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  1. Bruce Glymour (2013). The Wrong Equations: A Reply to Gildenhuys. Biology and Philosophy 28 (4):675-681.
    Glymour (Philos Sci 73:369–389, 2006) claims that classical population genetic models can reliably predict short and medium run population dynamics only given information about future fitnesses those models cannot themselves predict, and that in consequence the causal, ecological models which can predict future fitnesses afford a more foundational description of natural selection than do population genetic models. This paper defends the first claim from objections offered by Gildenhuys (Biol Philos, 2011).
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  2. Bruce Glymour (2011). Modeling Environments: Interactive Causation and Adaptations to Environmental Conditions. Philosophy of Science 78 (3):448-471.
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  3. Bruce Glymour (2011). 11 Predicting Populations by Modeling Individuals. In Joseph Keim Campbell, Michael O'Rourke & Matthew H. Slater (eds.), Carving Nature at its Joints: Natural Kinds in Metaphysics and Science. Mit Press. 231.
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  4. Marc Lange, Raphael van Riel, Maximilian Schlosshauer, Gregory Wheeler, Zalán Gyenis, Miklós Rédei, John Byron Manchak, James Owen Weatherall, Bruce Glymour & Bradford Skow (2011). 10. Discussion: Problems for Natural Selection as a Mechanism Discussion: Problems for Natural Selection as a Mechanism (Pp. 512-523). [REVIEW] Philosophy of Science 78 (3).
     
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  5. Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph Ramsey, Richard Scheines, Peter Spirtes, Choh Man Teng & Jiji Zhang (2010). Actual Causation: A Stone Soup Essay. Synthese 175 (2):169 - 192.
    We argue that current discussions of criteria for actual causation are ill-posed in several respects. (1) The methodology of current discussions is by induction from intuitions about an infinitesimal fraction of the possible examples and counterexamples; (2) cases with larger numbers of causes generate novel puzzles; (3) "neuron" and causal Bayes net diagrams are, as deployed in discussions of actual causation, almost always ambiguous; (4) actual causation is (intuitively) relative to an initial system state since state changes are relevant, but (...)
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  6. Bruce Glymour (2008). Correlated Interaction and Group Selection. British Journal for the Philosophy of Science 59 (4):835-855.
    argues that correlated interactions are necessary for group selection. His argument turns on a particular procedure for measuring the strength of selection, and employs a restricted conception of correlated interaction. It is here shown that the procedure in question is unreliable, and that while related procedures are reliable in special contexts, they do not require correlated interactions for group selection to occur. It is also shown that none of these procedures, all of which employ partial regression methods, are reliable when (...)
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  7. Bruce Glymour (2008). Stable Models and Causal Explanation in Evolutionary Biology. Philosophy of Science 75 (5):571-583.
    Models that fail to satisfy the Markov condition are unstable because changes in state variable values may cause changes in the values of background variables, and these changes in background lead to predictive error. Such error arises because non‐Markovian models fail to track the causal relations generating the values of response variables. This has implications for discussions of the level of selection: under certain plausible conditoins most standard models of group selection will not satisfy the Markov condition when fit to (...)
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  8. Bruce Glymour (2007). 7 In Defense of Explanatory Deductivism. In J. K. Campbell, M. O'Rourke & H. S. Silverstein (eds.), Causation and Explanation. Mit Press. 4--133.
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  9. Bruce Glymour (2006). Wayward Modeling: Population Genetics and Natural Selection. Philosophy of Science 73 (4):369-389.
    Since the introduction of mathematical population genetics, its machinery has shaped our fundamental understanding of natural selection. Selection is taken to occur when differential fitnesses produce differential rates of reproductive success, where fitnesses are understood as parameters in a population genetics model. To understand selection is to understand what these parameter values measure and how differences in them lead to frequency changes. I argue that this traditional view is mistaken. The descriptions of natural selection rendered by population genetics models are (...)
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  10. Bruce Glymour (2003). On the Metaphysics of Probabilistic Causation: Lessons From Social Epidemiology. Philosophy of Science 70 (5):1413-1423.
    I argue that the orthodox account of probabilistic causation, on which probabilistic causes determine the probability of their effects, is inconsistent with certain ontological assumptions implicit in scientific practice. In particular, scientists recognize the possibility that properties of populations can cause the behavior of members of the populations. Such emergent population‐level causation is metaphysically impossible on the orthodoxy.
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  11. Bruce Glymour (2001). Selection, Indeterminism, and Evolutionary Theory. Philosophy of Science 68 (4):518-535.
    I argue that results from foraging theory give us good reason to think some evolutionary phenomena are indeterministic and hence that evolutionary theory must be probabilistic. Foraging theory implies that random search is sometimes selectively advantageous, and experimental work suggests that it is employed by a variety of organisms. There are reasons to think such search will sometimes be genuinely indeterministic. If it is, then individual reproductive success will also be indeterministic, and so too will frequency change in populations of (...)
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  12. Bruce Glymour, Marcelo Sabatés & Andrew Wayne (2001). Quantum Java: The Upwards Percolation of Quantum Indeterminacy. Philosophical Studies 103 (3):271 - 283.
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  13. Bruce Glymour (2000). Data and Phenomena: A Distinction Reconsidered. [REVIEW] Erkenntnis 52 (1):29-37.
    Bogen and Woodward (1988) advance adistinction between data and phenomena. Roughly, theformer are the observations reported by experimentalscientists, the latter are objective, stable featuresof the world to which scientists infer based onpatterns in reliable data. While phenomena areexplained by theories, data are not, and so theempirical basis for an inference to a theory consistsin claims about phenomena. McAllister (1997) hasrecently offered a critique of their version of thisdistinction, offering in its place a version on whichphenomena are theory laden, and hence (...)
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  14. Bruce Glymour (1999). Is Pure R-Selection Really Selection? Philosophy of Science 66 (3):195.
    Lennox and Wilson (1994) critique dispositional accounts of selection on the grounds that such accounts will class evolutionary events as cases of selection whether or not the environment constrains population growth. Lennox and Wilson claim that pure r-selection involves no environmental checks on growth, and that accounts of natural selection ought to distinguish between the two sorts of cases. I argue that Lennox and Wilson are mistaken in claiming that pure r-selection involves no environmental checks, but suggest that two related (...)
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  15. Bruce Glymour (1999). Population Level Causation and a Unified Theory of Natural Selection. Biology and Philosophy 14 (4):521-536.
    Sober (1984) presents an account of selection motivated by the view that one property can causally explain the occurrence of another only if the first plays a unique role in the causal production of the second. Sober holds that a causal property will play such a unique role if it is a population level cause of its effect, and on this basis argues that there is selection for a trait T only if T is a population level cause of survival (...)
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  16. Bruce Glymour (1998). Contrastive, Non-Probabilistic Statistical Explanations. Philosophy of Science 65 (3):448-471.
    Standard models of statistical explanation face two intractable difficulties. In his 1984 Salmon argues that because statistical explanations are essentially probabilistic we can make sense of statistical explanation only by rejecting the intuition that scientific explanations are contrastive. Further, frequently the point of a statistical explanation is to identify the etiology of its explanandum, but on standard models probabilistic explanations often fail to do so. This paper offers an alternative conception of statistical explanations on which explanations of the frequency of (...)
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  17. Bruce Glymour, Rick Grush, Valerie Gray Hardcastle, Brian Keeley, Joe Ramsey, Oron Shagrir & Ellen Watson (1992). The Cartesian Theater Stance. Behavioral and Brain Sciences 15 (2):209-210.
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