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Bruce Glymour [18]Bruce David Glymour [1]
  1. Actual causation: a stone soup essay.Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph Ramsey & Richard Scheines - 2010 - 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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  2. Wayward Modeling: Population Genetics and Natural Selection.Bruce Glymour - 2006 - 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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  3. Modeling Environments: Interactive Causation and Adaptations to Environmental Conditions.Bruce Glymour - 2011 - Philosophy of Science 78 (3):448-471.
    I argue that a phenotypic trait can be an adaptation to a particular environmental condition, as against others, only if the environmental condition and the phenotype interactively cause fitness. Models of interactive environmental causes of fitness generally require that environments be individuated by explicit representation rather than by measures of environmental quality, although the latter understanding of ‘environment’ is more prominent in the philosophy of biology. Hence, talk of adaptations to some but not other environmental conditions relies on conceptions of (...)
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  4. Selection, indeterminism, and evolutionary theory.Bruce Glymour - 2001 - 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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  5.  53
    Cross-Unit Causation and the Identity of Groups.Bruce Glymour - 2017 - Philosophy of Science 84 (4):717-736.
    In this article I explore some statistical difficulties confronting going conceptions of ‘group’ as understood in accounts of group selection. Most such theories require real groups but define the reality of groups in ways that make it impossible to test for their reality. There are alternatives, but they either require or invite a nominalism about groups that many theorists abjure.
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  6.  96
    Contrastive, non-probabilistic statistical explanations.Bruce Glymour - 1998 - 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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  7.  42
    Measuring the Biases that Matter: The Ethical and Causal Foundations for Measures of Fairness in Algorithms.Jonathan Herington & Bruce Glymour - 2019 - Proceedings of the Conference on Fairness, Accountability, and Transparency 2019:269-278.
    Measures of algorithmic bias can be roughly classified into four categories, distinguished by the conditional probabilistic dependencies to which they are sensitive. First, measures of "procedural bias" diagnose bias when the score returned by an algorithm is probabilistically dependent on a sensitive class variable (e.g. race or sex). Second, measures of "outcome bias" capture probabilistic dependence between class variables and the outcome for each subject (e.g. parole granted or loan denied). Third, measures of "behavior-relative error bias" capture probabilistic dependence between (...)
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  8.  79
    Correlated interaction and group selection.Bruce Glymour - 2008 - 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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  9.  65
    Population level causation and a unified theory of natural selection.Bruce Glymour - 1999 - 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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  10.  85
    Stable models and causal explanation in evolutionary biology.Bruce Glymour - 2008 - Philosophy of Science 75 (5):571-583.
    : Models that fail to satisfy the Markov condition are unstable in the sense that changes in state variable values may cause changes in the values of background variables, and these changes in background lead to predictive error. This sort of error arises exactly from the failure of non-Markovian models to track the set of causal relations upon which the values of response variables depend. The result has implications for discussions of the level of selection: under certain plausible conditions the (...)
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  11.  20
    7 In Defense of Explanatory Deductivism.Bruce Glymour - 2007 - In Joseph Keim Campbell, Michael O'Rourke & Harry Silverstein (eds.), Causation and Explanation. Bradford. pp. 4--133.
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  12. On the metaphysics of probabilistic causation: Lessons from social epidemiology.Bruce Glymour - 2003 - 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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  13.  45
    The Cartesian Theater stance.Bruce Glymour, Rick Grush, Valerie Gray Hardcastle, Brian Keeley, Joe Ramsey, Oron Shagrir & Ellen Watson - 1992 - Behavioral and Brain Sciences 15 (2):209-210.
  14. Quantum java: The upwards percolation of quantum indeterminacy.Bruce Glymour, Marcelo Sabatés & Andrew Wayne - 2001 - Philosophical Studies 103 (3):271 - 283.
  15.  53
    The wrong equations: a reply to Gildenhuys.Bruce Glymour - 2013 - 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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  16.  73
    Is pure r-selection really selection?Bruce Glymour - 1999 - 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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  17.  16
    11 Predicting Populations by Modeling Individuals.Bruce Glymour - 2011 - In Joseph Keim Campbell, Michael O'Rourke & Matthew H. Slater (eds.), Carving nature at its joints: natural kinds in metaphysics and science. Cambridge, MA, USA: MIT Press. pp. 231.
  18.  21
    10. Discussion: Problems for Natural Selection as a Mechanism Discussion: Problems for Natural Selection as a Mechanism (pp. 512-523). [REVIEW]Marc Lange, Raphael van Riel, Maximilian Schlosshauer, Gregory Wheeler, Zalán Gyenis, Miklós Rédei, John Byron Manchak, James Owen Weatherall, Bruce Glymour & Bradford Skow - 2011 - Philosophy of Science 78 (3):376-392.
    Focused correlation compares the degree of association within an evidence set to the degree of association in that evidence set given that some hypothesis is true. Wheeler and Scheines have shown that a difference in incremental confirmation of two evidence sets is robustly tracked by a difference in their focus correlation. In this essay, we generalize that tracking result by allowing for evidence having unequal relevance to the hypothesis. Our result is robust as well, and we retain conditions for bidirectional (...)
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