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Collin Rice [22]Collin C. Rice [2]
  1. 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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  2. 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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  3. Models Don’t Decompose That Way: A Holistic View of Idealized Models.Collin Rice - 2019 - British Journal for the Philosophy of Science 70 (1):179-208.
    Many accounts of scientific modelling assume that models can be decomposed into the contributions made by their accurate and inaccurate parts. These accounts then argue that the inaccurate parts of the model can be justified by distorting only what is irrelevant. In this paper, I argue that this decompositional strategy requires three assumptions that are not typically met by our best scientific models. In response, I propose an alternative view in which idealized models are characterized as holistically distorted representations that (...)
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  4.  86
    Idealized models, holistic distortions, and universality.Collin Rice - 2018 - Synthese 195 (6):2795-2819.
    In this paper, I first argue against various attempts to justify idealizations in scientific models that explain by showing that they are harmless and isolable distortions of irrelevant features. In response, I propose a view in which idealized models are characterized as providing holistically distorted representations of their target system. I then suggest an alternative way that idealized modeling can be justified by appealing to universality.
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  5. Optimality explanations: a plea for an alternative approach.Collin Rice - 2012 - Biology and Philosophy 27 (5):685-703.
    Recently philosophers of science have begun to pay more attention to the use of highly idealized mathematical models in scientific theorizing. An important example of this kind of highly idealized modeling is the widespread use of optimality models within evolutionary biology. One way to understand the explanations provided by these models is as a censored causal explanation: an explanation that omits certain causal factors in order to focus on a modular subset of the causal processes that led to the explanandum. (...)
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  6.  50
    Understanding realism.Collin Rice - 2019 - Synthese 198 (5):4097-4121.
    Catherine Elgin has recently argued that a nonfactive conception of understanding is required to accommodate the epistemic successes of science that make essential use of idealizations and models. In this paper, I argue that the fact that our best scientific models and theories are pervasively inaccurate representations can be made compatible with a more nuanced form of scientific realism that I call Understanding Realism. According to this view, science aims at (and often achieves) factive scientific understanding of natural phenomena. I (...)
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  7. 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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  8.  61
    Autonomous-Statistical Explanations and Natural Selection.André Ariew, Collin Rice & Yasha Rohwer - 2015 - British Journal for the Philosophy of Science 66 (3):635-658.
    Shapiro and Sober claim that Walsh, Ariew, Lewens, and Matthen give a mistaken, a priori defense of natural selection and drift as epiphenomenal. Contrary to Shapiro and Sober’s claims, we first argue that WALM’s explanatory doctrine does not require a defense of epiphenomenalism. We then defend WALM’s explanatory doctrine by arguing that the explanations provided by the modern genetical theory of natural selection are ‘autonomous-statistical explanations’ analogous to Galton’s explanation of reversion to mediocrity and an explanation of the diffusion ofgases. (...)
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  9.  67
    (1 other version)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.
    Highly idealized models, such as the Hawk-Dove game, are pervasive in biological theorizing. We argue that the process and motivation that leads to the introduction of various idealizations into these models is not adequately captured by Michael Weisberg’s taxonomy of three kinds of idealization. Consequently, a fourth kind of idealization is required, which we call hypothetical pattern idealization. This kind of idealization is used to construct models that aim to be explanatory but do not aim to be explanations.
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  10. 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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  11.  22
    Leveraging distortions: explanation, idealization, and universality in science.Collin Rice - 2021 - Cambridge, Massachusetts: The MIT Press.
    An original argument about how scientific models often times distort reality rather than accurately reflect it. And it's this distortion that often gives scientific models their epistemic power.
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  12.  61
    Galton, reversion and the quincunx: The rise of statistical explanation.André Ariew, Yasha Rohwer & Collin Rice - 2017 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 66:63-72.
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  13.  55
    Concepts as Pluralistic Hybrids.Collin Rice - 2014 - Philosophy and Phenomenological Research 92 (3):597-619.
    In contrast to earlier views that argued for a particular kind of concept, several recent accounts have proposed that there are multiple distinct kinds of concepts, or that there is a plurality of concepts for each category. In this paper, I argue for a novel account of concepts as pluralistic hybrids. According to this view, concepts are pluralistic because there are several concepts for the same category whose use is heavily determined by context. In addition, concepts are hybrids because they (...)
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  14.  95
    Explanatory schema and the process of model building.Collin Rice, Yasha Rohwer & André Ariew - 2019 - Synthese 196 (11):4735-4757.
    In this paper, we argue that rather than exclusively focusing on trying to determine if an idealized model fits a particular account of scientific explanation, philosophers of science should also work on directly analyzing various explanatory schemas that reveal the steps and justification involved in scientists’ use of highly idealized models to formulate explanations. We develop our alternative methodology by analyzing historically important cases of idealized statistical modeling that use a three-step explanatory schema involving idealization, mathematical operation, and explanatory interpretation.
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  15.  83
    Interdisciplinary modeling: a case study of evolutionary economics.Collin Rice & Joshua Smart - 2011 - Biology and Philosophy 26 (5):655-675.
    Biologists and economists use models to study complex systems. This similarity between these disciplines has led to an interesting development: the borrowing of various components of model-based theorizing between the two domains. A major recent example of this strategy is economists’ utilization of the resources of evolutionary biology in order to construct models of economic systems. This general strategy has come to be called evolutionary economics and has been a source of much debate among economists. Although philosophers have developed literatures (...)
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  16.  38
    Modeling multiscale patterns: active matter, minimal models, and explanatory autonomy.Collin Rice - 2022 - Synthese 200 (6):1-35.
    Both ecologists and statistical physicists use a variety of highly idealized models to study active matter and self-organizing critical phenomena. In this paper, I show how universality classes play a crucial role in justifying the application of highly idealized ‘minimal’ models to explain and understand the critical behaviors of active matter systems across a wide range of scales and scientific fields. Appealing to universality enables us to see why the same minimal models can be used to explain and understand behaviors (...)
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  17. Concept empiricism, content, and compositionality.Collin Rice - 2013 - Philosophical Studies 162 (3):567-583.
    Concepts are the constituents of thoughts. Therefore, concepts are vital to any theory of cognition. However, despite their widely accepted importance, there is little consensus about the nature and origin of concepts. Thanks to the work of Lawrence Barsalou, Jesse Prinz and others concept empiricism has been gaining momentum within the philosophy and psychology literature. Concept empiricism maintains that all concepts are copies, or combinations of copies, of perceptual representations—that is, all concepts are couched in the codes of perceptual representation (...)
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  18. How to Reconcile a Unified Account of Explanation with Explanatory Diversity.Collin Rice & Yasha Rohwer - 2020 - Foundations of Science 26 (4):1025-1047.
    The concept of explanation is central to scientific practice. However, scientists explain phenomena in very different ways. That is, there are many different kinds of explanation; e.g. causal, mechanistic, statistical, or equilibrium explanations. In light of the myriad kinds of explanation identified in the literature, most philosophers of science have adopted some kind of explanatory pluralism. While pluralism about explanation seems plausible, it faces a dilemma Explanation beyond causation, Oxford University Press, Oxford, pp 39–56, 2018). Either there is nothing that (...)
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  19.  55
    Massive Modularity, Content Integration, and Language.Collin Rice - 2011 - Philosophy of Science 78 (5):800-812.
    One of the obstacles facing massive modularity is how a pervasively modular mind might generate non-domain-specific thoughts by integrating the content produced by various domain-specific modules. Peter Carruthers has recently argued that the operations of the language faculty are constitutive of the process by which the human mind is able to integrate content from heterogeneous conceptual domains. In this article, I first argue that Carruthers's data do not provide support for either of two possible interpretations of his thesis. In addition, (...)
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  20.  33
    Universality and Modeling Limiting Behaviors.Collin Rice - 2020 - Philosophy of Science 87 (5):829-840.
    Most attempts to justify the use of idealized models to explain appeal to the accuracy of the model with respect to difference-making causes. In this article, I argue for an alternative way to just...
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  21.  13
    Beyond reduction and emergence: a framework for tailoring multiscale modeling techniques to specific contexts.Collin Rice - 2024 - Biology and Philosophy 39 (4):1-23.
    This paper analyzes three multiscale modeling techniques that are commonly used in biology and physics and uses those cases to construct a normative framework for tailoring multiscale modeling techniques to specific modeling contexts. I argue that the selection of a multiscale modeling technique ought to focus on degrees of relative autonomy between scales, the measurable macroscale parameters of interest, indirect scaling relationships mediated by mesoscale features, and the degree of heterogeneity of the system’s mesoscale structures. The unique role that these (...)
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  22.  26
    The Epistemic Benefits of Diversity in Introductory Philosophy Classes.Collin Rice - 2023 - American Association of Philosophy Teachers Studies in Pedagogy 8:93-94.
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