understanding can be problematic for ethical, legal, or practical reasons. The research field Explainable AI (XAI) has therefore developed several approaches to explain image classifiers. The hope is to bring about understanding, e.g., regarding why certain images are classified as belonging to a particular target class. Most of these (...) approaches use visual explanations. Drawing on Elgin’s work (True enough. MIT Press, Cambridge, 2017), I argue that analyzing what those explanations exemplify can help to assess their suitability for producing understanding. More specifically, I suggest to distinguish between two forms of examples according to their suitability for producing understanding. I call these forms samples and exemplars, respectively. Samples are prone to misinterpretation and thus carry the risk of leading to misunderstanding. Exemplars, by contrast, are intentionally designed or chosen to meet contextual requirements and to mitigate the risk of misinterpretation. They are thus preferable for bringing about understanding. By reviewing several XAI approaches directed at image classifiers, I show that most of them explain with samples. If my analysis is correct, it will be beneficial if such explainability methods use explanations that qualify as exemplars. (shrink)

Supersymmetry (SUSY) has long been considered an exceptionally promising theory. A central role for the promise has been played by naturalness arguments. Yet, given the absence of experimental findings it is questionable whether the promise will ever be fulfilled. Here, I provide an analysis of the promises associated with SUSY, employing a concept of pursuitworthiness. A research program like SUSY is pursuitworthy if (1) it has the plausible potential to provide high epistemic gain and (2) that gain can be achieved (...) with manageable research efforts. Naturalness arguments have been employed to support both conditions (1) and (2). First, SUSY has been motivated by way of analogy: the proposed symmetry between fermions and bosons is supposed to 'protect' the small Higgs mass from large quantum corrections just as the electron mass is protected through the chiral symmetry. Thus, SUSY held the promise of solving a major problem of the Standard Model of particle physics. Second, naturalness arguments have been employed to indicate that such gain is achievable at relatively low cost: SUSY discoveries seemed to be well in reach of upcoming high-energy experiments. While the first part of the naturalness argument may have the right form to facilitate considerations of pursuitworthiness, the second part of the argument has been problematically overstated. (shrink)

One central aim of science is to provide explanations of natural phenomena. What role(s) does mathematics play in achieving this aim? How does mathematics contribute to the explanatory power of science? Rules to Infinity defends the thesis, common though perhaps inchoate among many members of the Vienna Circle, that mathematics contributes to the explanatory power of science by expressing conceptual rules, rules which allow the transformation of empirical descriptions. Mathematics should not be thought of as describing, in any substantive sense, (...) an abstract realm of eternal mathematical objects, as traditional platonists have thought. In Rules to Infinity, this view, which I call mathematical normativism, is updated with contemporary philosophical tools, and it is argued that normativism is compatible with mainstream semantic theory. This allows the normativist to accept that there are mathematical truths, while resisting the platonistic idea that there exist abstract mathematical objects that explain such truths. Furthermore, Rules to Infinity defends a particular account of the distinction between scientific explanations that are in some sense distinctively mathematical – those that explain natural phenomena in some uniquely mathematical way – and those that are only standardly mathematical, and it lays out desiderata for any account of this distinction. Normativism is compared with other prominent views in the philosophy of mathematics such as neo-Fregeanism, fictionalism, conventionalism, and structuralism. Rules to Infinity serves as an entry point into debates at the forefront of philosophy of science and mathematics, and it defends novel positions in these debates. (shrink)

I argue for an account of grasping, or understanding that, on which we grasp via a higher‐order mental act of Husserlian fulfillment. Fulfillment is the act of matching up the objects of our phenomenally presentational experiences with those of our phenomenally representational thought. Grasping‐by‐fulfilling is importantly different from standard epistemic aims, in part because it is phenomenal rather than inferential. (I endorse Bourget's (2017) arguments to that effect.) I show that grasping‐by‐fulfilling cannot be a species of propositional knowledge or belief, (...) and that it is not essentially connected to justification. I motivate a revisionary epistemology on which achieving propositional knowledge and coming to grasp are dual epistemic aims. My account makes sense of a common occurrence—that we are often unmoved to act on our beliefs until we come to phenomenally experience them in some way. It also explains puzzling features of human inquiry. (shrink)

I first quickly outline what I think grasping is, and suggest that it is both among our basic aims of inquiry and not essentially tied to belief, justification, or knowledge. Then, I briefly look at some places in the metaphysics of science in which it looks like our aim of grasping and our aim in knowing—or perhaps more specifically in knowing the explanations for things—might seem to conflict. I will use this conflict to support a broader view: sometimes, we might (...) develop philosophical views or theories—and even endorse them—in order to better grasp them, regardless of whether we genuinely believe them, or are justified in so doing. At other times, we may be aiming at propositional knowledge. These aims can come apart, and perhaps even systematically come apart. A pluralism about the value of those aims doesn’t entail an “anything goes” attitude with respect to how we think about what philosophical views to put forward, defend, or endorse, however—far from it. Instead, it suggests that what counts as a virtue of a philosophical theory depends on our aims in espousing it. If philosophers have distinct aims at the meta-level, it will be hard for them to engage in joint theory evaluation at the first-order level. At least all three of theoretical virtues, evaluative judgments of philosophical views, and what attitudes we ought to have towards the views we ourselves espouse will vary according to our guiding metaphilosophical aims. (shrink)

Here is a puzzling phenomenon. Moral theories are typically thought to be necessary. If act utilitarianism is true, for example, then it is necessarily true. However, scientific theories are typically thought to be contingent. If quantum field theory is true, it’s not necessarily true — the world could have been Newtonian. My aim is to explore this discrepancy between domains. -/- In particular, I explore the role of what I call `internality’ intuitions in motivating necessitism about both moral and scientific (...) domains. In effect, these internality intuitions tell against the explanatory role of certain `external’ entities – like moral or scientific principles. I’ll suggest that perhaps these internality intuitions are more compelling in the moral case than the scientific case. If so, this would rationalize the combination of moral necessitism and scientific contingentism. (shrink)

Hostile epistemology is the study of how environmental features exploit our cognitive vulnerabilities. I am particularly interested in those vulnerabilities arise from the basic character of our epistemic lives. We are finite beings with limited cognitive resources, perpetually forced to reasoning a rush. I focus on two sources of unavoidable vulnerability. First, we need to use cognitive shortcuts and heuristics to manage our limited time and attention. But hostile forces can always game the gap between the heuristic and the ideal. (...) Second, we need to trust others, and trust makes us vulnerable. In particular, we need to trust experts whose expertise lies beyond our individual ability to vet. So we must rely on various imperfect signals and proxies, like institutional affiliation, to manage our trust. We are forced to use these imperfect and exploitable forms of reasoning, in order to cope with a cognitively overwhelming world. I present the hostile epistemology framework as a counterbalance to vice epistemology, which tends to locate responsibility for bad beliefs in the character of the believer. I draw two conclusions from my analysis. First: suppose we accept that that only communities and institutions can know about the world. This deeply social approach doesn’t solve the problem of expert identification. The individual still faces a problem: given their incomplete understanding, how should the individual pick which group to trust? Second: we might have hoped to get some kind of static picture of the right principles of reasoning — but hostile epistemology presents a different picture. We have to use risky heuristics and shortcuts, which hostile forces can game and exploit. We can respond by shifting to a different set of heuristics, but this simply shifts the location of our vulnerabilities. These new heuristics are also exploitable. There is no end to this, only a constant struggle to adapt. Limited beings in a hostile epistemic environment are locked in an unending epistemic arms race. (shrink)

Practical ability manifested through robust and reliable task performance, as well as information relevance and well-structured representation, are key factors indicative of understanding in the philosophical literature. We explore these factors in the context of deep learning, identifying prominent patterns in how the results of these algorithms represent information. While the estimation applications of modern neural networks do not qualify as the mental activity of persons, we argue that coupling analyses from philosophical accounts with the empirical and theoretical basis for (...) identifying these factors in deep learning representations provides a framework for discussing and critically evaluating potential machine understanding given the continually improving task performance enabled by such algorithms. (shrink)

This paper offers a normative account of the speech act of explanation with understanding as its norm. The previous accounts of the speech act of explanation rely on the factive notion of understanding and maintain that proper explanations require knowledge. I argue, however, that such accounts are too demanding and do not reflect the everyday practice of explanation and the attribution of understanding. Instead, I argue that the non-factive, objectual attitude of understanding is sufficient for a proper explanation. On the (...) normative level, explanations are governed by an audience-centred norm, i.e., they are sensitive to the epistemic position of the audience. According to the proposed account, an explanation is a communicative act in which one puts the audience in a position to understand the explained phenomenon. This proposal fits into the recent wave of applications of the normative account and makes space for the pluralism of illocutionary acts. (shrink)

One of the main worries with machine learning model opacity is that we cannot know enough about how the model works to fully understand the decisions they make. But how much is model opacity really a problem? This chapter argues that the problem of machine learning model opacity is entangled with non-epistemic values. The chapter considers three different stages of the machine learning modeling process that corresponds to understanding phenomena: (i) model acceptance and linking the model to the phenomenon, (ii) (...) explanation, and (iii) attributions of understanding. At each of these stages, non-epistemic values can, in part, determine how much machine learning model opacity poses a problem. (shrink)

Many AI systems that make important decisions are black boxes: how they function is opaque even to their developers. This is due to their high complexity and to the fact that they are trained rather than programmed. Efforts to alleviate the opacity of black box systems are typically discussed in terms of transparency, interpretability, and explainability. However, there is little agreement about what these key concepts mean, which makes it difficult to adjudicate the success or promise of opacity alleviation methods. (...) I argue for a unified account of these key concepts that treats the concept of understanding as fundamental. This allows resources from the philosophy of science and the epistemology of understanding to help guide opacity alleviation efforts. A first significant benefit of this understanding account is that it defuses one of the primary, in-principle objections to post hoc explainable AI (XAI) methods. This “rationalization objection” argues that XAI methods provide mere rationalizations rather than genuine explanations. This is because XAI methods involve using a separate “explanation” system to approximate the original black box system. These explanation systems function in a completely different way than the original system, yet XAI methods make inferences about the original system based on the behavior of the explanation system. I argue that, if we conceive of XAI methods as idealized scientific models, this rationalization worry is dissolved. Idealized scientific models misrepresent their target phenomena, yet are capable of providing significant and genuine understanding of their targets. (shrink)

Wir Menschen streben danach, die Wirklichkeit zu verstehen. Eine Welt, die wir gut verstehen, ist eine, die wir "im Griff" haben, mit der wir gut umgehen können. Aber was heißt es genau, ein Phänomen der Wirklichkeit zu verstehen? Wie sieht unser Weltbild aus, wenn wir ein Phänomen verstanden haben? Welche Bedingungen müssen erfüllt sein, damit Verstehen gelingt? Die Kernthese des Buches ist, dass wir Phänomene der Wirklichkeit durch noetische Integration verstehen. Wir verstehen Phänomene, indem wir den entsprechenden Informationseinheiten eine sinnvolle (...) und angemessene Position in unserem Weltbild zuschreiben und insofern unser Weltbild in gewissem Maße der Wirklichkeit entspricht. (shrink)

Science and mathematics continually change in their tools, methods, and concepts. Many of these changes are not just modifications but progress---steps to be admired. But what constitutes progress? This dissertation addresses one central source of intellectual advancement in both disciplines: reformulating a problem-solving plan into a new, logically compatible one. For short, I call these cases of compatible problem-solving plans "reformulations." Two aspects of reformulations are puzzling. First, reformulating is often unnecessary. Given that we could already solve a problem using (...) an older formulation, what do we gain by reformulating? Second, some reformulations are genuinely trivial or insignificant. Merely replacing one symbol with another does not lead to intellectual progress. What distinguishes significant reformulations from trivial ones? According to what I call "conceptualism" (or "conceptual empiricism"), reformulations are intellectually significant when they provide a different plan for solving problems. Significant reformulations provide inferentially different routes to the same solution. In contrast, trivial reformulations provide exactly the same problem-solving plans, and hence they do not change our understanding. This answers the second question about what distinguishes trivial from significant reformulations. However, the first question remains: what makes a new way of solving an old problem valuable? Here, a bevy of practical considerations come to mind: one formulation might be faster, less complicated, or use more familiar concepts. According to "instrumentalism," these practical benefits are all there is to reformulating. Some reformulations are simply more instrumentally valuable for meeting the aims of science than others. At another extreme, "fundamentalism" contends that a reformulation is valuable when it provides a more fundamental description of reality. According to this view, some reformulations directly contribute to the metaphysical aim of carving reality at its joints. Conceptualism develops a middle ground between instrumentalism and fundamentalism, preserving their benefits without their costs. I argue that the epistemic value of significant reformulations does not reduce to either practical or metaphysical value. Reformulations are valuable because they are a constitutive part of problem-solving. Both science and mathematics aim at solving all possible problems within their respective domains. Meeting this aim requires being able to plan for any possible problem-solving context, and this requires reformulating. By reformulating, we clarify what we need to know to solve problems. Still, one might wonder whether the value of reformulations requires underlying differences in explanatory power. According to "explanationism," a reformulation is valuable only when it provides a better explanation. Explanationism stands as a rival middle ground position to my own. However, it faces numerous counterexamples. In many cases, two reformulations provide the same explanation while nonetheless providing different ways of understanding a phenomenon. Hence, reformulating can be valuable even when neither formulation is more explanatory. Methodologically, I draw on a variety of case studies to support my account of reformulation. These range from classical mechanics to quantum chemistry, along with examples from mathematics. Symmetry arguments provide a paradigmatic example: the mathematics of symmetry groups radically recasts quantum mechanics and quantum chemistry. Nevertheless, elementary approaches exist that eschew this additional mathematical apparatus, solving problems in a more tedious but less mathematically-demanding manner. Further examples include reformulations of quantum field theory, Arabic vs. Roman numerals, and Fermat's little theorem in number theory. In each case, my account identifies how reformulations change and improve our understanding of science and mathematics. (shrink)

Advancements in machine learning have fuelled the popularity of using AI decision algorithms in procedures such as bail hearings, medical diagnoses and recruitment. Academic articles, policy texts, and popularizing books alike warn that such algorithms tend to be opaque: they do not provide explanations for their outcomes. Building on a causal account of transparency and opacity as well as recent work on the value of causal explanation, I formulate a moral concern for opaque algorithms that is yet to receive a (...) systematic treatment in the literature: when such algorithms are used in life-changing decisions, they can obstruct us from effectively shaping our lives according to our goals and preferences, thus undermining our autonomy. I argue that this concern deserves closer attention as it furnishes the call for transparency in algorithmic decision-making with both new tools and new challenges. (shrink)

This thesis is an exercise in comparative metaontology. I am centrally concerned with how one might choose between competing metaontological theories. To make my project tractable, I compare two contemporary metaontological approaches dominant in the literature: neo-Quineanism (N-Q) and neo-Aristotelianism (N-A). Peter van Inwagen, a representative of N-Q, claims that ontological inquiry should be conducted in the quantifier-variable idiom of first-order predicate logic; to know what exists, or what a theory says exists, we read our commitments off the regimented sentences (...) that we affirm as true. E.J. Lowe, a representative of N-A objects to N-Q and claims that ontology should be done directly; that it is a mostly a priori activity which is the indispensable intellectual foundation for all rational inquiry. Both metaontological accounts are questionable and there seems to be no decisive way to choose between them. I claim, however, that considerations concerning the explanatory nature of ontology is a key and under-studied factor with respect to ontological method, pointing a way to a possible candidate for metaontological theory choice. I conclude that van Inwagen’s N-Q metaontology is wanting in many respects and further, that he does not provide adequate reasons to dispense with explanation as a feature of ontological inquiry. While explanatory considerations are central to Lowe’s N-A metaontology, I claim that the best that can be hoped for with his particular approach is a form of explanatory antirealism. (shrink)

In the last couple of years, a few seemingly independent debates on scientific explanation have emerged, with several key questions that take different forms in different areas. For example, the questions what makes an explanation distinctly mathematical and are there any non-causal explanations in sciences (i.e., explanations that don’t cite causes in the explanans) sometimes take a form of the question of what makes mathematical models explanatory, especially whether highly idealized models in science can be explanatory and in virtue of (...) what they are explanatory. These questions raise further issues about counterfactuals, modality, and explanatory asymmetries: i.e., do mathematical and non-causal explanations support counterfactuals, and how ought we to understand explanatory asymmetries in non-causal explanations? Even though these are very common issues in the philosophy of physics and mathematics, they can be found in different guises in the philosophy of biology where there is the statistical interpretation of the Modern Synthesis theory of evolution, according to which the post-Darwinian theory of natural selection explains evolutionary change by citing statistical properties of populations and not the causes of changes. These questions also arise in philosophy of ecology or neuroscience in regard to the nature of topological explanations. The question here is can the mathematical (or more precisely topological) properties in network models in biology, ecology, neuroscience, and computer science be explanatory of physical phenomena, or are they just different ways to represent causal structures. The aim of this special issue is to unify all these debates around several overlapping questions. These questions are: are there genuinely or distinctively mathematical and non-causal explanations?; are all distinctively mathematical explanations also non-causal; in virtue of what they are explanatory; does the instantiation, implementation, or in general, applicability of mathematical structures to a variety of phenomena and systems play any explanatory role? This special issue provides a platform for unifying the debates around several key issues and thus opens up avenues for better understanding of mathematical and non-causal explanations in general, but also, it will enable even better understanding of key issues within each of the debates. (shrink)

By appealing to resources found in the scientific understanding literature, I identify in what senses idealisations afford understanding in the context of the (magnetic) Aharonov-Bohm effect. Three types of concepts of understanding are discussed: understanding-what, which has to do with understanding a phenomenon; understanding-with, which has to do with understanding a scientific theory; and understanding-why, which has to do with the reason some phenomenon occurs. Consequently, I outline an account of understanding-with that is suggested by the historical controversy surrounding the (...) Aharonov-Bohm effect. (shrink)

In a recent article, Erasmus, Brunet, and Fisher (2021) argue that Artificial Neural Networks (ANNs) are explainable. They survey four influential accounts of explanation: the Deductive-Nomological model, the Inductive-Statistical model, the Causal-Mechanical model, and the New-Mechanist model. They argue that, on each of these accounts, the features that make something an explanation is invariant with regard to the complexity of the explanans and the explanandum. Therefore, they conclude, the complexity of ANNs (and other Machine Learning models) does not make them (...) less explainable. In this reply, it is argued that Erasmus et al. left out one influential account of explanation from their discussion: the Unificationist model. It is argued that, on the Unificationist model, the features that makes something an explanation is sensitive to complexity. Therefore, on the Unificationist model, ANNs (and other Machine Learning models) are not explainable. It is emphasized that Erasmus et al.’s general strategy is correct. The literature on explainable Artificial Intelligence can benefit by drawing from philosophical accounts of explanation. However, philosophical accounts of explanation do not settle the problem of whether ANNs are explainable because they do not unanimously declare that explanation is invariant with regard to complexity. (shrink)

We provide two programmatic frameworks for integrating philosophical research on understanding with complementary work in computer science, psychology, and neuroscience. First, philosophical theories of understanding have consequences about how agents should reason if they are to understand that can then be evaluated empirically by their concordance with findings in scientific studies of reasoning. Second, these studies use a multitude of explanations, and a philosophical theory of understanding is well suited to integrating these explanations in illuminating ways.

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)

Can understanding be transmitted by testimony, in the same sense that propositional knowledge can be transmitted by testimony? Some contemporary philosophers – call them testimonial understanding pessimists – say No, and others – call them testimonial understanding optimists – say Yes. In this chapter I will articulate testimonial understanding pessimism (§1) and consider some arguments for it (§2).

I want to combine two hitherto largely independent research projects, scientific understanding and mechanistic explanations. Understanding is not only achieved by answering why-questions, that is, by providing scientific explanations, but also by answering what-questions, that is, by providing what I call scientific descriptions. Based on this distinction, I develop three forms of understanding: understanding-what, understanding-why, and understanding-how. I argue that understanding-how is a particularly deep form of understanding, because it is based on mechanistic explanations, which answer why something happens in (...) virtue of what it is made of. I apply the three forms of understanding to two case studies: first, to the historical development of thermodynamics and, second, to the differences between the Clausius and the Boltzmann entropy in explaining thermodynamic processes. (shrink)

Hempel seems to hold the following three views: (H1) Understanding is pragmatic/relativistic: Whether one understands why X happened in terms of Explanation E depends on one's beliefs and cognitive abilities; (H2) Whether a scientific explanation is good, just like whether a mathematical proof is good, is a nonpragmatic and objective issue independent of the beliefs or cognitive abilities of individuals; (H3) The goal of scientific explanation is understanding: A good scientific explanation is the one that provides understanding. Apparently, H1, H2, (...) and H3 cannot be all true. Some philosophers think that Hempel is inconsistent, while some others claim that Hempel does not actually hold H3. I argue that Hempel does hold H3 and that he can consistently hold all of H1, H2, and H3 if he endorses what I call the “understanding argument.” I also show how attributing the understanding argument to Hempel can make more sense of his D-N model and his philosophical analysis of the pragmatic aspects of scientific explanation. (shrink)

What is scientific progress? This paper advances an interpretation of this question, and an account that serves to answer it. Roughly, the question is here understood to concern what type of cognitive change with respect to a topic X constitutes a scientific improvement with respect to X. The answer explored in the paper is that the requisite type of cognitive change occurs when scientific results are made publicly available so as to make it possible for anyone to increase their understanding (...) of X. This account is briefly compared to two rival accounts of scientific progress, based respectively on increasing truthlikeness and accumulating knowledge, and is argued to be preferable to both. (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)

What is the primary intellectual aim of philosophy? The standard view is that philosophy aims to provide true answers to philosophical questions. But if our aim is to settle controversy by answering such questions, our discipline is an embarrassing failure. Moreover, taking philosophy to aim at providing true answers to these questions leads to a variety of puzzles: How do we account for philosophical expertise? How is philosophical progress possible? Why do job search committees not care about the truth or (...) falsity of a candidate’s philosophical views? We argue that philosophy does not aim at discovering true answers to philosophical questions. Instead, its primary intellectual aim is understanding. We argue that many familiar aspects of philosophy become intelligible once we accept this hypothesis. (shrink)

The main purpose of this dissertation is to examine critically and discuss the role of imagination in science and religion, with particular emphasis on its possible epistemic, creative, and meaning-making functions. In order to answer my research questions, I apply theories and concepts from contemporary philosophy of mind on scientific and religious practices. This framework allows me to explore the mental state of imagination, not as an isolated phenomenon but, rather, as one of many mental states that co-exist and interplay (...) in our cogntive architecture. Based on the philosophical discourse of philosophy of mind, four types of imagination are indentified and conceptualized: sensory, propositional, experiential, and creative imagination. These categories are then employed on five phenomena that can be found in scientific and religious environments: metaphors, models, thought experiments, aspect perception, and - in the religious case - rituals. In relation to the concept of religious "seeing" I consider how imaginings may influence visionary experiences and visualization, and compare these phenomena with cases of scientific visualization and eureka experiences. In regard to scientific and religious models, a distinction is made between, on the one hand, two notions of truth and, on the other hand, truth-independent meaning-making. In light of these categories, I differentiate between, and critically discuss, the use of imagination in doxastic, non-doxastic and fictionalist accounts. In light of this investigation, I formulate and defend the position of interactivism, which acknowledges a constant interplay between different attitudes and mental states. In my examination of rituals and scientific and religious thought experiments, special attention is given to the mental capacity to recreate the experiences that are entailed in an imagined scenario. At the end of the investigation, I consider the possible impact that my study might have on how we view science and religion as well as the dialogue bewteen these two fields. (shrink)