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James H. Fetzer [109]James Henry Fetzer [1]
  1. Artificial Intelligence: Its Scope and Limits.James H. Fetzer - 1990 - Kluwer Academic Publishers.
    1. WHAT IS ARTIFICIAL INTELLIGENCE? One of the fascinating aspects of the field of artificial intelligence (AI) is that the precise nature of its subject ..
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  2.  39
    Scientific Explanation and the Causal Structure of the World. Wesley Salmon.James H. Fetzer - 1987 - Philosophy of Science 54 (4):597-610.
  3. Definitions and Definability Philosophical Perspectives.James H. Fetzer, George N. Schlesinger & David Shatz - 1991
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  4. Program Verification: The Very Idea.James H. Fetzer - 1988 - Communications of the Acm 31 (9):1048--1063.
    The notion of program verification appears to trade upon an equivocation. Algorithms, as logical structures, are appropriate subjects for deductive verification. Programs, as causal models of those structures, are not. The success of program verification as a generally applicable and completely reliable method for guaranteeing program performance is not even a theoretical possibility.
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  5.  29
    Mental Algorithms: Are Minds Computational Systems?James H. Fetzer - 1994 - Pragmatics and Cognition 21 (1):1-29.
    The idea that human thought requires the execution of mental algorithms provides a foundation for research programs in cognitive science, which are largely based upon the computational conception of language and mentality. Consideration is given to recent work by Penrose, Searle, and Cleland, who supply various grounds for disputing computationalism. These grounds in turn qualify as reasons for preferring a non-computational, semiotic approach, which can account for them as predictable manifestations of a more adquate conception. Thinking does not ordinarily require (...)
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  6.  16
    Mental Algorithms: Are Minds Computational Systems?James H. Fetzer - 1994 - Pragmatics and Cognition 2 (1):1-29.
    The idea that human thought requires the execution of mental algorithms provides a foundation for research programs in cognitive science, which are largely based upon the computational conception of language and mentality. Consideration is given to recent work by Penrose, Searle, and Cleland, who supply various grounds for disputing computationalism. These grounds in turn qualify as reasons for preferring a non-computational, semiotic approach, which can account for them as predictable manifestations of a more adquate conception. Thinking does not ordinarily require (...)
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  7.  33
    Connectionism and Cognition: Why Fodor and Pylyshyn Are Wrong.James H. Fetzer - 1992 - In A. Clark & Ronald Lutz (eds.), Connectionism in Context. Springer Verlag. pp. 305-319.
  8. Information: Does It Have to Be True? [REVIEW]James H. Fetzer - 2004 - Minds and Machines 14 (2):223-229.
    Luciano Floridi (2003) offers a theory of information as a strongly semantic notion, according to which information encapsulates truth, thereby making truth a necessary condition for a sentence to qualify as information. While Floridi provides an impressive development of this position, the aspects of his approach of greatest philosophical significance are its foundations rather than its formalization. He rejects the conception of information as meaningful data, which entails at least three theses – that information can be false; that tautologies are (...)
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  9.  37
    Information, Misinformation, and Disinformation.James H. Fetzer - 2004 - Minds and Machines 14 (2):223-229.
    Luciano Floridi offers a theory of information as a “strongly semantic” notion, according to which information encapsulates truth, thereby making truth a necessary condition for a sentence to qualify as “information”. While Floridi provides an impressive development of this position, the aspects of his approach of greatest philosophical significance are its foundations rather than its formalization. He rejects the conception of information as meaningful data, which entails at least three theses – that information can be false; that tautologies are information; (...)
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  10.  7
    The Nature of Explanation.James H. Fetzer - 1984 - Philosophy of Science 51 (3):516-519.
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  11.  55
    Syntax, Semantics, and Ontology: A Probabilistic Causal Calculus.James H. Fetzer & Donald E. Nute - 1979 - Synthese 40 (3):453 - 495.
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  12.  61
    A World of Dispositions.James H. Fetzer - 1977 - Synthese 34 (4):397 - 421.
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  13. Thinking and Computing: Computers as Special Kinds of Signs. [REVIEW]James H. Fetzer - 1997 - Minds and Machines 7 (3):345-364.
    Cognitive science has been dominated by the computational conception that cognition is computation across representations. To the extent to which cognition as computation across representations is supposed to be a purposive, meaningful, algorithmic, problem-solving activity, however, computers appear to be incapable of cognition. They are devices that can facilitate computations on the basis of semantic grounding relations as special kinds of signs. Even their algorithmic, problem-solving character arises from their interpretation by human users. Strictly speaking, computers as such — apart (...)
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  14.  57
    A Single Case Propensity Theory of Explanation.James H. Fetzer - 1974 - Synthese 28 (2):171 - 198.
  15.  57
    Reichenbach, Reference Classes, and Single Case 'Probabilities'.James H. Fetzer - 1977 - Synthese 34 (2):185 - 217.
  16.  28
    Probabilistic Explanations.James H. Fetzer - 1982 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1982:194-207.
    The purpose of this paper is to provide a systematic defense of the single-case propensity account of probabilistic explanation from the criticisms advanced by Hanna and by Humphreys and to offer a critical appraisal of the aleatory conception advanced by Humphreys and of the deductive-nomological-probabilistic approach Railton has proposed. The principal conclusion supported by this analysis is that the Requirements of Maximal Specificity and of Strict Maximal Specificity afford the foundation for completely objective explanations of probabilistic explananda, so long as (...)
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  17.  90
    Philosophical Aspects of Program Verification.James H. Fetzer - 1991 - Minds and Machines 1 (2):197-216.
    A debate over the theoretical capabilities of formal methods in computer science has raged for more than two years now. The function of this paper is to summarize the key elements of this debate and to respond to important criticisms others have advanced by placing these issues within a broader context of philosophical considerations about the nature of hardware and of software and about the kinds of knowledge that we have the capacity to acquire concerning their performance.
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  18.  89
    Probability and Objectivity in Deterministic and Indeterministic Situations.James H. Fetzer - 1983 - Synthese 57 (3):367--86.
    This paper pursues the question, To what extent does the propensity approach to probability contribute to plausible solutions to various anomalies which occur in quantum mechanics? The position I shall defend is that of the three interpretations — the frequency, the subjective, and the propensity — only the third accommodates the possibility, in principle, of providing a realistic interpretation of ontic indeterminism. If these considerations are correct, then they lend support to Popper's contention that the propensity construction tends to remove (...)
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  19.  67
    A Probabilistic Causal Calculus: Conflicting Conceptions.James H. Fetzer & Donald E. Nute - 1981 - Synthese 48 (3):241 - 246.
  20.  51
    The Role Of Models In Computer Science.James H. Fetzer - 1999 - The Monist 82 (1):20-36.
    Taking Brian Cantwell Smith’s study, “Limits of Correctness in Computers,” as its point of departure, this article explores the role of models in computer science. Smith identifies two kinds of models that play an important role, where specifications are models of problems and programs are models of possible solutions. Both presuppose the existence of conceptualizations as ways of conceiving the world “in certain delimited ways.” But high-level programming languages also function as models of virtual (or abstract) machines, while low-level programming (...)
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  21.  66
    A Probabilistic Causal Calculus: Conflicting Conceptions.James H. Fetzer & Donald E. Nute - 1980 - Synthese 44 (2):241 - 246.
  22. The Frame Problem: Artificial Intelligence Meets David Hume.James H. Fetzer - 1990 - International Journal of Expert Systems 3:219-232.
  23.  76
    Science, Explanation, and Rationality: Aspects of the Philosophy of Carl G. Hempel.James H. Fetzer (ed.) - 2000 - Oxford University Press.
    Carl G. Hempel exerted greater influence upon philosophers of science than any other figure during the 20th century. In this far-reaching collection, distinguished philosophers contribute valuable studies that illuminate and clarify the central problems to which Hempel was devoted. The essays enhance our understanding of the development of logical empiricism as the major intellectual influence for scientifically-oriented philosophers and philosophically-minded scientists of the 20th century.
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  24.  70
    Evolution, Rationality and Testability.James H. Fetzer - 1990 - Synthese 82 (3):423-39.
    Cosmides, Wason, and Johnson-Laird, among others, have suggested evidence that reasoning abilities tend to be domain specific, insofar as humans do not appear to acquire capacities for logical reasoning that are applicable across different contexts. Unfortunately, the significance of these findings depends upon the specific variety of logical reasoning under consideration. Indeed, there seem to be at least three grounds for doubting such conclusions, since: (1) tests of reasoning involving the use of material conditionals may not be appropriate for representing (...)
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  25.  56
    Logical Reasoning and Domain Specificity: A Critique of the Social Exchange Theory of Reasoning.Paul Sheldon Davies, James H. Fetzer & Thomas R. Foster - 1995 - Biology and Philosophy 10 (1):1-37.
    The social exchange theory of reasoning, which is championed by Leda Cosmides and John Tooby, falls under the general rubric evolutionary psychology and asserts that human reasoning is governed by content-dependent, domain-specific, evolutionarily-derived algorithms. According to Cosmides and Tooby, the presumptive existence of what they call cheater-detection algorithms disconfirms the claim that we reason via general-purpose mechanisms or via inductively acquired principles. We contend that the Cosmides/Tooby arguments in favor of domain-specific algorithms or evolutionarily-derived mechanisms fail and that the notion (...)
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  26. Propensities and Frequencies: Inference to the Best Explanation.James H. Fetzer - 2002 - Synthese 132 (1-2):27 - 61.
    An approach to inference to the best explanation integrating a Popperianconception of natural laws together with a modified Hempelian account of explanation, one the one hand, and Hacking's law of likelihood (in its nomicguise), on the other, which provides a robust abductivist model of sciencethat appears to overcome the obstacles that confront its inductivist,deductivist, and hypothetico-deductivist alternatives.This philosophy of scienceclarifies and illuminates some fundamental aspects of ontology and epistemology, especially concerning the relations between frequencies and propensities. Among the most important (...)
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  27. The Place of Probability in Science.Ellery Eells & James H. Fetzer (eds.) - 2010 - Springer.
  28.  17
    Dispositional Probabilities.James H. Fetzer - 1970 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1970:473 - 482.
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  29.  11
    Probabilistic Metaphysics.James H. Fetzer - 2010 - In Ellery Eells & James H. Fetzer (eds.), The Place of Probability in Science. Springer. pp. 81--98.
  30.  63
    Probability and Explanation.James H. Fetzer - 1981 - Synthese 48 (3):371 - 408.
  31.  98
    Disinformation: The Use of False Information. [REVIEW]James H. Fetzer - 2004 - Minds and Machines 14 (2):231-240.
    The distinction between misinformation and disinformation becomes especially important in political, editorial, and advertising contexts, where sources may make deliberate efforts to mislead, deceive, or confuse an audience in order to promote their personal, religious, or ideological objectives. The difference consists in having an agenda. It thus bears comparison with lying, because lies are assertions that are false, that are known to be false, and that are asserted with the intention to mislead, deceive, or confuse. One context in which disinformation (...)
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  32.  41
    Methodological Individualism: Singular Causal Systems and Their Population Manifestations.James H. Fetzer - 1986 - Synthese 68 (1):99 - 128.
    The purpose of this essay is to investigate the properties of singular causal systems and their population manifestations, with special concern for the thesis of methodological individualism, which claims that there are no properties of social groups that cannot be adequately explained exclusively by reference to properties of individual members of those groups, i.e., at the level of individuals. Individuals, however, may be viewed as singular causal systems, i.e., as instantiations of (arrangements of) dispositional properties. From this perspective, methodological individualism (...)
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  33.  7
    The Philosophy of Carl G. Hempel.Carl G. Hempel & James H. Fetzer - 2002 - Mind 111 (443):683-687.
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  34.  21
    On Mellor on Dispositions.James H. Fetzer - 1978 - Philosophia 7 (3-4):651-660.
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  35. Glossary of Epistemology/Philosophy of Science.James H. Fetzer - 1993 - Paragon House.
     
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  36. Principles of Philosophical Reasoning.James H. Fetzer (ed.) - 1984 - Rowman & Allanheld.
     
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  37.  60
    On “Epistemic Possibility”.James H. Fetzer - 1974 - Philosophia 4 (2-3):327-335.
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  38.  44
    In Memoriam: Wesley C. Salmon (1925-2001).James H. Fetzer - 2002 - Synthese 132 (1-2):1 - 3.
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  39.  17
    The Argument for Mental Models is Unsound.James H. Fetzer - 1993 - Behavioral and Brain Sciences 16 (2):347-348.
  40.  8
    Evolving Consciousness: The Very Idea!James H. Fetzer - 2013 - In Liz Swan (ed.), Origins of Mind. pp. 225--242.
  41.  28
    Mental Models: Reasoning Without Rules. [REVIEW]James H. Fetzer - 1999 - Minds and Machines 9 (1):119-126.
  42.  22
    Propensities and Frequencies.James H. Fetzer - 2010 - In Ellery Eells & James H. Fetzer (eds.), The Place of Probability in Science. Springer. pp. 323--351.
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  43.  23
    Deduction and Mental Models.James H. Fetzer - 1999 - Minds and Machines 9 (1):105-110.
  44.  61
    Consciousness and Cognition: Semiotic Conceptions of Bodies and Minds.James H. Fetzer - 2003 - In Quentin Smith & Aleksandar Jokic (eds.), Consciousness: New Philosophical Perspectives. Oxford University Press. pp. 295.
  45.  22
    Frequencies and Propensities: Inference to the Best Explanation.James H. Fetzer - 2002 - Synthese 132 (1-2):27-61.
    An approach to inference to the best explanation integrating a Popperianconception of natural laws together with a modified Hempelian account of explanation, one the one hand, and Hacking's law of likelihood, on the other, which provides a robust abductivist model of sciencethat appears to overcome the obstacles that confront its inductivist,deductivist, and hypothetico-deductivist alternatives.This philosophy of scienceclarifies and illuminates some fundamental aspects of ontology and epistemology, especially concerning the relations between frequencies and propensities. Among the most important elements of this (...)
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  46. Foundations of Philosophy of Science: Recent Developments.James H. Fetzer (ed.) - 1993 - Paragon House.
     
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  47.  23
    On the Historical Explanation of Unique Events.James H. Fetzer - 1975 - Theory and Decision 6 (1):87-97.
  48.  21
    The TTT is Not the Final Word.James H. Fetzer - 1993 - Think (misc) 2 (1):34-36.
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  49.  7
    What Makes Connectionism Different?James H. Fetzer - 1994 - Pragmatics and Cognition 2 (2):327-348.
  50.  10
    What’s Wrong with Salmon’s History: The Third Decade.James H. Fetzer - 1992 - Philosophy of Science 59 (2):246-262.
    My purpose here is to elaborate the reasons I maintain that Salmon has not been completely successful in reporting the history of work on explanation. The most important limitation of his account is that it does not emphasize the critical necessity to embrace a suitable conception of probability in the development of the theory of probabilistic explanation.
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