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Profile: Kevin Kelly
  1.  39
    The Logic of Reliable Inquiry.Kevin Kelly - 1996 - Oxford University Press USA.
    This book is devoted to a different proposal--that the logical structure of the scientist's method should guarantee eventual arrival at the truth given the scientist's background assumptions.
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  2.  47
    Propositional Reasoning That Tracks Probabilistic Reasoning.Hanti Lin & Kevin T. Kelly - 2012 - Journal of Philosophical Logic 41 (6):957-981.
    This paper concerns the extent to which uncertain propositional reasoning can track probabilistic reasoning, and addresses kinematic problems that extend the familiar Lottery paradox. An acceptance rule assigns to each Bayesian credal state p a propositional belief revision method B p , which specifies an initial belief state B p (T) that is revised to the new propositional belief state B(E) upon receipt of information E. An acceptance rule tracks Bayesian conditioning when B p (E) = B p|E (T), for (...)
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  3.  24
    Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.Clark Glymour, Richard Scheines, Peter Spirtes & Kevin T. Kelly - unknown
    Clark Glymour, Richard Scheines, Peter Spirtes and Kevin Kelly. Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.
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  4.  59
    A New Solution to the Puzzle of Simplicity.Kevin Kelly - 2007 - Philosophy of Science 74 (5):561-573.
    Explaining the connection, if any, between simplicity and truth is among the deepest problems facing the philosophy of science, statistics, and machine learning. Say that an efficient truth finding method minimizes worst case costs en route to converging to the true answer to a theory choice problem. Let the costs considered include the number of times a false answer is selected, the number of times opinion is reversed, and the times at which the reversals occur. It is demonstrated that (1) (...)
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  5.  65
    A Geo-Logical Solution to the Lottery Paradox, with Applications to Conditional Logic.Hanti Lin & Kevin T. Kelly - 2012 - Synthese 186 (2):531-575.
  6.  29
    Realism, Rhetoric, and Reliability.Kevin T. Kelly, Konstantin Genin & Hanti Lin - 2016 - Synthese 193 (4):1191-1223.
    Ockham’s razor is the characteristic scientific penchant for simpler, more testable, and more unified theories. Glymour’s early work on confirmation theory eloquently stressed the rhetorical plausibility of Ockham’s razor in scientific arguments. His subsequent, seminal research on causal discovery still concerns methods with a strong bias toward simpler causal models, and it also comes with a story about reliability—the methods are guaranteed to converge to true causal structure in the limit. However, there is a familiar gap between convergent reliability and (...)
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  7.  37
    Ockham's Razor, Empirical Complexity, and Truth-Finding Efficiency.Kevin Kelly - 2007 - Theoretical Computer Science 383:270-289.
    Theoretical Computer Science, 383: 270-289, 2007.
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  8.  91
    Justification as Truth-Finding Efficiency: How Ockham's Razor Works.Kevin T. Kelly - 2004 - Minds and Machines 14 (4):485-505.
    I propose that empirical procedures, like computational procedures, are justified in terms of truth-finding efficiency. I contrast the idea with more standard philosophies of science and illustrate it by deriving Ockham's razor from the aim of minimizing dramatic changes of opinion en route to the truth.
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  9.  46
    Why Probability Does Not Capture the Logic of Scientific Justification.Kevin Kelly - unknown
    Here is the usual way philosophers think about science and induction. Scientists do many things— aspire, probe, theorize, conclude, retract, and refine— but successful research culminates in a published research report that presents an argument for some empirical conclusion. In mathematics and logic there are sound deductive arguments that fully justify their conclusions, but such proofs are unavailable in the empirical domain because empirical hypotheses outrun the evidence adduced for them. Inductive skeptics insist that such conclusions cannot be justified. But (...)
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  10.  76
    Iterated Belief Revision, Reliability, and Inductive Amnesia.Kevin T. Kelly - 1999 - Erkenntnis 50 (1):11-58.
    Belief revision theory concerns methods for reformulating an agent's epistemic state when the agent's beliefs are refuted by new information. The usual guiding principle in the design of such methods is to preserve as much of the agent's epistemic state as possible when the state is revised. Learning theoretic research focuses, instead, on a learning method's reliability or ability to converge to true, informative beliefs over a wide range of possible environments. This paper bridges the two perspectives by assessing the (...)
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  11.  25
    Uncomputability: The Problem of Induction Internalized.Kevin T. Kelly - unknown
    I show that a version of Ockham’s razor (a preference for simple answers) is advantageous in both domains when infallible inference is infeasible. A familiar response to the empirical problem..
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  12.  93
    Convergence to the Truth and Nothing but the Truth.Kevin T. Kelly & Clark Glymour - 1989 - Philosophy of Science 56 (2):185-220.
    One construal of convergent realism is that for each clear question, scientific inquiry eventually answers it. In this paper we adapt the techniques of formal learning theory to determine in a precise manner the circumstances under which this ideal is achievable. In particular, we define two criteria of convergence to the truth on the basis of evidence. The first, which we call EA convergence, demands that the theorist converge to the complete truth "all at once". The second, which we call (...)
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  13.  13
    Church's Thesis and Hume's Problem.Kevin T. Kelly & Oliver Schulte - unknown
    We argue that uncomputability and classical scepticism are both reflections of inductive underdetermination, so that Church's thesis and Hume's problem ought to receive equal emphasis in a balanced approach to the philosophy of induction. As an illustration of such an approach, we investigate how uncomputable the predictions of a hypothesis can be if the hypothesis is to be reliably investigated by a computable scientific method.
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  14.  43
    The Learning Power of Belief Revision.Kevin Kelly - unknown
    Belief revision theory aims to describe how one should change one’s beliefs when they are contradicted by newly input information. The guiding principle of belief revision theory is to change one’s prior beliefs as little as possible in order to maintain consistency with the new information. Learning theory focuses, instead, on learning power: the ability to arrive at true beliefs in a wide range of possible environments. The goal of this paper is to bridge the two approaches by providing a (...)
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  15.  7
    Reliable Belief Revision.Kevin T. Kelly, Oliver Schulte & Vincent Hendricks - unknown
    Philosophical logicians proposing theories of rational belief revision have had little to say about whether their proposals assist or impede the agent's ability to reliably arrive at the truth as his beliefs change through time. On the other hand, reliability is the central concern of formal learning theory. In this paper we investigate the belief revision theory of Alchourron, Gardenfors and Makinson from a learning theoretic point of view.
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  16.  36
    Ockham Efficiency Theorem for Stochastic Empirical Methods.Kevin T. Kelly & Conor Mayo-Wilson - 2010 - Journal of Philosophical Logic 39 (6):679-712.
    Ockham’s razor is the principle that, all other things being equal, scientists ought to prefer simpler theories. In recent years, philosophers have argued that simpler theories make better predictions, possess theoretical virtues like explanatory power, and have other pragmatic virtues like computational tractability. However, such arguments fail to explain how and why a preference for simplicity can help one find true theories in scientific inquiry, unless one already assumes that the truth is simple. One new solution to that problem is (...)
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  17.  28
    Learning Theory and Descriptive Set Theory.Kevin T. Kelly - unknown
    then essentially characterized the hypotheses that mechanical scientists can successfully decide in the limit in terms of arithmetic complexity. These ideas were developed still further by Peter Kugel [4]. In this paper, I extend this approach to obtain characterizations of identification in the limit, identification with bounded mind-changes, and identification in the short run, both for computers and for ideal agents with unbounded computational abilities. The characterization of identification with n mind-changes entails, as a corollary, an exact arithmetic characterization of (...)
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  18.  43
    Simplicity, Truth, and the Unending Game of Science.Kevin Kelly - unknown
    This paper presents a new explanation of how preferring the simplest theory compatible with experience assists one in finding the true answer to a scientific question when the answers are theories or models. Inquiry is portrayed as an unending game between science and nature in which the scientist aims to converge to the true theory on the basis of accumulating information. Simplicity is a topological invariant reflecting sequences of theory choices that nature can force an arbitrary, convergent scientist to produce. (...)
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  19.  40
    Ockham's Razor, Truth, and Information.Kevin Kelly - manuscript
    in Handbook of the Philosophy of Information, J. van Behthem and P. Adriaans, eds., to appear.
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  20.  35
    Efficient Convergence Implies Ockham's Razor.Kevin Kelly - unknown
    A finite data set is consistent with infinitely many alternative theories. Scientific realists recommend that we prefer the simplest one. Anti-realists ask how a fixed simplicity bias could track the truth when the truth might be complex. It is no solution to impose a prior probability distribution biased toward simplicity, for such a distribution merely embodies the bias at issue without explaining its efficacy. In this note, I argue, on the basis of computational learning theory, that a fixed simplicity bias (...)
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  21.  35
    Learning Theory and the Philosophy of Science.Kevin T. Kelly, Oliver Schulte & Cory Juhl - 1997 - Philosophy of Science 64 (2):245-267.
    This paper places formal learning theory in a broader philosophical context and provides a glimpse of what the philosophy of induction looks like from a learning-theoretic point of view. Formal learning theory is compared with other standard approaches to the philosophy of induction. Thereafter, we present some results and examples indicating its unique character and philosophical interest, with special attention to its unified perspective on inductive uncertainty and uncomputability.
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  22. The Logic of Reliable Inquiry.Kevin Kelly - 1998 - British Journal for the Philosophy of Science 49 (2):351-354.
     
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  23.  16
    Reichenbach, Induction, and Discovery.Kevin T. Kelly - 1991 - Erkenntnis 35 (1-3):123 - 149.
    I have applied a fairly general, learning theoretic perspective to some questions raised by Reichenbach's positions on induction and discovery. This is appropriate in an examination of the significance of Reichenbach's work, since the learning-theoretic perspective is to some degree part of Reichenbach's reliabilist legacy. I have argued that Reichenbach's positivism and his infatuation with probabilities are both irrelevant to his views on induction, which are principally grounded in the notion of limiting reliability. I have suggested that limiting reliability is (...)
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  24.  39
    Inductive Inference From Theory Laden Data.Kevin T. Kelly & Clark Glymour - 1992 - Journal of Philosophical Logic 21 (4):391 - 444.
    Kevin T. Kelly and Clark Glymour. Inductive Inference from Theory-Laden Data.
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  25.  20
    The Logic of Discovery.Kevin T. Kelly - 1987 - Philosophy of Science 54 (3):435-452.
    There is renewed interest in the logic of discovery as well as in the position that there is no reason for philosophers to bother with it. This essay shows that the traditional, philosophical arguments for the latter position are bankrupt. Moreover, no interesting defense of the philosophical irrelevance or impossibility of the logic of discovery can be formulated or defended in isolation from computation-theoretic considerations.
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  26.  13
    Realism, Convergence, and Additivity.Cory Juhl & Kevin T. Kelly - 1994 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1994:181 - 189.
    In this paper, we argue for the centrality of countable additivity to realist claims about the convergence of science to the truth. In particular, we show how classical sceptical arguments can be revived when countable additivity is dropped.
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  27.  32
    A Further Tribute to Paul McGuire.Kevin T. Kelly - 1979 - The Chesterton Review 5 (2):323-323.
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  28.  19
    The Computable Testability of Theories Making Uncomputable Predictions.Kevin T. Kelly & Oliver Schulte - 1995 - Erkenntnis 43 (1):29 - 66.
  29.  4
    How Simplicity Helps You Find the Truth Without Pointing at It.Kevin T. Kelly - unknown
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  30.  34
    The Logic of Success.Kevin Kelly - 2000 - British Journal for the Philosophy of Science 51 (4):639-666.
    The problem of induction reminds us that science cannot wait for empirical hypotheses to be verified and Duhem’s problem reminds us that we cannot expect full refutations either. We must settle for something less. The shape of this something less depends on which features of full verification and refutation we choose to emphasize. If we conceive of verification and refutation as arguments in which evidence entails the hypothesis or its negation, then the central problem of the philosophy of science is (...)
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  31. 10. Philosophy of Chemistry.Cristina Bicchieri, Jason McKenzie Alexander, Kevin T. Kelly, Kevin Js Zollman, Malcolm R. Forster, Predrag Šustar, Patrick Forber, Kenneth Reisman, Jay Odenbaugh & Yoichi Ishida - 2007 - Philosophy of Science 74 (5).
  32.  54
    Why Bayesian Confirmation Does Not Capture the Logic of Scientific Justification.Kevin T. Kelly & Clark Glymour - unknown
    Kevin T. Kelly and Clark Glymour. Why Bayesian Confirmation Does Not Capture the Logic of Scientific Justification.
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  33.  14
    Thoroughly Modern Meno.Clark Glymour & Kevin T. Kelly - 1992 - In Inference, Explanation, and Other Frustrations: Essays in the Philosophy of Science. University of California Press: Berkeley. pp. 3--22.
    Clark Glymour and Kevin T. Kelly. Thoroughly Modern Meno.
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  34.  14
    Reliability, Realism, and Relativism.Kevin T. Kelly, Cory Juhl & Clark Glymour - unknown
    Kevin T. Kelly, Cory Juhl and Clark Glymour. Reliability, Realism, and Relativism.
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  35.  49
    A Close Shave with Realism: How Ockham's Razor Helps Us Find the Truth.Kevin Kelly - unknown
    Many distinct theories are compatible with current experience. Scientific realists recommend that we choose the simplest. Anti-realists object that such appeals to “Ockham’s razor” cannot be truth-conducive, since they lead us astray in complex worlds. I argue, on behalf of the realist, that always preferring the simplest theory compatible with experience is necessary for efficient convergence to the truth in the long run, even though it may point in the wrong direction in the short run. Efficiency is a matter of (...)
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  36.  45
    Learning Theory and Epistemology.Kevin Kelly - 2004 - In Ilkka Niiniluoto, Matti Sintonen & Jan Wolenski (eds.), Handbook of Epistemology. Dordrecht: Kluwer Academic. pp. 183--203.
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  37.  17
    Simplicity, Truth, and Probability.Kevin T. Kelly - unknown
    Simplicity has long been recognized as an apparent mark of truth in science, but it is difficult to explain why simplicity should be accorded such weight. This chapter examines some standard, statistical explanations of the role of simplicity in scientific method and argues that none of them explains, without circularity, how a reliance on simplicity could be conducive to finding true models or theories. The discussion then turns to a less familiar approach that does explain, in a sense, the elusive (...)
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  38.  35
    How to Do Things with an Infinite Regress.Kevin Kelly - manuscript
    Scientific methods may be viewed as procedures for converging to the true answer to a given empirical question. Typically, such methods converge to the truth only if certain empirical presuppositions are satisfied, which raises the question whether the presuppositions are satisfied. Another scientific method can be applied to this empirical question, and so forth, occasioning an empirical regress. So there is an obvious question about the point of such a regress. This paper explains how to assess the methodological worth of (...)
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  39.  11
    Theory Discovery From Data with Mixed Quantifiers.Kevin T. Kelly & Clark Glymour - 1990 - Journal of Philosophical Logic 19 (1):1 - 33.
    Convergent realists desire scientific methods that converge reliably to informative, true theories over a wide range of theoretical possibilities. Much attention has been paid to the problem of induction from quantifier-free data. In this paper, we employ the techniques of formal learning theory and model theory to explore the reliable inference of theories from data containing alternating quantifiers. We obtain a hierarchy of inductive problems depending on the quantifier prefix complexity of the formulas that constitute the data, and we provide (...)
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  40.  28
    Kevin T. Kelly and Oliver Schulte.Kevin Kelly - unknown
    We argue that uncomputability and classical scepticism are both re ections of inductive underdetermination, so that Church's thesis and Hume's problem ought to receive equal emphasis in a balanced approach to the philosophy of induction. As an illustration of such an approach, we investigate how uncomputable the predictions of a hypothesis can be if the hypothesis is to be reliably investigated by a computable scienti c method.
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  41. Kevin Kelly, Oliver Schulte, Vincent Hendricks.Kevin Kelly - unknown
    Philosophical logicians proposing theories of rational belief revision have had little to say about whether their proposals assist or impede the agent's ability to reliably arrive at the truth as his beliefs change through time. On the other hand, reliability is the central concern of formal learning theory. In this paper we investigate the belief revision theory of Alchourron, Gardenfors and Makinson from a learning theoretic point of view.
     
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  42.  11
    Why You'll Never Know Whether Roger Penrose is a Computer.Clark Glymour & Kevin Kelly - 1990 - Behavioral and Brain Sciences 13 (4):666-667.
  43.  26
    Project Page: Ockham's Razor and Truth.Kevin T. Kelly, Conor Mayo Wilson, Hanti Lin & Oliver Schulte - unknown
    Philosophy of science, statistics, and machine learning all recommend the selection of simple theories or models on the basis of empirical data, where simplicity has something to do with minimizing independent entities, principles, causes, or equational coefficients. This intuitive preference for simplicity is called Ockham's razor, after the fourteenth century theologian and logician William of Ockham. But in spite of its intuitive appeal, how could Ockham's razor help one find the true theory? For, in an updated version of Plato's Meno (...)
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  44.  18
    Formal Learning Theory and the Philosophy of Science.Kevin T. Kelly - 1988 - PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1988:413 - 423.
    Formal learning theory is an approach to the study of inductive inference that has been developed by computer scientists. In this paper, I discuss the relevance of formal learning theory to such standard topics in the philosophy of science as underdetermination, realism, scientific progress, methodology, bounded rationality, the problem of induction, the logic of discovery, the theory of knowledge, the philosophy of artificial intelligence, and the philosophy of psychology.
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  45.  9
    General Characteristics of Inductive Inference Over Arbitrary Sets of Data Representations.Kevin T. Kelly - unknown
    Kevin T. Kelly. General Characteristics of Inductive Inference Over Arbitrary Sets of Data Representations.
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  46.  8
    A Geo-Logical Solution to the Lottery Paradox, with Applications to Nonmonotonic Logic.Kevin T. Kelly & Hanti Lin - unknown
    We defend a set of acceptance rules that avoids the lottery paradox, that is closed under classical entailment, and that accepts uncertain propositions without ad hoc restrictions. We show that the rules we recommend provide a semantics that validates exactly Adams’ conditional logic and are exactly the rules that preserve a natural, logical structure over probabilistic credal states that we call probalogic. To motivate probalogic, we first expand classical logic to geologic, which fills the entire unit cube, and then we (...)
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  47.  14
    Review of Gilbert Harman, Sanjeev Kulkarni, Reliable Reasoning: Induction and Statistical Learning Theory[REVIEW]Kevin Kelly - 2008 - Notre Dame Philosophical Reviews 2008 (3).
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  48.  15
    Naturalism Logicized.Kevin Kelly - unknown
    The approach to scientific methodology developed in my recent book The Logic of Reliable Inquiry (LRI) shares many general features with that summarized in Larry Laudan’s concurrently published collection of papers Beyond Positivism and Relativism (BPR). Nonetheless, this fact might not be apparent, as my own work emphasizes mathematical theorems, whereas Laudan’s draws primarily upon historiography. It is, therefore, of some interest to discuss the extent of the agreement and the significance of the differences. More generally, the discussion will (I) (...)
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  49.  6
    Version Spaces, Structural Descriptions and NP-Completeness.Kevin T. Kelly - unknown
    Kevin T. Kelly. Version Spaces, Structural Descriptions and NP-Completeness.
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  50.  11
    The Expected Complexity of Problem Solving.Kevin Kelly & Peter Spirtes - unknown
    Worst case complexity analyses of algorithms are sometimes held to be less informative about the real difficulty of computation than are expected complexity analyses. We show that the two most common representations of problem solving in cognitive science each admit aigorithms that have constant expected complexity, and for one of these representations we obtain constant expected complexity bounds under a variety of probability measures.
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