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  • Andrés Páez (2009). Artificial Explanations: The Epistemological Interpretation of Explanation in Ai. Synthese 170 (1).
    In this paper I critically examine the notion of explanation used in artificial intelligence in general, and in the theory of belief revision in particular. I focus on two of the best known accounts in the literature: Pagnucco’s abductive expansion functions and Gärdenfors’ counterfactual analysis. I argue that both accounts are at odds with the way in which this notion has historically been understood in philosophy. They are also at odds with the explanatory strategies used in actual scientific practice. At the end of the paper I outline a set of desiderata for an epistemologically motivated, scientifically informed belief revision model for explanation.
    Formal Epistemology, Misc in Epistemology
    Philosophy of Artificial Intelligence in Philosophy of Cognitive Science
    Explanation in General Philosophy of Science
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  • 69.9Todd Jones (1997). Unification, Reduction, and Non-Ideal Explanations. Synthese 112 (1).
    Kitcher's unification theory of explanation seems to suggest that only the most reductive accounts can legitimately be termed explanatory. This is not what we find in actual scientific practice. In this paper, I attempt to reconcile these ideas. I claim that Kitcher's theory picks out ideal explanations, but that our term explanation is used to cover other accounts that have a certain relationship with the ideal accounts. At times, versions and portions of ideal explanations can also be considered explanatory.
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  • 68.2Till Grüne-Yanoff (2009). The Explanatory Potential of Artificial Societies. Synthese 169 (3).
    It is often claimed that artificial society simulations contribute to the explanation of social phenomena. At the hand of a particular example, this paper argues that artificial societies often cannot provide full explanations, because their models are not or cannot be validated. Despite that, many feel that such simulations somehow contribute to our understanding. This paper tries to clarify this intuition by investigating whether artificial societies provide potential explanations. It is shown that these potential explanations, if they contribute to our (...) understanding, considerably differ from potential causal explanations. Instead of possible causal histories, simulations offer possible functional analyses of the explanandum . The paper discusses how these two kinds explanatory strategies differ, and how potential functional explanations can be appraised. (shrink)
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  • 66.4Bernard Walliser, Denis Zwirn & Hervé Zwirn (2005). Abductive Logics in a Belief Revision Framework. Journal of Logic, Language and Information 14 (1).
    Abduction was first introduced in the epistemological context of scientific discovery. It was more recently analyzed in artificial intelligence, especially with respect to diagnosis analysis or ordinary reasoning. These two fields share a common view of abduction as a general process of hypotheses formation. More precisely, abduction is conceived as a kind of reverse explanation where a hypothesis H can be abduced from events E if H is a good explanation of E. The paper surveys four known schemes for abduction (...) that can be used in both fields. Its first contribution is a taxonomy of these schemes according to a common semantic framework based on belief revision. Its second contribution is to produce, for each non-trivial scheme, a representation theorem linking its semantic framework to a set of postulates. Its third contribution is to present semantic and axiomatic arguments in favor of one of these schemes, ordered abduction, which has never been vindicated in the literature. (shrink)
    Science, Logic, and Mathematics
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  • 64.9Denis J. Hilton (1996). Mental Models and Causal Explanation: Judgements of Probable Cause and Explanatory Relevance. Thinking and Reasoning 2 (4):273 – 308.
    Good explanations are not only true or probably true, but are also relevant to a causal question. Current models of causal explanation either only address the question of the truth of an explanation, or do not distinguish the probability of an explanation from its relevance. The tasks of scenario construction and conversational explanation are distinguished, which in turn shows how scenarios can interact with conversational principles to determine the truth and relevance of explanations. The proposed model distinguishes causal discounting from (...) causal backgrounding , and makes predictions concerning the differential effects of contextual information about alternative explanations on: (a) the kind of mental models constructed; (b) belief revision about probable cause; and (c) the perceived quality of a focal explanation. Four experiments are reported that test these predictions. The significance of the notion of explanatory relevance for research on causal explanation is then discussed. (shrink)
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  • 61.9Keith R. Sawyer (2004). Social Explanation and Computational Simulation. Philosophical Explorations 7 (3):219 – 231.
    I explore a type of computational social simulation known as artificial societies. Artificial society simulations are dynamic models of real-world social phenomena. I explore the role that these simulations play in social explanation, by situating these simulations within contemporary philosophical work on explanation and on models. Many contemporary philosophers have argued that models provide causal explanations in science, and that models are necessary mediators between theory and data. I argue that artificial society simulations provide causal mechanistic explanations. I conclude that (...) in their current form, these simulations are based on methodologically individualist assumptions that could limit their potential scope of social explanation. (shrink)
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  • 61.5Edmund Nierlich (1988). Die Deduktiv-Nomologische Erklärung AlS Hauptmotiv Empirisch-Wissenschaftlicher Tätigkeit. Erkenntnis 29 (1).
    In this paper an attempt is made at developing the notion of a real and complete empirical explanation as excluding all forms of potential or incomplete explanations. This explanation is, however, no longer conceived as the proper aim of empirical science, for it can certainly be gleaned from recent epistemological publications that no comprehensive notion of a real and complete scientific explanation is likely to be constructed from within empirical science. Contrary to common understanding the empirical explanation, deductive-nomological as well (...) as statistical explanation, is considered here only as motive of scientific activities, i.e., as common aim of a transcending cooperation of scientific and non-scientific social practice. Following from this the proper aim of empirical science now consists in the development of practically relevant explanatory theories.This redetermination of the aim of scientific activities of empirical science also means criticism of the unification of deductive-nomological and statistical explanations, as it has been proposed by Wolfgang Stegmüller in his pragmatisch-epistemische Wende. For both forms of empirical explanation must be referred to fundamentally different kinds of practical relevance, the former playing a more important role in the advancement of social practice. Stegmüller's development of a comprehensive probabilistic notion of empirical explanation, as tied up to pragmatic knowledge-situations, in a way already transcends a scientifically immanent determination of it, but he seems to have stopped halfway on the road to practically relevant empirical explanations. Several insufficiencies with his probabilistic notion of empirical explanation are shown up in this paper as a consequence of his abiding by pragmatic, and not penetrating to practical, knowledge-situations. The final result of it, however, consists in a clarification and a modification of the concept of deductive-nomological explanation, originally developed by Hempel and Oppenheim. (shrink)
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  • 61.5Andrés Páez, The Epistemic Value of Explanation.
    In this paper I defend the idea that there is a sense in which it is meaningful and useful to talk about objective understanding, and that to characterize that notion it is necessary to formulate an account of explanation that makes reference to the beliefs and epistemic goals of the participants in a cognitive enterprise. Using the framework for belief revision developed by Isaac Levi, I analyze the conditions that information must fulfill to be both potentially explanatory and epistemically valuable (...) to an inquiring agent and to a scientific community. To be potentially explanatory, the information must state the relations of probabilistic relevance that the explanans bares to the explanandum. But a potential explanation con only be a bona fide explanation if it becomes part of inquiry, that is, if an agent or a group of agents can see any value in it for their cognitive purposes. I provide a way to evaluate the epistemic value of a potential explanation as a function of its credibility and its informational content. (shrink)
    Attitude Ascriptions in Philosophy of Language
    Belief Revision in Epistemology
    Explanation in General Philosophy of Science
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  • 61.3Gerhard Schurz (1982). Ein Logisch-Pragmatisches Modell Von Deduktiv-Nomologischer Erklärung (Systematisierung). Erkenntnis 17 (3).
    The present paper first shows that the validity of deductive-nomological (D-N) explanations (systematizations) depends in general on the interpretation context of the predicates involved in the explanation. Therefore, no logical-semantical model can be adequate. This problem is solved by relativisation of the validity criteria on both the confirmation context and the definition context of the premisses. Based upon this, a logical-pragmatical model of D-N explanation is developed. Thereby, especially explanations of laws and global explanations are taken into consideration, since these (...) can be regarded as prototypes of scientific explanation. (shrink)
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  • 61.1Alexander Bird (2006). Selection and Explanation. In Alexander Bird (ed.), Rethinking Explanation.
    Selection explanations explain some non-accidental generalizations in virtue of a selection process. Such explanations are not particulaizable - they do not transfer as explanations of the instances of such generalizations. This is unlike many explanations in the physical sciences, where the explanation of the general fact also provides an explanation of its instances (i.e. standard D-N explanations). Are selection explanations (e.g. in biology) therefore a different kind of explanation? I argue that to understand this issue, we need to see that (...) a standard D-N explanation of some non-accidental generalization (al Fs are Gs) may also ipso facto explain its contrapositive (all non-Gs are non-Fs), but the explanation is particularizable with respect to the former but not to the latter. This can be seen by noting that the Raven Paradox counterexample to the H-D model of confirmation also generates a counterexample to the D-N model of explanation (all ravens are black does not explain why the non-black shoe is a non-raven). In such cases it is natural to take the generalization with the positive predicates to have a particularizable explanation. However, this need not be the case, and in selection explanations it is the generalization with the positive predicates whose explanation is no particularizable. Thus there is no need to suppose that selection explanations are fundamentally different. (shrink)
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  • 60.3Douglas Walton (2004). A New Dialectical Theory of Explanation. Philosophical Explorations 7 (1):71 – 89.
    This paper offers a dialogue theory of explanation. A successful explanation is defined as a transfer of understanding in a dialogue system in which a questioner and a respondent take part. The questioner asks a special sort of why-question that asks for understanding of something and the respondent provides a reply that transfers understanding to the questioner. The theory is drawn from recent work on explanation in artificial intelligence (AI), especially in expert systems, but applies to scientific, legal and everyday (...) conversational explanations. (shrink)
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