The epistemic account and the noetic account hold that the essence of scientific progress is the increase in knowledge and understanding, respectively. Dellsén (2018) criticizes the epistemic account (Park, 2017a) and defends the noetic account (Dellsén, 2016). I argue that Dellsén’s criticisms against the epistemic account fail, and that his notion of understanding, which he claims requires neither belief nor justification, cannot explain scientific progress, although it can explain fictional progress in science-fiction.

Without doubt, there is a widespread usage of visualisations in science. However, what exactly the _epistemic status_ of these visual representations in science may be remains an open question. In the following, I will argue that at least some scientific visualisations are indispensible for our cognitive processes. My thesis will be that, with regard to the activity of _learning_, visual representations are of relevance in the sense of contributing to the aim of _scientific_ _understanding_. Taking into account that (...) class='Hi'>understanding can be regarded as an epistemic desideratum in its own right, I will argue that, at least in some instances, no understanding can be achieved without the aid of visualisations. Consequently, they are of crucial importance in this process. Moreover, to support this thesis we will make use of some findings in educational psychology. (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)

In this paper, I outline a heuristic for thinking about the relation between explanation and understanding that can be used to capture various levels of “intimacy”, between them. I argue that the level of complexity in the structure of explanation is inversely proportional to the level of intimacy between explanation and understanding, i.e. the more complexity the less intimacy. I further argue that the level of complexity in the structure of explanation also affects the explanatory depth in a (...) similar way to intimacy between explanation and understanding, i.e. the less complexity the greater explanatory depth and vice versa. (shrink)

The work of harmonizing and integrating the various fields of knowledge is not left to the individual as much as it is already accomplished in and by the Complete Whole. Rather, the individual must become self-forgetful, which is achieved anyhow in the universalizing activity of science. And more than self-forgetful, the individual becomes a self-sacrificing or dedicating unit within the self-realizing Absolute. It is here that entrusting oneself to the intelligence and reason of the True, once it is scientifically realized, (...) introduces us to the concept of the mercy of the Absolute. This can be understood only when the essential negativity that characterizes reality is comprehended along with the positivity that is normally associated with Being. "Thinking" is negative activity because it is a determining - literally a terminating or delimiting activity. It is what produces distinction and differentiation - particularity, within the universality of mere positive being. (shrink)

Hegel considers the Concept (in German Begriffe) to be a spiritual entity - the soul, if you will, of a thing. Looked at in another way it may be considered (although not precisely) like the essence of a thing. It is what makes the thing whatever it is. Thus if we took the Concept of a room (let’s say “roomness”) away from a thing we would no longer have a room but something else. In this sense the Concept is essential (...) to the being of anything. As soon as we determine “what” a thing is we are involved with its Concept. The Concept is the “whatness” of things. (shrink)

Scientists often diverge widely when choosing between research programs. This can seem to be rooted in disagreements about which of several theories, competing to address shared questions or phenomena, is currently the most epistemically or explanatorily valuable—i.e. most successful. But many such cases are actually more directly rooted in differing judgments of pursuit-worthiness, concerning which theory will be best down the line, or which addresses the most significant data or questions. Using case studies from 16th-century astronomy and 20th-century geology and (...) biology, I argue that divergent theory choice is thus often driven by considerations of scientific process, even where direct epistemic or explanatory evaluation of its final products appears more relevant. Broadly following Kuhn’s analysis of theoretical virtues, I suggest that widely shared criteria for pursuit-worthiness function as imprecise, mutually-conflicting values. However, even Kuhn and others sensitive to pragmatic dimensions of theory ‘acceptance’, including the virtue of fruitfulness, still commonly understate the role of pursuit-worthiness—especially by exaggerating the impact of more present-oriented virtues, or failing to stress how ‘competing’ theories excel at addressing different questions or data. This framework clarifies the nature of the choice and competition involved in theory choice, and the role of alternative theoretical virtues. (shrink)

"Understanding Scientific Progress constitutes a potentially enormous and revolutionary advancement in philosophy of science. It deserves to be read and studied by everyone with any interest in or connection with physics or the theory of science. Maxwell cites the work of Hume, Kant, J.S. Mill, Ludwig Bolzmann, Pierre Duhem, Einstein, Henri Poincaré, C.S. Peirce, Whitehead, Russell, Carnap, A.J. Ayer, Karl Popper, Thomas Kuhn, Imre Lakatos, Paul Feyerabend, Nelson Goodman, Bas van Fraassen, and numerous others. He lauds Popper for (...) advancing beyond verificationism and Hume’s problem of induction, but faults both Kuhn and Popper for being unable to show that and how their work could lead nearer to the truth." —Dr. LLOYD EBY teaches philosophy at The George Washington University and The Catholic University of America, in Washington, DC "Maxwell's aim-oriented empiricism is in my opinion a very significant contribution to the philosophy of science. I hope that it will be widely discussed and debated." – ALAN SOKAL, Professor of Physics, New York University "Maxwell takes up the philosophical challenge of how natural science makes progress and provides a superb treatment of the problem in terms of the contrast between traditional conceptions and his own scientifically-informed theory—aim-oriented empiricism. This clear and rigorously-argued work deserves the attention of scientists and philosophers alike, especially those who believe that it is the accumulation of knowledge and technology that answers the question."—LEEMON McHENRY, California State University, Northridge "Maxwell has distilled the finest essence of the scientific enterprise. Science is about making the world a better place. Sometimes science loses its way. The future depends on scientists doing the right things for the right reasons. Maxwell's Aim-Oriented Empiricism is a map to put science back on the right track."—TIMOTHY McGETTIGAN, Professor of Sociology, Colorado State University - Pueblo "Maxwell has a great deal to offer with these important ideas, and deserves to be much more widely recognised than he is. Readers with a background in philosophy of science will appreciate the rigour and thoroughness of his argument, while more general readers will find his aim-oriented rationality a promising way forward in terms of a future sustainable and wise social order."—David Lorimer, Paradigm Explorer, 2017/2 "This is a book about the very core problems of the philosophy of science. The idea of replacing Standard Empiricism with Aim-Oriented Empiricism is understood by Maxwell as the key to the solution of these central problems. Maxwell handles his main tool masterfully, producing a fascinating and important reading to his colleagues in the field. However, Nicholas Maxwell is much more than just a philosopher of science. In the closing part of the book he lets the reader know about his deep concern and possible solutions of the biggest problems humanity is facing."—Professor PEETER MŰŰREPP, Tallinn University of Technology, Estonia “For many years, Maxwell has been arguing that fundamental philosophical problems about scientific progress, especially the problem of induction, cannot be solved granted standard empiricism (SE), a doctrine which, he thinks, most scientists and philosophers of science take for granted. A key tenet of SE is that no permanent thesis about the world can be accepted as a part of scientific knowledge independent of evidence. For a number of reasons, Maxwell argues, we need to adopt a rather different conception of science which he calls aim-oriented empiricism (AOE). This holds that we need to construe physics as accepting, as a part of theoretical scientific knowledge, a hierarchy of metaphysical theses about the comprehensibility and knowability of the universe, which become increasingly insubstantial as we go up the hierarchy. In his book “Understanding Scientific Progress: Aim-Oriented Empiricism”, Maxwell gives a concise and excellent illustration of this view and the arguments supporting it… Maxwell’s book is a potentially important contribution to our understanding of scientific progress and philosophy of science more generally. Maybe it is the time for scientists and philosophers to acknowledge that science has to make metaphysical assumptions concerning the knowability and comprehensibility of the universe. Fundamental philosophical problems about scientific progress, which cannot be solved granted SE, may be solved granted AOE.” Professor SHAN GAO, Shanxi University, China . (shrink)

Book Review: De Regt, H. W. Understanding Scientific Understanding. New York: Oxford University Press, 2017.

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

This is a copy of the presentation given at the "Workshop on Agency and Distributed Cognition" at Macquarie University, March 2012. What is noteworthy about this piece of work is that (i) it is a very early foray into the pedagogy, ontology, and epistemology of virtual worlds (it's 2012, way before David Chalmers' book "Reality+" in 2022); and (ii) it was my first foray into "social epistemology" beyond the standard "S knows that p" epistemology, drawing on Vygotskian collaborative approaches to (...) learning and teaching, moving beyond analytic philosophy into educational research. Finally, (iii) there's a hyperlink to the actual virtual world in Unity3D created by a cross-disciplinary team of researchers from Sydney University and Macquarie University for the purposes of teaching scientific inquiry. (shrink)

Bird argues that scientific progress consists in increasing knowledge. Dellsén objects that increasing knowledge is neither necessary nor sufficient for scientific progress, and argues that scientific progress rather consists in increasing understanding. Dellsén also contends that unlike Bird’s view, his view can account for the scientific practices of using idealizations and of choosing simple theories over complex ones. I argue that Dellsén’s criticisms against Bird’s view fail, and that increasing understanding cannot account for (...) class='Hi'>scientific progress, if acceptance, as opposed to belief, is required for scientific understanding. (shrink)

Review of Henk de Regt's "Understanding Scientific Understanding".

A common but overlooked form of explanation in psychiatry is what I label ‘empathetic explanation’. Empathetic explanations invoke empathetic variables, which, in addition to providing an explanation of the target phenomenon, also afford an empathetic understanding of it. Focusing on the case of auditory verbal hallucination (AVH), I argue that empathetic explanation fails to provide an adequate account of the phenomenon, perniciously shapes empirical research, and confuses empathetic understanding with scientific understanding. I close by providing a (...) general condition on the legitimate application of empathetic explanation. (shrink)

In my book Understanding Scientific Progress, I argue that fundamental philosophical problems about scientific progress, above all the problem of induction, cannot be solved granted standard empiricism (SE), a doctrine which most scientists and philosophers of science take for granted. A key tenet of SE is that no permanent thesis about the world can be accepted as a part of scientific knowledge independent of evidence. For a number of reasons, we need to adopt a rather different (...) conception of science which I call aim-oriented empiricism (AOE). This holds that we need to construe physics as accepting, as a part of theoretical scientific knowledge, a hierarchy of metaphysical theses about the comprehensibility and knowability of the universe, these theses becoming increasingly insubstantial as we go up the hierarchy. Fundamental philosophical problems about scientific progress, including the problems of induction, theory unity, verisimilitude and scientific discovery, which cannot be solved granted SE, can be solved granted AOE. In his review of Understanding Scientific Progress, Moti Mizrahi makes a number of criticisms, almost all of which are invalid in quite elementary ways. (shrink)

Dellsén (2017) has recently argued for an understanding-based account of scientific progress, the noetic account, according to which science (or a particular scientific discipline) makes cognitive progress precisely when it increases our understanding of some aspect of the world. Dellsén contrasts this account with Bird’s (2007, 2015) epistemic account, according to which such progress is made precisely when our knowledge of the world is increased or accumulated. In a recent paper, Park (2017) criticizes various aspects of (...) Dellsén’s account and his arguments in favor of the noetic account as against Bird’s epistemic account. This paper responds to Park’s objections. Since a number of Park’s arguments rely on the idea that scientific progress may merely consists in “achieving the means to increase knowledge” (Park 2017: 570), I will start by discussing this “means-end thesis”. (shrink)

Abstract The Greek composer and architect Iannis Xenakis has shown in Formalized Music (1963) how it is possible to compose or describe music and sound by means of probabilistic laws from mathematics, information theory and statistical mechanics. In his theory, scientific concepts and properties such as entropy take on a musical meaning in that they become also properties structurally instantiable by music. Philosophically speaking, this raises many important questions about the relation between science and the arts. One of these (...) questions concerns in particular the possibility for aesthetic symbols (like musical compositions) to convey scientific understanding, and understanding in general. In the present work, I claim that this question can be answered positively. In general, understanding does not necessarily depend on truth, explanation and propositionality (non-factualism, non-reductivism). Understanding can be conveyed also in non-propositional domains, in particular by means of exemplification. Since aesthetic and musical symbols are non-propositional, they can advance understanding possibly by exemplification, and in particular scientific understanding as long as they exemplify scientific concepts and properties. I moreover substantiate my claim by taking a case study: the concept of entropy in music. On the basis of Xenakis’ stochastic theory of music, I show how by exemplifying this concept, music can advance understanding of it. (shrink)

This book provides philosophers of science with new theoretical resources for making their own contributions to the scientific realism debate. Readers will encounter old and new arguments for and against scientific realism. They will also be given useful tips for how to provide influential formulations of scientific realism and antirealism. Finally, they will see how scientific realism relates to scientific progress, scientific understanding, mathematical realism, and scientific practice.

Can we sustain the idea, once expressed by Henri Poincaré, that science and values only touch but do not interpenetrate? Isn’t such an idea nothing more than an idealization? Is there no link between science and genuine human flourishing?

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 argues that scientific studies distinguish themselves from other studies by a combination of their processes, their (knowledge) elements and the roles of these elements. This is supported by constructing a process model. An illustrative example based on Newtonian mechanics shows how scientific knowledge is structured according to the process model. To distinguish scientific studies from research and scientific research, two additional process models are built for such processes. We apply these process models: (1) to (...) argue that scientific progress should emphasize both the process of change and the content of change; (2) to chart the major stages of scientific study development; and (3) to define “science”. (shrink)

At the intersection of taxonomy and nomenclature lies the scientific practice of typification. This practice occurs in biology with the use of holotypes (type specimens), in geology with the use of stratotypes, and in metrology with the use of measurement prototypes. In this paper I develop the first general definition of a scientific type and outline a new philosophical theory of types inspired by Pierre Duhem. I use this general framework to resolve the necessity-contingency debate about type specimens (...) in philosophy of biology, to advance the debate over the myth of the absolute accuracy of standards in metrology, and to address the definition-correlation debate in geology. I conclude that just as there has been a productive synergy between philosophical accounts of natural kinds and scientific taxonomic practices, so too there is much to be gained from developing a deeper understanding of the practices and philosophy of scientific types. (shrink)

This paper outlines the methodological and empirical limitations of analysing the potential relationship between complex social phenomena such as democracy and inequality. It shows that the means to assess how they may be related is much more limited than recognised in the existing literature that is laden with contradictory hypotheses and findings. Better understanding our scientific limitations in studying this potential relationship is important for research and policy because many leading economists and other social scientists such as Acemoglu (...) and Robinson mistakenly claim to identify causal linkages between inequality and democracy but at times still inform policy. In contrast to the existing literature, the paper argues that ‘structural’ or ‘causal’ mechanisms that may potentially link the distribution of economic wealth and different political regimes will remain unknown given reasons such as their highly complex and idiosyncratic characteristics, fundamental econometric constraints and analysis at the macro-level. Neither new data sources, different analysed time periods nor new data analysis techniques can resolve this question and provide robust, general conclusions about this potential relationship across countries. Researchers are thus restricted to exploring rough correlations over specific time periods and geographic contexts with imperfect data that are very limited for cross-country comparisons. (shrink)

A biophysical and biochemical perspective of Brahmajnaana will be advanced by viewing Upanishads and related books as “Texts of Science on human mind”. A biological and cognitive science insight of Atman and Maya, the results of breathing process; constituting and responsible for human consciousness and mental functions will be developed. The Advaita and Dvaita phases of human mind, its cognitive and functional states will be discussed. These mental activities will be modeled as brain-wave modulation and demodulation processes. The energy-forms and (...) their transformations as ideas/moods/experiences/thoughts/feelings/utterances/knowing/perception/experience/mood; and a theory of human cognition and communication will be advanced. The sameness of these and processes taking place and steps involved in human language acquisition and communication processes will be highlighted taking ideas from Sabdabrahma Siddhanta and Sphota Vaada, for which the basis is Brahmajnaana only. In fine, a physiological psychological and neurological model of human consciousness and function of mind based on Indian spiritual thought will be derived and discussed using concepts from modern science and technology. The application of these derivations in the fields of physiological psychology, mind-machine modeling, natural language comprehension branch of artificial intelligence and neurology to model and imitate human mental functions will be hinted. -/- . (shrink)

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

Philosophers of science are divided over the interpretations of scientific normativity. Larry Laudan defends a sort of goal-directed rules for scientific methodology. In contrast, Gerard Doppelt thinks methodological rules are a mixed batch of rules in that some are goal-oriented hypothetical rules and others are goal-independent categorical rules. David Resnik thinks that the debate between them is at a standstill now. He further thinks there are certain rules, such as the rule of consistency which is goal independent. However, (...) he proposes a holistic understanding of the scientific methodology. Taking a thread from Resnik, the present paper also advocates a holistic understanding of the scientific methodology. Given that many scientific practices deal with systems, the focus will be given to the systems by assuming each as a constellation of methodological norms. By taking each system as a set of mutually supportive methodological rules whose instrumental values underwrite the coherence relation among them, the paper aims to provide what could be a viable holistic epistemological account that can explain scientific normativity at work in a scientific system. The paper will lay down specific holistic criteria for understanding the scientific methodology. They will be used to show how a holistic account could satisfactorily account for the success of the Compact Muon Solenoid (CMS) experiment in discovering the Higgs boson and how the holistic account can account for the instrumental error behind the apparent faster-than-light neutrino anomaly of the Oscillation Project with Emulsion-t Racking Apparatus (OPERA) experiments respectively. (shrink)

There are three main accounts of scientific progress: 1) the epistemic account, according to which an episode in science constitutes progress when there is an increase in knowledge; 2) the semantic account, according to which progress is made when the number of truths increases; 3) the problem-solving account, according to which progress is made when the number of problems that we are able to solve increases. Each of these accounts has received several criticisms in the last decades. Nevertheless, some (...) authors think that the epistemic account is to be preferred if one takes a realist stance. Recently, Dellsén proposed the noetic account, according to which an episode in science constitutes progress when scientists achieve increased understanding of a phenomenon. Dellsén claims that the noetic account is a more adequate realist account of scientific progress than the epistemic account. This paper aims precisely at assessing whether the noetic account is a more adequate realist account of progress than the epistemic account. (shrink)

I examine the epistemological debate on scientific realism in the context of quantum physics, focusing on the empirical underdetermin- ation of different formulations and interpretations of QM. I will argue that much of the interpretational, metaphysical work on QM tran- scends the kinds of realist commitments that are well-motivated in the light of the history of science. I sketch a way of demarcating empirically well-confirmed aspects of QM from speculative quantum metaphysics in a way that coheres with anti-realist evidence (...) from the history of science. The minimal realist attitude sketched withholds realist com- mitment to what quantum state |Ψ⟩ represents. I argue that such commitment is not required for fulfilling the ultimate realist motiva- tion: accounting for the empirical success of quantum mechanics in a way that is in tune with a broader understanding of how theoretical science progresses and latches onto reality. (shrink)

Modern life sciences research is increasingly relying on artificial intelligence (AI) approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. As such, the interplay between these models and scientific understanding in biology is a topic with important implications for the future of (...) class='Hi'>scientific research, yet it is a subject that has received little attention. Here, we draw from an epistemological toolkit to contextualize recent applications of ML in biological sciences under modern philosophical theories of understanding, identifying general principles that can guide the design and application of ML systems to model biological phenomena and advance scientific knowledge. We propose that conceptions of scientific understanding as information compression, qualitative intelligibility, and dependency relation modelling provide a useful framework for interpreting ML-mediated understanding of biological systems. Through a detailed analysis of two key application areas of ML in modern biological research – protein structure prediction and single cell RNA-sequencing – we explore how these features have thus far enabled ML systems to advance scientific understanding of their target phenomena, how they may guide the development of future ML models, and the key obstacles that remain in preventing ML from achieving its potential as a tool for biological discovery. Consideration of the epistemological features of ML applications in biology will improve the prospects of these methods to solve important problems and advance scientific understanding of living systems. (shrink)

Philosophers of science are divided over the interpretations of scientific normativity. Larry Laudan defends a sort of goal-directed rules for scientific methodology. In contrast, Gerard Doppelt thinks methodological rules are a mixed batch of rules in that some are goal-oriented hypothetical rules and others are goal-independent categorical rules. David Resnik thinks that the debate between them is at a standstill now. He further thinks there are certain rules, such as the rule of consistency which is goal independent. However, (...) he proposes a holistic understanding of the scientific methodology. Taking a thread from Resnik, the present paper also advocates a holistic understanding of the scientific methodology. Given that many scientific practices deal with systems, the focus will be given to the systems by assuming each as a constellation of methodological norms. By taking each system as a set of mutually supportive methodological rules whose instrumental values underwrite the coherence relation among them, the paper aims to provide what could be a viable holistic epistemological account that can explain scientific normativity at work in a scientific system. The paper will lay down specific holistic criteria for understanding the scientific methodology. They will be used to show how a holistic account could satisfactorily account for the success of the Compact Muon Solenoid (CMS) experiment in discovering the Higgs boson and how the holistic account can account for the instrumental error behind the apparent faster-than-light neutrino anomaly of the Oscillation Project with Emulsion-t Racking Apparatus (OPERA) experiments respectively. (shrink)

Philosophy of Science in Practice (PoSiP) has the “practice of science” as its object of research. Notwithstanding, it does not possess yet any general or specific methodology in order to achieve its goal. Instead of sticking to one protocol, PoSiP takes advantage of a set of approaches from different fields. This thesis takes as a starting point a collaborative and interdisciplinary research between two Ph.D. students from distinct areas: ecology and philosophy. This collaboration showed how a scientist could benefit from (...) philosophy of science (in this case study the philosophical approach of. mechanistic explanation) to construct a model of his explanandum, by means of heuristics approach (heuristics as an instrument but also a methodological approach) and, also allowed philosophy of science take a closer look into the scientific practice to investigate how explanations are constructed and how scientific understanding is achieved (in this thesis, with a dialogue with the contextual theory of scientific understanding). As a result, it is asserted that (i) mechanistic explanation possess limitations but may work as epistemic instruments that mediate between theories, data, scientists, and models; (ii) explanation construction and scientific understanding deeply relies on intuition; (iii) scientific understanding is an instant, a moment, a temporary achievement, and its process may happen in degrees; (iv) philosophy of science, by means of heuristics process, may enhance scientists’ epistemic virtues, improving his academic skills, by means of self-evaluation. This research shows that interdisciplinarity and collaborative work can act, through heuristics, as a toolbox for PoSiP to achieve its goal of understanding how science is made. Despite its success, an analysis of this collaborative practice leads to some fundamental issues. First, philosophy of science in practice is a philosophy of past practice, in that the majority of examples used by mainstream PoSiP come from the final products of science. Second, is it philosophy of [science in practice] or philosophy of science [in practice]? How to practice philosophy of scientific practice and, how to practice interdisciplinarity in the philosophy of scientific practices simultaneously to its scientific activity? This research exposes the epistemic role heuristics and interdisciplinarity possess as methodological toolboxes for philosophy of science in practice. It is defended that other ways of constructing sciences would be through different dynamics such as collaborative networks and interdisciplinarity research contributing to the vision of Trading Zones from Peter Galison, in which bridges between specialized disciplines are created in order to exchange knowledge and information. (shrink)

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide (...) understanding misguided? In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding. (shrink)

Scientists are constantly making observations, carrying out experiments, and analyzing empirical data. Meanwhile, scientific theories are routinely being adopted, revised, discarded, and replaced. But when are such changes to the content of science improvements on what came before? This is the question of scientific progress. One answer is that progress occurs when scientific theories ‘get closer to the truth’, i.e. increase their degree of truthlikeness. A second answer is that progress consists in increasing theories’ effectiveness for solving (...) scientific problems. A third answer is that progress occurs when the stock of scientific knowledge accumulates. A fourth and final answer is that scientific progress consists in increasing scientific understanding, i.e. the capacity to correctly explain and reliably predict relevant phenomena. This paper compares and contrasts these four accounts of scientific progress, considers some of the most prominent arguments for and against each account, and briefly explores connections to different forms of scientific realism. (shrink)

Elgin offers an influential and far-reaching challenge to veritism. She takes scientific understanding to be non-factive and maintains that there are epistemically useful falsehoods that figure ineliminably in scientific understanding and whose falsehood is no epistemic defect. Veritism, she argues, cannot account for these facts. This paper argues that while Elgin rightly draws attention to several features of epistemic practices frequently neglected by veritists, veritists have numerous plausible ways of responding to her arguments. In particular, it (...) is not clear that false propositional commitments figure ineliminably in understanding in the manner supposed by Elgin. Moreover, even if scientific understanding were non-factive and false propositional commitments did figure ineliminably in understanding, the veritist can account for this in several ways without thereby abandoning veritism. (shrink)

Scientific realism is the position that the aim of science is to advance on truth and increase knowledge about observable and unobservable aspects of the mind-independent world which we inhabit. This book articulates and defends that position. In presenting a clear formulation and addressing the major arguments for scientific realism Sankey appeals to philosophers beyond the community of, typically Anglo-American, analytic philosophers of science to appreciate and understand the doctrine. The book emphasizes the epistemological aspects of scientific (...) realism and contains an original solution to the problem of induction that rests on an appeal to the principle of uniformity of nature. (shrink)

Scientific explanations must bear the proper relationship to the world: they must depict what, out in the world, is responsible for the explanandum. But explanations must also bear the proper relationship to their audience: they must be able to create human understanding. With few exceptions, philosophical accounts of explanation either ignore entirely the relationship between explanations and their audience or else demote this consideration to an ancillary role. In contrast, I argue that considering an explanation’s communicative role is (...) crucial to any satisfactory account of explanation. (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)

Ecological-enactive approaches to cognition aim to explain cognition in terms of the dynamic coupling between agent and environment. Accordingly, cognition of one’s immediate environment (which is sometimes labeled “basic” cognition) depends on enaction and the picking up of affordances. However, ecological-enactive views supposedly fail to account for what is sometimes called “higher” cognition, i.e., cognition about potentially absent targets, which therefore can only be explained by postulating representational content. This challenge levelled against ecological-enactive approaches highlights a putative explanatory gap between (...) basic and higher cognition. In this paper, we examine scientific cognition—a paradigmatic case of higher cognition—and argue that it shares fundamental features with basic cognition, for enaction and affordance selection are central to the scientific enterprise. Our argument focuses on modeling, and on how models promote scientific understanding. We base our argument on a non-representational account of scientific understanding and on the material engagement theory, for models are hereby conceived as material objects designed for scientific engagements. Having done so, we conclude that the explanatory gap is significantly less threatening to the ecological-enactive approach than it might appear. (shrink)

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

Several argue that truth cannot be science’s sole epistemic goal, for it would fail to do justice to several scientific practices that advance understanding. I challenge these arguments, but only after making a small concession: science’s sole epistemic goal is not truth as such; rather, its goal is finding true answers to relevant questions. Using examples from the natural and social sciences, I then show that scientific understanding’s epistemically valuable features are either true answers to relevant (...) questions or a means thereof. (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)

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

In a recent paper, Erasmus et al. (2021) defend the idea that the ambiguity of the term “explanation” in explainable AI (XAI) can be solved by adopting any of four different extant accounts of explanation in the philosophy of science: the Deductive Nomological, Inductive Statistical, Causal Mechanical, and New Mechanist models. In this chapter, I show that the authors’ claim that these accounts can be applied to deep neural networks as they would to any natural phenomenon is mistaken. I also (...) provide a more general argument as to why the notion of explainability as it is currently used in the XAI literature bears little resemblance to the traditional concept of scientific explanation. It would be more fruitful to use the label “understandable AI” to avoid the confusion that surrounds the goal and purposes of XAI. In the second half of the chapter, I argue for a pragmatic conception of understanding that is better suited to play the central role attributed to explanation in XAI. Following De Regt (2017) and Kuorikoski and Ylikoski (2015), the conditions of satisfaction for understanding an ML system are fleshed out in terms of an agent’s success in using the system. (shrink)

"Explanation and Understanding" (1971) by Georg Henrik von Wright is a modern classic in analytic hermeneutics, and in the philosophy of the social sciences and humanities in general. In this work, von Wright argues against naturalism, or methodological monism, i.e. the idea that both the natural sciences and the social sciences follow broadly the same general scientific approach and aim to achieve causal explanations. Against this view, von Wright contends that the social sciences are qualitatively different from the (...) natural sciences: according to his view, the natural sciences aim at causal explanations, whereas the purpose of the social sciences is to understand their subjects. In support of this conviction, von Wright also puts forward a version of the so-called logical connection argument. -/- Von Wright views scientific explanation along the lines of the traditional covering law model. He suggests that the social sciences, in contrast, utilize what he calls “practical syllogism” in understanding human actions. In addition, von Wright presents in this work an original picture on causation: a version of the manipulability theory of causation. -/- In the four decades following von Wright’s classic work, the overall picture in in the philosophy of science has changed significantly, and much progress has been made in various fronts. The aim of the contribution is to revisit the central ideas of "Explanation and Understanding" and evaluate them from this perspective. The covering law model of explanation and the regularity theory of causation behind it have since then fallen into disfavor, and virtually no one believes that causal explanations even in the natural sciences comply with the covering law model. No wonder then that covering law explanations are not found in the social sciences either. Ironically, the most popular theory of causal explanation in the philosophy of science nowadays is the interventionist theory, which is a descendant of the manipulability theory of von Wright and others. However, this theory can be applied with no special difficulties in both the natural sciences and the social sciences. -/- Von Wright’s logical connection argument and his ideas concerning practical syllogisms are also critically assessed. It is argued that in closer scrutiny, they do not pose serious problems for the view that the social sciences too provide causal explanations. In sum, von Wright’s arguments against naturalism do not appear, in today’s perspective, particularly convincing. (shrink)

Decades of debate about scientific realism notwithstanding, we find ourselves bemused by what different philosophers appear to think it is, exactly. Does it require any sort of belief in relation to scientific theories and, if so, what sort? Is it rather typified by a certain understanding of the rationality of such beliefs? In the following dialogue we explore these questions in hopes of clarifying some convictions about what scientific realism is, and what it could or should (...) be. En route, we encounter some profoundly divergent conceptions of the nature of science and of philosophy. (shrink)

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

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