Understanding

We all know unequivocally that we hold certain rights entitled to every human being, that are enshrined in and guaranteed by the constitution, that would have been there, even if we were not yet stepped into the world. These rights comprise a broad range from our right to food, to our right to education, our right to freedom, and the like. However, in her recent book, Watson points out an unnoticed rights framework regarding epistemic goods to which are all entitled. (...) Drawing from Watson’s work, this article concerned itself with the question ‘How can students’ epistemic rights be secured?’ Toward this aim, it first takes epistemic nudging as an adjunct to tease out whether there is a unity between everyday interactions and educational settings regarding which epistemic goods we end up in securing epistemic rights. It concludes that in educational settings, securing epistemic rights must end with understanding, not knowledge, and that in the virtue of grasping conditions, epistemic rights come in varying degrees. (shrink)

The primary aim of this paper is to argue that the value of understanding derives in part from a kind of subjective stability of belief that we call epistemic resilience. We think that this feature of understanding has been overlooked by recent work, and we think it’s especially important to the value of understanding for social cognitive agents such as us. We approach the concept of epistemic resilience via the idea of the experience of epistemic ownership and argue that the (...) former concept has Platonic pedigree. Contrary to longstanding exegetical tradition, we think that Plato solves the “Meno problem” with an appeal to the epistemic resilience characteristic of understanding, not the well-groundedness characteristic of canonical cases of propositional knowledge. Finally, we apply our discussion to the case of science outreach and the challenge of global warming skepticism and conclude with directions for future research. (shrink)

A short discussion of whether or not an error theorist of understanding should construe understanding in terms of belief. This is a comment on a discussion between Dean Pettit and Steven Gross.

This chapter introduces some central issues in Epistemology, and, like others in the open textbook series Introduction to Philosophy, is set up for rewarding college classroom use, with discussion/reflection questions matched to clearly-stated learning objectives,, a brief glossary of the introduced/bolded terms/concepts, links to further open source readings as a next step, and a readily-accessible outline of the classic between William Clifford and William James over the "ethics of belief." The chapter introduces questions of epistemic value through Plato's famous example (...) of the 'road to Larissa,' and goes on to explain work on doxastic responsibility, on intellectual “virtue”/“vice,” and on epistemic value monism vs. pluralism. Section 1; Epistemic Value and the Value Problem; Section 2: The Ethics of Belief; Section 3: Virtue & Vice Epistemologies; Section 4: Epistemic Paternalism. (shrink)

There is, among philosophers involved in the debate concerning the nature and epistemology of understanding, a general recognition that this state has a grasping component that accounts for some of its distinctive features. This paper defends the view that this component of understanding consists of a knowledge of how to use a particular explanation to account for a given phenomenon. The way in which a subject acquires this knowledge is examined and the result of this examination is shown to highlight (...) the particular contribution of the grasping component of understanding to the distinctive features of this state. (shrink)

Knowledge is widely regarded as being incompatible with epistemic luck, but according to several philosophers, the same does not hold for understanding. This paper examines to what extent understanding is vulnerable to epistemic luck. After discussing the weaknesses of some of the cases that have been offered to support the conclusion that understanding tolerates environmental epistemic luck, I turn to a more recent one offered in favour of the opposite conclusion. I argue that this case does not manage to establish (...) that understanding is vulnerable to environmental luck; this even if the fact that understanding comes in degrees is taken into account. Finally, I examine the vulnerability of understanding to intervening luck – the type of luck present in classical Gettier cases – and conclude that when such luck is present, one's understanding is necessarily sub-optimal; a conclusion that does not hold, according to what I argue, when it comes to environmental luck. (shrink)

Is art epistemically valuable? Catherine Z. Elgin answers this question in the affirmative. She argues for the epistemic value of art on the basis of her innovative epistemological theory, in which the focus is shifted from knowledge and truth to a non-factive account of understanding. After an exposition and critique of her view, as she develops it in her most recent book “True Enough”, I will build on some of her ideas in order to strengthen her account.

Duncan Pritchard (2008, 2009, 2010, forthcoming) has argued for an elegant solution to what have been called the value problems for knowledge at the forefront of recent literature on epistemic value. As Pritchard sees it, these problems dissolve once it is recognized that that it is understanding-why, not knowledge, that bears the distinctive epistemic value often (mistakenly) attributed to knowledge. A key element of Pritchard’s revisionist argument is the claim that understanding-why always involves what he calls strong cognitive achievement—viz., cognitive (...) achievement that consists always in either (i) the overcoming of a significant obstacle or (ii) the exercise of a significant level of cognitive ability. After outlining Pritchard’s argument, we show (contra Pritchard) that understanding-why does not essentially involve strong cognitive achievement. Interestingly, in the cases in which understanding-why is distinctively valuable, it is (we argue) only because there is sufficiently rich objectual understanding in the background. If that’s right, then a plausible revisionist solution to the value problems must be sensitive to different kinds of understanding and what makes them valuable, respectively. (shrink)

In the course of developing an account of scientific progress, C. D. McCoy (2022) appeals centrally to understanding as well as to problem-solving. On the face of it, McCoy’s account could thus be described as a kind of hybrid of the understanding-based account that I favor (Dellsén 2016, 2021) and the functional (a.k.a. problem-solving) account developed most prominently by Laudan (1977; see also Kuhn 1970; Shan 2019). In this commentary, I offer two possible interpretations of McCoy’s account and explain why (...) I do not find it entirely compelling on either interpretation. (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)

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)

I present a puzzle – the “puzzle of aesthetic testimony” – along with a solution to it that appeals to the impossibility of testimonial understanding. I'll criticize this solution by defending the possibility of testimonial understanding, including testimonial aesthetic understanding.

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).

Hot-cold empathy gaps are a pervasive phenomena wherein one’s predictions about others tend to skew ‘in the direction’ of one’s own current visceral states. For instance, when one predicts how hungry someone else is, one’s prediction will tend to reflect one’s own current hunger state. These gaps also obtain intrapersonally, when one attempts to predict what one oneself would do at a different time. In this paper, we do three things: We draw on empirical evidence to argue that so-called hot-cold (...) empathy gaps arise when one projects one’s own current state into a simulation about another. Second, we argue that this process does not typically confer knowledge, even when the predictions it produces happen to be accurate. Third, we suggest that these results can be used to develop a challenge for L.A. Paul's view that authentic action sometimes requires subjective knowledge of one’s own values and how these values relate to relevant outcomes. We then sketch an alternative view of the epistemic grounds of authenticity, one on which authenticity requires a kind of understanding. The relevant form of understanding can be achieved by subjective knowledge but can also be achieved elsewise, such as through testimony from a close friend about what one values. (shrink)

Some philosophers recently have objected that veritism cannot explain the epistemic value of understanding-why. And they have proposed two anti-veritist accounts. In this paper, I first introduce their objection and argue that it fails. Next, I consider a strengthened version of their objection and argue that it also fails. After that, I suggest a new veritist account: Understanding-why entails believing the truth that what is grasped is accurate, and it is this true belief, along with many other true beliefs understanding-why (...) entails, that makes understanding-why finally epistemically valuable. Then, I explain why the two anti-veritist accounts are both false. Finally, I briefly discuss the idea that understanding involves a kind of know-how and show how veritism can explain the epistemic value of know-how in general. (shrink)

Very plausibly, there is something important missing in our lives if we are thoroughly ignorant or misled about reality – even if, as in a kind of Truman Show scenario, intervention or fantastic luck prevents unhappiness and practical failure. But why? I argue that perfectionism about well-being offers the most promising explanation. My version says, roughly, that we flourish when we exercise our self-defining capacities successfully according to their constitutive standards. One of these self-defining capacities, or capacities whose exercise reveals (...) who we are, is Reason, our capacity for normative self-governance. I argue that in its practical use, Reason formally aims at competently realizing self-chosen valuable ends that are in harmony with each other, or valuable achievements. In its theoretical use, it formally aims at competently grasping fundamental enough subject matters, or a kind of understanding. Because success by reason’s own standards requires many things to go right, there are many different ways in which we can fall short. Some of them amount to partial success. But some, like incompetent inquiry that fails to yield understanding of its target, or taking inefficient means to a worthless end, are robust failures that amount to epistemic or agential unflourishing, and thus to a form of ill-being. (shrink)

Recent work on the imagination has stressed the epistemic role of imaginative experiences, notably in justifying modal beliefs. An immediate problem with this is that modal beliefs appear to admit of justification through the mere exercise of rational capacities. For instance, mastery of the concepts of pig, flying, and possibility should suffice to form a justified belief that flying pigs are possible, regardless of whether one imagines a flying pig. In this paper, I consider three ways to defend the epistemic (...) role of imagination in the face of this problem. One is that modal beliefs simply admit of justification by two separate sources: rational capacities and imaginative experience. Another is that while beliefs about logical or conceptual modality can be justified entirely by rational capacities, beliefs about metaphysical modality require imaginative experiences. The third, which I defend, is that imagination is relevant in the first instance not to modal knowledge but to modal understanding: even where imaginative experience is unnecessary for the justification of modal beliefs, it is indispensable for directly grasping certain modal facts. (shrink)

A problem-solving-based account of scientific progress that takes understanding as the principal epistemic aim of science is developed and defended against knowledge reductionism.

Wittgenstein seeks to throw light on our concept of understanding by looking at how misunderstandings arise and what kinds of failure they involve. He discerns a peculiar sort of misunderstanding in the writings of the social anthropologist James Frazer. In Frazer’s hands, the anthropological project of enabling us to understand human behavior seems to yield the result that there are certain forms of human behavior that simply cannot be understood. The source of Frazer’s misunderstanding, according to Wittgenstein, is that he (...) places narrow requirements on what could count as meaningful, prior to and independently of his encounter with the subjects of his interpretation. Frazer, like some of the philosophers Wittgenstein addresses in his other works, succumbs to nonsense in his very attempt to draw the limits of sense. My aim in this chapter is to clarify the connections between Wittgenstein’s criticism of Frazer and his criticism of his fellow philosophers, in particular of Frege. The materials I draw on stem from various periods in Wittgenstein’s career, and they reveal, in my mind, an important continuity in Wittgenstein’s thought: addressing misunderstanding, in Wittgenstein’s view, is fundamentally an ethical problem, not a theoretical one. (shrink)

The notion of understanding occupies an increasingly prominent place in contemporary epistemology, philosophy of science, and moral theory. A central and ongoing debate about the nature of understanding is how it relates to the truth. In a series of influential contributions, Catherine Elgin has used a variety of familiar motivations for antirealism in philosophy of science to defend a non- factive theory of understanding. Key to her position are: (i) the fact that false theories can contribute to the upwards trajectory (...) of scientific understanding, and (ii) the essential role of inaccurate idealisations in scientific research. Using Elgin’s arguments as a foil, I show that a strictly factive theory of understanding has resources with which to offer a unified response to both the problem of idealisations and the role of false theories in the upwards trajectory of scientific understanding. Hence, strictly factive theories of understanding are viable notwithstanding these forceful criticisms. (shrink)

This essay develops an interpretation of Kant’s conception of the faculty of reason as the capacity for what he calls "comprehension" (Begreifen). In doing so, it first discusses Kant's characterizations of reason in relation to what he describes as the two highest grades of cognition—insight and comprehension. Then it discusses how the resulting conception of reason relates to more familiar characterizations as the faculty for inference and the faculty of principles. In doing so, it focuses on how the idea of (...) reason as aiming at comprehension expresses the modesty about reason that is essential to Kant’s critical philosophy. It concludes by showing how this grounds an elegant account of the unity of theoretical and practical reason—one that does justice both to the "primacy" that Kant assigns to practical reason and the fact that this "primacy" is grounded in a more basic unity. (shrink)

Starting with the slogan that understanding is a 'knowledge of causes,' Stephen Grimm and John Greco have argued that understanding comes from a knowledge of dependence relations. Grounding is the trendiest dependence relation on the market, and if Grimm and Greco are correct, then instances of grounding should also give rise to understanding. In this paper, I will show that this prediction is correct-grounding does indeed generate understanding in just the way that Grimm and Greco anticipate. However, grounding examples of (...) understanding also show that Grimm and Greco are not telling the full story when it comes to understanding. Understanding can only be generated by a particular subset of dependence relations-those dependence relations that are also explanatory. Grimm and Greco should thus appeal to a privileged class of dependence relations, relations like grounding that can give rise to explanation as well. (shrink)

Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this paper, I argue that non-epistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (ii) an internal opacity (...) problem, where greater inductive risk demands a higher level of transparency regarding the inferences the model makes. (shrink)

Philosophy and Phenomenological Research, EarlyView.

It is natural to regard understanding as having a rational dimension, in the sense that understanding seems to require having justification for holding certain beliefs about the world. Some philosophers however argue that justification is not required to gain understanding of phenomena. In the present paper, my intention is to provide a critical examination of the arguments that have been offered against the view that understanding requires justification in order to show that, contrary to what they purport to establish, justification (...) remains a plausible requirement on understanding. (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)

Models are indispensable tools of scientific inquiry, and one of their main uses is to improve our understanding of the phenomena they represent. How do models accomplish this? And what does this tell us about the nature of understanding? While much recent work has aimed at answering these questions, philosophers' focus has been squarely on models in empirical science. I aim to show that pure mathematics also deserves a seat at the table. I begin by presenting two cases: Cramér’s random (...) model of the prime numbers and the function field model of the integers. These cases show that mathematicians, like empirical scientists, rely on unrealistic models to gain understanding of complex phenomena. They also have important implications for some much-discussed theses about scientific understanding. First, modeling practices in mathematics confirm that one can gain understanding without obtaining an explanation. Second, these cases undermine the popular thesis that unrealistic models confer understanding by imparting counterfactual knowledge. (shrink)

Roughly, the noetic account characterizes scientific progress in terms of increased understanding. This chapter outlines a version of the noetic account according to which scientific progress on some phenomenon consists in making scientific information publicly available so as to enable relevant members of society to increase their understanding of that phenomenon. This version of the noetic account is briefly compared with four rival accounts of scientific progress, viz. the truthlikeness account, the problem-solving account, the new functional account, and the epistemic (...) account. In addition, the chapter seeks to precisify the question that accounts of scientific progress are (or should be) aiming to answer, viz. “What type of cognitive change with respect to a given topic or phenomenon X constitutes a (greater or lesser degree of) scientific improvement with respect to X?”. (shrink)

Contemporary debate surrounding the nature of scientific progress has focused upon the precise role played by justification, with two realist accounts having dominated proceedings. Recently, however, a third realist account has been put forward, one which offers no role for justification at all. According to Finnur Dellsén’s (Stud Hist Philos Sci Part A 56:72–83, 2016) noetic account, science progresses when understanding increases, that is, when scientists grasp how to correctly explain or predict more aspects of the world that they could (...) before. In this paper, we argue that the noetic account is severely undermotivated. Dellsén provides three examples intended to show that understanding can increase absent the justification required for true belief to constitute knowledge. However, we demonstrate that a lack of clarity in each case allows for two contrasting interpretations, neither of which serves its intended purpose. On the first, the agent involved lacks both knowledge and understanding; and, on the second, the agent involved successfully gains both knowledge and understanding. While neither interpretation supports Dellsén’s claim that understanding can be prised apart from knowledge, we argue that, in general, agents in such cases ought to be attributed neither knowledge nor understanding. Given that the separability of knowledge and understanding is a necessary component of the noetic account, we conclude that there is little support for the idea that science progresses through increasing understanding rather than the accumulation of knowledge. (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)

This book presents fresh insights into analogue quantum simulation. It argues that these simulations are a new instrument of science. They require a bespoke philosophical analysis, sensitive to both the similarities to and the differences with conventional scientific practices such as analogical argument, experimentation, and classical simulation. -/- The analysis situates the various forms of analogue quantum simulation on the methodological map of modern science. In doing so, it clarifies the functions that analogue quantum simulation serves in scientific practice. To (...) this end, the authors introduce a number of important terminological distinctions. They establish that analogue quantum ‘computation' and ‘emulation' are distinct scientific practices and lead to distinct forms of scientific understanding. The authors also demonstrate the normative value of the computation vs. emulation distinction at both an epistemic and a pragmatic level. -/- The volume features a range of detailed case studies focusing on: i) cold atom computation of many-body localisation and the Higgs mode; ii) photonic emulation of quantum effects in biological systems; and iii) emulation of Hawking radiation in dispersive optical media. Overall, readers will discover a normative framework to isolate and support the goals of scientists undertaking analogue quantum simulation and emulation. This framework will prove useful to both working scientists and philosophers of science interested in cutting-edge scientific practice. (shrink)

What does it take to understand a phenomenon ideally, or to the highest conceivable extent? In this paper, we answer this question by arguing for five necessary conditions for ideal understanding: (i) representational accuracy, (ii) intelligibility, (iii) truth, (iv) reasonable endorsement, and (v) fitting. Even if one disagrees that there is some form of ideal understanding, these five conditions can be regarded as sufficient conditions for a particularly deep level of understanding. We then argue that grasping, novel predictions, and transparency (...) are not reasonable conditions for ideal understanding. (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.

This chapter gives an overview of the various themes and issues discussed in the volume. It includes summaries of all chapters and places the contributions, some of which are part of a critical conversation format, in the context of the larger literature and debates.

Imagine a zombie world that looks “from the outside” just like ours, but where there is no phenomenal consciousness. Creatures that look like us move about just as we do and make the same noises we do, but nobody experiences or feels anything. How much of the epistemic value that’s exemplified in our world survives in that one? The short answer is: any kind of epistemic value that requires the occurrence of consciousness for its exemplification cannot exist in that world, (...) but epistemic value that doesn’t require consciousness can exist. The real question, though, is what kinds of epistemic value require the occurrence of consciousness. We will consider four central epistemic values: justification, truth, acquaintance, and understanding. (shrink)

This volume brings together leading scholars working on understanding and representation in philosophy of science. It features a critical conversation format between contributors that advances debates concerning scientific understanding, scientific representation, and their delicate interplay.

The framework of the modern Western analysis of culture, in terms of the socio-historical situation of the subject and the reciprocal influence of one on the other, has its roots in nineteenth century discussions. This paper will examine two traditions: the hermeneutic approach of Wilhelm Dilthey, and the Völkerpsychologie of Moses Lazarus and Chajim Steinthal. The account will focus on two elements. First, Lazarus and Steinthal attempted to motivate an account based on collective structures, or forms, of rationality made manifest (...) by a people or Volk; the paper will explore their account of how collective structures can be employed in sociohistorical analysis. Second, Dilthey rejected Lazarus’s and Steinthal’s argument that it was possible to identify the norms of action governing social phenomena. Dilthey rejected any account of psychology that took it to be law-governed, even retrospectively, because he argued that the ‘nexus of life’ that is the ultimate basis of the human sciences cannot be reduced to any law-governed or explanatory relationship between the self, society, and nature. However, there is a deep tension in Dilthey’s position here, which is evident in the development of his work over time. The account below will explore this tension and its significance for the understanding of the subject and the role of psychology in the human sciences. (shrink)

This chapter responds to Michael Tamir and Elay Shech’s chapter “Understanding from Deep Learning Models in Context.”.

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)

A lot of philosophical energy has been devoted recently in trying to determine if mathematics can contribute to our understanding of physical phenomena. Not many philosophers are interested, though, if the converse makes sense, i.e., if our cognitive interaction (scientific or otherwise) with the physical world can be helpful (in an explanatory or non-explanatory way) in our efforts to make sense of mathematical facts. My aim in this paper is to try to fill this important lacuna in the recent literature. (...) My answer to the question of this paper is negative. As I will argue, there are serious problems with the main reasons for believing in the first place that it is possible to have physical understanding of mathematical facts. (shrink)

In this paper, I give an overview of different models of understanding attribution and advance a contextualist account of understanding attribution. Whereas other contextualist accounts make the degree in which the epistemic states of the relevant agents satisfy certain invariant conditions context-sensitive, the proposed account makes the conditions themselves context-sensitive.

We are often urged to figure things out for ourselves rather than to rely on other people’s say-so, and thus be ‘epistemically autonomous’ in one sense of the term. But why? For almost any important question, there will be someone around you who is at least as well placed to answer it correctly. So why bother making up your own mind at all? I consider, and then reject, two ‘egoistic’ answers to this question according to which thinking for oneself is (...) beneficial for the autonomous agent herself. I go on to suggest that the reason we should (sometimes) think for ourselves is that doing so (sometimes) increases the collective reliability of the epistemic community to which we belong. In many cases, this will do nothing at all to increase our own chances of forming correct beliefs. So, at least in this respect, the rationale for being epistemically autonomous is entirely ‘altruistic’. (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)

Contrastivism about interrogative understanding is the view that ‘S understands why p’ posits a three-place epistemic relation between a subject S, a fact p, and an alternative to p, q. This thesis stands in stark opposition to the natural idea that a subject S can be said to understand why psimpliciter. I argue that contrastivism offers the best explanation for the fact that evaluations of the form ‘S understands why p’ vary depending on the alternatives to p under consideration. I (...) also show that contrastivism offers valuable resources with which to explain the gradability of interrogative understanding attributions, as well as the sensitivity of these attributions to the perceived degree of epistemic demandingness of different contexts. (shrink)

The philosophical interest in the nature, value, and varieties of human understanding has swelled in recent years. This article will provide an overview of new research in the epistemology of understanding, with a particular focus on the following questions: What is understanding and why should we care about it? Is understanding reducible to knowledge? Does it require truth, belief, or justification? Can there be lucky understanding? Does it require ‘grasping’ or some kind of ‘know-how’? This cluster of questions has largely (...) set the research agenda for the study of understanding in epistemology. This article will conclude by discussing some varieties of understanding and highlight directions for future research. (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)

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)

Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these “stakeholders' desiderata”) in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability (...) of artificial systems and reviews their desiderata. We provide a model that explicitly spells out the main concepts and relations necessary to consider and investigate when evaluating, adjusting, choosing, and developing explainability approaches that aim to satisfy stakeholders' desiderata. This model can serve researchers from the variety of different disciplines involved in XAI as a common ground. It emphasizes where there is interdisciplinary potential in the evaluation and the development of explainability approaches. (shrink)