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  1. Why One Model is Never Enough: A Defense of Explanatory Holism.Hochstein Eric - 2017 - Biology and Philosophy 32 (6):1105-1125.
    Traditionally, a scientific model is thought to provide a good scientific explanation to the extent that it satisfies certain scientific goals that are thought to be constitutive of explanation. Problems arise when we realize that individual scientific models cannot simultaneously satisfy all the scientific goals typically associated with explanation. A given model’s ability to satisfy some goals must always come at the expense of satisfying others. This has resulted in philosophical disputes regarding which of these goals are in fact necessary (...)
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  • The Generality of Scientific Models: A Measure Theoretic Approach.Cory Travers Lewis & Christopher Belanger - 2015 - Synthese 192 (1):269-285.
    Scientific models are often said to be more or less general depending on how many cases they cover. In this paper we argue that the cardinality of cases is insufficient as a metric of generality, and we present a novel account based on measure theory. This account overcomes several problems with the cardinality approach, and additionally provides some insight into the nature of assessments of generality. Specifically, measure theory affords a natural and quantitative way of describing local spaces of possibility. (...)
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  • Hypothetical Pattern Idealization and Explanatory Models.Yasha Rohwer & Collin Rice - 2013 - Philosophy of Science 80 (3):334-355.
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  • Design Explanation and Idealization.Dingmar van Eck & Julie Mennes - 2016 - Erkenntnis 81 (5):1051-1071.
    In this paper we assess the explanatory role of idealizations in ‘design explanations’, a type of functional explanation used in biology. In design explanations, idealizations highlight which factors make a difference to phenomena to be explained: hypothetical, idealized, organisms are invoked to make salient which traits of extant organisms make a difference to organismal fitness. This result negates the view that idealizations serve only pragmatic benefits, and complements the view that idealizations highlight factors that do not make a difference. This (...)
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  • Trade-Offs in Model-Building: A More Target-Oriented Approach.John Matthewson - 2011 - Studies in History and Philosophy of Science Part A 42 (2):324-333.
    In his 1966 paper "The Strategy of model-building in Population Biology", Richard Levins argues that no single model in population biology can be maximally realistic, precise and general at the same time. This is because these desirable model properties trade-off against one another. Recently, philosophers have developed Levins' claims, arguing that trade-offs between these desiderata are generated by practical limitations on scientists, or due to formal aspects of models and how they represent the world. However this project is not complete. (...)
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  • Optimisation and Mathematical Explanation: Doing the Lévy Walk.Sam Baron - 2014 - Synthese 191 (3).
    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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  • The Heuristic Defense of Scientific Models: An Incentive-Based Assessment.Armin W. Schulz - 2015 - Perspectives on Science 23 (4):424-442.
    It is undeniable that much scientific work is model-based. Despite this, the justification for this reliance on models is still controversial. A particular difficulty here is the fact that many scientific models are based on assumptions that do not describe the exact details of many or even any empirical situations very well. This raises the question of why it is that, despite their frequent lack of descriptive accuracy, employing models is scientifically useful.One..
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  • Moving Beyond Causes: Optimality Models and Scientific Explanation.Collin Rice - 2015 - 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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  • Explanatory Schema and the Process of Model Building.Collin Rice, Yasha Rohwer & André Ariew - forthcoming - Synthese.
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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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  • Mechanism Discovery and Design Explanation: Where Role Function Meets Biological Advantage Function.Dingmar van Eck & Julie Mennes - 2018 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 49 (3):413-434.
    In the recent literature on explanation in biology, increasing attention is being paid to the connection between design explanation and mechanistic explanation, viz. the role of design principles and heuristics for mechanism discovery and mechanistic explanation. In this paper we extend the connection between design explanation and mechanism discovery by prizing apart two different types of design explanation and by elaborating novel heuristics that one specific type offers for mechanism discovery across species. We illustrate our claims in terms of two (...)
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  • Theoretical Models as Representations.Anguel Stefanov - 2012 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 43 (1):67-76.
    My aims here are, firstly, to suggest a minor amendment to R. I. G. Hughes’ DDI account of modeling, so that it could be viewed as a plausible epistemological “model” of how scientific models represent and secondly, to distinguish between two epistemological kinds of models that I call “descriptive” and “constitutive”. This aim is achieved by criticizing Michael Weisberg’s distinction between models and abstract direct representations and by following, at the same time, his own methodological approach for such a distinction.
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  • When Does ‘Folk Psychology’ Count as Folk Psychological?Eric Hochstein - 2017 - British Journal for the Philosophy of Science 68 (4):1125-1147.
    It has commonly been argued that certain types of mental descriptions, specifically those characterized in terms of propositional attitudes, are part of a folk psychological understanding of the mind. Recently, however, it has also been argued that this is the case even when such descriptions are employed as part of scientific theories in domains like social psychology and comparative psychology. In this paper, I argue that there is no plausible way to understand the distinction between folk and scientific psychology that (...)
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  • Models, Robustness, and Non-Causal Explanation: A Foray Into Cognitive Science and Biology.Elizabeth Irvine - 2015 - Synthese 192 (12):3943-3959.
    This paper is aimed at identifying how a model’s explanatory power is constructed and identified, particularly in the practice of template-based modeling (Humphreys, Philos Sci 69:1–11, 2002; Extending ourselves: computational science, empiricism, and scientific method, 2004), and what kinds of explanations models constructed in this way can provide. In particular, this paper offers an account of non-causal structural explanation that forms an alternative to causal–mechanical accounts of model explanation that are currently popular in philosophy of biology and cognitive science. Clearly, (...)
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  • How Are Models and Explanations Related?Yasha Rohwer & Collin Rice - 2016 - Erkenntnis 81 (5):1127-1148.
    Within the modeling literature, there is often an implicit assumption about the relationship between a given model and a scientific explanation. The goal of this article is to provide a unified framework with which to analyze the myriad relationships between a model and an explanation. Our framework distinguishes two fundamental kinds of relationships. The first is metaphysical, where the model is identified as an explanation or as a partial explanation. The second is epistemological, where the model produces understanding that is (...)
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  • Explanatory Independence and Epistemic Interdependence: A Case Study of the Optimality Approach.Angela Potochnik - 2010 - British Journal for the Philosophy of Science 61 (1):213-233.
    The value of optimality modeling has long been a source of contention amongst population biologists. Here I present a view of the optimality approach as at once playing a crucial explanatory role and yet also depending on external sources of confirmation. Optimality models are not alone in facing this tension between their explanatory value and their dependence on other approaches; I suspect that the scenario is quite common in science. This investigation of the optimality approach thus serves as a case (...)
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  • Optimality Explanations: A Plea for an Alternative Approach.Collin Rice - 2012 - Biology and Philosophy 27 (5):685-703.
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