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Profile: Paul Humphreys (University of Virginia)
  1. Paul W. Humphreys (1997). How Properties Emerge. Philosophy of Science 64 (1):1-17.
    A framework for representing a specific kind of emergent property instance is given. A solution to a generalized version of the exclusion argument is then provided and it is shown that upwards and downwards causation is unproblematical for that kind of emergence. One real example of this kind of emergence is briefly described and the suggestion made that emergence may be more common than current opinions allow.
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  2. Paul W. Humphreys (1997). Emergence, Not Supervenience. Philosophy of Science Supplement 64 (4):337-45.
    I argue that supervenience is an inadequate device for representing relations between different levels of phenomena. I then provide six criteria that emergent phenomena seem to satisfy. Using examples drawn from macroscopic physics, I suggest that such emergent features may well be quite common in the physical realm.
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  3. Paul Humphreys (2004). Extending Ourselves Computational Science, Empiricism, and Scientific Method. Monograph Collection (Matt - Pseudo).
     
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  4.  94
    Paul Humphreys (2009). The Philosophical Novelty of Computer Simulation Methods. Synthese 169 (3):615 - 626.
    Reasons are given to justify the claim that computer simulations and computational science constitute a distinctively new set of scientific methods and that these methods introduce new issues in the philosophy of science. These issues are both epistemological and methodological in kind.
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  5.  14
    Paul Humphreys (2013). Data Analysis: Models or Techniques? [REVIEW] Foundations of Science 18 (3):579-581.
    In this commentary to Napoletani et al. (Found Sci 16:1–20, 2011), we argue that the approach the authors adopt suggests that neural nets are mathematical techniques rather than models of cognitive processing, that the general approach dates as far back as Ptolemy, and that applied mathematics is more than simply applying results from pure mathematics.
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  6.  85
    Paul Humphreys (1985). Why Propensities Cannot Be Probabilities. Philosophical Review 94 (4):557-570.
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  7.  9
    Paul Humphreys (1980). Inference, Method, and Decision. International Studies in Philosophy 12 (1):90-91.
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  8. Paul Humphreys (2008). Synchronic and Diachronic Emergence. Minds and Machines 18 (4):431-442.
    I discuss here a number of different kinds of diachronic emergence, noting that they differ in important ways from synchronic conceptions. I argue that Bedau’s weak emergence has an essentially historical aspect, in that there can be two indistinguishable states, one of which is weakly emergent, the other of which is not. As a consequence, weak emergence is about tokens, not types, of states. I conclude by examining the question of whether the concept of weak emergence is too weak and (...)
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  9. Paul Humphreys (2008). Computational and Conceptual Emergence. Philosophy of Science 75 (5):584-594.
    A twofold taxonomy for emergence is presented into which a variety of contemporary accounts of emergence fit. The first taxonomy consists of inferential, conceptual, and ontological emergence; the second of diachronic and synchronic emergence. The adequacy of weak emergence, a computational form of inferential emergence, is then examined and its relationship to conceptual emergence and ontological emergence is detailed. †To contact the author, please write to: Corcoran Department of Philosophy, 120 Cocke Hall, University of Virginia, Charlottesville, VA 22904‐4780; e‐mail: pwh2a@virginia.edu.
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  10.  33
    Paul Humphreys (1995). Computational Science and Scientific Method. Minds and Machines 5 (4):499-512.
    The process of constructing mathematical models is examined and a case made that the construction process is an integral part of the justification for the model. The role of heuristics in testing and modifying models is described and some consequences for scientific methodology are drawn out. Three different ways of constructing the same model are detailed to demonstrate the claims made here.
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  11.  38
    Paul Humphreys (2004). Some Considerations on Conditional Chances. British Journal for the Philosophy of Science 55 (4):667-680.
    Four interpretations of single-case conditional propensities are described and it is shown that for each a version of what has been called ‘Humphreys' Paradox’ remains, despite the clarifying work of Gillies, McCurdy and Miller. This entails that propensities cannot be a satisfactory interpretation of standard probability theory. Introduction The basic issue The formal paradox Values of conditional propensities Interpretations of propensities McCurdy's response Miller's response Other possibilities 8.1 Temporal evolution 8.2 Renormalization 8.3 Causal influence Propensities to generate frequencies Conclusion.
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  12.  35
    Paul W. Humphreys (1996). Aspects of Emergence. Philosophical Topics 24 (1):53-71.
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  13.  32
    Paul Humphreys (2002). Computational Models. Proceedings of the Philosophy of Science Association 2002 (3):S1-S11.
    A different way of thinking about how the sciences are organized is suggested by the use of cross‐disciplinary computational methods as the organizing unit of science, here called computational templates. The structure of computational models is articulated using the concepts of construction assumptions and correction sets. The existence of these features indicates that certain conventionalist views are incorrect, in particular it suggests that computational models come with an interpretation that cannot be removed as well as a prior justification. A form (...)
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  14.  21
    Paul Humphreys (1995). Abstract and Concrete. [REVIEW] Philosophy and Phenomenological Research 55 (1):157-161.
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  15.  11
    Paul Humphreys (2014). Explanation as Condition Satisfaction. Philosophy of Science 81 (5):1103-1116.
    It is shown that three common conditions for scientific explanations are violated by a widely used class of domain-independent explanations. These explanations can accommodate both complex and noncomplex systems and do not require the use of detailed models of system-specific processes for their effectiveness, although they are compatible with such model-based explanations. The approach also shows how a clean separation can be maintained between mathematical representations and empirical content.
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  16.  26
    Paul Humphreys (1990). Computer Simulations. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1990:497 - 506.
    This article provides a survey of some of the reasons why computational approaches have become a permanent addition to the set of scientific methods. The reasons for this require us to represent the relation between theories and their applications in a different way than do the traditional logical accounts extant in the philosophical literature. A working definition of computer simulations is provided and some properties of simulations are explored by considering an example from quantum chemistry.
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  17.  25
    Paul Humphreys (2009). Network Epistemology. Episteme 6 (2):221-229.
    A comparison is made between some epistemological issues arising in computer networks and standard features of social epistemology. A definition of knowledge for computational devices is provided and the topics of nonconceptual content and testimony are discussed.
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  18.  4
    Paul Humphreys & Jim Woodward (1993). The Chances of Explanation: Causal Explanation in the Social, Medical and Physical Sciences. Philosophy of Science 60 (4):659.
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  19.  11
    Paul Humphreys (1993). Greater Unification Equals Greater Understanding? Analysis 53 (3):183 - 188.
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  20.  34
    P. Kyle Stanford, Paul Humphreys, Katherine Hawley, James Ladyman & Don Ross (2010). Protecting Rainforest Realism. Metascience 19 (2):161-185.
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  21.  32
    David Freedman & Paul Humphreys (1999). Are There Algorithms That Discover Causal Structure? Synthese 121 (1-2):29-54.
    There have been many efforts to infer causation from association byusing statistical models. Algorithms for automating this processare a more recent innovation. In Humphreys and Freedman[(1996) British Journal for the Philosophy of Science 47, 113–123] we showed that one such approach, by Spirtes et al., was fatally flawed. Here we put our arguments in a broader context and reply to Korb and Wallace [(1997) British Journal for thePhilosophy of Science 48, 543–553] and to Spirtes et al.[(1997) British Journal for the (...)
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  22.  46
    Paul Humphreys & David Freedman (1996). The Grand Leap. [REVIEW] British Journal for the Philosophy of Science 47 (1):113-123.
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  23.  11
    Paul Humphreys (2011). Computational Science and its Effects. In M. Carrier & A. Nordmann (eds.), Science in the Context of Application. Springer 131--142.
  24.  32
    Paul Humphreys (2004). Some Thoughts on Wesley Salmon's Contributions to the Philosophy of Probability. Philosophy of Science 71 (5):942-949.
    Wesley Salmon provided three classic criteria of adequacy for satisfactory interpretations of probability. A fourth criterion is suggested here. A distinction is drawn between frequency‐driven probability models and theory‐driven probability models and it is argued that single case accounts of chance are superior to frequency accounts at least for the latter. Finally it is suggested that theories of chance should be required only to be contingently true, a position which is a natural extension of Salmon's ontic account of probabilistic causality (...)
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  25.  25
    Paul Humphreys (1995). Computational Empiricism. Foundations of Science 1 (1):119-130.
    I argue here for a number of ways that modern computational science requires a change in the way we represent the relationship between theory and applications. It requires a switch away from logical reconstruction of theories in order to take surface mathematical syntax seriously. In addition, syntactically different versions of the same theory have important differences for applications, and this shows that the semantic account of theories is inappropriate for some purposes. I also argue against formalist approaches in the philosophy (...)
  26.  11
    Paul Humphreys (2006). Self‐Assembling Systems. Philosophy of Science 73 (5):595-604.
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  27.  68
    Paul Humphreys, The Truth of False Idealizations in Modeling.
    Modeling involves the use of false idealizations, yet there is typically a belief or hope that modeling somehow manages to deliver true information about the world. The paper discusses one possible way of reconciling truth and falsehood in modeling. The key trick is to relocate truth claims by reinterpreting an apparently false idealizing assumption in order to make clear what possibly true assertion is intended when using it. These include interpretations in terms of negligibility, applicability, tractability, early-step, and more. Elaborations (...)
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  28.  14
    Paul Humphreys (2006). Invariance, Explanation, and Understanding. Metascience 15 (1):39-66.
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  29.  30
    Paul Humphreys (1986). Causation in the Social Sciences: An Overview. Synthese 68 (1):1 - 12.
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  30.  6
    Paul Humphreys (1995). Review: Abstract and Concrete. [REVIEW] Philosophy and Phenomenological Research 55 (1):157 - 161.
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  31.  13
    Paul Humphreys (2013). Speculative Ontology. In Don Ross, James Ladyman & Harold Kincaid (eds.), Scientific Metaphysics. Oxford University Press 51.
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  32.  1
    Paul Humphreys (2004). Scientific Knowledge. In M. Sintonen, J. Wolenski & I. Niiniluoto (eds.), Handbook of Epistemology. Kluwer 549--569.
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  33. Paul Humphreys & James Fetzer (eds.) (1998). The New Theory of Reference. Kluwer.
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  34.  15
    Paul Humphreys (1996). Understanding in the Not-So-Special Sciences. Southern Journal of Philosophy 34 (S1):99-114.
  35.  15
    Paul W. Humphreys (1978). Is 'Physical Randomness' Just Indeterminism in Disguise? In Peter D. Asquith & Ian Hacking (eds.), PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association. University of Chicago Press 98--113.
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  36.  2
    Paul Humphreys (1992). Chapter Two. Probabilistic Causation. In The Chances of Explanation: Causal Explanation in the Social, Medical, and Physical Sciences. Princeton University Press 22-60.
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  37.  1
    Paul Humphreys (1992). Chapter Three. Cause and Chance. In The Chances of Explanation: Causal Explanation in the Social, Medical, and Physical Sciences. Princeton University Press 61-97.
  38.  8
    Paul Humphreys (2003). Mathematical Modeling in the Social Sciences. In Stephen P. Turner & Paul Andrew Roth (eds.), The Blackwell Guide to the Philosophy of the Social Sciences. Blackwell Pub. 166--184.
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  39.  36
    Philippe Huneman & Paul Humphreys (2008). Dynamical Emergence and Computation: An Introduction. [REVIEW] Minds and Machines 18 (4):425-430.
  40.  21
    Paul W. Humphreys (1984). Quantitative Probabilistic Causality and Structural Scientific Realism. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1984:329 - 342.
    The elements of structural models used in the social sciences are built up from four fundamental assumptions. It is then shown how the central idea of qualitative probabilistic causality follows as a special case of this covariational account. The relationships of both instrumentalism and common cause arguments for scientific realism to these structures is demonstrated. It is concluded that a predictivist argument against a thoroughgoing instrumentalism can be given, and hence why the difference between experimental and non-experimental contexts is important (...)
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  41.  10
    Paul W. Humphreys (1977). Randomness, Independence, and Hypotheses. Synthese 36 (4):415 - 426.
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  42.  13
    Paul Humphreys (2000). Causality and Explanation. Journal of Philosophy 97 (9):523-527.
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  43.  4
    Paul Humphreys & Ryszard Wójcicki (1995). Publisher's Note. Foundations of Science 1 (3):1-1.
    As Chinese Studies in Philosophy enters its twenty-fifth year, we wish to thank the editor since its inception, Professor Cheng Chung-ying of the University of Hawaii, for his many years of service, and to welcome with this issue our new editor, Professor Michael Schoenhals of Stockholm University.
  44.  14
    Paul Humphreys (1980). Probabilistic Causality and Multiple Causation. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1980:25 - 37.
    It is argued in this paper that although much attention has been paid to causal chains and common causes within the literature on probabilistic causality, a primary virtue of that approach is its ability to deal with cases of multiple causation. In doing so some ways are indicated in which contemporary sine qua non analyses of causation are too narrow (and ways in which probabilistic causality is not) and an argument by Reichenbach designed to provide a basis for the asymmetry (...)
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  45.  15
    Paul Humphreys (2010). Conceptual Sea Changes. Spontaneous Generations 4 (1):111-115.
    The reshaping of much scientific research around computational methods is not just a technological curiosity. It results in a significant reshaping of conceptual and representational resources within science in ways with which many traditional philosophical positions are ill-equipped to cope. Some illustrations of this are provided and a consequence for the roles of science and the arts is noted.
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  46.  3
    Paul Humphreys (1999). Observation and Reliable Detection. In Maria Luisa Dalla Chiara (ed.), Language, Quantum, Music. 19--24.
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  47.  3
    Paul Humphreys (2005). Teorías de causación y explicación:¿ necesariamente verdaderas o dominio-específicas? Enrahonar 37:19-33.
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  48.  9
    Paul Humphreys & David Freedman (1996). Review: The Grand Leap. [REVIEW] British Journal for the Philosophy of Science 47 (1):113 - 123.
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  49.  12
    Paul Humphreys (1990). A Conjecture Concerning the Ranking of the Sciences. Topoi 9 (2):157-160.
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  50.  12
    Paul Humphreys (1981). Aleatory Explanations. Synthese 48 (2):225 - 232.
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