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  1. Autonomous Patterns and Scientific Realism.Katherine Brading - 2010 - Philosophy of Science 77 (5):827-839.
    Taking Bogen and Woodward's discussion of data and phenomena as his starting point, McAllister presents a challenge to scientific realism. I discuss this challenge and offer a suggestion for how the scientific realist could respond to both its epistemic and ontological aspects. In so doing, I urge that the scientific realist should not reject ontological pluralism from the start, but should seek to explore versions of scientific realism that leave open the possibility of certain kinds of pluralist ontology. I investigate (...)
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  • Saving the phenomena.James Bogen & James Woodward - 1988 - Philosophical Review 97 (3):303-352.
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  • The law‐idealization.Paul Teller - 2004 - Philosophy of Science 71 (5):730-741.
    There are few, perhaps no known, exact, true, general laws. Some of the work of generalization is carried by ceteris paribus generalizations. I suggest that many models continue such work in more complex form, with the idea of ceteris paribus conditions thought of as extended to more general conditions of application. I use the term regularity guide to refer collectively to cp‐generalizations and such regularity‐purveying models. Laws in the traditional sense can then be thought of as idealizations, which idealize away (...)
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  • Model selection and the multiplicity of patterns in empirical data.James W. McAllister - 2007 - Philosophy of Science 74 (5):884-894.
    Several quantitative techniques for choosing among data models are available. Among these are techniques based on algorithmic information theory, minimum description length theory, and the Akaike information criterion. All these techniques are designed to identify a single model of a data set as being the closest to the truth. I argue, using examples, that many data sets in science show multiple patterns, providing evidence for multiple phenomena. For any such data set, there is more than one data model that must (...)
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  • Data models and the acquisition and manipulation of data.Todd Harris - 2003 - Philosophy of Science 70 (5):1508-1517.
    This paper offers an account of data manipulation in scientific experiments. It will be shown that in many cases raw, unprocessed data is not produced, but rather a form of processed data that will be referred to as a data model. The language of data models will be used to provide a framework within which to understand a recent debate about the status of data and data manipulation. It will be seen that a description in terms of data models allows (...)
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  • An agent-based conception of models and scientific representation.Ronald N. Giere - 2010 - Synthese 172 (2):269–281.
    I argue for an intentional conception of representation in science that requires bringing scientific agents and their intentions into the picture. So the formula is: Agents (1) intend; (2) to use model, M; (3) to represent a part of the world, W; (4) for some purpose, P. This conception legitimates using similarity as the basic relationship between models and the world. Moreover, since just about anything can be used to represent anything else, there can be no unified ontology of models. (...)
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