Search results for 'Explanation' (try it on Scholar)

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  1. Peter Lipton (2004). Inference to the Best Explanation. Routledge/Taylor and Francis Group.
    How do we go about weighing evidence, testing hypotheses, and making inferences? The model of " inference to the best explanation " -- that we infer the hypothesis that would, if correct, provide the best explanation of the available evidence--offers a compelling account of inferences both in science and in ordinary life. Widely cited by epistemologists and philosophers of science, IBE has nonetheless remained little more than a slogan. Now this influential work has been thoroughly revised and updated, (...)
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  2. James Woodward (2003). Making Things Happen: A Theory of Causal Explanation. Oxford University Press.
    Woodward's long awaited book is an attempt to construct a comprehensive account of causation explanation that applies to a wide variety of causal and explanatory claims in different areas of science and everyday life. The book engages some of the relevant literature from other disciplines, as Woodward weaves together examples, counterexamples, criticisms, defenses, objections, and replies into a convincing defense of the core of his theory, which is that we can analyze causation by appeal to the notion of manipulation.
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  3. Michael Strevens (2008). Depth: An Account of Scientific Explanation. Harvard University Press.
    Approaches to explanation -- Causal and explanatory relevance -- The kairetic account of /D making -- The kairetic account of explanation -- Extending the kairetic account -- Event explanation and causal claims -- Regularity explanation -- Abstraction in regularity explanation -- Approaches to probabilistic explanation -- Kairetic explanation of frequencies -- Kairetic explanation of single outcomes -- Looking outward -- Looking inward.
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  4. Carl F. Craver (2014). The Ontic Account of Scientific Explanation. In Marie I. Kaiser, Oliver R. Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the Special Sciences: The Case of Biology and History. Springer Netherlands 27-52.
    According to one large family of views, scientific explanations explain a phenomenon (such as an event or a regularity) by subsuming it under a general representation, model, prototype, or schema (see Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441; Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge: MIT Press; Darden (2006); Hempel, C. G. (1965). Aspects of (...)
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  5. David J. Chalmers & Frank Jackson (2001). Conceptual Analysis and Reductive Explanation. Philosophical Review 110 (3):315-61.
    Is conceptual analysis required for reductive explanation? If there is no a priori entailment from microphysical truths to phenomenal truths, does reductive explanation of the phenomenal fail? We say yes . Ned Block and Robert Stalnaker say no.
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  6. Carlos Zednik (forthcoming). Heuristics, Descriptions and the Scope of Mechanistic Explanation. In P. Braillard & C. Malaterre (eds.), Explanation in Biology. An Enquiry into the Diversity of Explanatory Patterns in the Life Sciences. Springer
    The philosophical conception of mechanistic explanation is grounded on a limited number of canonical examples. These examples provide an overly narrow view of contemporary scientific practice, because they do not reflect the extent to which the heuristic strategies and descriptive practices that contribute to mechanistic explanation have evolved beyond the well-known methods of decomposition, localization, and pictorial representation. Recent examples from evolutionary robotics and network approaches to biology and neuroscience demonstrate the increasingly important role played by computer simulations (...)
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  7. Iulian D. Toader (2015). Against Harmony: Infinite Idealizations and Causal Explanation. In Iulian D. Toader, Gabriel Sandu & Ilie Pȃrvu (eds.), Romanian Studies in Philosophy of Science. Boston Studies in the Philosophy and History of Science, Vol. 313, 291-301.
    This paper discusses the idea that some of the causal factors that are responsible for the production of a natural phenomenon are explanatorily irrelevant and, thus, may be omitted or distorted. It argues against Craig Callender’s suggestion that the standard explanation of phase transitions in statistical mechanics may be considered a causal explanation, in Michael Strevens’ sense, as a distortion that can nevertheless successfully represent causal relations.
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  8. Neil Sinclair & Uri D. Leibowitz (forthcoming). Introduction: Explanation in Ethics and Mathematics. In Uri D. Leibowitz & Neil Sinclair (eds.), Explanation in Ethics and Mathematics: Debunking and Indispensability. Oxford University Press
    Are moral properties intellectually indispensable, and, if so, what consequences does this have for our understanding of their nature, and of our talk and knowledge of them? Are mathematical objects intellectually indispensable, and, if so, what consequences does this have for our understanding of their nature, and of our talk and knowledge of them? What similarities are there, if any, in the answers to the first two questions? Can comparison of the two cases shed light on which answers are most (...)
     
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  9. Wesley C. Salmon (1998). Causality and Explanation. Oxford University Press.
    Wesley Salmon is renowned for his seminal contributions to the philosophy of science. He has powerfully and permanently shaped discussion of such issues as lawlike and probabilistic explanation and the interrelation of explanatory notions to causal notions. This unique volume brings together twenty-six of his essays on subjects related to causality and explanation, written over the period 1971-1995. Six of the essays have never been published before and many others have only appeared in obscure venues. The volume includes (...)
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  10. Michael Strevens (2013). No Understanding Without Explanation. Studies in History and Philosophy of Science Part A 44 (3):510-515.
    Scientific understanding, this paper argues, can be analyzed entirely in terms of a mental act of “grasping” and a notion of explanation. To understand why a phenomenon occurs is to grasp a correct explanation of the phenomenon. To understand a scientific theory is to be able to construct, or at least to grasp, a range of potential explanations in which that theory accounts for other phenomena. There is no route to scientific understanding, then, that does not go by (...)
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  11.  25
    Nevin Climenhaga (forthcoming). Inference to the Best Explanation Made Incoherent. Journal of Philosophy.
    Defenders of Inference to the Best Explanation (IBE) claim that explanatory factors should play an important role in empirical inference. They disagree, however, about how exactly to formulate this role. In particular, they disagree about whether to formulate IBE as an inference rule for full beliefs or for degrees of belief, and, if it is formulated as a rule for degrees of belief, how this rule relates to Bayesianism. In this essay I advance a new argument against non-Bayesian versions (...)
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  12.  23
    Alexander Reutlinger (forthcoming). Does the Counterfactual Theory of Explanation Apply to Non-Causal Explanations in Metaphysics? European Journal for Philosophy of Science:1-18.
    In the recent philosophy of explanation, a growing attention to and discussion of non-causal explanations has emerged, as there seem to be compelling examples of non-causal explanations in the sciences, in pure mathematics, and in metaphysics. I defend the claim that the counterfactual theory of explanation (CTE) captures the explanatory character of both non-causal scientific and metaphysical explanations. According to the CTE, scientific and metaphysical explanations are explanatory by virtue of revealing counterfactual dependencies between the explanandum and the (...)
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  13.  59
    Peter Achinstein (1983). The Nature of Explanation. Oxford University Press.
    Offering a new approach to scientific explanation, this book focuses initially on the explaining act itself.
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  14.  86
    Kareem Khalifa (2012). Inaugurating Understanding or Repackaging Explanation? Philosophy of Science 79 (1):15-37.
    Recently, several authors have argued that scientific understanding should be a new topic of philosophical research. In this article, I argue that the three most developed accounts of understanding--Grimm's, de Regt's, and de Regt and Dieks's--can be replaced by earlier accounts of scientific explanation without loss. Indeed, in some cases, such replacements have clear benefits.
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  15. L. R. Franklin-Hall (2014). High-Level Explanation and the Interventionist's 'Variables Problem'. British Journal for the Philosophy of Science:axu040.
    The interventionist account of causal explanation, in the version presented by Jim Woodward, has been recently claimed capable of buttressing the widely felt—though poorly understood—hunch that high-level, relatively abstract explanations, of the sort provided by sciences like biology, psychology and economics, are in some cases explanatorily optimal. It is the aim of this paper to show that this is mistaken. Due to a lack of effective constraints on the causal variables at the heart of the interventionist causal-explanatory scheme, as (...)
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  16.  95
    Ingo Brigandt (2013). Systems Biology and the Integration of Mechanistic Explanation and Mathematical Explanation. Studies in History and Philosophy of Biological and Biomedical Sciences 44 (4):477-492.
    The paper discusses how systems biology is working toward complex accounts that integrate explanation in terms of mechanisms and explanation by mathematical models—which some philosophers have viewed as rival models of explanation. Systems biology is an integrative approach, and it strongly relies on mathematical modeling. Philosophical accounts of mechanisms capture integrative in the sense of multilevel and multifield explanations, yet accounts of mechanistic explanation have failed to address how a mathematical model could contribute to such explanations. (...)
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  17. Holly Andersen (2012). The Case for Regularity in Mechanistic Causal Explanation. Synthese 189 (3):415-432.
    How regular do mechanisms need to be, in order to count as mechanisms? This paper addresses two arguments for dropping the requirement of regularity from the definition of a mechanism, one motivated by examples from the sciences and the other motivated by metaphysical considerations regarding causation. I defend a broadened regularity requirement on mechanisms that takes the form of a taxonomy of kinds of regularity that mechanisms may exhibit. This taxonomy allows precise explication of the degree and location of regular (...)
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  18. Daniel A. Weiskopf (2011). Models and Mechanisms in Psychological Explanation. Synthese 183 (3):313-338.
    Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of techniques for (...)
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  19. Marc Lange (2013). Grounding, Scientific Explanation, and Humean Laws. Philosophical Studies 164 (1):255-261.
    It has often been argued that Humean accounts of natural law cannot account for the role played by laws in scientific explanations. Loewer (Philosophical Studies 2012) has offered a new reply to this argument on behalf of Humean accounts—a reply that distinguishes between grounding (which Loewer portrays as underwriting a kind of metaphysical explanation) and scientific explanation. I will argue that Loewer’s reply fails because it cannot accommodate the relation between metaphysical and scientific explanation. This relation also (...)
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  20.  98
    David Michael Kaplan (2011). Explanation and Description in Computational Neuroscience. Synthese 183 (3):339-373.
    The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions (...)
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  21.  30
    Beate Krickel (forthcoming). A Regularist Approach to Mechanistic Type-Level Explanation. British Journal for the Philosophy of Science.
    Most defenders of the new mechanistic approach accept ontic constraints for successful scientific explanation (Illari 2013; Craver 2014). The minimal claim is that scientific explanations have objective truthmakers, namely mechanisms that exist in the physical world independently of any observer and that cause or constitute the phenomena-to- be-explained. How can this idea be applied to type-level explanations? Many authors at least implicitly assume that in order for mechanisms to be the truthmakers of type-level explanation they need to be (...)
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  22. William Bechtel & Cory D. Wright (2009). What is Psychological Explanation? In P. Calvo & J. Symons (eds.), Routledge Companion to the Philosophy of Psychology. Routledge 113--130.
    Due to the wide array of phenomena that are of interest to them, psychologists offer highly diverse and heterogeneous types of explanations. Initially, this suggests that the question "What is psychological explanation?" has no single answer. To provide appreciation of this diversity, we begin by noting some of the more common types of explanations that psychologists provide, with particular focus on classical examples of explanations advanced in three different areas of psychology: psychophysics, physiological psychology, and information-processing psychology. To analyze (...)
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  23. Cory D. Wright (2012). Mechanistic Explanation Without the Ontic Conception. European Journal of Philosophy of Science 2 (3):375-394.
    The ontic conception of scientific explanation has been constructed and motivated on the basis of a putative lexical ambiguity in the term explanation. I raise a puzzle for this ambiguity claim, and then give a deflationary solution under which all ontically-rendered talk of explanation is merely elliptical; what it is elliptical for is a view of scientific explanation that altogether avoids the ontic conception. This result has revisionary consequences for New Mechanists and other philosophers of science, (...)
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  24.  15
    T. Lombrozo & S. Carey (2006). Functional Explanation and the Function of Explanation. Cognition 99 (2):167-204.
    Teleological explanations (TEs) account for the existence or properties of an entity in terms of a function: we have hearts because they pump blood, and telephones for communication. While many teleological explanations seem appropriate, others are clearly not warranted-for example, that rain exists for plants to grow. Five experiments explore the theoretical commitments that underlie teleological explanations. With the analysis of [Wright, L. (1976). Teleological Explanations. Berkeley, CA: University of California Press] from philosophy as a point of departure, we examine (...)
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  25. Carlos Zednik (2011). The Nature of Dynamical Explanation. Philosophy of Science 78 (2):238-263.
    The received view of dynamical explanation is that dynamical cognitive science seeks to provide covering law explanations of cognitive phenomena. By analyzing three prominent examples of dynamicist research, I show that the received view is misleading: some dynamical explanations are mechanistic explanations, and in this way resemble computational and connectionist explanations. Interestingly, these dynamical explanations invoke the mathematical framework of dynamical systems theory to describe mechanisms far more complex and distributed than the ones typically considered by philosophers. Therefore, contemporary (...)
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  26. Frank Jackson & Philip Pettit (1990). Program Explanation: A General Perspective. Analysis 50 (2):107-17.
    Some properties are causally relevant for a certain effect, others are not. In this paper we describe a problem for our understanding of this notion and then offer a solution in terms of the notion of a program explanation.
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  27.  86
    Alan Millar (2004). Understanding People: Normativity and Rationalizing Explanation. Oxford University Press.
    Alan Millar examines our understanding of why people think and act as they do. His key theme is that normative considerations form an indispensable part of the explanatory framework in terms of which we seek to understand each other. Millar defends a conception according to which normativity is linked to reasons. On this basis he examines the structure of certain normative commitments incurred by having propositional attitudes. Controversially, he argues that ascriptions of beliefs and intentions in and of themselves attribute (...)
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  28.  55
    Alexander Reutlinger (forthcoming). Is There A Monist Theory of Causal and Non-Causal Explanations? The Counterfactual Theory of Scientific Explanation. Philosophy of Science.
    The goal of this paper is to develop a counterfactual theory of explanation. The CTE provides a monist framework for causal and non-causal explanations, according to which both causal and non-causal explanations are explanatory by virtue of revealing counterfactual dependencies between the explanandum and the explanans. I argue that the CTE is applicable to two paradigmatic examples of non-causal explanations: Euler’s explanation and renormalization group explanations of universality.
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  29. Adolfas Mackonis (2013). Inference to the Best Explanation, Coherence and Other Explanatory Virtues. Synthese 190 (6):975-995.
    This article generalizes the explanationist account of inference to the best explanation. It draws a clear distinction between IBE and abduction and presents abduction as the first step of IBE. The second step amounts to the evaluation of explanatory power, which consist in the degree of explanatory virtues that a hypothesis exhibits. Moreover, even though coherence is the most often cited explanatory virtue, on pain of circularity, it should not be treated as one of the explanatory virtues. Rather, coherence (...)
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  30. Sally Haslanger (2016). What is a Structural Explanation? Philosophical Studies 173 (1):113-130.
    A philosophically useful account of social structure must accommodate the fact that social structures play an important role in structural explanation. But what is a structural explanation? How do structural explanations function in the social sciences? This paper offers a way of thinking about structural explanation and sketches an account of social structure that connects social structures with structural explanation.
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  31. Cory D. Wright & William P. Bechtel (2007). Mechanisms and Psychological Explanation. In Paul Thagard (ed.), Philosophy of Psychology and Cognitive Science. Elsevier
    As much as assumptions about mechanisms and mechanistic explanation have deeply affected psychology, they have received disproportionately little analysis in philosophy. After a historical survey of the influences of mechanistic approaches to explanation of psychological phenomena, we specify the nature of mechanisms and mechanistic explanation. Contrary to some treatments of mechanistic explanation, we maintain that explanation is an epistemic activity that involves representing and reasoning about mechanisms. We discuss the manner in which mechanistic approaches serve (...)
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  32. Uri D. Leibowitz (2011). Scientific Explanation and Moral Explanation. Noûs 45 (3):472-503.
    Moral philosophers are, among other things, in the business of constructing moral theories. And moral theories are, among other things, supposed to explain moral phenomena. Consequently, one’s views about the nature of moral explanation will influence the kinds of moral theories one is willing to countenance. Many moral philosophers are (explicitly or implicitly) committed to a deductive model of explanation. As I see it, this commitment lies at the heart of the current debate between moral particularists and moral (...)
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  33.  27
    Ingo Brigandt (2016). Why the Difference Between Explanation and Argument Matters to Science Education. Science and Education 25:251-275.
    Contributing to the recent debate on whether or not explanations ought to be differentiated from arguments, this article argues that the distinction matters to science education. I articulate the distinction in terms of explanations and arguments having to meet different standards of adequacy. Standards of explanatory adequacy are important because they correspond to what counts as a good explanation in a science classroom, whereas a focus on evidence-based argumentation can obscure such standards of what makes an explanation explanatory. (...)
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  34.  83
    Tobias Wilsch (2015). The Deductive-Nomological Account of Metaphysical Explanation. Australasian Journal of Philosophy 94 (1):1-23.
    The paper explores a deductive-nomological account of metaphysical explanation: some truths metaphysically explain, or ground, another truth just in case the laws of metaphysics determine the latter truth on the basis of the former. I develop and motivate a specific conception of metaphysical laws, on which they are general rules that regulate the existence and features of derivative entities. I propose an analysis of the notion of ‘determination via the laws’, based on a restricted form of logical entailment. I (...)
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  35. Sorin Ioan Bangu (2008). Inference to the Best Explanation and Mathematical Realism. Synthese 160 (1):13-20.
    Arguing for mathematical realism on the basis of Field’s explanationist version of the Quine–Putnam Indispensability argument, Alan Baker has recently claimed to have found an instance of a genuine mathematical explanation of a physical phenomenon. While I agree that Baker presents a very interesting example in which mathematics plays an essential explanatory role, I show that this example, and the argument built upon it, begs the question against the mathematical nominalist.
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  36. Angela Potochnik (2011). Explanation and Understanding. European Journal for Philosophy of Science 1 (1):29-38.
    Michael Strevens offers an account of causal explanation according to which explanatory practice is shaped by counterbalanced commitments to representing causal influence and abstracting away from overly specific details. In this paper, I challenge a key feature of that account. I argue that what Strevens calls explanatory frameworks figure prominently in explanatory practice because they actually improve explanations. This suggestion is simple but has far-reaching implications. It affects the status of explanations that cite multiply realizable properties; changes the explanatory (...)
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  37.  56
    M. Chirimuuta (2014). Minimal Models and Canonical Neural Computations: The Distinctness of Computational Explanation in Neuroscience. Synthese 191 (2):127-153.
    In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I discuss work (...)
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  38.  22
    Daniel A. Wilkenfeld (2014). Functional Explaining: A New Approach to the Philosophy of Explanation. Synthese 191 (14):3367-3391.
    In this paper, I argue that explanations just ARE those sorts of things that, under the right circumstances and in the right sort of way, bring about understanding. This raises the question of why such a seemingly simple account of explanation, if correct, would not have been identified and agreed upon decades ago. The answer is that only recently has it been made possible to analyze explanation in terms of understanding without the risk of collapsing both to merely (...)
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  39. Laura Perini (2005). Explanation in Two Dimensions: Diagrams and Biological Explanation. Biology and Philosophy 20 (2-3):257-269.
    Molecular biologists and biochemists often use diagrams to present hypotheses. Analysis of diagrams shows that their content can be expressed with linguistic representations. Why do biologists use visual representations instead? One reason is simple comprehensibility: some diagrams present information which is readily understood from the diagram format, but which would not be comprehensible if the same information was expressed linguistically. But often diagrams are used even when concise, comprehensible linguistic alternatives are available. I explain this phenomenon by showing why diagrammatic (...)
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  40. Philip Gerrans (2002). A One-Stage Explanation of the Cotard Delusion. Philosophy, Psychiatry and Psychology 9 (1):47-53.
    Cognitive neuropsychiatry (CN) is the explanation of psychiatric disorder by the methods of cognitive neuropsychology. Within CN there are, broadly speaking, two approaches to delusion. The first uses a one-stage model, in which delusions are explained as rationalizations of anomalous experiences via reasoning strategies that are not, in themselves, abnormal. Two-stage models invoke additional hypotheses about abnormalities of reasoning. In this paper, I examine what appears to be a very strong argument, developed within CN, in favor of a twostage (...)
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  41.  51
    Sam Baron (2013). Optimisation and Mathematical Explanation: Doing the Lévy Walk. Synthese 3 (3):1-21.
    The indispensability argument seeks to establish the existence of mathematical objects. The success of the indispensability argument turns on finding cases of genuine extra- mathematical explanation. In this paper, I identify a new case of extra- mathematical explanation, involving the search patterns of fully-aquatic marine predators. I go on to use this case to predict the prevalence of extra- mathematical explanation in science.
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  42.  71
    Robert Northcott (2012). Genetic Traits and Causal Explanation. In Kathryn Plaisance & Thomas Reydon (eds.), Philosophy of Behavioral Biology. Springer 65-82.
    I use a contrastive theory of causal explanation to analyze the notion of a genetic trait. The resulting definition is relational, an implication of which is that no trait is genetic always and everywhere. Rather, every trait may be either genetic or non-genetic, depending on explanatory context. I also outline some other advantages of connecting the debate to the wider causation literature, including how that yields us an account of the distinction between genetic traits and genetic dispositions.
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  43.  17
    Daniele Molinini (2016). Evidence, Explanation and Enhanced Indispensability. Synthese 193 (2):403-422.
    In this paper I shall adopt a possible reading of the notions of ‘explanatory indispensability’ and ‘genuine mathematical explanation in science’ on which the Enhanced Indispensability Argument proposed by Alan Baker is based. Furthermore, I shall propose two examples of mathematical explanation in science and I shall show that, whether the EIA-partisans accept the reading I suggest, they are easily caught in a dilemma. To escape this dilemma they need to adopt some account of explanation and offer (...)
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  44. David Boersema (2003). Peirce on Explanation. Journal of Speculative Philosophy 17 (3):224-236.
    There has been a recent focused effort in philosophical scholarship to bridge the perceived divide between pragmatism and analytic philosophy. This divide, it has been suggested, is over philosophical doctrines, methods, and even aims. This is not to say there has not been fruitful—even if antagonistic—dialogue between these two philosophical traditions. Clearly there has been, e.g., Russell's famous (or infamous) disputes with James and Dewey. Clearly also, there has been direct philosophical influence from one tradition to the other, e.g., Peirce (...)
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  45.  49
    Alexander Reutlinger (2014). Why Is There Universal Macrobehavior? Renormalization Group Explanation as Noncausal Explanation. Philosophy of Science 81 (5):1157-1170.
    Renormalization group (RG) methods are an established strategy to explain how it is possible that microscopically different systems exhibit virtually the same macro behavior when undergoing phase-transitions. I argue – in agreement with Robert Batterman – that RG explanations are non-causal explanations. However, Batterman misidentifies the reason why RG explanations are non-causal: it is not the case that an explanation is non- causal if it ignores causal details. I propose an alternative argument, according to which RG explanations are non-causal (...)
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  46.  38
    Eric Hochstein (2016). One Mechanism, Many Models: A Distributed Theory of Mechanistic Explanation. Synthese 193 (5):1387-1407.
    There have been recent disagreements in the philosophy of neuroscience regarding which sorts of scientific models provide mechanistic explanations, and which do not. These disagreements often hinge on two commonly adopted, but conflicting, ways of understanding mechanistic explanations: what I call the “representation-as” account, and the “representation-of” account. In this paper, I argue that neither account does justice to neuroscientific practice. In their place, I offer a new alternative that can defuse some of these disagreements. I argue that individual models (...)
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  47. Max Seeger, The Reductive Explanation of Boiling Water in Levine's Explanatory Gap Argument.
    This paper examines a paradigm case of allegedly successful reductive explanation, viz. the explanation of the fact that water boils at 100°C based on facts about H2O. The case figures prominently in Joseph Levine’s explanatory gap argument against physicalism. The paper studies the way the argument evolved in the writings of Levine, focusing especially on the question how the reductive explanation of boiling water figures in the argument. It will turn out that there are two versions of (...)
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  48.  28
    Matteo Colombo (2016). Experimental Philosophy of Explanation Rising: The Case for a Plurality of Concepts of Explanation. Cognitive Science 40 (5).
    This paper brings together results from the philosophy and the psychology of explanation to argue that there are multiple concepts of explanation in human psychology. Specifically, it is shown that pluralism about explanation coheres with the multiplicity of models of explanation available in the philosophy of science, and it is supported by evidence from the psychology of explanatory judgment. Focusing on the case of a norm of explanatory power, the paper concludes by responding to the worry (...)
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  49. Michael Townsen Hicks & Peter van Elswyk (2015). Humean Laws and Circular Explanation. Philosophical Studies 172 (2):433-443.
    Humeans are often accused of accounting for natural laws in such a way that the fundamental entities that are supposed to explain the laws circle back and explain themselves. Loewer (Philos Stud 160(1):115–137, 2012) contends this is only the appearance of circularity. When it comes to the laws of nature, the Humean posits two kinds of explanation: metaphysical and scientific. The circle is then cut because the kind of explanation the laws provide for the fundamental entities is distinct (...)
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    James R. Griesemer & Michael J. Wade (1988). Laboratory Models, Causal Explanation and Group Selection. Biology and Philosophy 3 (1):67-96.
    We develop an account of laboratory models, which have been central to the group selection controversy. We compare arguments for group selection in nature with Darwin's arguments for natural selection to argue that laboratory models provide important grounds for causal claims about selection. Biologists get information about causes and cause-effect relationships in the laboratory because of the special role their own causal agency plays there. They can also get information about patterns of effects and antecedent conditions in nature. But to (...)
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