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  1. Systematizing the Theoretical Virtues.Michael N. Keas - 2017 - Synthese:1-33.
    There are at least twelve major virtues of good theories: evidential accuracy, causal adequacy, explanatory depth, internal consistency, internal coherence, universal coherence, beauty, simplicity, unification, durability, fruitfulness, and applicability. These virtues are best classified into four classes: evidential, coherential, aesthetic, and diachronic. Each virtue class contains at least three virtues that sequentially follow a repeating pattern of progressive disclosure and expansion. Systematizing the theoretical virtues in this manner clarifies each virtue and suggests how they might have a coordinated and cumulative (...)
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  • Systematizing the Theoretical Virtues.Michael Keas - 2018 - Synthese 195 (6):2761-2793.
    There are at least twelve major virtues of good theories: evidential accuracy, causal adequacy, explanatory depth, internal consistency, internal coherence, universal coherence, beauty, simplicity, unification, durability, fruitfulness, and applicability. These virtues are best classified into four classes: evidential, coherential, aesthetic, and diachronic. Each virtue class contains at least three virtues that sequentially follow a repeating pattern of progressive disclosure and expansion. Systematizing the theoretical virtues in this manner clarifies each virtue and suggests how they might have a coordinated and cumulative (...)
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  • Mathematical Modeling in Biology: Philosophy and Pragmatics.Rasmus Grønfeldt Winther - 2012 - Frontiers in Plant Evolution and Development 2012:1-3.
    Philosophy can shed light on mathematical modeling and the juxtaposition of modeling and empirical data. This paper explores three philosophical traditions of the structure of scientific theory—Syntactic, Semantic, and Pragmatic—to show that each illuminates mathematical modeling. The Pragmatic View identifies four critical functions of mathematical modeling: (1) unification of both models and data, (2) model fitting to data, (3) mechanism identification accounting for observation, and (4) prediction of future observations. Such facets are explored using a recent exchange between two groups (...)
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  • Pure Science and the Problem of Progress.Heather Douglas - 2014 - Studies in History and Philosophy of Science Part A 46:55-63.
    How should we understand scientific progress? Kuhn famously discussed science as its own internally driven venture, structured by paradigms. He also famously had a problem describing progress in science, as problem-solving ability failed to provide a clear rubric across paradigm change—paradigm changes tossed out problems as well as solving them. I argue here that much of Kuhn’s inability to articulate a clear view of scientific progress stems from his focus on pure science and a neglect of applied science. I trace (...)
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