Nersessian, Nancy. Creating Scientific Concepts
2008, MIT Press.
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Added by: Laura Jimenez
Publisher'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.Ney, Alyssa. Reductionism2008, Internet Encyclopedia of Philosophy.-
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Added by: Emily Paul
Introduction: 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.O'Connor, Cailin. The Evolution of Vagueness2013, Erkenntnis (S4):1-21.-
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Added by: Chris Blake-Turner, Contributed by: Cailin O'Connor
Abstract: 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 unavoidableOkasha, Samir. Philosophy of Science: A very short introduction2002, Oxford University Press.-
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Added by: Laura Jimenez
Back 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.Okasha, Samir. Experiment, observation and the confirmation of laws2011, Analysis 71(2): 222-232.-
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Added by: Laura Jimenez
Summary: 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.Okruhlik, Kathleen. Gender and the Biological Sciences1994, Canadian Journal of Philosophy 24(sup1): 21-42.-
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Added by: Nick Novelli
Summary: 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".Parke, Emily. Experiments, Simulations, and Epistemic Privilege2014, Philosophy of Science 81(4): 516-536.-
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Added by: Nick Novelli
Abstract: 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.Parker, Wendy. Model Evaluation: An Adequacy-for-Purpose View2020, Philosophy of Science 87 (3):457-477-
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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.Parker, Wendy S.. When Climate Models Agree: The Significance of Robust Model Predictions2011, Philosophy of Science 78 (4):579-600.-
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Added by: Clotilde Torregrossa, Contributed by: Simon Fokt
Abstract: 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 securePotochnik, Angela. Levels of Explanation Reconceived2010, Philosophy of Science 77(1): 59-72.-
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Added by: Nick Novelli
Abstract: A common argument against explanatory reductionism is that higher-level explanations are sometimes or always preferable because they are more general than reductive explanations. Here I challenge two basic assumptions that are needed for that argument to succeed. It cannot be assumed that higher-level explanations are more general than their lower-level alternatives or that higher-level explanations are general in the right way to be explanatory. I suggest a novel form of pluralism regarding levels of explanation, according to which explanations at different levels are preferable in different circumstances because they offer different types of generality, which are appropriate in different circumstances of explanation.Comment : An interesting anti-anti-reductionist article. Would be useful in a discussion of explanatory power or levels of explanation in a philosophy of science course.Can’t find it?Contribute the texts you think should be here and we’ll add them soon!
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