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)

Philosophers of logic are particularly interested in understanding the aims, epistemology, and methodology of logic. This raises the question of how the philosophy of logic should go about these enquires. According to the practice-based approach, the most reliable method we have to investigate the methodology and epistemology of a research field is by considering in detail the activities of its practitioners. This holds just as true for logic as it does for the recognised empirical and abstract sciences. If we wish (...) to systematically understand the aims and epistemology of logic, we are best placed achieving this by looking at what logicians do, rather than reflecting upon the nature of logic itself. In this entry, we outline the motivations for a practice-based approach towards the philosophy of logic and highlight its advantages over more “top-down” approaches, which attempt to infer conclusions about the nature of logic and its methodology on the basis of traditional assumptions about knowledge, metaphysics, or logic itself. We end by addressing several prominent concerns raised against the practice-based approach. (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)

In making sense of the world, we typically cooperate, join forces, and draw on one another’s competence and expertise. A group or community in which there is a well-functioning division of cognitive-epistemic labor can achieve levels of understanding that a single agent who relies exclusively on her own capacities would probably never achieve. However, is understanding also collective? I.e., is understanding something that can be possessed by a group or community rather than by individuals? In this paper, I develop an (...) account of understanding phenomena according to which understanding a phenomenon requires reasonably endorsing an adequate and intelligible epistemic mediator that accounts for this phenomenon. I then show that understanding, conceived along these lines, can be attributed to collective entities. An important result of my arguments will be that a collective entity’s understanding cannot (always) be reduced to the sum of the understandings of the individuals belonging to it. This is because a collective entity can sometimes be rightfully claimed to understand a phenomenon while none of its individual members understands it. (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)

The literature on values in science struggles with questions about how to describe and manage the role of values in scientific research. We argue that progress can be made by shifting this literature’s current emphasis. Rather than arguing about how non-epistemic values can or should figure into scientific assessment, we suggest analyzing how scientific assessment can accommodate non-epistemic values. For scientific assessment to do so, it arguably needs to incorporate goals that have been traditionally characterized as non-epistemic. Building on this (...) insight, we show how the adequacy-for-purpose framework recently developed for assessing scientific models can provide a general framework for describing scientific assessment so that it goes beyond purely epistemic considerations. Adopting this framework has significant advantages and opens the possibility of effecting a partial rapprochement between critics and proponents of the value-free ideal. (shrink)

The aim of this article is to develop an understanding-based argument for an explicitly political specification of the concept of race. It is argued that a specification of race in terms of hierarchical social positions is best equipped to guide causal reasoning about racial inequality in the public sphere. Furthermore, the article provides evidence that biological and cultural specifications of race mislead public reasoning by encouraging confusions between correlates and causes of racial inequality. The article concludes with a more general (...) case for incorporating empirical evidence about public reasoning into philosophical debates about competing specifications of the concept of race. (shrink)

In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine learning in (...) particular). These lie mainly in the creation of predictive models which performances increase as data set increases. Next, we will identify a tradeoff between predictive and explanatory performances by comparing the features of mechanistic and predictive models. Finally, we show how this a priori analysis of machine learning and mechanistic research applies to actual biological practice. This will be done by analyzing the publications of a consortium—The Cancer Genome Atlas—which stands at the forefront in integrating data science and molecular biology. The result will be that biologists have to deal with the tradeoff between explaining and predicting that we have identified, and hence the explanatory force of the ‘new’ biology is substantially diminished if compared to the ‘old’ biology. However, this aspect also emphasizes the existence of other research goals which make predictive force independent from explanation. (shrink)

Predictive processing (PP) has been repeatedly presented as a unificatory account of perception, action, and cognition. In this paper, we argue that this is premature: As a unifying theory, PP fails to deliver general, simple, homogeneous, and systematic explanations. By examining its current trajectory of development, we conclude that PP remains only loosely connected both to its computational framework and to its hypothetical biological underpinnings, which makes its fundamentals unclear. Instead of offering explanations that refer to the same set of (...) principles, we observe systematic equivocations in PP‐based models, or outright contradictions with its avowed principles. To make matters worse, PP‐based models are seldom empirically validated, and they are frequently offered as mere just‐so stories. The large number of PP‐based models is thus not evidence of theoretical progress in unifying perception, action, and cognition. On the contrary, we maintain that the gap between theory and its biological and computational bases contributes to the arrested development of PP as a unificatory theory. Thus, we urge the defenders of PP to focus on its critical problems instead of offering mere re‐descriptions of known phenomena, and to validate their models against possible alternative explanations that stem from different theoretical assumptions. Otherwise, PP will ultimately fail as a unified theory of cognition. (shrink)

The connection between understanding and explanation has recently been of interest to philosophers. Inglis and Mejía-Ramos (Synthese, 2019) propose that within mathematics, we should accept a functional account of explanation that characterizes explanations as those things that produce understanding. In this paper, I start with the assumption that this view of mathematical explanation is correct and consider what we can consequently learn about mathematical explanation. I argue that this view of explanation suggests that we should shift the question of explanation (...) away from why-questions and towards a “what’s going on here” question. Additionally, I argue that when we recognize the connection between understanding and explanation we naturally see how more than just proof can be explanatory. I expand this point by detailing how definitions and diagrams can be explanatory. In all, we see that when we take seriously the connection between understanding and explanation, we get a better sense of how explanation arises within mathematics. (shrink)

This dissertation starts with a concise overview of what philosophers of science have written about unification and its role in scientific explanation during the last 50 years to provide the reader with some background knowledge. In order to bring unification back into the picture, I have followed two strategies, resulting respectively in Parts I and II of this dissertation. In Part I the idea of unification is used to refine and enrich the dominant causalmechanist and causal-interventionist accounts of scientific explanation. (...) In this part of the dissertation I bracket the classical ideas about unification: deduction and derivation. I do grant, for the sake of argument, that explanations are causal and argue that unification is important from within this causalist perspective. In Part II I continue my strategy of digging into scientific practice to find cases of ontological unification. But here I distance myself from the dominant literature that all explanations must be causal. I will investigate whether explanatory unification is possible in non-causal explanations. Part III contains some further reflections and conclusions. I will formulate my primary results, and I will elaborate on their implications for thinking about unification and explanation. The different forms of ontological unification were quite diverse. This relates to the method I have used. Throughout this dissertation the types of unification that were discussed emerged from digging into scientific practice. This philosophy-of-science-practice approach steered me towards a pluralistic view on unification and on explanation. In this dissertation I do not try to develop a new model of explanation and compare it to existing models. The aim is to show that there are important types of explanatory practice which cannot be properly analyzed if we neglect unification as a desideratum for explanations. (shrink)

Science is a product of society: in its funding, its participation, and its application. This Element explores the relationship between science and the public with resources from philosophy of science. Chapter 1 defines the questions about science's relationship to the public and outlines science's obligation to the public. Chapter 2 considers the Vienna Circle as a case study in how science, philosophy, and the public can relate very differently than they do at present. Chapter 3 examines how public understanding of (...) science can have a variety of different goals and introduces philosophical discussions of scientific understanding as a resource. Chapter 4 addresses public trust in science, including responding to science denial. Chapter 5 considers how expanded participation in science can contribute to public trust of science. Finally, Chapter 6 casts light on how science might discharge its obligations to the public. (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)

Epistemic pluralists in the philosophy of science often argue that different epistemic perspectives in science are equally warranted. Sandra Mitchell – with her Integrative Pluralism (IP) – has notably advocated for this kind of epistemic pluralism. A problem arises for Mitchell however because she also wants to be an epistemological pluralist. She claims that, not only are different epistemic perspectives in science equally warranted in different contexts, but different understandings of these epistemic perspectives in science are also equally warranted in (...) different contexts. The problem is that Mitchell argues that her understanding of epistemic perspectives in science (IP) is the correct one, and it is dilemmic to then claim that there is more than one such understanding. As solution, I suggest that we abandon epistemological pluralism and instead follow Feyerabend in being opportunistic pluralists. We can adopt pluralism as a short-term strategy in the pursuit of a long-term unitary goal. This unitary goal is what philosophers of understanding call objectual understanding, the kind of understanding that Integrative Pluralism tacitly aspires to anyway. (shrink)

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

The question of whether Everettian quantum mechanics (EQM) justifies the existence of metaphysical indeterminacy has recently come to the fore. Metaphysical indeterminacy has been argued to emerge from three sources: coherent superpositions, the indefinite number of branches in the quantum multiverse and the nature of these branches. This paper reviews the evidence and concludes that those arguments don’t rely on EQM alone and rest on metaphysical auxiliary assumptions that transcend the physics of EQM. We show how EQM can be ontologically (...) interpreted without positing metaphysical indeterminacy by adopting a deflationary attitude towards branches. Two ways of developing the deflationary view are then proposed: one where branches are eliminated, and another where they are reduced to the universal quantum state. (shrink)

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

The Modern Synthesis has received criticism for its purported gene-centrism. That criticism relies on a concept of the gene as a unit of instructional information. In this paper I discuss information concepts and endorse one, developed from Floridi, that sees information as a functional relationship between data and context. I use this concept to inspect developmental criticisms of the Modern Synthesis and argue that the instructional gene arose as an idealization practice when evolutionary biologists made comment on development. However, a (...) closer inspection of key claims shows that at least some associated with the Modern Synthesis were in fact adopting the data led definition I favour and made clear arguments for the role of developmental processes beyond genetic input. There was no instructional gene. (shrink)

Undergraduate geoscience students are rarely exposed to history and philosophy of science. I will describe the experiences with a short course unfavourably placed in the first year of a bachelor of earth science. Arguments how HPS could enrich their education in many ways are sketched. One useful didactic approach is to develop a broader interest by connecting HPS themes to practical cases throughout the curriculum, and develop learning activities that allow students to reflect on their skills, methods and their field (...) in relation to other disciplines and interactions with society with abilities gained through exposure to HPS. Given support of the teaching staff, the tenets of philosophy of science in practice, of conceptual history of knowledge, and of ethics of science for society can fruitfully and directly be connected to the existing curriculum. This is ideally followed by a capstone HPS course late in the bachelor programme. (shrink)

Discussions on the status of definitions of life have long been dominated by a position known as definitional pessimism. Per the definitional pessimist, there is no point in trying to define life. This claim is defended in different ways, but one of the shared assumptions of all definitional pessimists is that our attempts to define life are attempts to provide a list of all necessary and sufficient conditions for something to count as alive. In other words, a definition of life (...) is a strict, descriptive definition. Against this, several pragmatic alternatives have been put forward. On these pragmatic accounts, definitions of life are not strictly, but rather loosely descriptive. Their purpose is not to be true, but to be useful to scientists by guiding scientific practice. More recently, this position has come under attack for not being able to explain how our attempts to define life are connected to scientific progress within the biological sciences. Here, I argue to the contrary by showing how pragmatic definitions of life can be, and in fact are, conducive to scientific progress. Additionally, I show how the pragmatic account of definitions of life can be brought to bear upon our normative discussions involving definitions of life. (shrink)

In earlier work, I criticized de Regt’s contextual theory of understanding, and advertised the advantages of my own, knowledge-based account. Using the early history of the standard model in particle physics as an illustration, I instead consider the benefits of unifying these two accounts of understanding. I argue that de Regt’s account substantially improves my own account of explanatory consideration, and that my account of explanatory comparison substantially improves upon his account of explanatory evaluation. De Regt and my apparent disagreement (...) about the so-called factivity of understanding is defused through a voluntaristic approach to the scientific realism debates. (shrink)

Many have argued that the human sciences feature a unique form of understanding that is absent from the natural sciences. However, in the last decade or so, epistemologists and philosop...

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.

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)

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

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)

The emerging field of synthetic biology aims to engineer novel biological entities. The envisioned future bio-based economy builds largely on “cell factories”: organisms that have been metabolically engineered to sustainably produce substances for human ends. In this paper, we argue that synthetic biology’s goal of creating efficient production vessels for industrial applications implies a set of ontological assumptions according to which living organisms are machines. Traditionally, a machine is understood as a technological, isolated and controllable production unit consisting of parts. (...) But modified organisms, or hybrids, require us to think beyond the machine paradigm and its associated dichotomies between artificial and natural, organisms and artefacts. We ask: How may we conceptualise hybrids beyond limiting ontological categories? Our main claim is that the hybrids created by synthetic biology should be considered not as machines but as metabolic systems. We shall show how the philosophical account of metabolism can inform an ontology of hybrids that moves beyond what we call the “machine ontology”, considering that metabolism enables thinking beyond the dominant dichotomies and allows us to understand and design lifeforms in a bio-based economy. Thus, the aim of this paper is twofold: first, to develop the philosophical ontology of hybrids, and second, to move synthetic biology beyond the problematically limiting view of hybrids. (shrink)

A widespread need to explain the behavior and outcomes of AI-based systems has emerged, due to their ubiquitous presence. Thus, providing renewed momentum to the relatively new research area of eXplainable AI (XAI). Nowadays, the importance of XAI lies in the fact that the increasing control transference to this kind of system for decision making -or, at least, its use for assisting executive stakeholders- already affects many sensitive realms (as in Politics, Social Sciences, or Law). The decision-making power handover to (...) opaque AI systems makes mandatory explaining those, primarily in application scenarios where the stakeholders are unaware of both the high technology applied and the basic principles governing the technological solutions. The issue should not be reduced to a merely technical problem; the explainer would be compelled to transmit richer knowledge about the system (including its role within the informational ecosystem where he/she works). To achieve such an aim, the explainer could exploit, if necessary, practices from other scientific and humanistic areas. The first aim of the paper is to emphasize and justify the need for a multidisciplinary approach that is beneficiated from part of the scientific and philosophical corpus on Explaining, underscoring the particular nuances of the issue within the field of Data Science. The second objective is to develop some arguments justifying the authors’ bet by a more relevant role of ideas inspired by, on the one hand, formal techniques from Knowledge Representation and Reasoning, and on the other hand, the modeling of human reasoning when facing the explanation. This way, explaining modeling practices would seek a sound balance between the pure technical justification and the explainer-explainee agreement. (shrink)

In the last decades, supervised machine learning has seen the widespread growth of highly complex, non-interpretable models, of which deep neural networks are the most typical representative. Due to their complexity, these models have showed an outstanding performance in a series of tasks, as in image recognition and machine translation. Recently, though, there has been an important discussion over whether those non-interpretable models are able to provide any sort of understanding whatsoever. For some scholars, only interpretable models can provide understanding. (...) More popular, however, is the idea that understanding can come from a careful analysis of the dataset or from the model’s theoretical basis. In this paper, I wish to examine the possible forms of obtaining understanding of such non-interpretable models. Two main strategies for providing understanding are analyzed. The first involves understanding without interpretability, either through external evidence for the model’s inner functioning or through analyzing the data. The second is based on the artificial production of interpretable structures, through three main forms: post hoc models, hybrid models, and quasi-interpretable structures. Finally, I consider some of the conceptual difficulties in the attempt to create explanations for these models, and their implications for understanding. (shrink)

The thesis proposes an account of the means of scientific representation focused on similarity, or more specifically, on the notion of “creative similarity”. I first distinguish between two different questions regarding the problem of representation: the question about the constituents and the question about the means of representation (following Suárez 2003; van Fraassen 2008). I argue that, although similarity is not a good candidate for constituent of representation, it can satisfactorily answer the question about the means of representation if adequately (...) characterized. To motivate this position, I dispute the main arguments offered against similarity as means of representation, namely the arguments from variety, vagueness, and misrepresentation, and contend that similarity plays a central epistemic role in practices of representing in science. The study of the role of similarity in scientific practice, I argue, requires the analysis of the uses of judgments of similarity in the construction of scientific models. I examine the cases of the Mississippi Basin Model and the San Francisco Bay Model to illustrate how judgments of similarity are directly involved in the production of epistemically fruitful models. Informed by the practical investigation developed throughout the thesis, I finally outline what I call the creative similarity account of the means of representation. The notion of “creative similarity” helps to capture the way in which similarity, as means of representation, intervenes in actual practices of representing, namely, in the form of a productive interplay of judgments of similarity and distortions (i.e. idealizations, abstractions, simplifications), which is employed by resourceful agents with the aim of understanding aspects of the natural world. (shrink)

Explainable artificial intelligence (XAI) is an emerging, multidisciplinary field of research that seeks to develop methods and tools for making AI systems more explainable or interpretable. XAI researchers increasingly recognise explainability as a context-, audience- and purpose-sensitive phenomenon, rather than a single well-defined property that can be directly measured and optimised. However, since there is currently no overarching definition of explainability, this poses a risk of miscommunication between the many different researchers within this multidisciplinary space. This is the problem we (...) seek to address in this paper. We outline a framework, called Explanatory Pragmatism, which we argue has two attractive features. First, it allows us to conceptualise explainability in explicitly context-, audience- and purpose-relative terms, while retaining a unified underlying definition of explainability. Second, it makes visible any normative disagreements that may underpin conflicting claims about explainability regarding the purposes for which explanations are sought. Third, it allows us to distinguish several dimensions of AI explainability. We illustrate this framework by applying it to a case study involving a machine learning model for predicting whether patients suffering disorders of consciousness were likely to recover consciousness. (shrink)

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

This paper analyzes the historical context and systematic importance of Kant's hypothetical use of reason. It does so by investigating the role of hypotheses in Kant's philosophy of science. We first situate Kant’s account of hypotheses in the context of eighteenth-century German philosophy of science, focusing on the works of Wolff, Meier, and Crusius. We contrast different conceptions of hypotheses of these authors and elucidate the different theories of probability informing them. We then adopt a more systematic perspective to discuss (...) Kant's idea that scientific hypotheses must articulate real possibilities. We argue that Kant's views on the intelligibility of scientific hypotheses constitute a valuable perspective on scientific understanding and the constraints it imposes on scientific rationality. (shrink)

Machine learning methods have recently created high expectations in the climate modelling context in view of addressing climate change, but they are often considered as non-physics-based ‘black boxes’ that may not provide any understanding. However, in many ways, understanding seems indispensable to appropriately evaluate climate models and to build confidence in climate projections. Relying on two case studies, we compare how machine learning and standard statistical techniques affect our ability to understand the climate system. For that purpose, we put five (...) evaluative criteria of understanding to work: intelligibility, representational accuracy, empirical accuracy, coherence with background knowledge, and assessment of the domain of validity. We argue that the two families of methods are part of the same continuum where these various criteria of understanding come in degrees, and that therefore machine learning methods do not necessarily constitute a radical departure from standard statistical tools, as far as understanding is concerned. (shrink)

The main objective of this dissertation is to philosophically assess how the use of informational concepts in the field of classical thermostatistical physics has historically evolved from the late 1940s to the present day. I will first analyze in depth the main notions that form the conceptual basis on which 'informational physics' historically unfolded, encompassing (i) different entropy, probability and information notions, (ii) their multiple interpretative variations, and (iii) the formal, numerical and semantic-interpretative relationships among them. In the following, I (...) will assess the history of informational thermophysics during the second half of the twentieth century. Firstly, I analyse the intellectual factors that gave rise to this current in the late forties (i.e., popularization of Shannon's theory, interest in a naturalized epistemology of science, etc.), then study its consolidation in the Brillouinian and Jaynesian programs, and finally claim how Carnap (1977) and his disciples tried to criticize this tendency within the scientific community. Then, I evaluate how informational physics became a predominant intellectual current in the scientific community in the nineties, made possible by the convergence of Jaynesianism and Brillouinism in proposals such as that of Tribus and McIrvine (1971) or Bekenstein (1973) and the application of algorithmic information theory into the thermophysical domain. As a sign of its radicality at this historical stage, I explore the main proposals to include information as part of our physical reality, such as Wheeler’s (1990), Stonier’s (1990) or Landauer’s (1991), detailing the main philosophical arguments (e.g., Timpson, 2013; Lombardi et al. 2016a) against those inflationary attitudes towards information. Following this historical assessment, I systematically analyze whether the descriptive exploitation of informational concepts has historically contributed to providing us with knowledge of thermophysical reality via (i) explaining thermal processes such as equilibrium approximation, (ii) advantageously predicting thermal phenomena, or (iii) enabling understanding of thermal property such as thermodynamic entropy. I argue that these epistemic shortcomings would make it impossible to draw ontological conclusions in a justified way about the physical nature of information. In conclusion, I will argue that the historical exploitation of informational concepts has not contributed significantly to the epistemic progress of thermophysics. This would lead to characterize informational proposals as 'degenerate science' (à la Lakatos 1978a) regarding classical thermostatistical physics or as theoretically underdeveloped regarding the study of the cognitive dynamics of scientists in this physical domain. (shrink)

In this talk I present the main results from Anta (2021), namely, that the theoretical division between Boltzmannian and Gibbsian statistical mechanics should be understood as a separation in the epistemic capabilities of this physical discipline. In particular, while from the Boltzmannian framework one can generate powerful explanations of thermal processes by appealing to their microdynamics, from the Gibbsian framework one can predict observable values in a computationally effective way. Finally, I argue that this statistical mechanical schism contradicts the Hempelian (...) (1958) thesis that the predictive power of a scientific theory is directly proportional to its explanatory potential, and vice versa. (shrink)

COVID-19 has substantially affected our lives during 2020. Since its beginning, several epidemiological models have been developed to investigate the specific dynamics of the disease. Early COVID-19 epidemiological models were purely statistical, based on a curve-fitting approach, and did not include causal knowledge about the disease. Yet, these models had predictive capacity; thus they were used to ground important political decisions, in virtue of the understanding of the dynamics of the pandemic that they offered. This raises a philosophical question about (...) how purely statistical models can yield understanding, and if so, what the relationship between prediction and understanding in these models is. Drawing on the model that was developed by the Institute of Health Metrics and Evaluation, we argue that early epidemiological models yielded a modality of understanding that we call descriptive understanding, which contrasts with the so-called explanatory understanding which is assumed to be the main form of scientific understanding. We spell out the exact details of how descriptive understanding works, and efficiently yields understanding of the phenomena. Finally, we vindicate the necessity of studying other modalities of understanding that go beyond the conventionally assumed explanatory understanding. (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)

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

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

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

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

Understanding is a valued trait in any epistemic practice, scientific or not. Yet, when it comes to characterizing its nature, the notion has not received the philosophical attention it deserves. We have set ourselves three tasks in this paper. First, we defend the importance of this endeavor. Second, we consider and criticize a number of proposals to this effect. Third, we defend an alternative account, focusing on abilities as the proper mark of understanding.

Definiciones de epistemología hay muchas, al igual que clases y estilos. Sin embargo, más allá de esta diversidad, es necesario contar con una definición básica que guíe nuestra comprensión del tema. Dos serán las preguntas que nos ayuden a ello en este artículo: a) ¿qué es la epistemología? y b) ¿para qué le sirve al científico?

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)

This chapter has four central aims. First, in §1, I distinguish two ideas within epistemology that sometimes travel under the name ‘contextualism’ — the ‘situational contextualist’ idea that an individual’s context, especially their social context, can make for a difference in what they know, and the ‘linguistic contextualist’ idea that discourse using the word ‘knows’ and its cognates is context-sensitive, expressing dif- ferent contents in different conversational contexts. -/- Second, in §2, I situate contextualism with respect to several influential ideas (...) in feminist epistemology. These ideas are thoroughgoingly contextualist in the situational sense; I’ll explore the prospects for linguistic contextualist analogues or implementations of them. Simple connections between these feminist ideas and linguistic contextualism will prove elusive, but more subtle ones are possible, and sometimes attractive. -/- §3 considers the degree to which contextual epistemic parameters are determined interpersonally, as opposed to individualistically. Should contextualists hold that speakers can individually determine the contextual parameters that influence the truth- conditions of their utterances? Or are they fixed at a broader social level? I’ll rehearse some influential reasons to opt for the latter, more social, form of contextualism. -/- In §4 I discuss the practical and moral significance of speakers’ choices of epistemic parameters, given contextualism. For example, I’ll consider how standards-raising can be used to discredit evidential sources, with an eye towards the social and moral consequences of such moves. (shrink)

Scientific understanding as a subject of inquiry has become widely discussed in philosophy of science and is often addressed through case studies from history of science. Even though these historical reconstructions engage with details of scientific practice, they usually provide only limited information about the gradual formation of understanding in ongoing processes of model and theory construction. Based on a qualitative ethnographic study of an ecological research project, this article shifts attention from understanding in the context of historical case studies (...) to evidence of current case studies. By taking de Regt’s contextual theory of scientific understanding into the field, it confirms core tenets of the contextual theory suggesting a normative character with respect to scientific activities. However, the case study also shows the limitations of de Regt’s latest version of this theory as an attempt to explain the development of understanding in current practice. This article provides a model representing the emergence of scientific understanding that exposes main features of scientific understanding such as its gradual formation, its relation to skills and imagination, and its capacity for knowledge selectivity. The ethnographic evidence presented here supports the claim that something unique can be learned by looking into ongoing research practices that can’t be gained by studying historical case studies. (shrink)

Elgin has presented an extensive defence of reflective equilibrium embedded in an epistemology which focuses on objectual understanding rather than ordinary propositional knowledge. This paper has two goals: to suggest an account of reflective equilibrium which is sympathetic to Elgin’s but includes a range of further developments, and to analyse its role in an account of understanding. We first address the structure of reflective equilibrium as a target state and argue that reflective equilibrium requires more than an equilibrium in the (...) sense of a coherent position. On the one hand, the position also needs to be stable between a ‘conservative’ pull of input commitments and a ‘progressive’ pull of epistemic goals; on the other hand, reflective equilibrium requires that enough of the resulting commitments have some credibility independent of the coherence of the position. We then turn to the dynamics of reflective equilibrium, the process of mutual adjustment of commitments and theories. Here, the most pressing internal challenges for defenders of reflective equilibrium arise: to characterize this process more exactly and to explain its status in relation to reflective equilibrium as a target state. Finally, we investigate the role of reflective equilibrium in Elgin’s account of understanding and argue that objectual understanding cannot be explained in terms of reflective equilibrium alone. An epistemic agent who understands a subject matter by means of a theory also needs to be able to use this theory and the theory needs to meet some external rightness condition. (shrink)