I discuss here the definition of computer simulations, and more specifically the views of Humphreys, who considers that an object is simulated when a computer provides a solution to a computational model, which in turn represents the object of interest. I argue that Humphreys's concepts are not able to analyse fully successfully a case of contemporary simulation in physics, which is more complex than the examples considered so far in the philosophical literature. I therefore modify Humphreys's definition of simulation. I (...) allow for several successive layers of computational models, and I discuss the relations that exist between these models, the computer, and the object under study. An aim of my proposal is to clarify the distinction between computational models and numerical methods, and to better understand the representational and the computational functions of models in simulations. (shrink)

The act of understanding is at the heart of all scientific activity; without it any ostensibly scientific activity is as sterile as that of a high school student substituting numbers into a formula. Ordinary language often uses visual metaphors in connection with understanding. When we finally understand what someone is trying to point out to us, we exclaim: “I see!” When someone really understands a subject matter, we say that she has “insight”. There appears to be a link between visualization (...) and understanding, and between visualizability and intelligibility. This applies in science no less than in daily life: visualization is regarded as a useful means of achieving scientific understanding, even in the .. (shrink)

By briefly reviewing three well-known scientific revolutions in fundamental physics (the discovery of inertia, of special relativity and of general relativity), I claim that problems that were supposed to be crying for a dynamical explanation in the old paradigm ended up receiving a structural explanation in the new one. This claim is meant to give more substance to Kuhn’s view that revolutions are accompanied by a shift in what needs to be explained, while suggesting at the same time the existence (...) of a pattern that is common to all of the discussed case-studies. It remains to be seen whether also quantum mechanics, in particular entanglement, conforms to this pattern. (shrink)

What is it that makes someone wise, or one person wiser than another? I argue that wisdom consists in knowledge of how to live well, and that this knowledge of how to live well is constituted by various further kinds of knowledge. One concern for this view is that knowledge is not needed for wisdom but rather some state short of knowledge, such as having rational or justified beliefs about various topics. Another concern is that the emphasis on knowing how (...) to live well fails to do justice to the ancient tradition of ‘theoretical wisdom’. I address both of these concerns in filling out the account. (shrink)

The aim of this dissertation is to create a naturalistic philosophical picture of creative capacities that are specific to our species, focusing on artistic ability, religious reflection, and scientific study. By integrating data from diverse domains within a philosophical anthropological framework, I have presented a cognitive and evolutionary approach to the question of why humans, but not other animals engage in such activities. Through an application of cognitive and evolutionary perspectives to the study of these behaviors, I have sought to (...) provide a more solid footing for philosophical anthropological discussions of uniquely human behavior. In particular, I have argued that art, religion and science, which are usually seen as achievements that are quite remote from ordinary modes of reasoning, are subserved by evolved cognitive processes that serve functions in everyday cognitive tasks, that arise early and spontaneously in cognitive development, that are shared cross-culturally, and that have evolved in response to selective pressures in our ancestral past. These mundane cognitive processes provide a measuring rod with which we can assess a diversity of cultural phenomena; they form a unified explanatory framework to approach human culture. I have argued that we can explain uncommon thoughts in terms of interactions between common minds. This dissertation is subdivided into four parts. Part I outlines the problem of human uniqueness, examining theories on how humans conceptualize the world, and what their mental tool box looks like. Part II discusses the evolutionary and cognitive origins of human artistic behavior. Part III focuses on the cognitive science of religion, especially on how it can be applied to the reasoning of theologians and philosophers of religion. Part IV considers the cognitive basis of scientific practice. (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)

Recently, it has been debated as to whether understanding is a species of explanatory knowledge. Those who deny this claim frequently argue that understanding, unlike knowledge, can be lucky. In this paper I argue that current arguments do not support this alleged compatibility between understanding and epistemic luck. First, I argue that understanding requires reliable explanatory evaluation, yet the putative examples of lucky understanding underspecify the extent to which subjects possess this ability. In the course of defending this claim, I (...) also provide a new account of the kind of ‘grasping’ taken to be central to understanding. Second, I show that putative examples of lucky understanding unwittingly deploy a kind of luck that is compatible with knowledge. Finally, appealing to a number of works on explanation and its attendant epistemology, I argue that alleged instances of lucky understanding that overcome these two obstacles will invariably violate certain norms of explanatory inquiry – our paradigmatic understanding-oriented practice. By contrast, knowledge of the same information is immune to these criticisms. Consequently, if understanding is environmentally lucky, it is always inferior to the understanding that a corresponding case of knowledge would provide. (shrink)

In the context of climate change, the adoption of pro-environment behaviour is favoured by the understanding of causal passages within climate science. The understanding of the causes of climate change is necessary in order to be able to take mitigation actions (the subject needs to be aware of its role as a causalagent). Conversely, the understanding of the consequences of climate change is essential for rationally managing the risks, especially in cases where adaptation is needed rather than simple mitigation. The (...) case of ozone depletion confirms this view: the understanding of its causal dynamics played a major role in people’sbehavioural response.Para afrontar el cambio climático, la adopción de un comportamiento en favor del medio ambiente se verá favorecida por la comprensión de los argumentos causales de la climatología. La comprensión de las causas del cambio climático es necesario para que seamos capaces de mitigarlo (cada individuo debe ser consciente de su papel como agente causal). La comprensión de las consecuencias del cambio climático es esencial para gestionar racionalmente los riesgos, especialmente en casos en los que más que mitigarlo, debemos intentar adaptarnos. El caso del agujero de la capa de ozono confirma este punto de vista: la comprensión de la dinámica causal desempeñó un papel principal en el cambio de conductas de la gente. (shrink)

In this article I argue that there are two different types of understanding: the understanding we get from explanations, and the understanding we get from unification. This claim is defended by first showing that explanation and unification are not as closely related as has sometimes been thought. A critical appraisal of recent proposals for understanding without explanation leads us to discuss the example of a purely classificatory biology: it turns out that such a science can give us understanding of the (...) world through unification of the phenomena, even though it does not give us any explanations. The two types of understanding identified in this paper, while strictly separate, do have in common that both consist in seeing how the individual phenomena of the universe hang together. Explanations give us connections between the phenomena through the asymmetric, ‘vertical’ relation of determination; unifications give us connections through the symmetric, ‘horizontal’ relation of kinship. We then arrive at a general definition of understanding as knowledge of connections between the phenomena, and indicate that there might be more than two types of understanding. (shrink)

The Linda paradox is a key topic in current debates on the rationality of human reasoning and its limitations. We present a novel analysis of this paradox, based on the notion of verisimilitude as studied in the philosophy of science. The comparison with an alternative analysis based on probabilistic confirmation suggests how to overcome some problems of our account by introducing an adequately defined notion of verisimilitudinarian confirmation.

Peter Lipton has argued that understanding can exist in the absence of explanation. We argue that this does not denigrate explanation's importance to understanding. Specifically, we show that all of Lipton's examples are consistent with the idea that explanation is the ideal of understanding, i.e. other modes of understanding ought to be assessed by how well they replicate the understanding provided by a good and correct explanation. We defend this idea by showing that for all of Lipton's examples of non-explanatory (...) understanding of why p , there exists a correct and reasonably good explanation that would provide greater understanding of p. (shrink)

Defenders of pragmatic encroachment in epistemology (or what I call practicalism) need to address two main problems. First, the view seems to imply, absurdly, that knowledge can come and go quite easily—in particular, that it might come and go along with our variable practical interests. We can call this the stability problem. Second, there seems to be no fully satisfying way of explaining whose practical interests matter. We can call this the “whose stakes?” problem. I argue that both problems can (...) be addressed in roughly the same terms. More exactly, I argue that by first clarifying the whose stakes? problem an answer to the stability problem naturally falls out. (shrink)

Recently, several authors have argued that scientific understanding should be a new topic of philosophical research. In this article, I argue that the three most developed accounts of understanding--Grimm's, de Regt's, and de Regt and Dieks's--can be replaced by earlier accounts of scientific explanation without loss. Indeed, in some cases, such replacements have clear benefits.

Over the last several years a number of leading philosophers – including Catherine Elgin, Linda Zagzebski, Jonathan Kvanvig, and Duncan Pritchard – have grown increasingly dissatisfied with the contemporary focus on knowledge in epistemology and have attempted to “recover” the notion of understanding. According to some of these philosophers, in fact, understanding deserves not just to be recovered, but to supplant knowledge as the focus of epistemological inquiry. This entry considers some of the main reasons why philosophers have taken understanding (...) to be more valuable than knowledge, focusing on claims that it is more transparent, that it better reflects or mirrors the world, and that it is a greater intellectual achievement. (shrink)

Claims pertaining to understanding are made in a variety of contexts and ways. As a result, few in the philosophical literature have made an attempt to precisely characterize the state that is y understanding x. This paper builds an account that does just that. The account is motivated by two main observations. First, understanding x is somehow related to being able to manipulate x. Second, understanding is a mental phenomenon, and so what manipulations are required to be an understander must (...) only be mental manipulations. Combining these two insights, the paper builds an account (URM) of understanding as a certain representational capacity—specifically, understanding x involves possessing a representation of x that could be manipulated in useful ways. By tying understanding to representation, the account correctly identifies that understanding is a fundamentally cognitive achievement. However, by also demanding that which representations count as understanding-conferring be determined by their practical effects, URM captures the insight that understanding is vitally connected to practice. URM is fully general, and can apply equally well to understanding states of affairs, understanding events, and even understanding people and works of art. The ultimate test of URM is its applicability in actual scientific and philosophical discourse. To that end the paper discusses the importance of understanding in the philosophy of science, psychology, and computer science. (shrink)

Jonathan Kvanvig has argued that “objectual” understanding, i.e. the understanding we have of a large body of information, cannot be reduced to explanatory concepts. In this paper, I show that Kvanvig fails to establish this point, and then propose a framework for reducing objectual understanding to explanatory understanding.

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)

This paper starts by looking at the coincidence of surprising behavior on the nanolevel in both matter and simulation. It uses this coincidence to argue that the simulation approach opens up a pragmatic mode of understanding oriented toward design rules and based on a new instrumental access to complex models. Calculations, and their variation by means of explorative numerical experimentation and visualization, can give a feeling for a model's behavior and the ability to control phenomena, even if the model itself (...) remains epistemically opaque. Thus, the investigation of simulation in nanoscience provides a good example of how science is adapting to a new instrument: computer simulation. (shrink)

Although prediction has been largely absent from discussions of explanation for the past 40 years, theories of explanation can gain much from a reintroduction. I review the history that divorced prediction from explanation, examine the proliferation of models of explanation that followed, and argue that accounts of explanation have been impoverished by the neglect of prediction. Instead of a revival of the symmetry thesis, I suggest that explanation should be understood as a cognitive tool that assists us in generating new (...) predictions. This view of explanation and prediction clarifies what makes an explanation scientific and why inference to the best explanation makes sense in science. *Received August 2009; revised September 2009. †To contact the author, please write to: Department of Philosophy, University of Tennessee, 801 McClung Tower, Knoxville, TN 37920‐0480; e‐mail: [email protected]. (shrink)

This paper is intended to sketch the definition of a methodological tool -- the notion of a format of representation -- for the study of scientific theorising. One of its main assumption is that a philosophical study of theorising needs to pay attention to other types of units of analysis than the traditional ones, namely, theories and models approached in a logical and structural way, since scientific reasoning is always led on concrete representational devices and depends upon their specific properties. (...) The notion of a format is intended to characterise the differences between various types of representational devices, in terms of the inferences they enable cognitive agents to draw. (shrink)

"Though this be madness, yet there is method in't." Hamlet , act II, scene ii Abstract: Inherent normativity is the claim that intentional action explanations necessarily have to comply with normatively understood rationality constraints on the ascribed propositional attitudes. This paper argues against inherent normativity in three steps. First, it presents three examples of actions successfully explained with propositional attitudes, where the ascribed attitudes violate relevant rationality constraints. Second, it argues that the inference rules that systematise propositional attitudes are qualitatively (...) different from rationality constraints both in their justification and their recipients. Third, it rejects additional conditions on propositional attitudes, which purport to necessitate a normative commitment. Thus, inherent normativity is rejected; and with it the claim that intentional action explanations differ substantially from other explanations because they are inherently normative. (shrink)

Traditional accounts of explanation fail to illuminate the explanatory relevance of “models that are descriptively false” in the sense that the regularities they entail fail to obtain. In this paper, I propose an account of explanation, which I call ‘explanation by concretization’, that serves to explicate the explanatory relevance of such models. Starting from a highly abstract and idealized model, causal explanations of the absence of regularities are sought by adding complexity to the model or by concretizing it. Whether this (...) process is successful depends on whether the abstractions and idealizations in the basic model succeed in isolating a mechanism, i.e. in representing how it operates when interfering factors are absent. This account is developed in the context of economics and contrasted to those of Daniel Hausman and Nancy Cartwright. I go on to provide an account of how unrealistic models can be used for providing understanding of the way mechanisms work. (shrink)

The basis for adoption of pro-environment behaviour is the understanding of causal passages within climate dynamics. The understanding of the causes of climate change is necessary in order _to be able_ to take mitigation actions. Conversely, the understanding of the consequences of climate change is essential in _motivating_ action. The case of ozone depletion confirms this view: the understanding of its causal dynamics played a determining role in people’s behavioural response.

Could groups ever be an understanding subject (an epistemic agent ascribed with understanding) or should we keep our focus exclusively on the individuals that make up the group? The way this paper will shape an answer to this question is by starting from a case we are most willing to accept as group understanding, then mark out the crucial differences with an unconvincing case, and, ultimately, explain why these differences matter. In order to concoct the cases, however, we need to (...) elucidate what it means to be attributed with understanding and what makes up an epistemic agent. I shall argue that it is abilities, above all, that guide our attributions of understanding. While it is true that understanding must go beyond single acts, this is not going beyond as in going behind them (to private occurrences which are impossible to discern and don’t themselves contribute anything of value), but beyond as in considering what people could and would do: their discernable and valuable abilities. To conceptualize whom the abilities belong to, I will specify what it means to be an epistemic agent. I shall argue that it is being a successful target of the epistemic stance. The epistemic stance (heavily inspired by Dennett’s intentional stance) forms an instrumental abstraction by attributing epistemic properties (i.e., beliefs, epistemic aims, problem-solving tactics) to an entity to explain or predict its behavior. Macro-systematicity (a higher-level pattern which a theory can exploit) is what makes the epistemic stance’s abstraction an explanatory powerful one (regardless of how that macro-systematicity is realized) and emergence (the lack of a straightforward mapping-relation between the micro and the macro) is what makes its power unique to a particular level of explanation. To end, I suggest two kinds of mapping-relations and walk through the shift from reducibility to emergence. (shrink)

In his later years, Wesley Salmon believed that the two dominant models of scientific explanation (his own causal-mechanical model and the unificationist model) were reconcilable. Salmon envisaged a 'new consensus' about explanation: he suggested that the two models represent two 'complementary' types of explanation, which may 'peacefully coexist' because they illuminate different aspects of scientific understanding. This paper traces the development of Salmon's ideas and presents a critical analysis of his complementarity thesis. Salmon's thesis is rejected on the basis of (...) two objections, and an alternative view of the relation between different types of explanation is proposed. (shrink)

Roughly put, explanatory circles — if any exist — would be propositions such that (i) each explains the next, and (ii) the last explains the first. In this paper, I give two arguments for the view that there are explanatory circles. The first argument appeals to general relativistic worlds in which time is circular. The second argument appeals to special science theories that describe feedback loops. In addition, I show that three standard arguments against explanatory circles are unsuccessful.

In the natural and social sciences, it is common to use toy models—extremely simple and highly idealized representations—to understand complex phenomena. Some of the simple surrogate models used to understand opaque machine learning (ML) models, such as rule lists and sparse decision trees, bear some resemblance to scientific toy models. They allow non-experts to understand how an opaque ML model works globally via a much simpler model that highlights the most relevant features of the input space and their effect on (...) the output. The obvious difference is that the common target of a toy and a full-scale model in the sciences is some phenomenon in the world, while the target of a surrogate model is another model. This essential difference makes toy surrogate models (TSMs) a new object of study for theories of understanding, one that is not easily accommodated under current analyses. This paper provides an account of what it means to understand an opaque ML model globally with the aid of such simple models. (shrink)

In recent years, the question of the reliability of Machine Learning (ML) methods has acquired significant importance, and the analysis of the associated uncertainties has motivated a growing amount of research. However, most of these studies have applied standard error analysis to ML models—and in particular Deep Neural Network (DNN) models—which represent a rather significant departure from standard scientific modelling. It is therefore necessary to integrate the standard error analysis with a deeper epistemological analysis of the possible differences between DNN (...) models and standard scientific modelling and the possible implications of these differences in the assessment of reliability. This article offers several contributions. First, it emphasises the ubiquitous role of model assumptions (both in ML and traditional science) against the illusion of theory-free science. Secondly, model assumptions are analysed from the point of view of their (epistemic) complexity, which is shown to be language-independent. It is argued that the high epistemic complexity of DNN models hinders the estimate of their reliability and also their prospect of long term progress. Some potential ways forward are suggested. Thirdly, this article identifies the close relation between a model’s epistemic complexity and its interpretability, as introduced in the context of responsible AI. This clarifies in which sense—and to what extent—the lack of understanding of a model (black-box problem) impacts its interpretability in a way that is independent of individual skills. It also clarifies how interpretability is a precondition for a plausible assessment of the reliability of any model, which cannot be based on statistical analysis alone. This article focuses on the comparison between traditional scientific models and DNN models. However, Random Forest (RF) and Logistic Regression (LR) models are also briefly considered. (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)

Scientific understanding is a fundamental goal of science. However, there is currently no good way to measure the scientific understanding of agents, whether these be humans or Artificial Intelligence systems. Without a clear benchmark, it is challenging to evaluate and compare different levels of scientific understanding. In this paper, we propose a framework to create a benchmark for scientific understanding, utilizing tools from philosophy of science. We adopt a behavioral conception of understanding, according to which genuine understanding should be recognized (...) as an ability to perform certain tasks. We extend this notion of scientific understanding by considering a set of questions that gauge different levels of scientific understanding, covering information retrieval, the capability to arrange information to produce an explanation, and the ability to infer how things would be different under different circumstances. We suggest building a Scientific Understanding Benchmark (SUB), formed by a set of these tests, allowing for the evaluation and comparison of scientific understanding. Benchmarking plays a crucial role in establishing trust, ensuring quality control, and providing a basis for performance evaluation. By aligning machine and human scientific understanding we can improve their utility, ultimately advancing scientific understanding and helping to discover new insights within machines. (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)

The philosophical debate around the impact of machine learning in science is often framed in terms of a choice between AI and classical methods as mutually exclusive alternatives involving difficult epistemological trade-offs. A common worry regarding machine learning methods specifically is that they lead to opaque models that make predictions but do not lead to explanation or understanding. Focusing on the field of molecular biology, I argue that in practice machine learning is often used with explanatory aims. More specifically, I (...) argue that machine learning can be tightly integrated with other, more traditional, research methods and in a clear sense can contribute to insight into the causal processes underlying phenomena of interest to biologists. One could even say that machine learning is not the end of theory in important areas of biology, as has been argued, but rather a new beginning. I support these claims with a detailed discussion of a case study involving gene regulation by microRNAs. (shrink)

Is understanding subject to a factivity constraint? That is, must the agent’s representation of some subject matter be accurate in order for her to understand that subject matter? ‘No’, I argue in this paper. As an alternative, I formulate a novel manipulationist account of understanding. Rather than correctly representing, understanding, on this account, is a matter of being able to manipulate a representation of the world to satisfy contextually salient interests. This account of understanding is preferable to factivism, I argue, (...) mainly for simplicity reasons. While it explains the intuitive data about understanding as successfully as factivist accounts, it is simpler by virtue of reducing the value truth bestows on understanding to that of usability. (shrink)

This paper aims to clarify the nature of understanding in medicine. The first part describes in more detail what it means to understand something and links a type of understanding (i.e., objectual understanding) to explanations. The second part proceeds to investigate what objectual understanding of a disease (i.e., biomedical understanding) requires by considering the case of scurvy from the history of medicine. The main hypothesis is that grasping a mechanistic explanation of a condition is necessary for a biomedical understanding of (...) that condition. The third part of the paper argues that biomedical understanding is necessary, but not sufficient for understanding in a clinical context (i.e., clinical understanding). The hypothesis is that clinical understanding combines biomedical understanding of a _disease_ or pathological condition with understanding _illness,_ which involves some degree of personal understanding of the patient. It is argued that, in many cases, clinical understanding necessitates adopting a particular second-personal stance and using cognitive resources _in addition_ to those involved in biomedical understanding. (shrink)

This paper aims to clarify the nature of understanding in medicine. The first part describes in more detail what it means to understand something and links a type of understanding (i.e., objectual understanding) to explanations. The second part proceeds to investigate what objectual understanding of a disease (i.e., biomedical understanding) requires by considering the case of scurvy from the history of medi- cine. The main hypothesis is that grasping a mechanistic explanation of a condi- tion is necessary for a biomedical (...) understanding of that condition. The third part of the paper argues that biomedical understanding is necessary, but not sufficient for understanding in a clinical context (i.e., clinical understanding). The hypothe- sis is that clinical understanding combines biomedical understanding of a disease or pathological condition with understanding illness, which involves some degree of personal understanding of the patient. It is argued that, in many cases, clinical understanding necessitates adopting a particular second-personal stance and using cognitive resources in addition to those involved in biomedical understanding. (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)

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)

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)

Perhaps no term in the geological lexicon excites more passions than uniformitarianism, whose motto is “the present is the key to the past.” The term is controversial in part because it contains several meanings, which have been implicated in creating a situation of “semantic chaos” in the geological literature. Yet I argue that debates about uniformitarianism do not arise from a simple chaos of meanings. Instead, they arise from legitimate disagreements about substantive questions. This paper examines these questions and relates (...) them to several “forms of understanding” pursued by researchers in geohistory. (shrink)

Philosophy Compass, Volume 17, Issue 6, June 2022.

Certain characteristics make machine learning a powerful tool for processing large amounts of data, and also particularly unsuitable for explanatory purposes. There are worries that its increasing use in science may sideline the explanatory goals of research. We analyze the key characteristics of ML that might have implications for the future directions in scientific research: epistemic opacity and the ‘theory-agnostic’ modeling. These characteristics are further analyzed in a comparison of ML with the traditional statistical methods, in order to demonstrate what (...) it is specifically that makes ML methodology substantially unsuitable for reaching explanations. The analysis is given broader philosophical context by connecting it with the views on the role of prediction and explanation in science, their relationship, and the value of explanation. We proceed to show, first, that ML disrupts the relationship between prediction and explanation commonly understood as a functional relationship. Then we show that the value of explanation is not exhausted in purely predictive functions, but rather has a ubiquitously recognized value for both science and everyday life. We then invoke two hypothetical scenarios with different degrees of automatization of science, which help test our intuitions on the role of explanation in science. The main question we address is whether ML will reorient or otherwise impact our standard explanatory practice. We conclude with a prognosis that ML would diversify science into purely predictively oriented research based on ML-like techniques and, on the other hand, remaining faithful to anthropocentric research focused on the search for 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)

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)