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  1. AIC and the challenge of complexity: A case study from ecology.Remington J. Moll, Daniel Steel & Robert A. Montgomery - 2016 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 60 (C):35-43.
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  • Explaining Engineered Computing Systems’ Behaviour: the Role of Abstraction and Idealization.Nicola Angius & Guglielmo Tamburrini - 2017 - Philosophy and Technology 30 (2):239-258.
    This paper addresses the methodological problem of analysing what it is to explain observed behaviours of engineered computing systems, focusing on the crucial role that abstraction and idealization play in explanations of both correct and incorrect BECS. First, it is argued that an understanding of explanatory requests about observed miscomputations crucially involves reference to the rich background afforded by hierarchies of functional specifications. Second, many explanations concerning incorrect BECS are found to abstract away from descriptions of physical components and processes (...)
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  • Interdisciplinary lessons for the teaching of biology from the practice of Evo-devo.Alan C. Love - 2013 - Science & Education 22 (2):255–278.
    Evolutionary developmental biology (Evo-devo) is a vibrant area of contemporary life science that should be (and is) increasingly incorporated into teaching curricula. Although the inclusion of this content is important for biological pedagogy at multiple levels of instruction, there are also philosophical lessons that can be drawn from the scientific practices found in Evo-devo. One feature of particular significance is the interdisciplinary nature of Evo-devo investigations and their resulting explanations. Instead of a single disciplinary approach being the most explanatory or (...)
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  • Difference making, explanatory relevance, and mechanistic models.Dingmar van Eck & Raoul Gervais - 2016 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 31 (1):125-134.
    In this paper we consider mechanistic explanations for biologic malfunctions. Drawing on Lipton’s work on difference making, we offer three reasons why one should distinguish i) mechanistic features that only make a difference to the malfunction one aims to explain, from ii) features that make a difference to both the malfunction and normal functioning. Recognition of the distinction is important for a) repair purposes, b) mechanism discovery, and c) understanding. This analysis extends current mechanistic thinking, which fails to appreciate the (...)
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  • Model templates within and between disciplines: from magnets to gases – and socio-economic systems.Tarja Knuuttila & Andrea Loettgers - 2016 - European Journal for Philosophy of Science 6 (3):377-400.
    One striking feature of the contemporary modelling practice is its interdisciplinary nature. The same equation forms, and mathematical and computational methods, are used across different disciplines, as well as within the same discipline. Are there, then, differences between intra- and interdisciplinary transfer, and can the comparison between the two provide more insight on the challenges of interdisciplinary theoretical work? We will study the development and various uses of the Ising model within physics, contrasting them to its applications to socio-economic systems. (...)
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  • Lightning in a Bottle: Complexity, Chaos, and Computation in Climate Science.Jon Lawhead - 2014 - Dissertation, Columbia University
    Climatology is a paradigmatic complex systems science. Understanding the global climate involves tackling problems in physics, chemistry, economics, and many other disciplines. I argue that complex systems like the global climate are characterized by certain dynamical features that explain how those systems change over time. A complex system's dynamics are shaped by the interaction of many different components operating at many different temporal and spatial scales. Examining the multidisciplinary and holistic methods of climatology can help us better understand the nature (...)
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  • Scientific progress: Knowledge versus understanding.Finnur Dellsén - 2016 - Studies in History and Philosophy of Science Part A 56 (C):72-83.
    What is scientific progress? On Alexander Bird’s epistemic account of scientific progress, an episode in science is progressive precisely when there is more scientific knowledge at the end of the episode than at the beginning. Using Bird’s epistemic account as a foil, this paper develops an alternative understanding-based account on which an episode in science is progressive precisely when scientists grasp how to correctly explain or predict more aspects of the world at the end of the episode than at the (...)
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  • Isolating Representations Versus Credible Constructions? Economic Modelling in Theory and Practice.Tarja Knuuttila - 2009 - Erkenntnis 70 (1):59-80.
    This paper examines two recent approaches to the nature and functioning of economic models: models as isolating representations and models as credible constructions. The isolationist view conceives of economic models as surrogate systems that isolate some of the causal mechanisms or tendencies of their respective target systems, while the constructionist approach treats them rather like pure constructions or fictional entities that nevertheless license different kinds of inferences. I will argue that whereas the isolationist view is still tied to the representationalist (...)
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  • Factive scientific understanding without accurate representation.Collin C. Rice - 2016 - Biology and Philosophy 31 (1):81-102.
    This paper analyzes two ways idealized biological models produce factive scientific understanding. I then argue that models can provide factive scientific understanding of a phenomenon without providing an accurate representation of the features of their real-world target system. My analysis of these cases also suggests that the debate over scientific realism needs to investigate the factive scientific understanding produced by scientists’ use of idealized models rather than the accuracy of scientific models themselves.
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  • How Idealizations Provide Understanding.Michael Strevens - 2016 - In Stephen Robert Grimm, Christoph Baumberger & Sabine Ammon (eds.), Explaining Understanding: New Perspectives From Epistemology and Philosophy of Science. London: Routledge.
    How can a model that stops short of representing the whole truth about the causal production of a phenomenon help us to understand the phenomenon? I answer this question from the perspective of what I call the simple view of understanding, on which to understand a phenomenon is to grasp a correct explanation of the phenomenon. Idealizations, I have argued in previous work, flag factors that are casually relevant but explanatorily irrelevant to the phenomena to be explained. Though useful to (...)
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  • Rawlsian Stability.Jon Garthoff - 2016 - Res Publica 22 (3):285-299.
    Despite great advances in recent scholarship on the political philosophy of John Rawls, Rawls’s conception of stability is not fully appreciated. This essay aims to remedy this by articulating a more complete understanding of stability and its role in Rawls’s theory of justice. I argue that even in A Theory of Justice Rawls maintains that within liberal democratic constitutionalism judgments of relative stability typically adjudicate decisively among conceptions of justice and is committed to more deeply than to the substantive content (...)
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  • Robustness, Diversity of Evidence, and Probabilistic Independence.Jonah N. Schupbach - 2015 - In Uskali Mäki, Stéphanie Ruphy, Gerhard Schurz & Ioannis Votsis (eds.), Recent Developments in the Philosophy of Science. Cham: Springer. pp. 305-316.
    In robustness analysis, hypotheses are supported to the extent that a result proves robust, and a result is robust to the extent that we detect it in diverse ways. But what precise sense of diversity is at work here? In this paper, I show that the formal explications of evidential diversity most often appealed to in work on robustness – which all draw in one way or another on probabilistic independence – fail to shed light on the notion of diversity (...)
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  • The diverse aims of science.Angela Potochnik - 2015 - Studies in History and Philosophy of Science Part A 53:71-80.
    There is increasing attention to the centrality of idealization in science. One common view is that models and other idealized representations are important to science, but that they fall short in one or more ways. On this view, there must be an intermediary step between idealized representation and the traditional aims of science, including truth, explanation, and prediction. Here I develop an alternative interpretation of the relationship between idealized representation and the aims of science. In my view, continuing, widespread idealization (...)
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  • Scientific Collaboration: Do Two Heads Need to Be More than Twice Better than One?Thomas Boyer-Kassem & Cyrille Imbert - 2015 - Philosophy of Science 82 (4):667-688.
    Epistemic accounts of scientific collaboration usually assume that, one way or another, two heads really are more than twice better than one. We show that this hypothesis is unduly strong. We present a deliberately crude model with unfavorable hypotheses. We show that, even then, when the priority rule is applied, large differences in successfulness can emerge from small differences in efficiency, with sometimes increasing marginal returns. We emphasize that success is sensitive to the structure of competing communities. Our results suggest (...)
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  • Explanatory completeness and idealization in large brain simulations: a mechanistic perspective.Marcin Miłkowski - 2016 - Synthese 193 (5):1457-1478.
    The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: Blue Brain, used for particular simulations of the cortical column in hybrid models, and Eliasmith’s SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of computational explanation, I (...)
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  • Network representation and complex systems.Charles Rathkopf - 2018 - Synthese (1).
    In this article, network science is discussed from a methodological perspective, and two central theses are defended. The first is that network science exploits the very properties that make a system complex. Rather than using idealization techniques to strip those properties away, as is standard practice in other areas of science, network science brings them to the fore, and uses them to furnish new forms of explanation. The second thesis is that network representations are particularly helpful in explaining the properties (...)
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  • Why We Cannot Learn from Minimal Models.Roberto Fumagalli - 2016 - Erkenntnis 81 (3):433-455.
    Philosophers of science have developed several accounts of how consideration of scientific models can prompt learning about real-world targets. In recent years, various authors advocated the thesis that consideration of so-called minimal models can prompt learning about such targets. In this paper, I draw on the philosophical literature on scientific modelling and on widely cited illustrations from economics and biology to argue that this thesis fails to withstand scrutiny. More specifically, I criticize leading proponents of such thesis for failing to (...)
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  • Caricatures, Myths, and White Lies.Kirsten Walsh & Adrian Currie - 2015 - Metaphilosophy 46 (3):414-435.
    Pedagogical situations require white lies: in teaching philosophy we make decisions about what to omit, what to emphasise, and what to distort. This article considers when it is permissible to distort the historical record, arguing for a tempered respect for the historical facts. It focuses on the rationalist/empiricist distinction, which still frames most undergraduate early modern courses despite failing to capture the intellectual history of that period. It draws an analogy with Michael Strevens's view on idealisation in causal explanation to (...)
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  • Are climate models credible worlds? Prospects and limitations of possibilistic climate prediction.Gregor Betz - 2015 - European Journal for Philosophy of Science 5 (2):191-215.
    Climate models don’t give us probabilistic forecasts. To interpret their results, alternatively, as serious possibilities seems problematic inasmuch as climate models rely on contrary-to-fact assumptions: why should we consider their implications as possible if their assumptions are known to be false? The paper explores a way to address this possibilistic challenge. It introduces the concepts of a perfect and of an imperfect credible world, and discusses whether climate models can be interpreted as imperfect credible worlds. That would allow one to (...)
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  • Against Harmony: Infinite Idealizations and Causal Explanation.Iulian D. Toader - 2015 - In Ilie Parvu, Gabriel Sandu & Iulian D. Toader (eds.), Romanian Studies in Philosophy of Science. Boston Studies in the Philosophy and History of Science, vol. 313: Springer. pp. 291-301.
    This paper argues against the view that the standard explanation of phase transitions in statistical mechanics may be considered a causal explanation, a distortion that can nevertheless successfully represent causal relations.
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  • Idealization, epistemic logic, and epistemology.Audrey Yap - 2014 - Synthese 191 (14):3351-3366.
    Many criticisms of epistemic logic have centered around its use of devices such as idealized knowers with logical omniscience and perfect self-knowledge. One possible response to such criticisms is to say that these idealizations are normative devices, and that epistemic logic tells us how agents ought to behave. This paper will take a different approach, treating epistemic logic as descriptive, and drawing the analogy between its formal models and idealized scientific models on that basis. Treating it as descriptive matches the (...)
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  • Validating Function-Based Design Methods: an Explanationist Perspective.Dingmar van Eck - 2015 - Philosophy and Technology 28 (4):511-531.
    Analysis of the adequacy of engineering design methods, as well as analysis of the utility of concepts of function often invoked in these methods, is a neglected topic in both philosophy of technology and in engineering proper. In this paper, I present an approach—dubbed an explanationist perspective—for assessing the adequacy of function-based design methods. Engineering design is often intertwined with explanation, for instance, in reverse engineering and subsequent redesign, knowledge base-assisted designing, and diagnostic reasoning. I argue that the presented approach (...)
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  • Minimal Model Explanations.Robert W. Batterman & Collin C. Rice - 2014 - Philosophy of Science 81 (3):349-376.
    This article discusses minimal model explanations, which we argue are distinct from various causal, mechanical, difference-making, and so on, strategies prominent in the philosophical literature. We contend that what accounts for the explanatory power of these models is not that they have certain features in common with real systems. Rather, the models are explanatory because of a story about why a class of systems will all display the same large-scale behavior because the details that distinguish them are irrelevant. This story (...)
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  • Better Best Systems – Too Good To Be True.Marius Backmann & Alexander Reutlinger - 2014 - Dialectica 68 (3):375-390.
    Craig Callender, Jonathan Cohen and Markus Schrenk have recently argued for an amended version of the best system account of laws – the better best system account (BBSA). This account of lawhood is supposed to account for laws in the special sciences, among other desiderata. Unlike David Lewis's original best system account of laws, the BBSA does not rely on a privileged class of natural predicates, in terms of which the best system is formulated. According to the BBSA, a contingently (...)
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  • Causal patterns and adequate explanations.Angela Potochnik - 2015 - Philosophical Studies 172 (5):1163-1182.
    Causal accounts of scientific explanation are currently broadly accepted (though not universally so). My first task in this paper is to show that, even for a causal approach to explanation, significant features of explanatory practice are not determined by settling how causal facts bear on the phenomenon to be explained. I then develop a broadly causal approach to explanation that accounts for the additional features that I argue an explanation should have. This approach to explanation makes sense of several aspects (...)
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  • Neural Findings and Economic Models: Why Brains Have Limited Relevance for Economics.Roberto Fumagalli - 2014 - Philosophy of the Social Sciences 44 (5):606-629.
    Proponents of neuroeconomics often argue that better knowledge of the human neural architecture enables economists to improve standard models of choice. In their view, these improvements provide compelling reasons to use neural findings in constructing and evaluating economic models. In a recent article, I criticized this view by pointing to the trade-offs between the modeling desiderata valued by neuroeconomists and other economists, respectively. The present article complements my earlier critique by focusing on three modeling desiderata that figure prominently in economic (...)
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  • Function Ascription and Explanation: Elaborating an Explanatory Utility Desideratum for Ascriptions of Technical Functions.Dingmar van Eck & Erik Weber - 2014 - Erkenntnis 79 (6):1367-1389.
    Current philosophical theorizing about technical functions is mainly focused on specifying conditions under which agents are justified in ascribing functions to technical artifacts. Yet, assessing the precise explanatory relevance of such function ascriptions is, by and large, a neglected topic in the philosophy of technical artifacts and technical functions. We assess the explanatory utility of ascriptions of technical functions in the following three explanation-seeking contexts: why was artifact x produced?, why does artifact x not have the expected capacity to ϕ?, (...)
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  • Nonepistemic Values and the Multiple Goals of Science.Kevin C. Elliott & Daniel J. McKaughan - 2014 - Philosophy of Science 81 (1):1-21.
    Recent efforts to argue that nonepistemic values have a legitimate role to play in assessing scientific models, theories, and hypotheses typically either reject the distinction between epistemic and nonepistemic values or incorporate nonepistemic values only as a secondary consideration for resolving epistemic uncertainty. Given that scientific representations can legitimately be evaluated not only based on their fit with the world but also with respect to their fit with the needs of their users, we show in two case studies that nonepistemic (...)
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  • Entangled Life: Organism and Environment in the Biological and Social Sciences.Gillian Barker, Eric Desjardins & Trevor Pearce (eds.) - 2014 - Dordrecht: Springer.
    Despite the burgeoning interest in new and more complex accounts of the organism-environment dyad by biologists and philosophers, little attention has been paid in the resulting discussions to the history of these ideas and to their deployment in disciplines outside biology—especially in the social sciences. Even in biology and philosophy, there is a lack of detailed conceptual models of the organism-environment relationship. This volume is designed to fill these lacunae by providing the first multidisciplinary discussion of the topic of organism-environment (...)
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  • Addressing the Conflict Between Relativity and Quantum Theory: Models, Measurement and the Markov Property.Gareth Ernest Boardman - 2013 - Cosmos and History 9 (2):86-115.
    Twenty-first century science faces a dilemma. Two of its well-verified foundation stones - relativity and quantum theory - have proven inconsistent. Resolution of the conflict has resisted improvements in experimental precision leaving some to believe that some fundamental understanding in our world-view may need modification or even radical reform. Employment of the wave-front model of electrodynamics, as a propagation process with a Markov property, may offer just such a clarification.
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  • Narratives, mechanisms and progress in historical science.Adrian Mitchell Currie - 2014 - Synthese 191 (6):1-21.
    Geologists, Paleontologists and other historical scientists are frequently concerned with narrative explanations targeting single cases. I show that two distinct explanatory strategies are employed in narratives, simple and complex. A simple narrative has minimal causal detail and is embedded in a regularity, whereas a complex narrative is more detailed and not embedded. The distinction is illustrated through two case studies: the ‘snowball earth’ explanation of Neoproterozoic glaciation and recent attempts to explain gigantism in Sauropods. This distinction is revelatory of historical (...)
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  • Fiction, Creation and Fictionality : An Overview.Matthieu Fontaine & Shahid Rahman - 2010 - Methodos 10:1-75.
    La réflexion philosophique sur la non-existence est une thématique qui a été abordée au commencement même de la philosophie et qui suscite, depuis la publication en 1905 de « On Denoting » par Russell, les plus vifs débats en philosophie analytique. Cependant, le débat féroce sur la sémantique des noms propres et des descriptions définies qui surgirent suite à la publication du « On Referring » par Strawson en 1950 n’engagea pas d’étude systématique de la sémantique des fictions. En fait, (...)
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  • Moving Beyond Causes: Optimality Models and Scientific Explanation.Collin Rice - 2013 - Noûs 49 (3):589-615.
    A prominent approach to scientific explanation and modeling claims that for a model to provide an explanation it must accurately represent at least some of the actual causes in the event's causal history. In this paper, I argue that many optimality explanations present a serious challenge to this causal approach. I contend that many optimality models provide highly idealized equilibrium explanations that do not accurately represent the causes of their target system. Furthermore, in many contexts, it is in virtue of (...)
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  • A Simulacrum Account of Dispositional Properties.Marco J. Nathan - 2013 - Noûs 49 (2):253-274.
    This essay presents a model-theoretic account of dispositional properties, according to which dispositions are not ordinary properties of real entities; dispositions capture the behavior of abstract, idealized models. This account has several payoffs. First, it saves the simple conditional analysis of dispositions. Second, it preserves the general connection between dispositions and regularities, despite the fact that some dispositions are not grounded in actual regularities. Finally, it brings together the analysis and the explanation of dispositions under a unified framework.
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  • The Concrete Universal and Cognitive Science.Richard Shillcock - 2014 - Axiomathes 24 (1):63-80.
    Cognitive science depends on abstractions made from the complex reality of human behaviour. Cognitive scientists typically wish the abstractions in their theories to be universals, but seldom attend to the ontology of universals. Two sorts of universal, resulting from Galilean abstraction and materialist abstraction respectively, are available in the philosophical literature: the abstract universal—the one-over-many universal—is the universal conventionally employed by cognitive scientists; in contrast, a concrete universal is a material entity that can appear within the set of entities it (...)
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  • Don’t Blame the Idealizations.Nicholaos Jones - 2013 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 44 (1):85-100.
    Idealizing conditions are scapegoats for scientific hypotheses, too often blamed for falsehood better attributed to less obvious sources. But while the tendency to blame idealizations is common among both philosophers of science and scientists themselves, the blame is misplaced. Attention to the nature of idealizing conditions, the content of idealized hypotheses, and scientists’ attitudes toward those hypotheses shows that idealizing conditions are blameless when hypotheses misrepresent. These conditions help to determine the content of idealized hypotheses, and they do so in (...)
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  • Modeling without representation.Alistair M. C. Isaac - 2013 - Synthese 190 (16):3611-3623.
    How can mathematical models which represent the causal structure of the world incompletely or incorrectly have any scientific value? I argue that this apparent puzzle is an artifact of a realist emphasis on representation in the philosophy of modeling. I offer an alternative, pragmatic methodology of modeling, inspired by classic papers by modelers themselves. The crux of the view is that models developed for purposes other than explanation may be justified without reference to their representational properties.
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  • Psa 2012.-Preprint Volume- - unknown
    These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2012.
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  • Berkeley and Proof in Geometry.Richard J. Brook - 2012 - Dialogue 51 (3):419-435.
    Berkeley in his Introduction to the Principles of Human knowledge uses geometrical examples to illustrate a way of generating “universal ideas,” which allegedly account for the existence of general terms. In doing proofs we might, for example, selectively attend to the triangular shape of a diagram. Presumably what we prove using just that property applies to all triangles.I contend, rather, that given Berkeley’s view of extension, no Euclidean triangles exist to attend to. Rather proof, as Berkeley would normally assume, requires (...)
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  • A verisimilitudinarian analysis of the Linda paradox.Gustavo Cevolani, Vincenzo Crupi & Roberto Festa - 2012 - VII Conference of the Spanish Society for Logic, Methodology and Philosphy of Science.
    The Linda paradox is a key topic in current debates on the rationality of human reasoning and its limitations. We present a novel analysis of this paradox, based on the notion of verisimilitude as studied in the philosophy of science. The comparison with an alternative analysis based on probabilistic confirmation suggests how to overcome some problems of our account by introducing an adequately defined notion of verisimilitudinarian confirmation.
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  • Feminist implications of model-based science.Angela Potochnik - 2012 - Studies in History and Philosophy of Science Part A 43 (2):383-389.
    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 (...)
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  • Special Issue: Philosophical Considerations in the Teaching of Biology. Part II, Evolution, Development and Genetics.Kostas Kampourakis (ed.) - 2013 - Springer (Science & Education).
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  • Scientific Models.Stephen M. Downes - 2011 - Philosophy Compass 6 (11):757-764.
    This contribution provides an assessment of the epistemological role of scientific models. The prevalent view that all scientific models are representations of the world is rejected. This view points to a unified way of resolving epistemic issues for scientific models. The emerging consensus in philosophy of science that models have many different epistemic roles in science is presented and defended.
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  • Explanation and description in computational neuroscience.David Michael Kaplan - 2011 - 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 of phenomena. It (...)
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  • On the neural enrichment of economic models: tractability, trade-offs and multiple levels of description.Roberto Fumagalli - 2011 - Biology and Philosophy 26 (5):617-635.
    In the recent literature at the interface between economics, biology and neuroscience, several authors argue that by adopting an interdisciplinary approach to the analysis of decision making, economists will be able to construct predictively and explanatorily superior models. However, most economists remain quite reluctant to import biological or neural insights into their account of choice behaviour. In this paper, I reconstruct and critique one of the main arguments by means of which economists attempt to vindicate their conservative position. Furthermore, I (...)
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  • The truth of false idealizations in modeling.Uskali Mäki - 2011 - In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. New York: Routledge.
    Modeling involves the use of false idealizations, yet there is typically a belief or hope that modeling somehow manages to deliver true information about the world. The paper discusses one possible way of reconciling truth and falsehood in modeling. The key trick is to relocate truth claims by reinterpreting an apparently false idealizing assumption in order to make clear what possibly true assertion is intended when using it. These include interpretations in terms of negligibility, applicability, tractability, early-step, and more. Elaborations (...)
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  • Philosophy of chemistry.Michael Weisberg, Paul Needham & Robin Hendry - 2011 - Stanford Encyclopedia of Philosophy.
    Chemistry is the study of the structure and transformation of matter. When Aristotle founded the field in the 4th century BCE, his conceptual grasp of the nature of matter was tailored to accommodate a relatively simple range of observable phenomena. In the 21st century, chemistry has become the largest scientific discipline, producing over half a million publications a year ranging from direct empirical investigations to substantial theoretical work. However, the specialized interest in the conceptual issues arising in chemistry, hereafter Philosophy (...)
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  • Scientific Models and Representation.Gabriele Contessa - 2011 - In Steven French & Juha Saatsi (eds.), Continuum Companion to the Philosophy of Science. Continuum. pp. 120--137.
    My two daughters would love to go tobogganing down the hill by themselves, but they are just toddlers and I am an apprehensive parent, so, before letting them do so, I want to ensure that the toboggan won’t go too fast. But how fast will it go? One way to try to answer this question would be to tackle the problem head on. Since my daughters and their toboggan are initially at rest, according to classical mechanics, their final velocity will (...)
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  • Idealization and the structure of theories in biololgy.Alfonso Arroyo-Santos & Xavier De Donato-Rodríguez - 2008
    In this paper we present a new framework of idealization in biology. We characterize idealizations as a network of counterfactual conditionals that can exhibit different degrees of contingency. We use the idea of possible worlds to say that, in departing more or less from the actual world, idealizations can serve numerous epistemic, methodological or heuristic purposes within scientific research. We defend that, in part, it is this structure what helps explain why idealizations, despite being deformations of reality, are so successful (...)
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  • Beyond Spacetime: The Foundations of Quantum Gravity.Patrick Shields & Nicholas Teh - 2024 - Philosophical Review 133 (1):106-111.
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