Scientific understanding, this paper argues, can be analyzed entirely in terms of a mental act of “grasping” and a notion of explanation. To understand why a phenomenon occurs is to grasp a correct explanation of the phenomenon. To understand a scientific theory is to be able to construct, or at least to grasp, a range of potential explanations in which that theory accounts for other phenomena. There is no route to scientific understanding, then, that does not go by way of (...) scientific explanation. (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)

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)

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)

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)

Modest pessimism about philosophical progress is the view that while philosophy may sometimes make some progress, philosophy has made, and can be expected to make, only very little progress (where the extent of philosophical progress is typically judged against progress in the hard sciences). The paper argues against recent attempts to defend this view on the basis of the pervasiveness of disagreement within philosophy. The argument from disagreement for modest pessimism assumes a teleological conception of progress, according to which the (...) attainment of true answers to the big philosophical questions, or knowledge of them, is the primary goal of philosophy. The paper argues that this assumption involves a misconception of the goal of philosophy: if philosophy has a primary goal, its goal is the understanding of philosophical problems rather than knowledge of answers to philosophical questions. Moreover, it is argued that if the primary goal of philosophy is such understanding, then widespread disagreement within philosophy does not indicate that philosophy makes little progress. (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)

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.

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

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

Catherine Elgin has recently argued that a nonfactive conception of understanding is required to accommodate the epistemic successes of science that make essential use of idealizations and models. In this paper, I argue that the fact that our best scientific models and theories are pervasively inaccurate representations can be made compatible with a more nuanced form of scientific realism that I call Understanding Realism. According to this view, science aims at (and often achieves) factive scientific understanding of natural phenomena. I (...) contend that this factive scientific understanding is provided by grasping a set of true modal information about the phenomenon of interest. Furthermore, contrary to Elgin’s view, I argue that the facticity of this kind of scientific understanding can be separated from the inaccuracy of the models and theories used to produce it. (shrink)

In a number of very influential publications, Epstein and Hoover (among other authors) have recently argued that a thoroughly micro-foundationalist approach towards economics is unconvincing for metaphysical reasons. However, as we show in this article, this metaphysical/social ontological approach to the debate fails to resolve the status of micro-foundations in the practice of economic modelling. To overcome this, we argue that endogenizing a model—that is, providing micro-foundations for it—correlates with making that model more complex. Specifically, we show that models with (...) more micro-foundations tend to have more parameters or variables. This matters, as there are well-established methodological reasons for preferring models with fewer parameters or variables—ceteris paribus. We therefore conclude that, from a practice-based point of view, micro-foundations are only defensible to the extent that they significantly improve the ability of the relevant model to fit the data of interest. In this way, we arrive at a practice-based, methodological reconceptualization of the debate surrounding the need for micro-foundations in economics. (shrink)

Brian Epstein has recently argued that a thoroughly microfoundationalist approach towards economics is unconvincing for metaphysical reasons. Generally, Epstein argues that for an improvement in the methodology of social science we must adopt social ontology as the foundation of social sciences; that is, the standing microfoundationalist debate could be solved by fixing economics’ ontology. However, as I show in this paper, fixing the social ontology prior to the process of model construction is optional instead of necessary and that metaphysical-ontological commitments (...) are often the outcome of model construction, not its starting point. By focusing on the practice of modeling in economics the paper provides a useful inroad into the debate about the role of metaphysics in the natural and social sciences more generally. (shrink)

In this paper I explore a method for refusing uptake when explanations are morally and epistemically troubling. Gaile Pohlhaus Jr has shown that imploring marginalised people to “understand” marginalising practices amounts to a request that they legitimise their own marginalisation. In this paper, I expand upon this analysis with the aim of describing a method for withholding understanding. First, I analyse understanding through its association with explanation. Drawing on pragmatic theories, I describe explanations as speech acts whose success depends on (...) the explainee granting the explainer uptake by revising their background assumptions. Those background assumptions sometimes reference troubling generalisations, and in those cases, the explanation must be blocked. Accordingly, I formalise a form of explanatory resistance in which the explainee feigns misunderstanding to corner the explainer into exposing the problematic assumptions upon which their explanation depends. Second, I situate wrongful requests for understanding within the epistemic injustice schema as “explanatory injustices,” emphasising the fact that marginalised groups are also specifically marginalised within the explanation economy. I conclude that we should be more cognisant of the way in which explanations track power, and be prepared to undertake resistance in order to expose moral and epistemic shortcomings in how we explain. (shrink)

The feeling of clarity can be dangerously seductive. It is the feeling associated with understanding things. And we use that feeling, in the rough-and-tumble of daily life, as a signal that we have investigated a matter sufficiently. The sense of clarity functions as a thought-terminating heuristic. In that case, our use of clarity creates significant cognitive vulnerability, which hostile forces can try to exploit. If an epistemic manipulator can imbue a belief system with an exaggerated sense of clarity, then they (...) can induce us to terminate our inquiries too early — before we spot the flaws in the system. How might the sense of clarity be faked? Let’s first consider the object of imitation: genuine understanding. Genuine understanding grants cognitive facility. When we understand something, we categorize its aspects more easily; we see more connections between its disparate elements; we can generate new explanations; and we can communicate our understanding. In order to encourage us to accept a system of thought, then, an epistemic manipulator will want the system to provide its users with an exaggerated sensation of cognitive facility. The system should provide its users with the feeling that they can easily and powerfully create categorizations, generate explanations, and communicate their understanding. And manipulators have a significant advantage in imbuing their systems with a pleasurable sense of clarity, since they are freed from the burdens of accuracy and reliability. I offer two case studies of seductively clear systems: conspiracy theories; and the standardized, quantified value systems of bureaucracies. (shrink)

Science frequently gives us multiple, compatible ways of solving the same problem or formulating the same theory. These compatible formulations change our understanding of the world, despite providing the same explanations. According to what I call "conceptualism," reformulations change our understanding by clarifying the epistemic structure of theories. I illustrate conceptualism by analyzing a typical example of symmetry-based reformulation in chemical physics. This case study poses a problem for "explanationism," the rival thesis that differences in understanding require ontic explanatory differences. (...) To defend conceptualism, I consider how prominent accounts of explanation might accommodate this case study. I argue that either they do not succeed, or they generate a skeptical challenge. (shrink)

Reformulating a scientific theory often leads to a significantly different way of understanding the world. Nevertheless, accounts of both theoretical equivalence and scientific understanding have neglected this important aspect of scientific theorizing. This essay provides a positive account of how reformulation changes our understanding. My account simultaneously addresses a serious challenge facing existing accounts of scientific understanding. These accounts have failed to characterize understanding in a way that goes beyond the epistemology of scientific explanation. By focusing on cases in which (...) we have differences in understanding without differences in explanation, I show that understanding does not reduce to explanation. (shrink)

What exactly do we need in order to enjoy the cognitive benefit that is supposed to be provided by an explanation? Some philosophers :15–37, 2012, Episteme 10:1–17, 2013, Eur J Philos Sci 5:377–385, 2015, Understanding, explanation, and scientific knowledge, Cambridge University Press, Cambridge, 2017) would say that all that we need is to know the explanation. Others :1–26, 2012; Strevens in Stud Hist Philos Sci Part A 44:510–515, 2013) would say that achieving understanding with the help of an explanation requires (...) more than that, that it requires a grasping or an understanding of the explanation. My aim in this paper is to come up with a new answer to this problem by exploring the shortcomings of the received view of understanding. In my view, besides having testimonial knowledge of an explanation, obtaining explanatory understanding requires full cognitive access to the explanation. (shrink)

This paper connects two important debates in epistemology—to wit, on the goal of inquiry and on the nature of understanding—and offers a unified knowledge-based account of both.

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

What is human understanding and why should we care about it? I propose a method of philosophical investigation called ‘function-first epistemology’ and use this method to investigate the nature and value of understanding-why. I argue that the concept of understanding-why serves the practical function of identifying good explainers, which is an important role in the general economy of our concepts. This hypothesis sheds light on a variety of issues in the epistemology of understanding including the role of explanation, the relationship (...) between understanding-why and knowledge, and the value of understanding-why. I conclude that understanding-why is valuable and yet knowledge plays more important roles in our epistemic life. (shrink)

Can we transmit understanding via testimony in more or less the same way in which we transmit knowledge? The standard view in social epistemology has a straightforward answer: no, we cannot. Three arguments supporting the standard view have been formulated so far. The first appeals to the claim that gaining understanding requires a greater cognitive effort than acquiring testimonial knowledge does. The second appeals to a certain type of epistemic trust that is supposedly characteristic of knowledge transmission (and maybe of (...) the transmission of epistemic goods in general) and that is allegedly incompatible with understanding. The third aims to show that there is a certain aspect of understanding (what epistemologists these days like to call “grasping”) that cannot be passed on to another person via testimony alone. In this article, I show that all of these arguments can be resisted. Thus, there seem to be no compelling reasons to embrace the standard view. (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)

Analytic Philosophy, Volume 62, Issue 1, Page 33-56, March 2021.

In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will (...) lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post hoc interpretability that seems to be predominant in most recent literature. (shrink)

I argue for the unity of imagination in two prima facie diverse contexts: experiences of beauty and achievements of understanding. I develop my argument in three steps. First, I begin by describing a type of aesthetic experience that is grounded in a set of imaginative activities on the part of the person having the experience. Second, I argue that the same set of imaginative activities that grounds this type of aesthetic experience also contributes to achievements of understanding. Third, I show (...) that my unified account of imagination has important implications: it sheds light on two puzzling phenomena, the aesthetic value of science and the cognitive value of art. (shrink)

The paper argues that an account of understanding should take the form of a Carnapian explication and acknowledge that understanding comes in degrees. An explication of objectual understanding is defended, which helps to make sense of the cognitive achievements and goals of science. The explication combines a necessary condition with three evaluative dimensions: an epistemic agent understands a subject matter by means of a theory only if the agent commits herself sufficiently to the theory of the subject matter, and to (...) the degree that the agent grasps the theory (i.e., is able to make use of it), the theory answers to the facts and the agent’s commitment to the theory is justified. The threshold for outright attributions of understanding is determined contextually. The explication has descriptive as well as normative facets and allows for the possibility of understanding by means of non-explanatory (e.g., purely classificatory) theories. (shrink)

Testimony spreads information. It is also commonly agreed that it can transfer knowledge. Whether it can work as an epistemic source of understanding is a matter of dispute. However, testimony certainly plays a pivotal role in the proliferation of understanding in the epistemic community. But how exactly do we learn, and how do we make advancements in understanding on the basis of one another’s words? And what can we do to maximize the probability that the process of acquiring understanding from (...) one another succeeds? These are very important questions in our current epistemological landscape, especially in light of the attention that has been paid to understanding as an epistemic achievement of purely epistemic value. Somewhat surprisingly, the recent literature in social epistemology does not offer much on the topic. The overarching aim of this paper is to provide a tentative model of understanding that goes in-depth enough to safely address the question of how understanding and testimony are related to one another. The hope is to contribute, in some measure, to the effort to understand understanding, and to explain two facts about our epistemic practices: the fact that knowledge and understanding relate differently to testimony, and the fact that some pieces of testimonial information are better than others for the sake of providing one with understanding and of yielding advancements in one’s epistemic standing. (shrink)

Can we gain understanding from testifiers who themselves fail to understand? At first glance, this looks counterintuitive. How could a hearer who has no understanding or very poor understanding of a certain subject matter non-accidentally extract items of information relevant to understanding from a speaker’s testimony if the speaker does not understand what she is talking about? This paper shows that, when there are theories or representational devices working as mediators, speakers can intentionally generate understanding in their hearers by engaging (...) in relevant speech acts without understanding the topic of these speech acts themselves. More specifically, I argue that testifiers can intentionally elicit understanding of empirical phenomena in their hearers even if they themselves lack such understanding – granted that they properly understood the epistemic mediators involved. (shrink)

In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will (...) lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post hoc interpretability that seems to be predominant in most recent literature. (shrink)

Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization that was developed to address just this sort of epistemic opacity. The visualization (...) is unusual in that it depicts the dynamics and structure of a computer model instead of that model’s target system, and because it is generated algorithmically. Using considerations from epistemology and aesthetics, we explore how this new kind of visualization increases scientific understanding of the content and function of computer models in systems biology to reduce epistemic opacity. (shrink)

One can find in the literature two sets of views concerning the relationship between understanding and explanation: that one understands only if 1) one has knowledge of causes and 2) that knowledge is provided by an explanation. Taken together, these tenets characterize what I call the narrow knowledge account of understanding. While the first tenet has recently come under severe attack, the second has been more resistant to change. I argue that we have good reasons to reject it on the (...) basis of theoretical models that provide how-possibly explanations. These models, while they do not explain in the strict sense, afford understanding. In response, I propose an alternative epistemology of understanding, broad KAU, that takes cases of theoretical modelling into account. (shrink)

In epistemology and philosophy of science, there has been substantial debate about truth’s relation to understanding. “Non-factivists” hold that radical departures from the truth are not always barriers to understanding; “quasi-factivists” demur. The most discussed example concerns scientists’ use of idealizations in certain derivations of the ideal gas law from statistical mechanics. Yet, these discussions have suffered from confusions about the relevant science, as well as conceptual confusions. Addressing this example, we shall argue that the ideal gas law is best (...) interpreted as favoring non-factivism about understanding, but only after delving a bit deeper into the statistical mechanics that has informed these arguments and stating more precisely what non-factivism entails. Along the way, we indicate where earlier discussions have gone astray, and highlight how a naturalistic approach furnishes more nuanced normative theses about the interaction of rationality, understanding, and epistemic value. (shrink)

There has been a burst of work in the last couple of decades on mechanistic explanation, as an alternative to the traditional covering-law model of scientific explanation. That work makes some interesting claims about mechanistic explanations rendering phenomena ‘intelligible’, but does not develop this idea in great depth. There has also been a growth of interest in giving an account of scientific understanding, as a complement to an account of explanation, specifically addressing a three-place relationship between explanation, world, and the (...) scientific community. The aim of this paper is to use the contextual theory of scientific understanding to build an account of understanding phenomena using mechanistic explanations. This account will be developed and illustrated by examining the mechanisms of supernovae, which will allow synthesis of treatment of the life sciences and social sciences on the one hand, where many accounts of mechanisms were originally developed, and treatment of physics on the other hand, where the contextual theory drew its original inspiration. (shrink)

Philosophical debates on how to account for the progress of science have traditionally divided along the realism-anti-realism axis. Relatively recent developments in epistemology, however, have opened up a new knowledge-understanding axis to the debate. This chapter presents a novel understanding-based account of scientific progress that takes its motivation from problem-solving practices in science. Problem-solving is characterized as a means of measuring degree of understanding, which is argued to be the principal epistemic (or cognitive) aim of science, over and against knowledge. (...) This account is then defended against knowledge reductionism by showing how each plays an important and distinctive role in science. (shrink)

The paper argues that an account of understanding should take the form of a Carnapian explication and acknowledge that understanding comes in degrees. An explication of objectual understanding is defended, which helps to make sense of the cognitive achievements and goals of science. The explication combines a necessary condition with three evaluative dimensions: An epistemic agent understands a subject matter by means of a theory only if the agent commits herself sufficiently to the theory of the subject matter, and to (...) the degree that the agent grasps the theory (i.e., is able to make use of it), the theory answers to the facts and the agent’s commitment to the theory is justified. The threshold for outright attributions of understanding is determined contextually. The explication has descriptive as well as normative facets and allows for the possibility of understanding by means of non-explanatory (e.g., purely classificatory) theories. (shrink)

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

Explaining phenomena is a primary goal of science. Consequently, it is unsurprising that gaining a proper understanding of the nature of explanation is an important goal of science education. In order to properly understand explanation, however, it is not enough to simply consider theories of the nature of explanation. Properly understanding explanation requires grasping the relation between explanation and understanding, as well as how explanations can lead to scientific knowledge. This article examines the nature of explanation, its relation to understanding, (...) and how explanations are used to generate scientific knowledge via inferences to the best explanation. Studying these features and applications of explanation not only provides insight into a concept that is important for science education in its own right, but also sheds light on an aspect of recent debates concerning the so-called consensus view of nature of science (NOS). Once the relation between explanation, understanding, and knowledge is clear, it becomes apparent that science is unified in important ways. Seeing this unification provides some support for thinking that there are general features of NOS of the sort proposed by the consensus view and that teaching about these general features of NOS should be a goal of science education. (shrink)

We might think that thought experiments are at their most powerful or most interesting when they produce new knowledge. This would be a mistake; thought experiments that seek understanding are just as powerful and interesting, and perhaps even more so. A growing number of epistemologists are emphasizing the importance of understanding for epistemology, arguing that it should supplant knowledge as the central notion. In this chapter, I bring the literature on understanding in epistemology to bear on explicating the different ways (...) that thought experiments increase three important kinds of understanding: explanatory, objectual and practical. (shrink)

The literature on the nature of understanding can be divided into two broad camps. Explanationists believe that it is knowledge of explanations that is key to understanding. In contrast, their manipulationist rivals maintain that understanding essentially involves an ability to manipulate certain representations. The aim of this paper is to provide a novel knowledge based account of understanding. More specifically, it proposes an account of maximal understanding of a given phenomenon in terms of fully comprehensive and maximally well-connected knowledge of (...) it and of degrees of understanding in terms of approximations to such knowledge. It is completed by a contextualist semantics for outright attributions of understanding according to which an attribution of understanding is true of one just in case one knows enough about it to perform some contextually determined task. It is argued that this account has an edge over both its explanationist and manipulationist competitors. (shrink)

How can we use models to understand real phenomena if models misrepresent the very phenomena we seek to understand? Some accounts suggest that models may afford understanding by providing causal knowledge about phenomena via how-possibly explanations. However, general equilibrium models, for example, pose a challenge to this solution since their contribution appears to be purely mathematical results. Despite this, practitioners widely acknowledge that it improves our understanding of the world. I argue that the Arrow–Debreu model provides a mathematical how-possibly explanation (...) which establishes claims of mathematical dependence. The account developed reveals how mathematical knowledge can inform claims about the world, allow ‘what-if-things-had-been-different’ inferences, and thus improve our understanding. (shrink)

Philosophers of science since Nagel have been interested in the links between intertheoretic reduction and explanation, understanding and other forms of epistemic progress. Although intertheoretic reduction is widely agreed to occur in pure mathematics as well as empirical science, the relationship between reduction and explanation in the mathematical setting has rarely been investigated in a similarly serious way. This paper examines an important particular case: the reduction of arithmetic to set theory. I claim that the reduction is unexplanatory. In defense (...) of this claim, I offer evidence from mathematical practice, and I respond to contrary suggestions due to Steinhart, Maddy, Kitcher and Quine. I then show how, even if set-theoretic reductions are generally not explanatory, set theory can nevertheless serve as a legitimate foundation for mathematics. Finally, some implications of my thesis for philosophy of mathematics and philosophy of science are discussed. In particular, I suggest that some reductions in mathematics are probably explanatory, and I propose that differing standards of theory acceptance might account for the apparent lack of unexplanatory reductions in the empirical sciences. (shrink)

Some experiences change who we are in ways we cannot understand until we have that very experience. In this paper I argue that so-called “transformative experiences” can not only bring about new understanding, but can actually be brought out by the gain of understanding itself. Coming to understand something new can change you. I argue that not only is understanding acquisition potentially a kind of transformative experience; given some of the recent philosophy of the phenomenology of understanding, it is a (...) kind that is potentially rare in not being dependent on a particular subjective phenomenology. The goal of this paper threefold. First, I argue that coming to gain cognitive understanding of an academic subject matter can, under some circumstances, itself be a transformative experience. A second, subsidiary goal of this paper is to argue that such transformative understanding merits further study. Finally, I give a rough taxonomy of under what conditions we should expect understanding acquisition to be transformative. (shrink)

The paper provides a systematic overview of recent debates in epistemology and philosophy of science on the nature of understanding. We explain why philosophers have turned their attention to understanding and discuss conditions for “explanatory” understanding of why something is the case and for “objectual” understanding of a whole subject matter. The most debated conditions for these types of understanding roughly resemble the three traditional conditions for knowledge: truth, justification and belief. We discuss prominent views about how to construe these (...) conditions for understanding, whether understanding indeed requires conditions of all three types and whether additional conditions are needed. (shrink)

In this dissertation, I argue that understanding possesses unique epistemic value. I propose and defend a novel account of understanding that I call the management account of understanding, which is the view that an agent A understands a subject matter S just in case A has the ability to extract the relevant information and exploit it with the relevant cognitive capacities to answer questions in S. Since inquiry is the process of raising and answering questions, I argue that without understanding, (...) it would be impossible to engage in successful inquiry. I argue that understanding is indispensable for effective cognition and that it is irreducible to other epistemic categories, such as knowledge, justification, or rationality. Understanding is an irreducible component of epistemic excellence. In arguing for this account, I focus on the nature and requirements of cognition and pay less attention to intuitions about hypothetical cases, in contrast to previous approaches to understanding. I draw upon psychology and cognitive science, especially work on the frame problem, to demonstrate the importance (and difficulty) of finding relevant information and exploiting it in relevant ways to answer questions. One’s cognitive system must be organized in such a way that one has the ability to think the relevant thoughts in the relevant ways to answer the relevant questions. This reveals well-defined directions in which future research on understanding should move. In addition to proposing and defending the management account of understanding, I explore several features of understanding, including some interesting consequences of the management account. I argue that the management account and commonly used methods of measuring degrees of understanding imply that understanding can be based on false beliefs. I argue that the management account (and any account of understanding that implies understanding is an ability) implies that understanding can be acquired by luck. I also argue that understanding is ontologically prior to explanation: a good explanation is essentially an act that produces understanding in the relevant cognitive background. (shrink)