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  1. Michael Baumgartner (2009). Uncovering Deterministic Causal Structures: A Boolean Approach. Synthese 170 (1):71 - 96.
    While standard procedures of causal reasoning as procedures analyzing causal Bayesian networks are custom-built for (non-deterministic) probabilistic structures, this paper introduces a Boolean procedure that uncovers deterministic causal structures. Contrary to existing Boolean methodologies, the procedure advanced here successfully analyzes structures of arbitrary complexity. It roughly involves three parts: first, deterministic dependencies are identified in the data; second, these dependencies are suitably minimalized in order to eliminate redundancies; and third, one or—in case of ambiguities—more than one causal structure is assigned (...)
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  2. Brian Ellis (1988). Solving the Problem of Induction Using a Values-Based Epistemology. British Journal for the Philosophy of Science 39 (2):141-160.
  3. Gilbert Harman & Sanjeev R. Kulkarni (2006). The Problem of Induction. Philosophy and Phenomenological Research 72 (3):559-575.
    The problem of induction is sometimes motivated via a comparison between rules of induction and rules of deduction. Valid deductive rules are necessarily truth preserving, while inductive rules are not.
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  4. James Hawthorne (1994). On the Nature of Bayesian Convergence. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1994:241 - 249.
    The objectivity of Bayesian induction relies on the ability of evidence to produce a convergence to agreement among agents who initially disagree about the plausibilities of hypotheses. I will describe three sorts of Bayesian convergence. The first reduces the objectivity of inductions about simple "occurrent events" to the objectivity of posterior probabilities for theoretical hypotheses. The second reveals that evidence will generally induce converge to agreement among agents on the posterior probabilities of theories only if the convergence is 0 or (...)
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  5. James Hawthorne (1993). Bayesian Induction IS Eliminative Induction. Philosophical Topics 21 (1):99-138.
    Eliminative induction is a method for finding the truth by using evidence to eliminate false competitors. It is often characterized as "induction by means of deduction"; the accumulating evidence eliminates false hypotheses by logically contradicting them, while the true hypothesis logically entails the evidence, or at least remains logically consistent with it. If enough evidence is available to eliminate all but the most implausible competitors of a hypothesis, then (and only then) will the hypothesis become highly confirmed. I will argue (...)
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  6. Jürgen Hollatz (1999). Analogy Making in Legal Reasoning with Neural Networks and Fuzzy Logic. Artificial Intelligence and Law 7 (2-3):289-301.
    Analogy making from examples is a central task in intelligent system behavior. A lot of real world problems involve analogy making and generalization. Research investigates these questions by building computer models of human thinking concepts. These concepts can be divided into high level approaches as used in cognitive science and low level models as used in neural networks. Applications range over the spectrum of recognition, categorization and analogy reasoning. A major part of legal reasoning could be formally interpreted as an (...)
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  7. Tokuyasu Kakuta, Makoto Haraguchi & Yoshiaki Okubo (1997). A Goal-Dependent Abstraction for Legal Reasoning by Analogy. Artificial Intelligence and Law 5 (1-2):97-118.
    This paper presents a new algorithm to find an appropriate similarityunder which we apply legal rules analogically. Since there may exist a lotof similarities between the premises of rule and a case in inquiry, we haveto select an appropriate similarity that is relevant to both thelegal rule and a top goal of our legal reasoning. For this purpose, a newcriterion to distinguish the appropriate similarities from the others isproposed and tested. The criterion is based on Goal-DependentAbstraction (GDA) to select a (...)
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  8. Nicholas Maxwell (forthcoming). Popper's Paradoxical Pursuit of Natural Philosophy. In J. Shearmur & G. Stokes (eds.), Cambridge Companion to Popper. Cambridge University Press.
    Philosophy of science is seen by most as a meta-discipline – one that takes science as its subject matter, and seeks to acquire knowledge and understanding about science without in any way affecting, or contributing to, science itself. Karl Popper’s approach is very different. His first love is natural philosophy or, as he would put it, cosmology. This intermingles cosmology and the rest of natural science with epistemology, methodology and metaphysics. Paradoxically, however, one of his best known contributions, his proposed (...)
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  9. Nicholas Maxwell, The Problem of Induction and Metaphysical Assumptions Concerning the Comprehensibility and Knowability of the Universe. PhilSci Archive.
    Even though evidence underdetermines theory, often in science one theory only is regarded as acceptable in the light of the evidence. This suggests there are additional unacknowledged assumptions which constrain what theories are to be accepted. In the case of physics, these additional assumptions are metaphysical theses concerning the comprehensibility and knowability of the universe. Rigour demands that these implicit assumptions be made explicit within science, so that they can be critically assessed and, we may hope improved. This leads to (...)
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  10. Michael Morreau (1998). Review of Isaac Levi, For the Sake of the Argument: Ramsey Test Conditionals, Inductive Inference and Nonmonotonic Reasoning. [REVIEW] Journal of Philosophy 95 (10):540-546.
  11. Ronald Pisaturo (2011). The Longevity Argument. self.
    J. Richard Gott III (1993) has used the “Copernican principle” to derive a probability density function for the total longevity of any phenomenon, based solely on the phenomenon’s past longevity. John Leslie (1996) and others have used an apparently similar probabilistic argument, the “Doomsday Argument,” to claim that conventional predictions of longevity must be adjusted, based on Bayes’ Theorem, in favor of shorter longevities. Here I show that Gott’s arguments are flawed and contradictory, but that one of his conclusions—his delta (...)
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  12. Ronald Pisaturo (2009). Past Longevity as Evidence for the Future. Philosophy of Science 76 (1):73-100.
    Gott ( 1993 ) has used the ‘Copernican principle’ to derive a probability distribution for the total longevity of any phenomenon, based solely on the phenomenon’s past longevity. Leslie ( 1996 ) and others have used an apparently similar probabilistic argument, the ‘Doomsday Argument’, to claim that conventional predictions of longevity must be adjusted, based on Bayes’s Theorem, in favor of shorter longevities. Here I show that Gott’s arguments are flawed and contradictory, but that one of his conclusions is plausible (...)
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  13. Joel Pust (1996). Induction, Focused Sampling and the Law of Small Numbers. Synthese 108 (1):89 - 104.
    Hilary Kornblith (1993) has recently offered a reliabilist defense of the use of the Law of Small Numbers in inductive inference. In this paper I argue that Kornblith's defense of this inferential rule fails for a number of reasons. First, I argue that the sort of inferences that Kornblith seeks to justify are not really inductive inferences based on small samples. Instead, they are knowledge-based deductive inferences. Second, I address Kornblith's attempt to find support in the work of Dorrit Billman (...)
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  14. Alfred Schramm (2006). Methodological Objectivism and Critical Rationalist ’Induction’. In Ian Jarvie, Karl Milford & David Miller (eds.), Karl Popper: A Centenary Assessment, Volume II. Ashgate.
    This paper constitutes one extended argument, which touches on various topics of Critical Rationalism as it was initiated by Karl Popper and further developed (although into different directions) in his aftermath. The result of the argument will be that critical rationalism either offers no solution to the problem of induction at all , or that it amounts, in the last resort, to a kind of Critical Rationalist Inductivism as it were, a version of what I call Good Old Induction. One (...)
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  15. Alfred Schramm (2002). Rationalitätsbegriffe und Begründungsurteile. In Jan M. Böhm, Heiko Holweg & Claudia Hoock (eds.), Karl Poppers kritischer Rationalismus heute. Mohr Siebeck.
    In einer früheren Arbeit habe ich neben anderem auch Poppers Theorie der rationalen Präferabilität der bestbewährten Theorie kritisiert und festgestellt, daß sie weder als Theorie der theoretischen Rationalität (d.i. die Rationalität des bloßen, an keinem anderen Zweck als dem des an der wissenschaftlichen Neugierde orientierten Glaubens, Meinens oder Führwahrhaltens)noch als Theorie der praktischen Rationalität (d.i. die Rationalität der auch an sonstigen Zwecken orientierten Handlungen bzw. Handlungsentscheidungen)tauglich sei. Im wesentlichen habe ich argumentiert, daß es bei der Verfolgung des Zieles, sich der (...)
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  16. David Slutsky (2012). Confusion and Dependence in Uses of History. Synthese 184 (3):261-286.
    Many people argue that history makes a special difference to the subjects of biology and psychology, and that history does not make this special difference to other parts of the world. This paper will show that historical properties make no more or less of a difference to biology or psychology than to chemistry, physics, or other sciences. Although historical properties indeed make a certain kind of difference to biology and psychology, this paper will show that historical properties make the same (...)
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  17. Paul D. Thorn (2013). Defeasible Conditionalization. Journal of Philosophical Logic:1-20.
    The applicability of Bayesian conditionalization in setting one’s posterior probability for a proposition, α, is limited to cases where the value of a corresponding prior probability, PPRI(α|∧E), is available, where ∧E represents one’s complete body of evidence. In order to extend probability updating to cases where the prior probabilities needed for Bayesian conditionalization are unavailable, I introduce an inference schema, defeasible conditionalization, which allows one to update one’s personal probability in a proposition by conditioning on a proposition that represents a (...)
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