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  1. The Impossibility of Coherence.Erik J. Olsson - 2005 - Erkenntnis 63 (3):387-412.
    There is an emerging consensus in the literature on probabilistic coherence that such coherence cannot be truth conducive unless the information sources providing the cohering information are individually credible and collectively independent. Furthermore, coherence can at best be truth conducive in a ceteris paribus sense. Bovens and Hartmann have argued that there cannot be any measure of coherence that is truth conducive even in this very weak sense. In this paper, I give an alternative impossibility proof. I provide a relatively (...)
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  • Reliability conducive measures of coherence.Erik J. Olsson & Stefan Schubert - 2007 - Synthese 157 (3):297-308.
    A measure of coherence is said to be truth conducive if and only if a higher degree of coherence (as measured) results in a higher likelihood of truth. Recent impossibility results strongly indicate that there are no (non-trivial) probabilistic coherence measures that are truth conducive. Indeed, this holds even if truth conduciveness is understood in a weak ceteris paribus sense (Bovens & Hartmann, 2003, Bayesian epistemology. New York, Oxford: Oxford University Press; Olsson, 2005, Against coherence: Truth probability and justification. Oxford: (...)
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  • Thoughts are facts in possible worlds, truths are facts of a given world.Leszek NowAK - 1991 - Dialectica 45 (4):273-288.
    Mentalism preserves the triad: brain's state — thought — state of affairs whereas phy‐sicalism identifies the former two elements of it. Both stands meet the famous difficulties. But these presuppose ontological actualism. On the ground of ontological possibilism, claiming the existence of all possible worlds, one may identify a thought with the corresponding state of affairs in a possible world. Yet, possibilism turns out to be too narrow to carry such an identification and requires a significant generalization.
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  • Psychology, statistics, and analytical epistemology.Richard E. Nisbett & Paul Thagard - 1983 - Behavioral and Brain Sciences 6 (2):257-258.
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  • Lay arbitration of rules of inference.Richard E. Nisbett - 1981 - Behavioral and Brain Sciences 4 (3):349-350.
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  • L. J. Cohen versus Bayesianism.Ilkka Niiniluoto - 1981 - Behavioral and Brain Sciences 4 (3):349-349.
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  • Can philosophy resolve empirical issues?Clifford R. Mynatt, Ryan D. Tweney & Michael E. Doherty - 1983 - Behavioral and Brain Sciences 6 (3):506.
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  • On the nature of the conjunction fallacy.Rodrigo Moro - 2009 - Synthese 171 (1):1 - 24.
    In a seminal work, Tversky and Kahneman showed that in some contexts people tend to believe that a conjunction of events (e.g., Linda is a bank teller and is active in the feminist movement) is more likely to occur than one of the conjuncts (e.g., Linda is a bank teller). This belief violates the conjunction rule in probability theory. Tversky and Kahneman called this phenomenon the “conjunction fallacy”. Since the discovery of the phenomenon in 1983, researchers in psychology and philosophy (...)
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  • False memories of the future: A critique of the applications of probabilistic reasoning to the study of cognitive processes.Mihnea Moldoveanu & Ellen Langer - 2002 - Psychological Review 109 (2):358-375.
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  • Contrapositivism; or, The only evidence worth paying for is contained in the negatives.David Miller - 1983 - Behavioral and Brain Sciences 6 (2):256-257.
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  • Optimization and connectionism are two different things.Drew McDermott - 1989 - Behavioral and Brain Sciences 12 (3):483-484.
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  • Acceptability, analogy, and the acceptability of analogies.Robert N. McCauley - 1989 - Behavioral and Brain Sciences 12 (3):482-483.
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  • The irrational, the unreasonable, and the wrong.Avishai Margalit & Maya Bar-Hillel - 1981 - Behavioral and Brain Sciences 4 (3):346-349.
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  • A system of communication rules for justifying and explaining beliefs about facts in civil trials.João Marques Martins - 2020 - Artificial Intelligence and Law 28 (1):135-150.
    This paper addresses the problems of justifying and explaining beliefs about facts in the context of civil trials. The first section contains some remarks about the nature of adjudicative fact-finding and highlights the communicative features of deciding about facts in judicial context. In Sect. 2, some difficulties and the incompleteness presented by Bayesian and coherentist frameworks, which are taken as methods suitable to solve the above-mentioned problems, are pointed out. In the third section, the purely epistemic approach to the justification (...)
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  • New science for old.Bruce Mangan & Stephen Palmer - 1989 - Behavioral and Brain Sciences 12 (3):480-482.
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  • Propensity, evidence, and diagnosis.J. L. Mackie - 1981 - Behavioral and Brain Sciences 4 (3):345-346.
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  • Explanationism, ECHO, and the connectionist paradigm.William G. Lycan - 1989 - Behavioral and Brain Sciences 12 (3):480-480.
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  • “Is” and “ought” in cognitive science.William G. Lycan - 1981 - Behavioral and Brain Sciences 4 (3):344-345.
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  • Performing competently.Lola L. Lopes - 1981 - Behavioral and Brain Sciences 4 (3):343-344.
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  • Normative theories of rationality: Occam's razor, Procrustes' bed?Lola L. Lopes - 1983 - Behavioral and Brain Sciences 6 (2):255-256.
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  • Who commits the base rate fallacy?Isaac Levi - 1983 - Behavioral and Brain Sciences 6 (3):502.
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  • Should Bayesians sometimes neglect base rates?Isaac Levi - 1981 - Behavioral and Brain Sciences 4 (3):342-343.
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  • Explanatory coherence in neural networks?Daniel S. Levine - 1989 - Behavioral and Brain Sciences 12 (3):479-479.
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  • Conjunctive bliss.Isaac Levi - 1983 - Behavioral and Brain Sciences 6 (2):254-255.
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  • The role of logic in reason, inference, and decision.Henry E. Kyburg - 1983 - Behavioral and Brain Sciences 6 (2):263-273.
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  • Intuition, competence, and performance.Henry E. Kyburg - 1981 - Behavioral and Brain Sciences 4 (3):341-342.
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  • Rational belief.Henry E. Kyburg - 1983 - Behavioral and Brain Sciences 6 (2):231-245.
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  • Improvements in human reasoning and an error in L. J. Cohen's.David H. Krantz - 1981 - Behavioral and Brain Sciences 4 (3):340-340.
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  • Does ECHO explain explanation? A psychological perspective.Joshua Klayman & Robin M. Hogarth - 1989 - Behavioral and Brain Sciences 12 (3):478-479.
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  • Who shall be the arbiter of our intuitions?Daniel Kahneman - 1981 - Behavioral and Brain Sciences 4 (3):339-340.
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  • Philosophical arguments, psychological experiments, and the problem of consistency.D. Kahneman - 1983 - Behavioral and Brain Sciences 6 (2):253-254.
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  • Norms, competence, and the explanation of reasoning.Gary S. Kahn & Lance J. Rips - 1983 - Behavioral and Brain Sciences 6 (3):501.
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  • Can irrationality be intelligently discussed?Daniel Kahneman & Amos Tversky - 1983 - Behavioral and Brain Sciences 6 (3):509.
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  • Legal reasoning with subjective logic.Audun Jøsang & Viggo A. Bondi - 2000 - Artificial Intelligence and Law 8 (4):289-315.
    Judges and jurors must make decisions in an environment of ignoranceand uncertainty for example by hearing statements of possibly unreliable ordishonest witnesses, assessing possibly doubtful or irrelevantevidence, and enduring attempts by the opponents to manipulate thejudge''s and the jurors'' perceptions and feelings. Three importantaspects of decision making in this environment are the quantificationof sufficient proof, the weighing of pieces of evidence, and therelevancy of evidence. This paper proposes a mathematical frameworkfor dealing with the two first aspects, namely the quantification ofproof (...)
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  • Inference to the best explanation is basic.John R. Josephson - 1989 - Behavioral and Brain Sciences 12 (3):477-478.
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  • Which comes first: Logic or rationality?P. N. Johnson-Laird - 1983 - Behavioral and Brain Sciences 6 (2):252-253.
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  • Inductive reasoning: Competence or skill?Christopher Jepson, David H. Krantz & Richard E. Nisbett - 1983 - Behavioral and Brain Sciences 6 (3):494.
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  • Is the mind Bayesian? The case for agnosticism.Jean Baratgin & Guy Politzer - 2006 - Mind and Society 5 (1):1-38.
    This paper aims to make explicit the methodological conditions that should be satisfied for the Bayesian model to be used as a normative model of human probability judgment. After noticing the lack of a clear definition of Bayesianism in the psychological literature and the lack of justification for using it, a classic definition of subjective Bayesianism is recalled, based on the following three criteria: an epistemic criterion, a static coherence criterion and a dynamic coherence criterion. Then it is shown that (...)
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  • Weak Bayesian coherentism.Michael Huemer - 2007 - Synthese 157 (3):337-346.
    Recent results in probability theory have cast doubt on coherentism, purportedly showing (a) that coherence among a set of beliefs cannot raise their probability unless individual beliefs have some independent credibility, and (b) that no possible measure of coherence makes coherence generally probability-enhancing. I argue that coherentists can reject assumptions on which these theorems depend, and I derive a general condition under which the concurrence of two information sources lacking individual credibility can raise the probability of what they report.
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  • Are explanatory coherence and a connectionist model necessary?Jerry R. Hobbs - 1989 - Behavioral and Brain Sciences 12 (3):476-477.
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  • Another vote for rationality.Mary Henle - 1981 - Behavioral and Brain Sciences 4 (3):339-339.
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  • A hybrid formal theory of arguments, stories and criminal evidence.Floris J. Bex, Peter J. van Koppen, Henry Prakken & Bart Verheij - 2010 - Artificial Intelligence and Law 18 (2):123-152.
    This paper presents a theory of reasoning with evidence in order to determine the facts in a criminal case. The focus is on the process of proof, in which the facts of the case are determined, rather than on related legal issues, such as the admissibility of evidence. In the literature, two approaches to reasoning with evidence can be distinguished, one argument-based and one story-based. In an argument-based approach to reasoning with evidence, the reasons for and against the occurrence of (...)
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  • Overcoming Luck: Two Trends in Legal Philosophy.Jeffrey S. Helmreich - 2018 - Analysis 78 (2):335-347.
    © The Author 2018. Published by Oxford University Press on behalf of The Analysis Trust. All rights reserved. For Permissions, please email: [email protected] article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model...Philosophy of law was until recently dominated by abstract investigation into the nature of law, a pursuit known as ‘general jurisprudence’. In this way, it resembled a branch of metaphysics or mid-twentieth century philosophy of mind, seeking to uncover the essential properties (...)
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  • Legal Probabilism: A Qualified Defence.Brian Hedden & Mark Colyvan - 2019 - Journal of Political Philosophy 27 (4):448-468.
    Journal of Political Philosophy, EarlyView.
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  • Logic and probability theory versus canons of rationality.Gilbert Harman - 1983 - Behavioral and Brain Sciences 6 (2):251-251.
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  • Kyburg on practical certainty.Willam L. Harper - 1983 - Behavioral and Brain Sciences 6 (2):251-252.
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  • Human reasoning: Can we judge before we understand?Richard A. Griggs - 1981 - Behavioral and Brain Sciences 4 (3):338-339.
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  • Intuition and inconsistency.Richard E. Grandy - 1983 - Behavioral and Brain Sciences 6 (3):494.
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  • Wigmore's Chart Method.Jean Goodwin & Alec Fisher - 2000 - Informal Logic 20 (3).
    A generation before Beardsley, legal scholar John Henry Wigmore invented a scheme for representing arguments in a tree diagram, aimed to help advocates analyze the proof of facts at trial. In this essay, I describe Wigmore's "Chart Method" and trace its origin and influence. Wigmore, I argue, contributes to contemporary theory in two ways. His rhetorical approach to diagramming provides a novel perspective on problems about the theory of reasoning, premise adequacy, and dialectical obligations. Further, he advances a novel solution (...)
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  • Modeling Corroborative Evidence: Inference to the Best Explanation as Counter–Rebuttal.David Godden - 2014 - Argumentation 28 (2):187-220.
    Corroborative evidence has a dual function in argument. Primarily, it functions to provide direct evidence supporting the main conclusion. But it also has a secondary, bolstering function which increases the probative value of some other piece of evidence in the argument. This paper argues that the bolstering effect of corroborative evidence is legitimate, and can be explained as counter–rebuttal achieved through inference to the best explanation. A model (argument diagram) of corroborative evidence, representing its structure and operation as a schematic (...)
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