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  1. Inductive Risk and Values in Science.Heather Douglas - 2000 - Philosophy of Science 67 (4):559-579.
    Although epistemic values have become widely accepted as part of scientific reasoning, non-epistemic values have been largely relegated to the "external" parts of science (the selection of hypotheses, restrictions on methodologies, and the use of scientific technologies). I argue that because of inductive risk, or the risk of error, non-epistemic values are required in science wherever non-epistemic consequences of error should be considered. I use examples from dioxin studies to illustrate how non-epistemic consequences of error can and should be considered (...)
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  • Understanding Scientific Understanding.Henk de Regt - 2017 - Oup Usa.
    Understanding is a central aim of science and highly important in present-day society. But what precisely is scientific understanding and how can it be achieved? This book answers these questions, through philosophical analysis and historical case studies, and presents a philosophical theory of scientific understanding that highlights its contextual nature.
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  • Hypothetical Pattern Idealization and Explanatory Models.Yasha Rohwer & Collin C. Rice - 2013 - Philosophy of Science 80 (3):334-355.
  • Depth. An Account of Scientific Explanation.Michael Strevens - 2008 - 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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  • The Content of Model-Based Information.Raphael van Riel - 2015 - Synthese 192 (12):3839-3858.
    The paper offers an account of the structure of information provided by models that relevantly deviate from reality. It is argued that accounts of scientific modeling according to which a model’s epistemic and pragmatic relevance stems from the alleged fact that models give access to possibilities fail. First, it seems that there are models that do not give access to possibilities, for what they describe is impossible. Secondly, it appears that having access to a possibility is epistemically and pragmatically idle. (...)
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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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  • 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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  • Understanding (With) Toy Models.Alexander Reutlinger, Dominik Hangleiter & Stephan Hartmann - 2016 - British Journal for the Philosophy of Science:axx005.
    Toy models are highly idealized and extremely simple models. Although they are omnipresent across scientific disciplines, toy models are a surprisingly under-appreciated subject in the philosophy of science. The main philosophical puzzle regarding toy models is that it is an unsettled question what the epistemic goal of toy modeling is. One promising proposal for answering this question is the claim that the epistemic goal of toy models is to provide individual scientists with understanding. The aim of this paper is to (...)
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  • Idealizations and Understanding: Much Ado About Nothing?Emily Sullivan & Kareem Khalifa - 2019 - Australasian Journal of Philosophy 97 (4):673-689.
    Because idealizations frequently advance scientific understanding, many claim that falsehoods play an epistemic role. In this paper, we argue that these positions greatly overstate idealiza...
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  • Understanding Why, Knowing Why, and Cognitive Achievements.Insa Lawler - 2018 - Synthese (11):4583-4603.
    Duncan Pritchard argues that a feature that sets understanding-why apart from knowledge-why is that whereas (I) understanding-why is a kind of cognitive achievement in a strong sense, (II) knowledge-why is not such a kind. I argue that (I) is false and that (II) is true. (I) is false because understanding-why featuring rudimentary explanations and understanding-why concerning very simple causal connections are not cognitive achievements in a strong sense. Knowledge-why is not a kind of cognitive achievement in a strong sense for (...)
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  • Empiricism Without Magic: Transformational Abstraction in Deep Convolutional Neural Networks.Cameron Buckner - 2018 - Synthese (12):1-34.
    In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to (...)
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  • Understanding (with) Toy Models.Alexander Reutlinger, Dominik Hangleiter & Stephan Hartmann - 2018 - British Journal for the Philosophy of Science 69 (4):1069-1099.
    Toy models are highly idealized and extremely simple models. Although they are omnipresent across scientific disciplines, toy models are a surprisingly under-appreciated subject in the philosophy of science. The main philosophical puzzle regarding toy models concerns what the epistemic goal of toy modelling is. One promising proposal for answering this question is the claim that the epistemic goal of toy models is to provide individual scientists with understanding. The aim of this article is to precisely articulate and to defend this (...)
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  • The Philosophical Novelty of Computer Simulation Methods.Paul Humphreys - 2009 - Synthese 169 (3):615 - 626.
    Reasons are given to justify the claim that computer simulations and computational science constitute a distinctively new set of scientific methods and that these methods introduce new issues in the philosophy of science. These issues are both epistemological and methodological in kind.
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  • Learning From Minimal Economic Models.Till Grüne-Yanoff - 2009 - Erkenntnis 70 (1):81-99.
    It is argued that one can learn from minimal economic models. Minimal models are models that are not similar to the real world, do not resemble some of its features, and do not adhere to accepted regularities. One learns from a model if constructing and analysing the model affects one’s confidence in hypotheses about the world. Economic models, I argue, are often assessed for their credibility. If a model is judged credible, it is considered to be a relevant possibility. Considering (...)
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  • Douglas on Values: From Indirect Roles to Multiple Goals.Kevin C. Elliott - 2013 - Studies in History and Philosophy of Science Part A 44 (3):375-383.
    In recent papers and a book, Heather Douglas has expanded on the well-known argument from inductive risk, thereby launching an influential contemporary critique of the value-free ideal for science. This paper distills Douglas’s critique into four major claims. The first three claims provide a significant challenge to the value-free ideal for science. However, the fourth claim, which delineates her positive proposal to regulate values in science by distinguishing direct and indirect roles for values, is ambiguous between two interpretations, and both (...)
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  • Explanation and Understanding: An Alternative to Strevens’ Depth.Angela Potochnik - 2011 - 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 role (...)
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  • MISSing the World: Models as Isolations, Representations, and Credible Worlds.Uskali Mäki - 2009 - Erkenntnis 70 (1):29-43.
    This article shows how the MISS account of models—as isolations and surrogate systems—accommodates and elaborates Sugden’s account of models as credible worlds and Hausman’s account of models as explorations. Theoretical models typically isolate by means of idealization, and they are representatives of some target system, which prompts issues of resemblance between the two to arise. Models as representations are constrained both ontologically (by their targets) and pragmatically (by the purposes and audiences of the modeller), and these relations are coordinated by (...)
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  • Are We There Yet?Nello Cristianini - 2010 - Neural Networks 23 (4):466-470.
    Statistical approaches to Artificial Intelligence are behind most success stories of the field in the past decade. The idea of generating non-trivial behaviour by analysing vast amounts of data has enabled recommendation systems, search engines, spam filters, optical character recognition, machine translation and speech recognition, among other things. As we celebrate the spectacular achievements of this line of research, we need to assess its full potential and its limitations. What are the next steps to take towards machine intelligence?
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  • MISSing the World. Models as Isolations and Credible Surrogate Systems.Uskali Mäki - 2009 - Erkenntnis 70 (1):29-43.
    This article shows how the MISS account of models—as isolations and surrogate systems—accommodates and elaborates Sugden’s account of models as credible worlds and Hausman’s account of models as explorations. Theoretical models typically isolate by means of idealization, and they are representatives of some target system, which prompts issues of resemblance between the two to arise. Models as representations are constrained both ontologically (by their targets) and pragmatically (by the purposes and audiences of the modeller), and these relations are coordinated by (...)
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  • External Representations and Scientific Understanding.Jaakko Kuorikoski & Petri Ylikoski - 2015 - Synthese 192 (12):3817-3837.
    This paper provides an inferentialist account of model-based understanding by combining a counterfactual account of explanation and an inferentialist account of representation with a view of modeling as extended cognition. This account makes it understandable how the manipulation of surrogate systems like models can provide genuinely new empirical understanding about the world. Similarly, the account provides an answer to the question how models, that always incorporate assumptions that are literally untrue of the model target, can still provide factive explanations. Finally, (...)
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  • 10. Referees for Philosophy of Science Referees for Philosophy of Science (Pp. 479-482).Justin Garson, Yasha Rohwer, Collin Rice, Matteo Colombo, Peter Brössel, Davide Rizza, Simon M. Huttegger, Richard Healey, Alyssa Ney & Kathryn Phillips - 2013 - Philosophy of Science 80 (3):334-355.
  • The Goal of Explanation.Stephen R. Grimm - 2010 - Studies in History and Philosophy of Science Part A 41 (4):337-344.
    I defend the claim that understanding is the goal of explanation against various persistent criticisms, especially the criticism that understanding is not truth-connected in the appropriate way, and hence is a merely psychological state. Part of the reason why understanding has been dismissed as the goal of explanation, I suggest, is because the psychological dimension of the goal of explanation has itself been almost entirely neglected. In turn, the psychological dimension of understanding—the Aha! experience, the sense that a certain explanation (...)
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  • Understanding Why.Alison Hills - 2016 - Noûs 50 (4):661-688.
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  • Understanding: Not Know-How.Emily Sullivan - 2018 - Philosophical Studies 175 (1):221-240.
    There is considerable agreement among epistemologists that certain abilities are constitutive of understanding-why. These abilities include: constructing explanations, drawing conclusions, and answering questions. This agreement has led epistemologists to conclude that understanding is a kind of know-how. However, in this paper, I argue that the abilities constitutive of understanding are the same kind of cognitive abilities that we find in ordinary cases of knowledge-that and not the kind of practical abilities associated with know-how. I argue for this by disambiguating between (...)
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  • Values and Uncertainties in the Predictions of Global Climate Models.Eric Winsberg - 2012 - Kennedy Institute of Ethics Journal 22 (2):111-137.
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  • Understanding Why.Alison Hills - 2015 - Noûs 49 (2):661-688.
    I argue that understanding why p involves a kind of intellectual know how and differsfrom both knowledge that p and knowledge why p (as they are standardly understood).I argue that understanding, in this sense, is valuable.
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  • Understanding Without Explanation.Peter Lipton - 2009 - In H. W. de Regt, S. Leonelli & K. Eigner (eds.), Scientific Understanding: Philosophical Perspectives. University of Pittsburgh Press. pp. 43-63.
  • Segregation That No One Seeks.Ryan Muldoon, Tony Smith & Michael Weisberg - 2012 - Philosophy of Science 79 (1):38-62.
    This paper examines a series of Schelling-like models of residential segregation, in which agents prefer to be in the minority. We demon- strate that as long as agents care about the characteristics of their wider community, they tend to end up in a segregated state. We then investigate the process that causes this, and conclude that the result hinges on the similarity of informational states amongst agents of the same type. This is quite di erent from Schelling-like behavior, and sug- (...)
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  • The Goal of Explanation.Stephen Bird - 2010 - Studies in History and Philosophy of Science Part A 41 (4):337-344.
    I defend the claim that understanding is the goal of explanation against various persistent criticisms, especially the criticism that understanding is not truth-connected in the appropriate way, and hence is a merely psychological state. Part of the reason why understanding has been dismissed as the goal of explanation, I suggest, is because the psychological dimension of the goal of explanation has itself been almost entirely neglected. In turn, the psychological dimension of understanding—the Aha! experience, the sense that a certain explanation (...)
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  • Queer Science.Simon LeVay - 1996 - MIT Press.