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Information Theory

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  • Rudolf Arnheim (1959). Information Theory: An Introductory Note. Journal of Aesthetics and Art Criticism 17 (4):501-503.
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  • Massimiliano Badino (2004). An Application of Information Theory to the Problem of the Scientific Experiment. Synthese 140 (3).
    There are two basic approaches to the problem of induction:the empirical one, which deems that the possibility of induction depends on how theworld was made (and how it works) and the logical one, which considers the formation(and function) of language. The first is closer to being useful for induction, whilethe second is more rigorous and clearer. The purpose of this paper is to create an empiricalapproach to induction that contains the same formal exactitude as the logical approach.This requires: (a) that (...)
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  • Yehoshua Bar-Hillel (1955). An Examination of Information Theory. Philosophy of Science 22 (2):86-105.
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  • Léon Brillouin (1956/2004). Science and Information Theory. Dover Publications.
    A classic source for understanding the connections between information theory and physics, this text was written by one of the giants of 20th-century physics and is appropriate for upper-level undergraduates and graduate students. Topics include the principles of coding, coding problems and solutions, the analysis of signals, a summary of thermodynamics, thermal agitation and Brownian motion, and thermal noise in an electric circuit. A discussion of the negentropy principle of information introduces the author's renowned examination of Maxwell's demon. Concluding chapters (...)
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  • Julio A. Camargo (2008). Revisiting the Relation Between Species Diversity and Information Theory. Acta Biotheoretica 56 (4).
    The Shannon information function (H) has been extensively used in ecology as a statistic of species diversity. Yet, the use of Shannon diversity index has also been criticized, mainly because of its ambiguous ecological interpretation and because of its relatively great sensitivity to the relative abundances of species in the community. In my opinion, the major shortcoming of the traditional perspective (on the possible relation of species diversity with information theory) is that species need for an external receiver (the scientist (...)
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  • Rob Clifton (2002). The Subtleties of Entanglement and its Role in Quantum Information Theory. Proceedings of the Philosophy of Science Association 2002 (3).
    My aim in this paper is a modest one. I do not have any particular thesis to advance about the nature of entanglement, nor can I claim novelty for any of the material I shall discuss. My aim is simply to raise some questions about entanglement that spring naturally from certain developments in quantum information theory and are, I believe, worthy of serious consideration by philosophers of science. The main topics I discuss are different manifestations of quantum nonlocality, entanglement-assisted communication, (...)
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  • John Collier, Information Theory as a General Language for Functional Systems.
    Function refers to a broad family of concepts of varying abstractness and range of application, from a many-one mathematical relation of great generality to, for example, highly specialized roles of designed elements in complex machines such as degaussing in a television set, or contributory processes to control mechanisms in complex metabolic pathways, such as the inhibitory function of the appropriate part of the lac-operon on the production of lactase through its action on the genome in the absence of lactose. We (...)
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  • Jeff Coulter (1995). The Informed Neuron: Issues in the Use of Information Theory in the Behavioral Sciences. Minds and Machines 5 (4):583-96.
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  • Hilmi Demir (2008). Counterfactuals Vs. Conditional Probabilities: A Critical Analysis of the Counterfactual Theory of Information. Australasian Journal of Philosophy 86 (1):45 – 60.
    Cohen and Meskin 2006 recently offered a counterfactual theory of information to replace the standard probabilistic theory of information. They claim that the counterfactual theory fares better than the standard account on three grounds: first, it provides a better framework for explaining information flow properties; second, it requires a less expensive ontology; and third, because it does not refer to doxastic states of the information-receiving organism, it provides an objective basis. In this paper, I show that none of these is (...)
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  • David Ellerman, A Short Note on the Logico-Conceptual Foundations of Information Theory in Partition Logic.
    A new logic of partitions has been developed that is dual to ordinary logic when the latter is interpreted as the logic of subsets of a fixed universe rather than the logic of propositions. For a finite universe, the logic of subsets gave rise to finite probability theory by assigning to each subset its relative size as a probability. The analogous construction for the dual logic of partitions gives rise to a notion of logical entropy that is precisely related to (...)
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  • David Ellerman (2009). Counting Distinctions: On the Conceptual Foundations of Shannon's Information Theory. Synthese 168 (1).
    Categorical logic has shown that modern logic is essentially the logic of subsets (or “subobjects”). In “subset logic,” predicates are modeled as subsets of a universe and a predicate applies to an individual if the individual is in the subset. Partitions are dual to subsets so there is a dual logic of partitions where a “distinction” [an ordered pair of distinct elements (u, u′) from the universe U] is dual to an “element”. A predicate modeled by a partition π on (...)
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  • Wesley Elsberry & Jeffrey Shallit (forthcoming). Information Theory, Evolutionary Computation, and Dembski's “Complex Specified Information”. Synthese.
    Intelligent design advocate William Dembski has introduced a measure of information called “complex specified information”, or CSI. He claims that CSI is a reliable marker of design by intelligent agents. He puts forth a “Law of Conservation of Information” which states that chance and natural laws are incapable of generating CSI. In particular, CSI cannot be generated by evolutionary computation. Dembski asserts that CSI is present in intelligent causes and in the flagellum of Escherichia coli , and concludes that neither (...)
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  • Attila Grandpierre (2006). A Review Of: "Information Theory, Evolution and the Origin of Life as a Digital Message How Life Resembles a Computer". World Futures 62 (5):401 – 403.
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  • Amit Hagar (2003). A Philosopher Looks at Quantum Information Theory. Philosophy of Science 70 (4):752-775.
    Recent suggestions to supply quantum mechanics (QM) with realistic foundations by reformulating it in light of quantum information theory (QIT) are examined and are found wanting by pointing to a basic conceptual problem that QIT itself ignores, namely, the measurement problem. Since one cannot ignore the measurement problem and at the same time pretend to be a realist, as they stand, the suggestions to reformulate QM in light of QIT are nothing but instrumentalism in disguise.
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  • Joseph F. Hanna (1969). Explanation, Prediction, Description, and Information Theory. Synthese 20 (3).
    The distinction between explanation and prediction has received much attention in recent literature, but the equally important distinction between explanation and description (or between prediction and description) remains blurred. This latter distinction is particularly important in the social sciences, where probabilistic models (or theories) often play dual roles as explanatory and descriptive devices. The distinction between explanation (or prediction) and description is explicated in the present paper in terms of information theory. The explanatory (or predictive) power of a probabilistic model (...)
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  • William F. Harms (1998). The Use of Information Theory in Epistemology. Philosophy of Science 65 (3):472-501.
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  • Aaron Meskin & Jonathan Cohen (2008). Counterfactuals, Probabilities, and Information: Response to Critics. Australasian Journal of Philosophy 86 (4):635 – 642.
    In earlier work we proposed an account of information grounded in counterfactual conditionals rather than probabilities, and argued that it might serve philosophical needs that more familiar probabilistic alternatives do not. Demir [2008] and Scarantino [2008] criticize the counterfactual approach by contending that its alleged advantages are illusory and that it fails to secure attractive desiderata. In this paper we defend the counterfactual account from these criticisms, and suggest that it remains a useful account of information.
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  • Leonard B. Meyer (1957). Meaning in Music and Information Theory. Journal of Aesthetics and Art Criticism 15 (4):412-424.
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  • Wayne Myrvold, From Physics to Information Theory and Back.
    Quantum information theory has given rise to a renewed interest in, and a new perspective on, the old issue of understanding the ways in which quantum mechanics differs from classical mechanics. The task of distinguishing between quantum and classical theory is facilitated by neutral frameworks that embrace both classical and quantum theory. In this paper, I discuss two approaches to this endeavour, the algebraic approach, and the convex set approach, with an eye to the strengths of each, and the relations (...)
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  • Derek Partridge (1981). Information Theory and Redundancy. Philosophy of Science 48 (2):308-316.
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  • Panu Raatikainen (2000). Algorithmic Information Theory and Undecidability. Synthese 123 (2).
    Algorithmic information theory, or the theory of Kolmogorov complexity, has become an extraordinarily popular theory, and this is no doubt due, in some part, to the fame of Chaitin’s incompleteness results arising from this field. Actually, there are two rather different results by Chaitin: the earlier one concerns the finite limit of the provability of complexity (see Chaitin, 1974a, 1974b, 1975a); and the later is related to random reals and the halting probability (see Chaitin, 1986, 1987a, 1987b, 1988, 1989.
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  • J. F. Schouten (1955). On the Concepts of Endechy and Manipulation and Their Application to Information Theory. Synthese 9 (1).
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  • Jacob T. Schwartz, A Note on Monte Carlo Primality Tests and Algorithmic Information Theory.
    clusions are only probably correct. On the other hand, algorithmic information theory provides a precise mathematical definition of the notion of random or patternless sequence. In this paper we shall describe conditions under which if the sequence of coin tosses in the Solovay– Strassen and Miller–Rabin algorithms is replaced by a sequence of heads and tails that is of maximal algorithmic information content, i.e., has maximal algorithmic randomness, then one obtains an error-free test for primality. These results are only of (...)
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  • James V. Stone (1997). Information Theory: The Holy Grail of Cortical Computation? Behavioral and Brain Sciences 20 (4):698-698.
    Simple hypotheses are intrinsically attractive, and, for this reason, need to be formulated with utmost precision if they are to be testable. Unfortunately, it is hard to see how Phillips & Singer's hypothesis might be unambiguously refuted. Despite this, the authors have provided much evidence consistent with the hypothesis, and have proposed a natural and powerful extension for information theoretic approaches to learning.
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  • Matthew Usher (2001). A Statistical Referential Theory of Content: Using Information Theory to Account for Misrepresentation. Mind and Language 16 (3):331-334.
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  • Michiel van Lambalgen (1989). Algorithmic Information Theory. Journal of Symbolic Logic 54 (4):1389-1400.
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  • Bruce Vermazen (1971). Information Theory and Musical Value. Journal of Aesthetics and Art Criticism 29 (3):367-370.
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