Search results for 'David Danks Clark Glymour' (try it on Scholar)

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  1.  10
    Tianjiao Chu, David Danks & Clark Glymour, Data Driven Methods for Nonlinear Granger Causality: Climate Teleconnection Mechanisms.
    Tianjaou Chu, David Danks, and Clark Glymour. Data Driven Methods for Nonlinear Granger Causality: Climate Teleconnection Mechanisms.
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  2.  37
    Alison Gopnik, Clark Glymour, David M. Sobel, Laura Schulz, Tamar Kushnir & David Danks, A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.
    We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...)
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  3.  1
    David Danks, Stephen Fancsali, Clark Glymour & Richard Scheines (2010). Comorbid Science? Behavioral and Brain Sciences 33 (2-3):153 - 155.
    We agree with Cramer et al.'s goal of the discovery of causal relationships, but we argue that the authors' characterization of latent variable models (as deployed for such purposes) overlooks a wealth of extant possibilities. We provide a preliminary analysis of their data, using existing algorithms for causal inference and for the specification of latent variable models.
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  4.  87
    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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  5.  1
    Alison Gopnik, Clark Glymour, David M. Sobel, Laura E. Schulz, Tamar Kushnir & David Danks (2004). A Theory of Causal Learning in Children: Causal Maps and Bayes Nets. Psychological Review 111 (1):3-32.
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  6.  33
    Clark Glymour & David Danks (2007). Reasons as Causes in Bayesian Epistemology. Journal of Philosophy 104 (9):464-474.
    In everyday matters, as well as in law, we allow that someone’s reasons can be causes of her actions, and often are. That correct reasoning accords with Bayesian principles is now so widely held in philosophy, psychology, computer science and elsewhere that the contrary is beginning to seem obtuse, or at best quaint. And that rational agents should learn about the world from energies striking sensory inputs nerves in people—seems beyond question. Even rats seem to recognize the difference between correlation (...)
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  7.  27
    David Danks & Clark Glymour, Linearity Properties of Bayes Nets with Binary Variables.
    It is “well known” that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved cause jointly influences several observed variables; (2) the technique of “instrumental variables” sometimes permits an estimation of the influence of one variable on another even when the association between the variables may be confounded by unobserved common causes; (3) the association (or conditional probability distribution of one variable given another) of two variables connected by a (...)
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  8.  20
    David Danks, Clark Glymour & Peter Spirtes (2003). The Computational and Experimental Complexity of Gene Perturbations for Regulatory Network Search. In W. H. Hsu, R. Joehanes & C. D. Page (eds.), Proceedings of IJCAI-2003 workshop on learning graphical models for computational genomics.
    Various algorithms have been proposed for learning (partial) genetic regulatory networks through systematic measurements of differential expression in wild type versus strains in which expression of specific genes has been suppressed or enhanced, as well as for determining the most informative next experiment in a sequence. While the behavior of these algorithms has been investigated for toy examples, the full computational complexity of the problem has not received sufficient attention. We show that finding the true regulatory network requires (in the (...)
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  9.  14
    Frank Wimberly, David Danks, Clark Glymour & Tianjiao Chu, Problems for Structure Learning: Aggregation and Computational Complexity.
  10.  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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  11. Clark Glymour & David Danks (2007). Reasons as Causes in Bayesian Epistemology. Journal of Philosophy 104 (9):464-474.
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  12.  8
    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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  13.  2
    Clark Glymour, Alison Gopnik, David M. Sobel & Laura E. Schulz, Causal Learning Mechanisms in Very Young Children: Two-, Three-, and Four-Year-Olds Infer Causal Relations From Patterns of Variation and Covariation.
  14. Alison Gopnik, Clark Glymour, David M. Sobel & Laura E. Schultz, Causal Learning in Children: Causal Maps and Bayes Nets.
    We outline a cognitive and computational account of causal learning in children. We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent representation of the causal relations among events. This kind of knowledge can be perspicuously represented by the formalism of directed graphical causal models, or “Bayes nets”. Human causal learning and inference may involve computations similar to those for learnig causal Bayes nets and for predicting with (...)
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  15.  4
    Clark Glymour, David Madigan, Daniel Pregibon & Padhraic Smyth, Statistical Inference and Data Mining.
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  16. Marc Lange, Peter Vickers, John Michael, Miles MacLeod, Alexander R. Pruss, David John Baker, Clark Glymour & Simon Fitzpatrick (2013). 1. Really Statistical Explanations and Genetic Drift Really Statistical Explanations and Genetic Drift (Pp. 169-188). Philosophy of Science 80 (2).
     
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  17.  2
    Ned Block, Richard Boyd, Robert Butts, Ronald Giere, Clark Glymour, Adolf Grunbaum, Erwin Hiebert, Colin Howson, David Hull & Paul Humphreys (1990). Consensus Institute Staff. In C. Wade Savage (ed.), Scientific Theories. University of Minnesota Press 417.
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  18.  2
    Nicoleta Serban, Larry Wasserman, David Peters, Peter Spirtes, Robert O'Doherty, Daniel Handley, Richard Scheines & Clark Glymour, Analysis of Microarray Data for Treated Fat Cells.
    DNA microarrays are perfectly suited for comparing gene expression in different populations of cells. An important application of microarray techniques is identifying genes which are activated by a particular drug of interest. This process will allow biologists to identify therapies targeted to particular diseases, and, eventually, to gain more knowledge about the biological processes in organisms. Such an application is described in this paper. It is focused on diabetes and obesity, which is a genetically heterogeneous disease, meaning that multiple defective (...)
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  19.  16
    Peter Spirtes & Clark Glymour (1982). Space-Time and Synonymy. Philosophy of Science 49 (3):463-477.
    In "The Epistemology of Geometry" Glymour proposed a necessary structural condition for the synonymy of two space-time theories. David Zaret has recently challenged this proposal, by arguing that Newtonian gravitational theory with a flat, non-dynamic connection (FNGT) is intuitively synonymous with versions of the theory using a curved dynamical connection (CNGT), even though these two theories fail to satisfy Glymour's proposed necessary condition for synonymy. Zaret allowed that if FNGT and CNGT were not equally well (bootstrap) tested (...)
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  20.  11
    Clark Glymour, Richard Scheines, Peter Spirtes & Kevin T. Kelly, Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.
    Clark Glymour, Richard Scheines, Peter Spirtes and Kevin Kelly. Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.
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  21.  44
    Kevin T. Kelly & Clark Glymour, Why Bayesian Confirmation Does Not Capture the Logic of Scientific Justification.
    Kevin T. Kelly and Clark Glymour. Why Bayesian Confirmation Does Not Capture the Logic of Scientific Justification.
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  22.  32
    Kevin T. Kelly & Clark Glymour (1992). Inductive Inference From Theory Laden Data. Journal of Philosophical Logic 21 (4):391 - 444.
    Kevin T. Kelly and Clark Glymour. Inductive Inference from Theory-Laden Data.
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  23.  10
    Clark Glymour & Kevin T. Kelly (1992). Thoroughly Modern Meno. In Inference, Explanation, and Other Frustrations: Essays in the Philosophy of Science. University of California Press: Berkeley 3--22.
    Clark Glymour and Kevin T. Kelly. Thoroughly Modern Meno.
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  24.  10
    Kevin T. Kelly, Cory Juhl & Clark Glymour, Reliability, Realism, and Relativism.
    Kevin T. Kelly, Cory Juhl and Clark Glymour. Reliability, Realism, and Relativism.
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  25.  8
    Peter Spirtes, Clark Glymour & Rcihard Scheines, Causality From Probability.
    Peter Spirtes, Clark Glymour and Richard Scheines. Causality From Probability.
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  26.  4
    Peter Spirtes, Clark Glymour, Richard Scheines, Christopher Meek, S. Fineberg & E. Slate, Prediction and Experimental Design with Graphical Causal Models.
    Peter Spirtes, Clark Glymour, Richard Scheines, Christopher Meek, S. Fineberg, E. Slate. Prediction and Experimental Design with Graphical Causal Models.
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  27.  10
    David Danks, Psychological Theories of Categorizations as Probabilistic Models.
    David Danks. Psychological Theories of Categorizations as Probabilistic Models.
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  28.  9
    Clark Glymour, Richard Scheines, Peter Spirtes & Christopher Meek, Regression and Causation.
    Clark Glymour, Richard Scheines, Peter Spirtes, and Christopher Meek. Regression and Causation.
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  29.  2
    Clark Glymour, Psychology as Physics.
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  30.  1
    Peter Spirtes & Clark N. Glymour, Causal Structure Among Measured Variables Preserved with Unmeasured Variables.
    Peter Spirtes and Clark Glymour. Casual Structure Among Measured Variables Preserved with Unmeasured Variables.
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  31.  1
    Peter Spirtes, Richard Scheines & Clark Glymour, Simulated Studies of the Reliability of Computer-Aided Model Specification Using the TETRAD, EQS and LISREL Programs.
    Peter Spirtes, Richard Scheines and Clark Glymour. Simulated Studies of the Reliability of Computer-Aided Model Specification Using the TETRAD, EQS and LISREL Programs.
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  32.  30
    Clark Glymour (1999). Rabbit Hunting. Synthese 121 (1-2):55-78.
    Twenty years ago, Nancy Cartwright wrote a perceptive essay in which she clearly distinguished causal relations from associations, introduced philosophers to Simpson’s paradox, articulated the difficulties for reductive probabilistic analyses of causation that flow from these observations, and connected causal relations with strategies of action (Cartwright 1979). Five years later, without appreciating her essay, I and my (then) students began to develop formal representations of causal and probabilistic relations, which, subsequently informed by the work of computer scientists and statisticians, led (...)
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  33. David Rose & David Danks (2013). In Defense of a Broad Conception of Experimental Philosophy. Metaphilosophy 44 (4):512-532.
    Experimental philosophy is often presented as a new movement that avoids many of the difficulties that face traditional philosophy. This article distinguishes two views of experimental philosophy: a narrow view in which philosophers conduct empirical investigations of intuitions, and a broad view which says that experimental philosophy is just the colocation in the same body of (i) philosophical naturalism and (ii) the actual practice of cognitive science. These two positions are rarely clearly distinguished in the literature about experimental philosophy, both (...)
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  34. David Rose & David Danks (2012). Causation: Empirical Trends and Future Directions. Philosophy Compass 7 (9):643-653.
    Empirical research has recently emerged as a key method for understanding the nature of causation, and our concept of causation. One thread of research aims to test intuitions about the nature of causation in a variety of classic cases. These experiments have principally been used to try to resolve certain debates within analytic philosophy, most notably that between proponents of transference and dependence views of causation. The other major thread of empirical research on our concept of causation has investigated the (...)
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  35.  6
    David Danks & Joseph H. Danks, Beyond Machines: Humans in Cyber Operations, Espionage, and Conflict.
    It is the height of banality to observe that people, not bullets, fight kinetic wars. The machinery of kinetic warfare is obviously relevant to the conduct of each particular act of warfare, but the reasons for, and meanings of, those acts depend critically on the fact that they are done by humans. Any attempt to understand warfare—its causes, strategies, legitimacy, dynamics, and resolutions—must incorporate humans as an intrinsic part, both descriptively and normatively. Humans from general staff to “boots on the (...)
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  36.  12
    David Danks & Joseph H. Danks (2013). The Moral Permissibility of Automated Responses During Cyberwarfare. Journal of Military Ethics 12 (1):18-33.
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  37.  36
    Clark Glymour (1980). Theory and Evidence. Princeton University Press.
  38. Peter Spirtes, Clark Glymour & Richard Scheines (1996). Causation, Prediction, and Search. British Journal for the Philosophy of Science 47 (1):113-123.
     
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  39. Clark Glymour (1971). Determinism, Ignorance, and Quantum Mechanics. Journal of Philosophy 68 (21):744-751.
    is every bit as intelligible and philosophically respectable as many other doctrines currently in favor, e.g., the doctrine that mental events are identical with brain events; the attempt to give a linguistic construal of this latter doctrine meets many of the same sorts of difficulties encountered above (see Hempel, op. cit.). Secondly, I think that evidence for universal determinism may not, as a matter of fact, be so hard to come by as one might imagine. It is a striking fact (...)
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  40. David Danks, David Rose & Edouard Machery (2013). Demoralizing Causation. Philosophical Studies (2):1-27.
    There have recently been a number of strong claims that normative considerations, broadly construed, influence many philosophically important folk concepts and perhaps are even a constitutive component of various cognitive processes. Many such claims have been made about the influence of such factors on our folk notion of causation. In this paper, we argue that the strong claims found in the recent literature on causal cognition are overstated, as they are based on one narrow type of data about a particular (...)
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  41. Clark Glymour & Frank Wimberly (2007). 3 Actual Causes and Thought Experiments. In J. K. Campbell, M. O'Rourke & H. S. Silverstein (eds.), Causation and Explanation. MIT Press 4--43.
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  42.  24
    David Danks (2015). Goal-Dependence in Ontology. Synthese 192 (11):3601-3616.
    Our best sciences are frequently held to be one way, perhaps the optimal way, to learn about the world’s higher-level ontology and structure. I first argue that which scientific theory is “best” depends in part on our goals or purposes. As a result, it is theoretically possible to have two scientific theories of the same domain, where each theory is best for some goal, but where the two theories posit incompatible ontologies. That is, it is possible for us to have (...)
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  43.  53
    Clark Glymour (2013). Theoretical Equivalence and the Semantic View of Theories. Philosophy of Science 80 (2):286-297.
  44.  54
    Clark Glymour (2015). Probability and the Explanatory Virtues. British Journal for the Philosophy of Science 66 (3):591-604.
    Recent literature in philosophy of science has addressed purported notions of explanatory virtues—‘explanatory power’, ‘unification’, and ‘coherence’. In each case, a probabilistic relation between a theory and data is said to measure the power of an explanation, or degree of unification, or degree of coherence. This essay argues that the measures do not capture cases that are paradigms of scientific explanation, that the available psychological evidence indicates that the measures do not capture judgements of explanatory power, and, finally, that the (...)
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  45. Conor Mayo-Wilson, Kevin J. S. Zollman & David Danks (2011). The Independence Thesis: When Individual and Social Epistemology Diverge. Philosophy of Science 78 (4):653-677.
    In the latter half of the twentieth century, philosophers of science have argued (implicitly and explicitly) that epistemically rational individuals might compose epistemically irrational groups and that, conversely, epistemically rational groups might be composed of epistemically irrational individuals. We call the conjunction of these two claims the Independence Thesis, as they together imply that methodological prescriptions for scientific communities and those for individual scientists might be logically independent of one another. We develop a formal model of scientific inquiry, define four (...)
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  46. Christopher Meek & Clark Glymour (1994). Conditioning and Intervening. British Journal for the Philosophy of Science 45 (4):1001-1021.
    We consider the dispute between causal decision theorists and evidential decision theorists over Newcomb-like problems. We introduce a framework relating causation and directed graphs developed by Spirtes et al. (1993) and evaluate several arguments in this context. We argue that much of the debate between the two camps is misplaced; the disputes turn on the distinction between conditioning on an event E as against conditioning on an event I which is an action to bring about E. We give the essential (...)
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  47. Peter Spirtes, Clark Glymour, Scheines N. & Richard (2000). Causation, Prediction, and Search. MIT Press: Cambridge.
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  48. Clark Glymour (2002). A Semantics and Methodology for Ceteris Paribus Hypotheses. Erkenntnis 57 (3):395-405.
    Taking seriously the arguments of Earman, Roberts and Smith that ceteris paribus laws have no semantics and cannot be tested, I suggest that ceteris paribus claims have a kind of formal pragmatics, and that at least some of them can be verified or refuted in the limit.
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  49. Conor Mayo-Wilson, Kevin Zollman & David Danks (2013). Wisdom of the Crowds Vs. Groupthink: Learning in Groups and in Isolation. International Journal of Game Theory 42 (3):695-723.
    We evaluate the asymptotic performance of boundedly-rational strategies in multi-armed bandit problems, where performance is measured in terms of the tendency (in the limit) to play optimal actions in either (i) isolation or (ii) networks of other learners. We show that, for many strategies commonly employed in economics, psychology, and machine learning, performance in isolation and performance in networks are essentially unrelated. Our results suggest that the appropriateness of various, common boundedly-rational strategies depends crucially upon the social context (if any) (...)
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  50.  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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