In Extragalactic Reality: The Case of Gravitational Lensing Hacking resumes the discussion of scientific realism from the last chapter of Representing and Intervening. Since the criterion of manipulability cannot be applied to astronomical objects, experimental entity realism seems to be restricted to terrestrial entities. In fact, Hacking explicitly argues against astronomical realism. The case at issue is the existence of gravitational lenses. In this paper, I question Hacking 's chief witness for astronomical antirealism: the gravitational lens system “0957+ 561”. It (...) will be shown that Hacking 's argumentation is misleading. Discussing astronomical realism as theory realism, Hacking focuses on the question of how to infer the existence of gravitational lenses from the truth of gravitational lens theory. But neither the reconstruction of gravitational lensing in terms of inference to the best explanation nor the argument of underdetermination are tenable under closer inspection. My thesis is that a realist account of gravitational lensing can be given by relying on observation, causal capacities and home truths. (shrink)

In this paper, we argue that rather than exclusively focusing on trying to determine if an idealized model fits a particular account of scientific explanation, philosophers of science should also work on directly analyzing various explanatory schemas that reveal the steps and justification involved in scientists’ use of highly idealized models to formulate explanations. We develop our alternative methodology by analyzing historically important cases of idealized statistical modeling that use a three-step explanatory schema involving idealization, mathematical operation, and explanatory interpretation.

In this paper, I aim to provide access to the current debate on non-causal explanations in philosophy of science. I will first present examples of non-causal explanations in the sciences. Then, I will outline three alternative approaches to non-causal explanations – that is, causal reductionism, pluralism, and monism – and, corresponding to these three approaches, different strategies for distinguishing between causal and non-causal explanation. Finally, I will raise questions for future research on non-causal explanations.

The patent system appears to make three ontological assumptions often associated with scientific realism: there is a natural world that is independent of human knowledge and technology; invented products can be unobservable things; and invented products have causal powers. Although a straightforward reading of patent laws implies these ontological commitments, it is not at all clear that what the patent system has to say about the world has any bearing on issues of scientific realism. While realists might embrace the patent (...) system’s apparent ontology as providing additional support for their view, anti-realists could propose an alternative interpretation of patent law that does not involve realist ontological commitments. Thus, questions about the patent system’s ontology depend on whether one takes patent laws at face value or defends an interpretation of patent law motivated by philosophical theory. Philosophers of science should pay attention to the practice of patenting, even if they ultimately reject the patent system’s apparent ontology and offer an alternative interpretation. (shrink)

Rational models vary in their goals and sources of justification. While the assumptions of some are grounded in the environment, those of others are induced and so require more traditional sources of justification, such as generalizability to dissimilar tasks and making novel predictions. Their contribution to scientific understanding will remain uncertain until standards of evidence are clarified.

In the context of calls for "postpositivist" sociology, realism has emerged as a powerful and compelling epistemology for social science. In transferring and transforming scientific realism --a philosophy of natural science--into a justificatory discourse for social science, realism splits into two parts: a strict, highly naturalistic realism and a reflexive, more mediated, and critical realism. Both forms of realism, however, suffer from conceptual ambiguities, omissions, and elisions that make them an inappropriate epistemology for social science. Examination of these problems in (...) detail reveals how a different perspective--centered on the interpretation of meaning--could provide a better justification for social inquiry, and in particular a better understanding of sociological theory and the construction of sociological explanations. (shrink)

In what sense do the sciences explain? Or do they merely describe what is going on without answering why-questions at all. But cannot description at an appropriate ‘level’ provide all that we can reasonably ask of an explanation? Well, what do we mean by explanation anyway? What, if anything, gets left out when we provide a so-called scientific explanation? Are there limits of explanation in general, and scientific explanation, in particular? What are the criteria for a good explanation? Is it (...) possible to satisfy all the desiderata simultaneously? If not, which should we regard as paramount? What is the connection between explanation and prediction? What exactly is it that statistical explanations explain? These are some of the questions that have generated a very extensive literature in the philosophy of science. In attempting to answer them, definite views will have to be taken on related matters, such as physical laws, causality, reduction, and questions of evidence and confirmation, of theory and observation, realism versus antirealism, and the objectivity and rationality of science. I will state my own views on these matters, in the course of this essay. To argue for everything in detail and to do justice to all the alternative views, would fill a book, perhaps several books. I want to lead up fairly quickly to modern physics, and review the explanatory situation there in rather more detail. (shrink)

In this essay we show that some thesis -contended by some authors searching on physical causation- about causal pseudoprocesses, processes of which the most salient aspect seems to be travelling faster than the speed of light, must be rejected o seriously modified. Secondly, we will argue that there is just one non causal entities category and that special relativity does not provide any particular motivation to appeal to an ontological distinction based on identity over time. In addition, this conclusion we (...) will enable us to offer a more accurate definition of a pseudoprocess than that found in the relevant literature. In third place, we will show in some detail how such processes, understood the way that we suggest here, are and have been valuable methodological tools when studying –through usual measurement procedures- effective causal relations holding in the physical world. Finally, we will connect our conclusions with R. Torretti’s criticism on the relation between ordinary causation and governed-by-differential-equations physical processes and we will show that, although neutral on his anthropocentric background, the account here argued provides, against what Torretti claims, a natural explanation to the physical connection between ordinary causation and causal processes such as studied by physics. (shrink)

This paper aims to cast light on the reasons that explain the shift of opinion—from scepticism to realism—concerning the reality of atoms and molecules in the beginning of the twentieth century, in light of Jean Perrin’s theoretical and experimental work on the Brownian movement. The story told has some rather interesting repercussions for the rationality of accepting the reality of explanatory posits. Section 2 presents the key philosophical debate concerning the role and status of explanatory hypotheses c. 1900, focusing on (...) the work of Duhem, Stallo, Ostwald, Poincaré and Boltzmann. Section 3 examines in detail Perrin’s theoretical account of the molecular origins of Brownian motion, reconstructs the structure and explains the strength of Perrin’s argument for the reality of molecules. Section 4 draws three important lessons for the current debate over scientific realism. (shrink)

Lange’s collection of expanded, mostly previously published essays, packed with numerous, beautiful examples of putatively non-causal explanations from biology, physics, and mathematics, challenges the increasingly ossified causal consensus about scientific explanation, and, in so doing, launches a new field of philosophic investigation. However, those who embraced causal monism about explanation have done so because appeal to causal factors sorts good from bad scientific explanations and because the explanatory force of good explanations seems to derive from revealing the relevant causal (or (...) ontic) structures. The taxonomic project of collecting examples and sorting their types is an essential starting place for a theory of non-causal explanation. But the title of Lange’s book requires something further: showing that the putative explanations are, in fact, explanatory and revealing the non-causal source of their explanatory power. This project is incomplete if there are examples of putative non-causal explanations that fit the form but that nobody would accept as explanatory (absent a radical revision of intuitions). Here we provide some reasons for thinking that there are such examples. (shrink)

Defending or attacking either functionalism or computationalism requires clarity on what they amount to and what evidence counts for or against them. My goal here is not to evaluate their plausibility. My goal is to formulate them and their relationship clearly enough that we can determine which type of evidence is relevant to them. I aim to dispel some sources of confusion that surround functionalism and computationalism, recruit recent philosophical work on mechanisms and computation to shed light on them, and (...) clarify how functionalism and computationalism may or may not legitimately come together. (shrink)

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

The debate between the dynamical and the statistical interpretations of natural selection is centred on the question of whether all explanations that employ the concepts of natural selection and drift are reducible to causal explanations. The proponents of the statistical interpretation answer negatively, but insist on the fact that selection/drift arguments are explanatory. However, they remain unclear on where the explanatory power comes from. The proponents of the dynamical interpretation answer positively and try to reduce selection/drift arguments to some of (...) the most prominent accounts of causal explanation. In turn, they face the criticism raised by statisticalists that current accounts of causation have to be violated in some of their core conditions or otherwise used in a very loose manner in order to account for selection/drift explanations. We propose a reconciliation of both interpretations by conveying evolutionary explanations within the unificationist model of scientific explanation. Therefore, we argue that the explanatory power in natural selection arguments is a result of successful unification of individual- and population-level facts. A short case study based on research on sympatric speciation will be presented as an example of how population- and individual-level facts are unified to explain the morphological mosaic of bill shape in island scrub jays. (shrink)

Arguments from explanation, i.e. arguments in which the explanatory value of a hypothesis or premise is appealed to, are common in science, and explanatory considerations are becoming more popular in metaphysics. The paper begins by arguing that explanatory arguments in science—even when these are metaphysical explanations— may fail to be explanatory in metaphysics; there is a distinction to be drawn between metaphysical explanation and explanation in metaphysics. This makes it potentially problematic to deploy arguments from explanation in, for instance, metaphysics (...) of science. Part of this problem has its source in that the explanatory concept differs between contexts. The paper discusses a few explanatory concepts and their corresponding arguments from explanation. Towards the end of the paper, I identify two allegedly explanatory arguments in metaphysical discourse by the concluding decisions they give rise to: the rejection of X as a metaphysical fact if X does not explain anything (the argument from explanatory inability) and the rejection of X as a metaphysical fact if X can be non-metaphysically explained (the argument from the non-metaphysically explained). I ask: What kind of concept of explanation do these arguments rely upon, and is that concept suited to the metaphysical task? Two recent examples of these arguments are used as illustration. The preliminary conclusion is that several of the strengths of arguments from explanation in science seem not to be present in metaphysical contexts. (shrink)

If well-designed, the results of a Randomised Clinical Trial can justify a causal claim between treatment and effect in the study population; however, additional information might be needed to carry over this result to another population. RCTs have been criticized exactly on grounds of failing to provide this sort of information Evidence, inference and enquiry. Oxford University Press, New York, 2011), as well as to black-box important details regarding the mechanisms underpinning the causal law instantiated by the RCT result. On (...) the other side, so-called In Silico Clinical Trials face the same criticisms addressed against standard modelling and simulation techniques, and cannot be equated to experiments Philosophy of molecular medicine: foundational issues in research and practice, Routledge, New York, 2017; Parker in Synthese 169:483–496, 2009; Parke in Philos Sci 81:516–536, 2014; Diez Roux in Am J Epidemiol 181:100–102, 2015 and related discussions in Frigg and Reiss in Synthese 169:593–613, 2009; Winsberg in Synthese 169:575–592, 2009; Beisbart and Norton in Int Stud Philos Sci 26:403–422, 2012). We undertake a formal analysis of both methods in order to identify their distinct contribution to causal inference in the clinical setting. Britton et al.’s study :E2098–E2105, 2013) on the impact of ion current variability on cardiac electrophysiology is used for illustrative purposes. We deduce that, by predicting variability through interpolation, ISCTs aid with problems regarding extrapolation of RCTs results, and therefore in assessing their external validity. Furthermore, ISCTs can be said to encode “thick” causal knowledge —as opposed to “thin” difference-making information inferred from RCTs. Hence, ISCTs and RCTs cannot replace one another but rather, they are complementary in that the former provide information about the determinants of variability of causal effects, while the latter can, under certain conditions, establish causality in the first place. (shrink)

This essay presents a long, detailed, in many ways critical but also appreciative account, of David Bloor’s recent book The Enigma of the Aerofoil. I take that work as the crowning statement of ideas and principles developed over the past four decades by Bloor and other exponents of the ‘strong programme’ in the sociology of scientific knowledge. It therefore offers both a test-case of that approach and a welcome opportunity to review, clarify and extend some of the arguments brought against (...) it by critical realists. This purpose is particularly well served by Bloor’s having here assembled such a wealth of scientific, historical, political and socio-cultural data concerning one specific, very crucial period of research into theories of aerodynamic lift. His book thus provides an exceptionally useful point of engagement for anyone seeking to adjudicate these issues from a critically angled but engaged and constructive standpoint. (shrink)

This article offers a critical review of various ontological-relativist arguments, mostly deriving from the work of W. V. Quine and Thomas K hn. I maintain that these arguments are (1) internally contradictory, (2) incapable of accounting for our knowledge of the growth of scientific knowledge, and (3) shown up as fallacious from the standpoint of a causal-realist approach to issues of truth, meaning, and interpretation. Moreover, they have often been viewed as lending support to such programmes as the 'strong' sociology (...) of knowledge and the turn towards wholesale cultural-relativist doctrines, whether of the Wittgensteinian ('language-games') or Heideggerian (depth-hermeneutic) varieties. Thus Richard Rorty recommends that we should henceforth drop all that old-fashioned talk of 'truth', 'knowledge', or 'reality', since whatever warrants those descriptions from time to time is just the product of some currently favoured language-game or range of elective metaphors. Such ideas have little in common with Quine's general outlook of robust physicalism, nor again - though the case is less clear - with Kuhn's reconsidered approach to these issues as set forth in his 1969 Postscript to The Structure of Scientific Revolutions . However, both thinkers have left themselves open to misconstrual by adopting a sceptical-relativist fallback position in response to the well-known problems with logical empiricism. This essay therefore reviews those problems with reference to alternative, more adequate accounts of what is involved in the process of scientific discovery and theory-change. (shrink)

La question de savoir si l'épistémologie est encore possible, et quelle voie elle doit emprunter, est à l'ordre du jour. En effet, suite à l'échec du modèle standard proposé par l'empirisme logique, nous nous retrouvons pour ainsi dire a la croisée des chemins. C'est ainsi que, par exemple, nous prenons progressivement conscience des enjeux opposant les partisans de la « LSE Position » aussi bien aux empiristes qu'à ceux qui favorisent plutôt une sorte de « virage sociologiste », ou encore (...) que nous nous trouvons confrontés à l'inadéquation des deux principales façons d'envisager la testabilité des théories scientifiques, à savoir la conception hypothético-déductive d'un côté et la conception bayésienne de l'autre. (shrink)

In a series of insightful publications, Philip Pettit and Frank Jackson have argued for an explanatory ecumenism that is designed to justify a variety of types of social scientific explanation of different , including structural and rational choice explanations. Their arguments are put in terms of different kinds of explanatory information; the distinction between causal efficacy, causal relevance and explanatory relevance within their program model of explanation; and virtual reality and resilience explanation. The arguments are here assessed from the point (...) of view of the illumination they are able to cast on the issue of economics imperialism, the project of privileging rational choice as a unifying basis for explanations. While the Jackson–Pettit arguments turn out to be helpful in specifying some of the ontological and pragmatic constraints on economics imperialism, they are also shown to conflate distinct dimensions in the purported explanantia (such as small grain and particular grain, and the macro and the existentially quantified) and thereby to miss an important class of individualist causal process explanations of social phenomena. (shrink)

A newly emerged field within economics, known as geographical economics claims to have provided a unified approach to the study of spatial agglomerations at different spatial scales by showing how these can be traced back to the same basic economic mechanisms. We analyze this contemporary episode of explanatory unification in relation to major philosophical accounts of unification. In particular, we examine the role of argument patterns in unifying derivations, the role of ontological convictions and mathematical structures in shaping unification, the (...) distinction between derivational and ontological unification, the issue of how explanation and unification relate and finally the idea that unification comes in degrees. (shrink)

In 1966 the population biologist Richard Levins gave a forceful and in?uential defence of a method called “robustness analysis” (RA). RA is a way of assessing the result of a model by showing that different but related models give the same result. As Levins put it, “our truth is the intersection of independent lies” (1966, 423). Steven Orzack and Elliott Sober (1993) responded with an equally forceful critique of this method, concluding that the idea of robustness “lacks proper de?nition and (...) that its bearing on the question of whether a proposition is true is highly problematic” (533). Replies to Orzack and Sober, from Levins (1993) and Weisberg (2006b), have rejected the idea that RA, on its own, can con?rm the results of models. I argue that these replies have not properly addressed Orzack and Sober’s real criticisms, which focus not on the role of empirical data in RA but on the problem of ensuring that the models used in RA are independent. By drawing on accounts of RA in experimental science, I argue that there is in fact a fallible but viable form of RA that can both con?rm the results of models and incorporate empirical data. However, for reasons other than those of Orzack and Sober, I conclude that RA may be of limited use in model-based science, as the example of ?shery biology can show. (shrink)

This article demonstrates that non-mechanistic, dynamical explanations are a viable approach to explanation in the special sciences. The claim that dynamical models can be explanatory without reference to mechanisms has previously been met with three lines of criticism from mechanists: the causal relevance concern, the genuine laws concern, and the charge of predictivism. I argue, however, that these mechanist criticisms fail to defeat non-mechanistic, dynamical explanation. Using the examples of Haken et al.’s model of bimanual coordination, and Thelen et al.’s (...) dynamical field model of infant perseverative reaching, I show how each mechanist criticism fails once the standards of Woodward’s interventionist framework are applied to dynamical models. An even-handed application of Woodwardian interventionism reveals that dynamical models are capable of producing genuine explanations without appealing to underlying mechanistic details. 1Introduction2Interventionism and Mechanistic Explanation 2.1Causal relevance and ideal interventions2.2Invariance2.3Explanation3Covering-Laws and Dynamical Explanation 3.1Dynamical models3.2Covering-law explanation3.3Prediction4Causal Relevance 4.1The causal relevance concern4.2Intervening on dynamical models4.3Test case I: The Haken–Kelso–Bunz model4.4Test case II: Dynamical field model5Genuine Laws 5.1The genuine laws concern5.2Using invariance in place of laws5.3Test case I: The Haken–Kelso–Bunz model5.4Test case II: Dynamical field model6Prediction 6.1Predictivism6.2Crude and invariant prediction7Interventionist Criticism of the Haken–Kelso–Bunz Model8Dynamical Explanation8Conclusion. (shrink)

Physicalism ? or roughly the view that the stuff that physics talks about is all the stuff there is ? has had a popular press in philosophical circles during the twentieth century. And yet, at the same time, it has become quite fashionable lately to believe that the mind matters in this world after all and that psychology is an autonomous science irreducible to physics. However, if (true, downward) mental causation implies non-reducibility and Physicalism implies the converse, it is hard (...) to see how these two views could be compatible. This paper reviews some classical arguments purportedly showing how the autonomy of the special sciences can be upheld without violating the laws of physics or the principle that physics constitutes a complete and closed system. These arguments are presented in order of increasing strength, indicating how the more popular arguments in fact fall short of establishing anti-reductionism of the intended kind. New arguments are added which claim to demonstrate quite effectively how downward causation is possible compatibly with the reign of physics. The paper begins with a section which distinguishes various kinds of reductionism. (shrink)

Der Artikel führt den Begriff der epistemischen Konkurrenz ein. Im Gegensatz zu „wissenschaftliche Kontroverse“ beschreibt er eine Situation, in der sich zwei Forschungsfelder gegenseitig als mit demselben Bereich von Phänomen befasst wahrnehmen, wobei ihre methodischen Ansätze und theoretischen Erklärungen jedoch so unterschiedlich sind, dass ein offener Konflikt über die Wahrheit oder Falschheit bestimmter Aussagen oder die Genauigkeit in der Anwendung einer Methode nicht stattfindet. Nichtsdestotrotz streben beide Parteien danach, die maßgebliche Erklärung der entsprechenden Phänomene anzubieten. Indem die erweiterte Gemeinschaft der (...) Forschenden oder die Öffentlichkeit einen Ansatz als maßgeblich anerkennt, handeln sie als eine dritte, gewissermaßen neutrale Partei, die einen Preis vergibt, um den die anderen Parteien konkurrieren. Der Artikel beschreibt das Verhältnis von Genetik und Entwicklungsbiologie um 1900 als epistemische Konkurrenz. Diese Forschungsfelder geben unterschiedliche Erklärungen für die Phänomene der organischen Reproduktion. Die Erklärungen unterscheiden sich bezüglich der Formbegriffe und entsprechend hinsichtlich der Begriffe der Vererbung von Aspekten der Form eines Organismus. Zudem basieren die Erklärungen der beiden Felder auf unterschiedlichen Arten kausalen Schließens, die in unterschiedliche experimentelle Ansätze eingebettet sind. Diese Unterschiede können als Instanzen eines allgemeineren Unterschieds zwischen der Tradition der Naturgeschichte, die sich mit Unterschieden zwischen Organismen auseinandersetzt, und der Tradition der Anatomie, die sich mit den Teilen der Organismen beschäftigt, angesehen werden. Dieses Bild der Genetik und Entwicklungsbiologie als Zweige unterschiedlicher fest verankerter Traditionen widerspricht dem Narrativ der Trennung des Begriffs der Vererbung von dem der Entwicklung im Zuge einer Trennung der Disziplinen der Genetik und der Embryologie, das in der Historiographie der Biologie nach wie vor weit verbreitet ist. (shrink)

Causal analysis in the social sciences takes advantage of a variety of methods and of a multi-fold source of information and evidence. This pluralistic methodology and source of information raises the question of whether we should accordingly have a pluralistic metaphysics and epistemology. This paper focuses on epistemology and argues that a pluralistic methodology and evidence don’t entail a pluralistic epistemology. It will be shown that causal models employ a single rationale of testing, based on the notion of variation. Further, (...) I shall argue that this monistic epistemology is also involved in alternative philosophical theories of causation. (shrink)

According to Antoine Augustine Cournot, chance events are the result of the intersection between independent causal chains. This coincidental notion of chance is not a new one, but—as Cournot remarks—it comes from Saint Thomas Aquinas, Boethius, and more probably from Jean de La Placette. Such a conception of chance phenomena seems to be very important, not only because it is closely related to the Principle of Causality, but also since it grounds Cournot’s theory of objective probability. Starting from Martin’s work, (...) the main attempt of this survey is to endorse the idea that Cournot’s coincidental notion of hasard is objective, that is it is ontic and it does not depend—in some sense—on our degree of knowledge. In order to do that, a central role in the discussion will be given to the meaning of the independence between the intersecting causal chains and to Cournot’s conception of causation. (shrink)

The principle of common cause asserts that positive correlations between causally unrelated events ought to be explained through the action of some shared causal factors. Reichenbachian common cause systems are probabilistic structures aimed at accounting for cases where correlations of the aforesaid sort cannot be explained through the action of a single common cause. The existence of Reichenbachian common cause systems of arbitrary finite size for each pair of non-causally correlated events was allegedly demonstrated by Hofer-Szabó and Rédei in 2006. (...) This paper shows that their proof is logically deficient, and we propose an improved proof. (shrink)

Grounding theorists insist that grounding and explanation are intimately related. This claim could be understood as saying either that grounding ‘inherits’ its properties from explanation or it could be interpreted as saying that grounding plays an important—possibly an indispensable—role in metaphysical explanation. Or both. I argue that saying that grounding ‘inherits’ its properties from explanation can only be justified if grounding is explanatory by nature, but that this view is untenable. We ought therefore to be ‘separatists’ and view grounding and (...) explanation as distinct. As it turns out, though, once grounding has been in this sense distinguished from the explanation it backs, the view that the role grounding plays in explanation justifies its introduction ends up in serious trouble. I conclude that the role grounding plays in explanation does not justify attributing to grounding whatever nature we think it has, and it most likely does not give us any special reason to think grounding exists. (shrink)

In this paper I critically investigate an unorthodox attempt to metaphysically explain in virtue of what there are states of affairs. This is a suggestion according to which states of affairs exist thanks to, rather than, as is the common view, in spite of, the infinite regress their metaphysical explanation seems to engender. I argue that, no matter in which form it is defended, or in which theoretical framework it is set, this suggestion cannot provide us with the explanation we (...) crave. (shrink)

A review of Paul Thompson's semantic interpretation of evolutionary theory.

Recently, Luk mentioned that scientific knowledge both explains and predicts. Do these two functions of scientific knowledge have equal significance, or is one of the two functions more important than the other? This commentary explains why prediction may be mandatory but explanation may be only desirable and optional.

The ArgumentIn this paper it will be shown that Newton'sPrincipiagives an explication of and an argument for the first Law of Motion, that seems to be outside the scope of today's philosophy of science but was familiar to seventeenth-century commentators: The foundation of classical mechanics is possible only by recurrence to results of a successful technical practice. Laws of classical mechanics gain their meaning as well as their claims to validity only when considered as statements about artifacts whose production belongs (...) to the shared know-how of a scientific community. (shrink)

In pedagogical contexts and in everyday life, we often come to believe something because it would best explain the data. What is it about the explanatory endeavor that makes it essential to everyday learning and to scientific progress? There are at least two plausible answers. On one view, there is something special about having true explanations. This view is highly intuitive: it’s clear why true explanations might improve one’s epistemic position. However, there is another possibility—it could be that the process (...) of seeking, generating, or evaluating explanations itself puts one in a better epistemic position, even when the outcome of the process is not a true explanation. In other words, it could be that accurate explanations are beneficial, or it could be that high-quality explaining is beneficial, where there is something about the activity of looking for an explanation that improves our epistemic standing. The main goal of this paper is to tease apart these two possibilities, both theoretically and empirically, which we align with “Inference to the Best Explanation” and “Explaining for the Best Inference”, respectively. We also provide some initial support for EBI and identify promising directions for future research. (shrink)

According to Skow (2016, 2017), correct answers to why-questions only cite causes or grounds, but not non-accidental regularities. Accounts that cite non-accidental regularities typically confuse second-level reasons with first-level reasons. Only causes and grounds are first-level reasons why. Non-accidental regularities are second-level reasons why. I first show that Skow's arguments for the accusation of confusion depend on the independent thesis that only citations of first-level reasons why are (parts of) answers to why-questions. Then, I argue that this thesis is false. (...) Consequently, the claim that correct answers to why-questions only cite causes or grounds is refuted as well. (shrink)

Philosophical discussions on causal inference in medicine are stuck in dyadic camps, each defending one kind of evidence or method rather than another as best support for causal hypotheses. Whereas Evidence Based Medicine advocates the use of Randomised Controlled Trials and systematic reviews of RCTs as gold standard, philosophers of science emphasise the importance of mechanisms and their distinctive informational contribution to causal inference and assessment. Some have suggested the adoption of a pluralistic approach to causal inference, and an inductive (...) rather than hypothetico-deductive inferential paradigm. However, these proposals deliver no clear guidelines about how such plurality of evidence sources should jointly justify hypotheses of causal associations. We here develop such guidelines by first giving a philosophical analysis of the underpinnings of Hill’s viewpoints on causality. We then put forward an evidence-amalgamation framework adopting a Bayesian net approach to model causal inference in pharmacology for the assessment of harms. Our framework accommodates a number of intuitions already expressed in the literature concerning the EBM vs. pluralist debate on causal inference, evidence hierarchies, causal holism, relevance, and reliability. (shrink)

Biomedical researchers claim there is significant biomedical information about humans which can be discovered only through experiments on intact animal systems (AMA p. 2). Although epidemiological studies, computer simulations, clinical investigation, and cell and tissue cultures have become important weapons in the biomedical scientists' arsenal, these are primarily "adjuncts to the use of animals in research" (Sigma Xi p. 76). Controlled laboratory experiments are the core of the scientific enterprise. Biomedical researchers claim these should be conducted on intact biological systems, (...) whole animals. By observing the effects of various stimuli in non-human animals, we can form legitimate expectations about the likely effects of these stimuli in humans. Perhaps more importantly, we can understand the biomedical condition's causal mechanisms. (shrink)

Volume 32, Issue 1, March 2019, Page 69-72.