Search results for 'Frederick Eberhardt Bruce Glymour' (try it on Scholar)

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  1.  82
    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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  2.  1
    Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph Ramsey & Richard Scheines (2010). Actual Causation: A Stone Soup Essay. Synthese 175 (2):169-192.
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  3.  10
    Frederick Eberhardt & Clark Glymour (2004). Hans Reichenbach's Probability Logic. In Dov M. Gabbay, John Woods & Akihiro Kanamori (eds.), Handbook of the History of Logic. Elsevier 10--357.
  4.  8
    Frederick Eberhardt, Clark Glymour & Richard Scheines, N-1 Experiments Suffice to Determine the Causal Relations Among N Variables.
    By combining experimental interventions with search procedures for graphical causal models we show that under familiar assumptions, with perfect data, N - 1 experiments suffice to determine the causal relations among N > 2 variables when each experiment randomizes at most one variable. We show the same bound holds for adaptive learners, but does not hold for N > 4 when each experiment can simultaneously randomize more than one variable. This bound provides a type of ideal for the measure of (...)
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  5.  23
    Flavia Padovani (2011). Hans Reichenbach.The Concept of Probability in the Mathematical Representation of Reality. Trans. And Ed. Frederick Eberhardt and Clark Glymour. Chicago: Open Court, 2008. Pp. Xi+154. $34.97. [REVIEW] Hopos: The Journal of the International Society for the History of Philosophy of Science 1 (2):344-347.
    Hans Reichenbach has been not only one of the founding fathers of logical empiricism but also one of the most prominent figures in the philosophy of science of the past century. While some of his ideas continue to be of interest in current philosophical programs, an important part of his early work has been neglected, and some of it has been unavailable to English readers. Among Reichenbach’s overlooked (and untranslated) early works, his doctoral thesis of 1915, The Concept of Probability (...)
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  6.  24
    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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  7.  12
    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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  8.  38
    Frederick Eberhardt & Richard Scheines (2007). Interventions and Causal Inference. Philosophy of Science 74 (5):981-995.
    The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of ‘hard' and ‘soft' interventions and discuss what they can contribute to causal discovery. We also describe how the choice of the optimal intervention(s) depends heavily on the (...)
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  9.  9
    Frederick Eberhardt (forthcoming). Green and Grue Causal Variables. Synthese:1-18.
    The causal Bayes net framework specifies a set of axioms for causal discovery. This article explores the set of causal variables that function as relata in these axioms. Spirtes showed how a causal system can be equivalently described by two different sets of variables that stand in a non-trivial translation-relation to each other, suggesting that there is no “correct” set of causal variables. I extend Spirtes’ result to the general framework of linear structural equation models and then explore to what (...)
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  10.  46
    Frederick Eberhardt & David Danks (2011). Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models. [REVIEW] Minds and Machines 21 (3):389-410.
    Bayesian models of human learning are becoming increasingly popular in cognitive science. We argue that their purported confirmation largely relies on a methodology that depends on premises that are inconsistent with the claim that people are Bayesian about learning and inference. Bayesian models in cognitive science derive their appeal from their normative claim that the modeled inference is in some sense rational. Standard accounts of the rationality of Bayesian inference imply predictions that an agent selects the option that maximizes the (...)
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  11.  86
    Bruce Glymour (2011). Modeling Environments: Interactive Causation and Adaptations to Environmental Conditions. Philosophy of Science 78 (3):448-471.
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  12.  42
    Frederick Eberhardt (2011). Reliability Via Synthetic a Priori: Reichenbach's Doctoral Thesis on Probability. Synthese 181 (1):125 - 136.
    Hans Reichenbach is well known for his limiting frequency view of probability, with his most thorough account given in The Theory of Probability in 1935/1949. Perhaps less known are Reichenbach's early views on probability and its epistemology. In his doctoral thesis from 1915, Reichenbach espouses a Kantian view of probability, where the convergence limit of an empirical frequency distribution is guaranteed to exist thanks to the synthetic a priori principle of lawful distribution. Reichenbach claims to have given a (...)
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  13.  12
    Frederick Eberhardt (2013). Direct Causes and the Trouble with Soft Interventions. Erkenntnis 79 (4):1-23.
    An interventionist account of causation characterizes causal relations in terms of changes resulting from particular interventions. I provide a new example of a causal relation for which there does not exist an intervention satisfying the common interventionist standard. I consider adaptations that would save this standard and describe their implications for an interventionist account of causation. No adaptation preserves all the aspects that make the interventionist account appealing. Part of the fallout is a clearer account of the difficulties in characterizing (...)
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  14.  37
    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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  15. Michael Friedman, Robert DiSalle, J. D. Trout, Shaun Nichols, Maralee Harrell, Clark Glymour, Carl G. Wagner, Kent W. Staley, Jesús P. Zamora Bonilla & Frederick M. Kronz (2002). 10. Interpreting Quantum Field Theory Interpreting Quantum Field Theory (Pp. 348-378). Philosophy of Science 69 (2).
     
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  16.  15
    Frederick Eberhardt (2013). Experimental Indistinguishability of Causal Structures. Philosophy of Science 80 (5):684-696.
    Using a variety of different results from the literature, I show how causal discovery with experiments is limited unless substantive assumptions about the underlying causal structure are made. These results undermine the view that experiments, such as randomized controlled trials, can independently provide a gold standard for causal discovery. Moreover, I present a concrete example in which causal underdetermination persists despite exhaustive experimentation and argue that such cases undermine the appeal of an interventionist account of causation as its dependence on (...)
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  17.  24
    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 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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  18.  27
    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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  19.  47
    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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  20.  13
    Marshall Abrams, Frederick Eberhardt & Michael Strevens (2015). Equidynamics and Reliable Reasoning About Frequencies. Metascience 24 (2):173-188.
    A symposium on Michael Strevens' book "Tychomancy", concerning the psychological roots and historical significance of physical intuition about probability in physics, biology, and elsewhere.
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  21. Frederick Eberhardt (2011). Reliability Via Synthetic a Priori: Reichenbach’s Doctoral Thesis on Probability. Synthese 181 (1):125-136.
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  22.  80
    Frederick Eberhardt (2009). Introduction to the Epistemology of Causation. Philosophy Compass 4 (6):913-925.
    This survey presents some of the main principles involved in discovering causal relations. They belong to a large array of possible assumptions and conditions about causal relations, whose various combinations limit the possibilities of acquiring causal knowledge in different ways. How much and in what detail the causal structure can be discovered from what kinds of data depends on the particular set of assumptions one is able to make. The assumptions considered here provide a starting point to explore further the (...)
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  23.  22
    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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  24.  21
    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 (...)
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  25.  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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  26.  18
    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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  27.  14
    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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  28.  3
    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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  29.  45
    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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  30.  4
    Frederick Eberhardt, Direct Cause.
    An interventionist account of causation characterizes causal relations in terms of changes resulting from particular interventions. We provide an example of a causal relation for which there does not exist an intervention satisfying the common interventionist standard. We consider adaptations that would save this standard and describe their implications for an interventionist account of causation. No adaptation preserves all the aspects that make the interventionist account appealing.
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  31.  36
    David Danks & Frederick Eberhardt (2009). Explaining Norms and Norms Explained. Behavioral and Brain Sciences 32 (1):86-87.
    Oaksford & Chater (O&C) aim to provide teleological explanations of behavior by giving an appropriate normative standard: Bayesian inference. We argue that there is no uncontroversial independent justification for the normativity of Bayesian inference, and that O&C fail to satisfy a necessary condition for teleological explanations: demonstration that the normative prescription played a causal role in the behavior's existence.
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  32.  1
    Gregory F. Cooper, Constantin F. Aliferis, Richard Ambrosino, John Aronis, Bruce G. Buchanon, Richard Caruana, Michael J. Fine, Clark Glymour, Geoffrey Gordon, Barbara H. Hanusa, Janine E. Janosky, Christopher Meek, Tom Mitchell, Thomas Richardson & Peter Spirtes, An Evaluation of Machine-Learning Methods for Predicting Pneumonia Mortality.
    This paper describes the application of eight statistical and machine-learning methods to derive computer models for predicting mortality of hospital patients with pneumonia from their findings at initial presentation. The eight models were each constructed based on 9847 patient cases and they were each evaluated on 4352 additional cases. The primary evaluation metric was the error in predicted survival as a function of the fraction of patients predicted to survive. This metric is useful in assessing a model’s potential to assist (...)
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  33.  6
    David Danks & Frederick Eberhardt (2011). Keeping Bayesian Models Rational: The Need for an Account of Algorithmic Rationality. Behavioral and Brain Sciences 34 (4):197-197.
    We argue that the authors’ call to integrate Bayesian models more strongly with algorithmic- and implementational-level models must go hand in hand with a call for a fully developed account of algorithmic rationality. Without such an account, the integration of levels would come at the expense of the explanatory benefit that rational models provide.
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  34.  18
    Frederick Eberhardt (2008). A Sufficient Condition for Pooling Data. Synthese 163 (3):433 - 442.
    We consider the problems arising from using sequences of experiments to discover the causal structure among a set of variables, none of whom are known ahead of time to be an “outcome”. In particular, we present various approaches to resolve conflicts in the experimental results arising from sampling variability in the experiments. We provide a sufficient condition that allows for pooling of data from experiments with different joint distributions over the variables. Satisfaction of the condition allows for an independence test (...)
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  35.  15
    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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  36.  2
    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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  37.  1
    Richard Scheines, Frederick Eberhardt & Patrik O. Hoyer, Combining Experiments to Discover Linear Cyclic Models with Latent Variables.
    We present an algorithm to infer causal relations between a set of measured variables on the basis of experiments on these variables. The algorithm assumes that the causal relations are linear, but is otherwise completely general: It provides consistent estimates when the true causal structure contains feedback loops and latent variables, while the experiments can involve surgical or `soft' interventions on one or multiple variables at a time. The algorithm is `online' in the sense that it combines the results from (...)
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  38.  4
    David Danks & Frederick Eberhardt, Conceptual Problems in Statistics, Testing and Experimentation.
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  39. 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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  40.  33
    David Danks Clark Glymour, Frederick Eberhardt Bruce Glymour, Richard Scheines Joseph Ramsey, Choh Man Teng Peter Spirtes & Jiji Zhang (forthcoming). Actual Causation: A Stone Soup Essay. Synthese.
    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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  41.  6
    Clark Glymour (2016). Clark Glymour’s Responses to the Contributions to the Synthese Special Issue “Causation, Probability, and Truth: The Philosophy of Clark Glymour”. Synthese 193 (4):1251-1285.
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  42.  2
    Ivan L. Little & Bruce Waters (1962). Thomas Frederick Storer 1918-1961. Proceedings and Addresses of the American Philosophical Association 36:121 -.
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  43. Frederick J. Crosson (1993). Rejoinder to Bruce Marshall. The Thomist 57 (2):299-303.
     
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  44. Bruce Williams (2004). Charles Frederick Carter, 1919-2002. Proceedings of the British Academy 124:38-47.
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  45.  58
    Kevin J. S. Zollman (2007). The Communication Structure of Epistemic Communities. Philosophy of Science 74 (5):574-587.
    Increasingly, epistemologists are becoming interested in social structures and their effect on epistemic enterprises, but little attention has been paid to the proper distribution of experimental results among scientists. This paper will analyze a model first suggested by two economists, which nicely captures one type of learning situation faced by scientists. The results of a computer simulation study of this model provide two interesting conclusions. First, in some contexts, a community of scientists is, as a whole, more reliable when its (...)
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  46.  39
    Robert D. Rupert (1999). The Best Test Theory of Extension: First Principle(S). Mind and Language 14 (3):321–355.
    This paper presents the leading idea of my doctoral dissertation and thus has been shaped by the reactions of all the members of my thesis committee: Charles Chastain, Walter Edelberg, W. Kent Wilson, Dorothy Grover, and Charles Marks. I am especially grateful for the help of Professors Chastain, Edelberg, and Wilson; each worked closely with me at one stage or another in the development of the ideas contained in the present work. Shorter versions of this paper were presented at the (...)
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  47.  29
    Peter Gildenhuys (2011). Righteous Modeling: The Competence of Classical Population Genetics. [REVIEW] Biology and Philosophy 26 (6):813-835.
    In a recent article, “Wayward Modeling: Population Genetics and Natural Selection,” Bruce Glymour claims that population genetics is burdened by serious predictive and explanatory inadequacies and that the theory itself is to blame. Because Glymour overlooks a variety of formal modeling techniques in population genetics, his arguments do not quite undermine a major scientific theory. However, his arguments are extremely valuable as they provide definitive proof that those who would deploy classical population genetics over natural systems must (...)
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  48.  80
    Jochen Apel (2011). On the Meaning and the Epistemological Relevance of the Notion of a Scientific Phenomenon. Synthese 182 (1):23-38.
    In this paper I offer an appraisal of James Bogen and James Woodward’s distinction between data and phenomena which pursues two objectives. First, I aim to clarify the notion of a scientific phenomenon. Such a clarification is required because despite its intuitive plausibility it is not exactly clear how Bogen and Woodward’s distinction has to be understood. I reject one common interpretation of the distinction, endorsed for example by James McAllister and Bruce Glymour, which identifies phenomena with patterns (...)
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  49. Glymour Bruce (1992). The Catesian Theatre Stance. Behavioral and Brain Sciences 15:209-211.
     
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  50.  57
    Thomas William Barrett & Hans Halvorson (forthcoming). Glymour and Quine on Theoretical Equivalence. Journal of Philosophical Logic:1-17.
    Glymour and Quine propose two different formal criteria for theoretical equivalence. In this paper we examine the relationships between these criteria.
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