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Added by: Clotilde Torregrossa, Contributed by: Juan R. LoaizaAbstract: There are no "special sciences" in Fodor's sense. There is a large group of sciences, "historical sciences," that differ fundamentally from the physical sciences because they quantify over a different kind of natural or real kind, nor are the generalizations supported by these kinds exceptionless. Heterogeneity, however, is not characteristic of these kinds. That there could be an univocal empirical science that ranged over multiple realizations of a functional property is quite problematic. If psychological predicates name multiply realized functionalist properties, then there is no single science dealing with these: human psychology, ape psychology, Martian psychology and robot psychology are necessarily different sciences
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, Contributed by: Quentin PharrPublisher’s Note: C.S. Peirce, the founder of pragmatism, argued that truth is what we would agree upon, were inquiry to be pursued as far as it could fruitfully go. In this book, Misak argues for and elucidates the pragmatic account of truth, paying attention both to Peirce's texts and to the requirements of a suitable account of truth. An important argument of the book is that we must be sensitive to the difference between offering a definition of truth and engaging in a distinctively pragmatic project. The pragmatic project spells out the relationship between truth and inquiry; it articulates the consequences of a statement's being true. The existence of a distinct pragmatic enterprise has implications for the status of the pragmatic account of truth and for the way in which philosophy should be conducted.
Comment: For students wanting to know more about Peirce's conception of truth, as it relates to the end of inquiry, Misak's book is an excellent first book to study. It is highly readable and authoritative. It is also a great book for understanding some of the major differences between Peirce and James, as early proponents of pragmatism who disagreed on the nature of truth. Prior readings of Peirce's philosophy will help - but, by and large, Misak provides everything that is needed for a first appreciation of the substance of his views. It is also a helpful guide for students to gather a sense of how pragmatists who are not necessarily Jamesians believe we should philosophize and inquire.
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Added by: Laura JimenezAbstract: Biological knowledge does not fit the image of science that philosophers have developed. Many argue that biology has no laws. Here I criticize standard normative accounts of law and defend an alternative, pragmatic approach. I argue that a multidimensional conceptual framework should replace the standard dichotomous law/ accident distinction in order to display important differences in the kinds of causal structure found in nature and the corresponding scientific representations of those structures. To this end I explore the dimensions of stability, strength, and degree of abstraction that characterize the variety of scientific knowledge claims found in biology and other sciences.
Comment: Really interesting paper that examines the nature of scientific laws by focusing on the case of laws in biology. It would be recommendable to read Carnap's analysis of the acceptance of different linguistic forms within science before reading this article. Could be used as a paper for a senior undergraduate course or for postgraduate courses in Philosophy of Science.
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Added by: Jamie CollinSummary: Uses Maxwell's model of the ether as a case study in accounting for the role of fictions in science. Argues that we should understand idealisation and abstraction as being different from fiction. Fictional models for Morrison are those that are deliberately intended to be such that the relationship between their structure and the structure of the concrete systems they model is not (immediately) apparent. This is different from mere idealisation, where certain structural features are omitted to make calculations more tractable.
Comment: Very useful as a primary or secondary reading in an advanced undergraduate course on philosophy of science (or perhaps on philosophy of fiction). It is philosophically sophisticated, but also treats the science in enough detail to provide students with some clear ideas about the nature of scientific representational practices themselves. Would be appropriate in sections on scientific representation or modelling.
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Added by: Jamie CollinSummary: Morrison and Morgan argue for a view of models as 'mediating instruments' whose role in scientific theorising goes beyond applying theory. Models are partially independent of both theories and the world. This autonomy allows for a unified account of their role as instruments that allow for exploration of both theories and the world.
Comment: Useful as a primary or secondary reading in an advanced undergraduate course on philosophy of science, particularly within a section on scientific modeling. The paper is particularly useful in teaching because it is not unduly technical.
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Added by: Laura JimenezPublisher's Note: How do novel scientific concepts arise? In Creating Scientific Concepts, Nancy Nersessian seeks to answer this central but virtually unasked question in the problem of conceptual change. She argues that the popular image of novel concepts and profound insight bursting forth in a blinding flash of inspiration is mistaken. Instead, novel concepts are shown to arise out of the interplay of three factors: an attempt to solve specific problems; the use of conceptual, analytical, and material resources provided by the cognitive-social-cultural context of the problem; and dynamic processes of reasoning that extend ordinary cognition. Focusing on the third factor, Nersessian draws on cognitive science research and historical accounts of scientific practices to show how scientific and ordinary cognition lie on a continuum, and how problem-solving practices in one illuminate practices in the other.
Comment: Nersessian’s book has a two-fold foundation, first, the empirical analysis of two cases of scientific thinking (one from Maxwell and one from a verbal protocol of a scientist); second, philosophical and cognitive analysis of the overall picture of meaning change in science that is the result of her work. The book presents her argument via an introductory chapter, followed by five chapters that develop the argument. Chapter 4 is particularly interesting for the cognitive-scientist: in this chapter Nersessian develops her account of the basic cognitive processes that underlie model-based reasoning. The new approach to mental modeling and analogy, together with Nersessian’s cognitive-historical approach, make Creating Scientific Concepts equally valuable to cognitive science and philosophy of science. The book is accessible and well-written, and should be a relatively quick read for anyone with a previous background in the mentioned fields. It is mainly recommended for postgraduate courses.
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Added by: Emily PaulIntroduction: Reductionists are those who take one theory or phenomenon to be reducible to some other theory or phenomenon. For example, a reductionist regarding mathematics might take any given mathematical theory to be reducible to logic or set theory. Or, a reductionist about biological entities like cells might take such entities to be reducible to collections of physico-chemical entities like atoms and molecules. The type of reductionism that is currently of most interest in metaphysics and philosophy of mind involves the claim that all sciences are reducible to physics. This is usually taken to entail that all phenomena (including mental phenomena like consciousness) are identical to physical phenomena. The bulk of this article will discuss this latter understanding of reductionism.
Comment: An excellent overview of reductionism, its history, and different ways to interpret it. Clear and accessible, and useful for an intermediate metaphysics course - perhaps after having studied an applied case of reductionism - e.g. about modality. Then, students will be able to have this in mind when considering different senses of reduction. Could then be a useful gateway into metaphysics of mind. Alternatively, this article could be used near the start of a philosophy of mind course.
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Added by: Chris Blake-Turner, Contributed by: Cailin O'ConnorAbstract: Vague predicates, those that exhibit borderline cases, pose a persistent problem for philosophers and logicians. Although they are ubiquitous in natural language, when used in a logical context, vague predicates lead to contradiction. This paper will address a question that is intimately related to this problem. Given their inherent imprecision, why do vague predicates arise in the first place? I discuss a variation of the signaling game where the state space is treated as contiguous, i.e., endowed with a metric that captures a similarity relation over states. This added structure is manifested in payoffs that reward approximate coordination between sender and receiver as well as perfect coordination. I evolve these games using a variation of Herrnstein reinforcement learning that better reflects the generalizing learning strategies real-world actors use in situations where states of the world are similar. In these simulations, signaling can develop very quickly, and the signals are vague in much the way ordinary language predicates are vague - they each exclusively apply to certain items, but for some transition period both signals apply to varying degrees. Moreover, I show that under certain parameter values, in particular when state spaces are large and time is limited, learning generalization of this sort yields strategies with higher payoffs than standard Herrnstein reinforcement learning. These models may then help explain why the phenomenon of vagueness arises in natural language: the learning strategies that allow actors to quickly and effectively develop signaling conventions in contiguous state spaces make it unavoidable
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Added by: Laura JimenezSummary: It is customary to distinguish experimental from purely observational sciences. The former include physics and molecular biology, the latter astronomy and palaeontology. Surprisingly, mainstream philosophy of science has had rather little to say about the observational/experimental distinction. For example, discussions of confirmation usually invoke a notion of 'evidence', to be contrasted with 'theory' or 'hypothesis'; the aim is to understand how the evidence bears on the hypothesis. But whether this 'evidence' comes from observation or experiment generally plays no role in the discussion; this is true of both traditional and modern confirmation theories, Bayesian and non-Bayesian. In this article, the author sketches one possible explanation, by suggesting that observation and experiment will often differ in their confirmatory power. Based on a simple Bayesian analysis of confirmation, Okasha argues that universal generalizations (or 'laws') are typically easier to confirm by experimental intervention than by pure observation. This is not to say that observational confirmation of a law is impossible, which would be flatly untrue. But there is a general reason why confirmation will accrue more easily from experimental data, based on a simple though oft-neglected feature of Bayesian conditionalization.
Comment: Previous knowledge of Bayesian conditioning might be needed. The article is suitable for postgraduate courses in philosophy of science focusing in the distinction between observational and experimental science.
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Added by: Laura JimenezBack Matter: What is science? Is there a real difference between science and myth? Is science objective? Can science explain everything? This Very Short Introduction provides a concise overview of the main themes of contemporary philosophy of science. Beginning with a short history of science to set the scene, Samir Okasha goes on to investigate the nature of scientific reasoning, scientific explanation, revolutions in science, and theories such as realism and anti-realism. He also looks at philosophical issues in particular sciences, including the problem of classification in biology, and the nature of space and time in physics. The final chapter touches on the conflicts between science and religion, and explores whether science is ultimately a good thing.
Comment: The book is extremely readable and clear. It is perfect as an introduction for undergraduate students to philosophy of science. It offers an overview of the most important topics of the field including philosophical problems in biology, physics, and linguistics.
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Added by: Nick NovelliSummary: Okhrulik offers a feminist critique of biology, a "real" science, to show that it is not just the "soft" social sciences that are affected by bias. She argues that preconceptions can interfere not only in cases of "bad science", but even when the rules of scientific practice are followed. There is no safeguard against the effects of bias in the context of discovery. Even if theories are rigorously tested to remove bias, some theories might not even be generated and so would not get to the point of being counted as competitors in the testing stage. This is illustrated by a number of case studies. Okhrulik concludes that a diversity of viewpoints is crucial.
Comment: Presents a good case for why feminist critiques are relevant even to "harder" sciences, made more salient with easy-to-understand examples. Raises issues of theory-ladenness of observation and underdetermination of theory. A good introduction to reasons to doubt that science is completely "objective".
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Added by: Nick NovelliAbstract: Experiments are commonly thought to have epistemic privilege over simulations. Two ideas underpin this belief: first, experiments generate greater inferential power than simulations, and second, simulations cannot surprise us the way experiments can. In this article I argue that neither of these claims is true of experiments versus simulations in general. We should give up the common practice of resting in-principle judgments about the epistemic value of cases of scientific inquiry on whether we classify those cases as experiments or simulations, per se. To the extent that either methodology puts researchers in a privileged epistemic position, this is context sensitive.
Comment: Valuable in raising questions about preconceptions of "science experiments". This article would be useful as part of a look at scientific methodology and the real value obtained from our scientific practices.
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Added by: Simon Fokt
Abstract: According to an adequacy-for-purpose view, models should be assessed with respect to their adequacy or fitness for particular purposes. Such a view has been advocated by scientists and philosophers alike. Important details, however, have yet to be spelled out. This article attempts to make progress by addressing three key questions: What does it mean for a model to be adequate-for-purpose? What makes a model adequate-for-purpose? How does assessing a model’s adequacy-for-purpose differ from assessing its representational accuracy? In addition, responses are given to some objections that might be raised against an adequacy-for-purpose view.
Comment: A good overview (and a defence) of the adequacy-for-purpose view on models. Makes the case that models should be assessed with respect to their adequacy for particular purposes.
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Added by: Clotilde Torregrossa, Contributed by: Simon FoktAbstract: This article identifies conditions under which robust predictive modeling results have special epistemic significance---related to truth, confidence, and security---and considers whether those conditions hold in the context of present-day climate modeling. The findings are disappointing. When today's climate models agree that an interesting hypothesis about future climate change is true, it cannot be inferred---via the arguments considered here anyway---that the hypothesis is likely to be true or that scientists' confidence in the hypothesis should be significantly increased or that a claim to have evidence for the hypothesis is now more secure
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Added by: Clotilde Torregrossa, Contributed by: Simon FoktAbstract: Recent philosophy of science has witnessed a shift in focus, in that significantly more consideration is given to how scientists employ models. Attending to the role of models in scientific practice leads to new questions about the representational roles of models, the purpose of idealizations, why multiple models are used for the same phenomenon, and many more besides. In this paper, I suggest that these themes resonate with central topics in feminist epistemology, in particular prominent versions of feminist empiricism, and that model-based science and feminist epistemology each has crucial resources to offer the other's project.
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