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  1. James H. Fetzer (2013). Evolving Consciousness: The Very Idea!. In. In Liz Swan (ed.), Origins of Mind. 225--242.
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  2. James H. Fetzer (2011). Evolution and Atheism: Has Griffin Reconciled Science and Religion? Synthese 178 (2):381 - 396.
    The distinguished theologian, David Ray Griffin, has advanced a set of thirteen theses intended to characterize (what he calls) "Neo-Darwinism" and which he contrasts with "Intelligent Design". Griffin maintains that Neo-Darwinism is "atheistic" in forgoing a creator but suggests that, by adopting a more modest scientific naturalism and embracing a more naturalistic theology, it is possible to find "a third way" that reconciles religion and science. The considerations adduced here suggest that Griffin has promised more than he can deliver. On (...)
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  3. Ellery Eells & James H. Fetzer (eds.) (2010). The Place of Probability in Science. Springer.
  4. James H. Fetzer (2010). Is Evolution An Optimizing Process?. In. In Ellery Eells & James H. Fetzer (eds.), The Place of Probability in Science. Springer. 163--177.
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  5. James H. Fetzer (2010). Propensities and Frequencies. In. In Ellery Eells & James H. Fetzer (eds.), The Place of Probability in Science. Springer. 323--351.
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  6. James H. Fetzer (2010). Probabilistic Metaphysics. In. In Ellery Eells & James H. Fetzer (eds.), The Place of Probability in Science. Springer. 81--98.
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  7. James H. Fetzer (2004). Disinformation: The Use of False Information. [REVIEW] 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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  8. James H. Fetzer (2004). Information: Does It Have to Be True? [REVIEW] 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. James H. Fetzer (2004). What is Abduction?: An Assessment of Jaakko Hintikka's Conception. In. In D. Kolak & J. Symons (eds.), Quantifiers, Questions and Quantum Physics. Springer. 127--155.
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  10. James H. Fetzer (2003). Consciousness and Cognition: Semiotic Conceptions of Bodies and Minds. In Quentin Smith & Aleksandar Jokic (eds.), Consciousness: New Philosophical Perspectives. Oxford University Press. 295.
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  11. James H. Fetzer (ed.) (2002). Consciousness Evolving. John Benjamins.
  12. James H. Fetzer (2002). Introduction. Synthese 132 (1-2):5-8.
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  13. James H. Fetzer (2002). In Memoriam: Wesley C. Salmon (1925-2001). Synthese 132 (1-2):1 - 3.
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  14. James H. Fetzer (2002). Propensities and Frequencies: Inference to the Best Explanation. 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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  15. James H. Fetzer (2000). Computing is at Best a Special Kind of Thinking. In The Proceedings of the Twentieth World Congress of Philosophy, Volume 9: Philosophy of Mind. Charlottesville: Philosophy Doc Ctr. 103-113.
    When computing is defined as the causal implementation of algorithms and algorithms are defined as effective decision procedures, human thought is mental computation only if it is governed by mental algorithms. An examination of ordinary thinking, however, suggests that most human thought processes are non-algorithmic. Digital machines, moreover, are mark-manipulating or string-processing systems whose marks or strings do not stand for anything for those systems, while minds are semiotic (or “signusing”) systems for which signs stand for other things for those (...)
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  16. James H. Fetzer (ed.) (2000). Science, Explanation, and Rationality: Aspects of the Philosophy of Carl G. Hempel. 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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  17. James H. Fetzer (2000). The Proceedings of the Twentieth World Congress of Philosophy, Volume 9: Philosophy of Mind. Charlottesville: Philosophy Doc Ctr.
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  18. James H. Fetzer (1999). Deduction and Mental Models. Minds and Machines 9 (1):105-110.
  19. James H. Fetzer (1999). Mental Models: Reasoning Without Rules. [REVIEW] Minds and Machines 9 (1):119-126.
  20. James H. Fetzer (1999). The Role Of Models In Computer Science. 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. James H. Fetzer (1998). Philosophy and Computer Science: Reflections on the Program Verification Debate. In Terrell Ward Bynum & James Moor (eds.), The Digital Phoenix: How Computers Are Changing Philosophy. Blackwell Publishers. 253--73.
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  22. James H. Fetzer (1997). Escaping the Propositional Prison. The Monist 80 (3):368-388.
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  23. James H. Fetzer (1997). Thinking and Computing: Computers as Special Kinds of Signs. [REVIEW] 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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  24. James H. Fetzer (1996). Computer Reliability and Public Policy: Limits of Knowledge of Computer-Based Systems. Social Philosophy and Policy 13 (02):229-.
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  25. Paul Sheldon Davies, James H. Fetzer & Thomas R. Foster (1995). Logical Reasoning and Domain Specificity. 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. James H. Fetzer & Paul W. Humphreys (1995). Editorial Preface. Synthese 104 (2):177-177.
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  27. James H. Fetzer (1994). Creative Thinking Presupposes the Capacity for Thought. Behavioral and Brain Sciences 17 (3):539-540.
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  28. James H. Fetzer (1994). Mental Algorithms: Are Minds Computational Systems? Pragmatics and Cognition 21 (1):1-29.
  29. James H. Fetzer (1994). What Makes Connectionism Different? Pragmatics and Cognition 2 (2):327-348.
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  30. James H. Fetzer (1993). Evolution Needs a Modern Theory of the Mind. Behavioral and Brain Sciences 16 (4):759.
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  31. James H. Fetzer (ed.) (1993). Foundations of Philosophy of Science: Recent Developments. Paragon House.
  32. James H. Fetzer (1993). Goldman has Not Defeated Folk Functionalism. Behavioral and Brain Sciences 16 (1):42.
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  33. James H. Fetzer (1993). Glossary of Epistemology/Philosophy of Science. Paragon House.
     
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  34. James H. Fetzer (1993). Philosophy of Science. Paragon House Publishers.
  35. James H. Fetzer (1993). The Argument for Mental Models is Unsound. Behavioral and Brain Sciences 16 (2):347.
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  36. James H. Fetzer (1993). The TTT is Not the Final Word. Think 2 (1):34-36.
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  37. James H. Fetzer (1992). Connectionism and Cognition: Why Fodor and Pylyshyn Are Wrong. In A. Clark & Ronald Lutz (eds.), Connectionism in Context. Springer-Verlag. 305-319.
  38. James H. Fetzer (1992). What's Wrong with Salmon's History: The Third Decade. 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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  39. James H. Fetzer (1991). Are There Laws of Nature? Philosophical Books 32 (2):65-75.
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  40. James H. Fetzer (1991). Frederick Suppe, The Semantic Conception of Theories and Scientific Realism Reviewed By. Philosophy in Review 11 (5):364-367.
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  41. James H. Fetzer (1991). Philosophy and Cognitive Science. Paragon House.
  42. James H. Fetzer (1991). Philosophical Aspects of Program Verification. 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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  43. James H. Fetzer (1991). Book Review:Scientific Explanation Philip Kitcher, Wesley C. Salmon; Four Decades of Scientific Explanation Wesley C. Salmon. [REVIEW] Philosophy of Science 58 (2):288-.
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  44. James H. Fetzer (1990). Artificial Intelligence: Its Scope and Limits. Kluwer.
    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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  45. James H. Fetzer (1990). Evolution, Rationality and Testability. 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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  46. James H. Fetzer (1990). The Frame Problem: Artificial Intelligence Meets David Hume. International Journal of Expert Systems 3:219-232.
  47. James H. Fetzer (1989). Language and Mentality: Computational, Representational, and Dispositional Conceptions. Behaviorism 17:21-39.
  48. James H. Fetzer (1989). Michael Tooley, Causation: A Realist Approach Reviewed By. Philosophy in Review 9 (3):121-124.
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  49. James H. Fetzer (ed.) (1988). Aspects of AI. D.
  50. James H. Fetzer (1988). Program Verification: The Very Idea. 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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