Reliable Reasoning is a simple, accessible, beautifully explained introduction to Vapnik and Chervonenkis’s statistical learning theory. It includes a modest discussion of the application of the theory to the philosophy of induction; the purpose of these remarks is to say something more. 27.

An overview of how mechanisms work in explanations.

Though some influentially critical objections have been raised during the ‘classical’ pre-computational simulation philosophy of science tradition, suggesting a more nuanced methodological category for experiments, it safe to say such critical objections have greatly proliferated in philosophical studies dedicated to the role played by computational simulations in science. For instance, Eric Winsberg suggests that computer simulations are methodologically unique in the development of a theory’s models suggesting new epistemic notions of application. This is also echoed in Jeffrey Ramsey’s notions of (...) “transformation reduction,”—i.e., a notion of reduction of a more highly constructive variety. Computer simulations create a broadly continuous arena spanned by normative and descriptive aspects of theory-articulation, as entailed by the notion of transformation reductions occupying a continuous region demarcated by Ernest Nagel’s logical-explanatory “domain-combining reduction” on the one hand, and Thomas Nickels’ heuristic “domain-preserving reduction,” on the other. I extend Winsberg’s and Ramsey’s points here, by arguing that in the field of computational fluid dynamics as well as in other branches of applied physics, the computer plays a constitutively experimental role—supplanting in many cases the more traditional experimental methods such as flow-visualization, etc. In this case, however CFD algorithms act as substitutes, not supplements when it comes to experimental practices. I bring up the constructive example involving the Clifford-Algebraic algorithms for modeling singular phenomena in CFD by Gerik Scheuermann and Steven Mann & Alyn Rockwood who demonstrate that their algorithms offer greater descriptive and explanatory scope than the standard Navier-Stokes approaches. The mathematical distinction between Navier-Stokes-based and Clifford-Algebraic based CFD has essentially to do with the regularization features exhibited to a far greater extent by the latter, than the former. Hence, CACFD indicate that the utilization of computational techniques can be based on principled reasons, as opposed to merely practical. CACFD hence exhibit a new generative role in the field of fluid mechanics, by offering categories of experimental evidence that are optimally descriptive and explanatory—i.e., pace Batterman can be both ontologically and epistemically fundamental. (shrink)

One of the main goals of scientific research is to provide a description of the empirical data which is as accurate and comprehensive as possible, while relying on as few and simple assumptions as possible. In this paper, I propose a definition of the notion of few and simple assumptions that is not affected by known problems. This leads to the introduction of a simple model of scientific progress that is based only on empirical accuracy and conciseness. An essential point (...) in this task is the understanding of the role played by measurability in the formulation of a scientific theory. This is the key to prevent artificially concise formulations. The model is confronted here with many possible objections and with challenging cases of real progress. Although I cannot exclude that the model might have some limitations, it includes all the cases of genuine progress examined here, and no spurious one. In this model, I stress the role of the state of the art, which is the collection of all the theories that are the only legitimate source of scientific predictions. Progress is a global upgrade of the state of the art. (shrink)

In order to tackle the question posed by the title – notoriously answered in the positive, among others, by Heisenberg, Margenau, Popper and Redhead – I first discuss some attempts at distinguishing dispositional from non-dispositional properties, and then relate the distinction to the formalism of quantum mechanics. Since any answer to the question titling the paper must be interpretation-dependent, I review some of the main interpretations of quantum mechanics in order to argue that the ontology of theories regarding “wave collapse” (...) as a genuine physical process could be interpreted as being irreducibly dispositional. In non-collapse interpretations, on the contrary, the appeal to dispositions is simply a way to reformulate the predictive content of the algorithm of the theory in a fancier metaphysical language. (shrink)

Resumen: La capacidad unificadora de una teoría científica es un rasgo usualmente contemplado a la hora de evaluar su adecuación. Kitcher ha elucidado satisfactoriamente tal noción mediante su enfoque de los patrones explicativos. Sin embargo, su perspectiva adolece de ciertas carencias. Concretamente, sostendremos que el requisito de rigurosidad de los patrones para evaluar la capacidad unificadora debe ser repensado, pues atenta contra la heterogeneidad característica de las diferentes aplicaciones de teorías unificadoras. A su vez, mostraremos cómo estas dificultades bien pueden (...) ser subsanadas desde el marco del estructuralismo metateórico sin por ello resignar ninguna de las acertadas intuiciones de Kitcher. -/- Palabras clave: Explicación, Kitcher, estructuralismo metateórico, unificación, unificación espuria. -/- Abstract: Kitcher has satisfactorily explicated unification using his particular approach to scientific explanation. However, we believe that his perspective has certain problems, which have been inherited by more recent approaches to the topic. Specifically, the requirement of stringency of patterns that Kitcher proposes to determine the unifying power of an explanatory pattern undermines the typical and peculiar heterogeneity we can find in different applications of unifying theories. We will show how this topic can be better addressed by the perspective built by metatheoretical structuralism without sacrificing any of Kitcher’s correct intuitions. -/- Keywords: Explanation, Kitcher, metatheoretical structuralism, spurious unification, unification. (shrink)

Due to the variation, contingency and complexity of living systems, biology is often taken to be a science without fundamental theories, laws or general principles. I revisit this question in light of the quest for design principles in systems biology and show that different views can be reconciled if we distinguish between different types of generality. The philosophical literature has primarily focused on generality of specific models or explanations, or on the heuristic role of abstraction. This paper takes a different (...) approach in emphasizing a theory-constituting role of general principles. Design principles signify general dependencyrelations between structures and functions, given a set of formally defined constraints. I contend that design principles increase our understanding of living systems by relating specific models to general types. The categorization of types is based on a delineation of the scope of biological possibilities, which serves to identify and define the generic features of classes of systems. To characterize the basis for general principles through generic abstraction and reasoning about possibility spaces, I coin the term constraint-based generality. I show that constraintbased generality is distinct from other types of generality in biology, and argue that general principles play a unifying role that does not entail theory reduction. (shrink)

Students in interdisciplinary science educations are faced with the challenge of combining knowledge and standards from different disciplines. To help them overcome this challenge it would be helpful to reflect more explicitly on what differences in epistemic aims there may be between the different disciplines. To aid further studies that will strengthen such discussions this paper outlines an empirical method that can be used to expose possible qualitative differences in explanations from different disciplines. The method is based on the use (...) of science textbooks as sources of explanations. I will argue that it is important to be very clear about how explanations are identified in the sources if the aim of the study is to make a strong empirical claim about the nature of explanations. Following a discussion of similar studies I will present an alternative procedure for identifying explanations in written sources that suits the purposes of this study better. A pilot study is then presented to illustrate the method and its limitations. (shrink)

A number of areas of biology raise questions about what is of value in the natural environment and how we ought to behave towards it: conservation biology, environmental science, and ecology, to name a few. Based on my experience teaching students from these and similar majors, I argue that the field of environmental ethics has much to teach these students. They come to me with pent-up questions and a feeling that more is needed to fully engage in their subjects, and (...) I believe some exposure to environmental ethics can help focus their interests and goals. I identify three primary areas in which environmental ethics can con- tribute to their education. The first is an examination of who (or what) should be considered to be part of our moral community (i.e., the community to whom we owe direct duties). Is it humans only? Or does it include all sentient life? Or all life? Or ecosystems considered holistically? Often, readings implicitly assume one or more of these answers; the goal is to make the student more sensitive to these implicit claims and to get them to think about the different reasons that support them. The second area, related to the first, is the application of the different answers concerning the extent of the ethical community to real environmental issues and problems. Students need to be aware of how the different answers concerning the moral community can imply conflicting answers for how we should act in certain cases and to think about ways to move toward conflict resolution. The third area in which environmental ethics can contribute is a more conceptual one, focusing on central concepts such as biodiversity, sustainability, species, and ecosystems. Exploring and evaluating various meanings of these terms will make students more reflective and thoughtful citizens and biologists, sensitive to the implications that different conceptual choices make. (shrink)

The three cardinal aims of science are prediction, control, and explanation; but the greatest of these is explanation. Also the most inscrutable: prediction aims at truth, and control at happiness, and insofar as we have some independent grasp of these notions, we can evaluate science’s strategies of prediction and control from the outside. Explanation, by contrast, aims at scientific understanding, a good intrinsic to science and therefore something that it seems we can only look to science itself to explicate.

Advances in biology, at least over the past two centuries, have mostly relied on theories that were subsequently revised, expanded or eventually refuted using experimental and other means. The field of theoretical biology used to primarily provide a basis, similar to theoretical physics in the physical sciences, to rationally examine the frameworks within which biological experiments were carried out and to shed light on overlooked gaps in understanding. Today, however, theoretical biology has generally become synonymous with computational and mathematical biology. (...) This could in part be explained by a relatively recent tendency in which a "data first", rather than a "theory first", approach is preferred. Moreover, generating hypotheses has at times become procedural rather than theoretical, therefore perhaps inadvertently leading some hypotheses to become perfunctory in nature. This situation leaves our understanding enmeshed in data, which should be disentangled from much noise. Given the many unresolved questions in biology and medicine, big and small, ranging from the problem of protein folding to unifying causative frameworks of complex non-Mendelian human diseases, it seems apt to revive the role of pure theory in the biological sciences. This paper, using the current biomedical literature and historical precedents, makes the case for a "philosophical biology" (philbiology), distinct from but quite complementary to philosophy of biology (philobiology), which would entail biological investigation through philosophical approaches. Philbiology would thus be a reincarnation of theoretical biology, adopting the true sense of the word "theory" and making use of a rich tradition of serious philosophical approaches in the natural sciences. A philbiological investigation, after clearly defining a given biological problem, would aim to propose a set of empirical questions, along with a class of possible solutions, about that problem. Importantly, whether or not the questions can be tested using current experimental paradigms would be secondary to whether the questions are inherently empirical or not. These issues will be illustrated using a range of specific examples. The final goal of a philbiological investigation would be to develop a theoretical framework that can lead observational and/or interventional experimental studies of the defined problem, a framework that is structured, generative and expandable, and, crucially, one that simplifies some aspect(s) of the said problem. (shrink)

The relation between probabilistic and explanatory reasoning is a classical topic in philosophy of science. Most philosophical analyses are concerned with the compatibility of Inference to the Best Explanation with probabilistic, Bayesian inference, and the impact of explanatory considerations on the assignment of subjective probabilities. This paper reverses the question and asks how causal and explanatory considerations are affected by probabilistic information. We investigate how probabilistic information determines the explanatory value of a hypothesis, and in which sense folk explanatory practice (...) can be said to be rational. Our study identifies three main factors in reasoning about a explanatory hypothesis: cognitive salience, rational acceptability and logical entailment. This corresponds well to the variety of philosophical accounts of explanation. Moreover, we show that these factors are highly sensitive to manipulations of probabilistic information. This finding suggests that probabilistic reasoning is a crucial part of explanatory inferences, and it motivates new avenues of research in the debate about Inference to the Best Explanation and probabilistic measures of explanatory power. (shrink)

It is argued that rather than a well defined F-Twist, Milton Friedman’s “Methodology of positive economics” offers an F-Mix: a pool of ambiguous and inconsistent ingredients that can be used for putting together a number of different methodological positions. This concerns issues such as the very concept of being unrealistic, the goal of predictive tests, the as-if formulation of theories, explanatory unification, social construction, and more. Both friends and foes of Friedman’s essay have ignored its open-ended unclarities. Their removal may (...) help create new common ground for more focused debate in economics. Here I show how F53 can be reread/rewritten as a socially constructivist fallibilist, and realist statement – in contrast to the received instrumentalist interpretation. (shrink)

One of the central aims of science is explanation: scientists seek to uncover why things happen the way they do. This chapter addresses what kinds of explanations are formulated in biology, how explanatory aims influence other features of the field of biology, and the implications of all of this for biology education. Philosophical treatments of scientific explanation have been both complicated and enriched by attention to explanatory strategies in biology. Most basically, whereas traditional philosophy of science based explanation on derivation (...) from scientific laws, there are many biological explanations in which laws play little or no role. Instead, the field of biology is a natural place to turn for support for the idea that causal information is explanatory. Biology has also been used to motivate mechanistic accounts of explanation, as well as criticisms of that approach. Ultimately, the most pressing issue about explanation in biology may be how to account for the wide range of explanatory styles encountered in the field. This issue is crucial, for the aims of biological explanation influence a variety of other features of the field of biology. Explanatory aims account for the continued neglect of some central causal factors, a neglect that would otherwise be mysterious. This is linked to the persistent use of models like evolutionary game theory and population genetic models, models that are simplified to the point of unreality. These explanatory aims also offer a way to interpret many biologists’ total commitment to one or another methodological approach, and the intense disagreements that result. In my view, such debates are better understood as arising not from different theoretical commitments, but commitments to different explanatory projects. Biology education would thus be enriched by attending to approaches to biological explanation, as well as the unexpected ways that these explanatory aims influence other features of biology. I suggest five lessons for teaching about explanation in biology that follow from the considerations of this chapter. (shrink)

The mantra that "the best way to predict the future is to invent it" (attributed to the computer scientist Alan Kay) exemplifies some of the expectations from the technical and innovative sides of biomedical research at present. However, for technical advancements to make real impacts both on patient health and genuine scientific understanding, quite a number of lingering challenges facing the entire spectrum from protein biology all the way to randomized controlled trials should start to be overcome. The proposal in (...) this chapter is that philosophy is essential in this process. By reviewing select examples from the history of science and philosophy, disciplines which were indistinguishable until the mid-nineteenth century, I argue that progress toward the many impasses in biomedicine can be achieved by emphasizing theoretical work (in the true sense of the word 'theory') as a vital foundation for experimental biology. Furthermore, a philosophical biology program that could provide a framework for theoretical investigations is outlined. (shrink)

La discusión acerca de funciones es de larga data en filosofía. Normalmente se describe a la revolución científica del siglo XVII como eliminando las causas finales y la teleología de la física. Sin embargo, el lenguaje funcional cumple un papel central en ciertas áreas de la práctica biológica. Esto ha llevado a muchos filósofos a intentar elucidar el concepto de función, en algunos casos para defender la relevancia de estos usos, en otros para mostrar que se trata de meras formas (...) de hablar que podrían ser eliminadas sin pérdida de ningún tipo de información relevante. El objetivo principal de este trabajo es mostrar la necesidad de apelar a conceptos funcionales para una reconstrucción adecuada de la teoría de la selección natural darwiniana. Pues, se utiliza lenguaje funcional en la conceptualización del campo de aplicación de esta teoría, y tal campo debe ser determinable con independencia la teoría de la selección natural. (shrink)

Scientific antirealists run the argument from underconsideration against scientific realism. I argue that the argument from underconsideration backfires on antirealists’ positive philosophical theories, such as the contextual theory of explanation (van Fraassen, 1980), the English model of rationality (van Fraassen, 1989), the evolutionary explanation of the success of science (Wray, 2008; 2012), and explanatory idealism (Khalifa, 2013). Antirealists strengthen the argument from underconsideration with the pessimistic induction against current scientific theories. In response, I construct a pessimistic induction against antirealists that (...) since antirealists generated problematic philosophical theories in the past, they must be generating problematic philosophical theories now. (shrink)

In this paper (which is, at best, a work in progress), I discuss different modes of scientific explanation identified by philosophers (Hempel, Salmon, Kitcher, Friedman, Hughes) and examine how well or badly they capture the "explanations" of phenomena that modern quantum theory provides. I tentatively conclude that quantum explanation is best seen as "structural explanation", and spell out in detail how this works in the case of explaining vacuum correlations. Problems and prospects for structural explanation in quantum theory are also (...) discussed. (shrink)

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

Scientific realists and antirealists believe that a successful scientific theory is true and merely empirically adequate, respectively. In contrast, epistemic reciprocalists believe that realists’ positive theories are true, and that antirealists’ positive theories are merely empirically adequate, treating their target agents as their target agents treat other epistemic agents. Antirealists cannot convince reciprocalists that their positive theories are true, no matter how confident they might be that they are true. In addition, reciprocalists criticize antirealists’ positive theories exactly in the way (...) that antirealists criticize their epistemic colleagues’ theories. Reciprocalism is a better epistemic policy than realism and antirealism in the epistemic battleground in which we strive to be epistemically safe vis-à-vis our epistemic colleagues’ theories and strive to convince our epistemic colleagues that our theories are true. (shrink)

Van Fraassen (1980) and Winther (2009) claim that we can explain phenomena in terms of scientific theories without believing that they are true. I argue that we ought to believe that they are true in order to use them to explain and understand phenomena. A scientific antirealist who believes that scientific theories are merely empirically adequate cannot use them to explain or to understand phenomena. The mere belief that they are empirically adequate produces neither explanation nor understanding of phenomena. Explanation (...) and understanding are the benefits of science only a scientific realist is entitled to. (shrink)

In natural sciences, the most interesting and relevant questions are the so-called why-questions. There are several different approaches to why-questions and explanations in the literature, however, most of the literature deals with why-questions about particular events, such as ``Why did Adam eat the apple?''. Even the best known theory of explanation, Hempel's covering law model, is designed for explaining particular events. Here we only deal with purely theoretical why-questions about general phenomena of physics, for instance ``Why can no observer move (...) faster than light?'' or ``Why are Kepler's laws valid?''. Here we are not going to develop a whole new theory of why-questions in physics. We will just touch upon some ideas and examples relevant to our subject. (shrink)

I argue for three central theses: ‘intuition’ is ambiguous, in material object metaphysics ‘intuition’ refers to pre-theoretical beliefs, and these pre-theoretical beliefs are generated by an innate physical reasoning system. I begin by outlining the relevant background discussions on the nature of intuitions and their role in philosophy to motivate the need for a more careful investigation of the meaning of ‘intuition’ and the role of intuitions in specific sub-disciplines of philosophy. In chapters one and two I argue that ‘intuition’ (...) is ambiguous between an inflationary and deflationary sense. In the inflationary sense, ‘intuition’ refers to a priori intellectual seemings with a special phenomenology, conceptual etiology, and modal content. In the deflationary sense, ‘intuition’ refers to beliefs or inclinations to believe. In chapter three I specifically examine the use of intuitions in material object metaphysics and conclude that in this sub-community ‘intuition’ is used in the deflationary sense to refer to pre-theoretical beliefs. Drawing from research on infant cognition, in the final chapter I argue that intuitions regarding material object metaphysics are those judgments that arise from an innate physical reasoning system. Based on this empirical observation, I argue that metaphysicians ought to give preference to abstract intuitions over intuitions regarding concrete cases because these abstract intuitions reflect the innate structure of our physical reasoning mechanisms. (shrink)

De acordo com Aristóteles, conhecemos algo cientificamente quando apreendemos a causa pela qual essa coisa é e apreendemos, também, certa relação necessária entre aquilo que pretendemos conhecer e o que descobrimos ser a causa adequada que explica por que tal fato é o caso. Além disso, o filósofo identifica o conhecimento científico com a posse de um silogismo científico ou demonstração. Neste trabalho, analisamos a relação entre a teoria demonstrativa que Aristóteles desenvolve, principalmente, no livro I dos Segundos Analíticos e (...) sua teoria silogística dos Primeiros Analíticos I e tentamos responder por que o conhecimento científico deve ser via silogismo. Também procuramos explicitar como as noções de causa e de necessidade, pelas quais Aristóteles define o conhecimento científico, são contempladas pela exigência de que as proposições de uma demonstração sejam per se. Finalmente, discutiremos como essas noções de per se, necessidade e causa se encaixam na estrutura silogística, uma vez que conhecer algo cientificamente é possuir um silogismo científico. (shrink)

In this paper, I would like to show that considering technological models as they arise in engineering disciplines can greatly enrich the philosophical perspective on models. In fluid mechanics, (at least) three types of models are distinguished: mathematical, computer and physical models. Very often, the choice of a particular mathematical, computer or physical model highly affects the type of solutions and the computational time needed for it. Technological models not only aim at a correct description of the physical phenomena, but (...) also for an efficient and accurate simulation. The problem arises how heterogeneous models of an engineering problem can be brought together and be compared to each other as regards their function and technological efficiency. There are two developments in the history of fluid mechanics that have greatly influenced the use of models in the field: The introduction of the concept of the boundary layer by Ludwig Prandtl in 1904 made it possible to apply ideal analytical solutions, which at the time were almost entirely based on Euler’s equations for inviscid fluids, to interesting real cases and to approximate the theoretical Navier-Stokes equations to practical engineering problems, i.e. to cases at high Reynolds numbers. This made it possible to link the empirical tradition of hydraulics with the theoretical tradition of analytical mechanics and therefore lead to a kind of equilibrium in the use of mathematical and physical models. In the 1970s the introduction of the computer has greatly pushed back the importance of both physical and mathematical (analytic) models alike without making them superfluous. There remain, however, three different ways to conceive of physical models in fluid mechanics, and thus of the experimental ingredient, depending on whether they are devised from an analytical, computational or measurement theoretical point of view. Yet even inside the tradition of computer simulation, different practices have formed according to the programming methods used. The choice of method either depends on considerations of efficiency in terms of costs and time, or on historically contingent factors, like availability of instruments and programming packages or the arbitrary choice of a forerunner. Seen from a technological point of view the factors that make models “autonomous agents” and thus (relatively) independent from theory depend on efficiency constraints. Models are means to solve problems in a certain practical perspective by the most efficient means available. To develop a model is a “fast and frugal way” to get to grips with a certain region of reality, whereas the theoretical approach stresses the importance of universal features. (shrink)