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Profile: Kevin Kelly
  1. Kevin Kelly, Ockham's Razor, Empirical Complexity, and Truth-Finding Efficiency.
    Theoretical Computer Science, 383: 270-289, 2007.
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  2. Kevin Kelly, Ockham's Razor, Truth, and Information.
    in Handbook of the Philosophy of Information, J. van Behthem and P. Adriaans, eds., to appear.
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  3. Kevin T. Kelly, Learning Theory and Descriptive Set Theory.
    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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  4. Kevin T. Kelly, The Automated Discovery of Universal Theories.
    This thesis examines the prospects for mechanical procedures that can identify true, complete, universal, first-order logical theories on the basis of a complete enumeration of true atomic sentences. A sense of identification is defined that is..
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  5. Kevin T. Kelly, Uncomputability: The Problem of Induction Internalized.
    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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  6. Kevin Kelly & Peter Spirtes, The Expected Complexity of Problem Solving.
    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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  7. Kevin Kelly, A Close Shave with Realism: How Ockham's Razor Helps Us Find the Truth.
    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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  8. Kevin Kelly, Efficient Convergence Implies Ockham's Razor.
    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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  9. Kevin Kelly, How to Do Things with an Infinite Regress.
    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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  10. Kevin Kelly, Kevin Kelly, Oliver Schulte, Vincent Hendricks.
    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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  11. Kevin Kelly, Kevin T. Kelly and Oliver Schulte.
    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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  12. Kevin Kelly, Naturalism Logicized.
    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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  13. Kevin Kelly, Simplicity, Truth, and the Unending Game of Science.
    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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  14. Kevin Kelly, The Learning Power of Belief Revision.
    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. Kevin Kelly, Why Probability Does Not Capture the Logic of Scientific Justification.
    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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  16. Kevin T. Kelly, Conor Mayo Wilson, Hanti Lin & Oliver Schulte, Participants:.
    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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  17. Kevin T. Kelly (2012). 50 Years Receiving Vatican Ii: A Personal Odyssey. Columba Press.
     
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  18. Hanti Lin & Kevin T. Kelly (2012). A Geo-Logical Solution to the Lottery Paradox, with Applications to Conditional Logic. Synthese 186 (2):531-575.
  19. Hanti Lin & Kevin T. Kelly (2012). Propositional Reasoning That Tracks Probabilistic Reasoning. 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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  20. Kevin T. Kelly & Hanti Lin, A Geo-Logical Solution to the Lottery Paradox, with Applications to Nonmonotonic Logic.
    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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  21. Kevin T. Kelly, Simplicity, Truth, and Probability.
    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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  22. Conor Mayo-Wilson & Kevin Kelly, Ockham Efficiency Theorem for Stochastic Empirical Methods.
    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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  23. Kevin Kelly (2008). Review of Gilbert Harman, Sanjeev Kulkarni, Reliable Reasoning: Induction and Statistical Learning Theory. [REVIEW] Notre Dame Philosophical Reviews 2008 (3).
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  24. Kevin T. Kelly, Julie Clague, Bernard Hoose & Gerard Mannion (eds.) (2008). Moral Theology for the Twenty-First Century: Essays in Celebration of Kevin Kelly. T & T Clark.
     
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  25. 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). 10. Philosophy of Chemistry. Philosophy of Science 74 (5).
     
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  26. Kevin Kelly (2007). A New Solution to the Puzzle of Simplicity. 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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  27. Kevin T. Kelly, How Simplicity Helps You Find the Truth Without Pointing at It.
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  28. Kevin Kelly (2004). Justification as Truth-Finding Efficiency: How Ockham's Razor Works. Minds and Machines 14 (4):485-505.
    6 Abstract. I propose that empirical procedures, like computational procedures, are justified in 7 terms of truth-finding efficiency. I contrast the idea with more standard philosophies of science 8 and illustrate it by deriving Ockham’s razor from the aim of minimizing dramatic changes of 9 opinion en route to the truth.
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  29. Kevin Kelly (2004). Learning Theory and Epistemology. In. In Ilkka Niiniluoto, Matti Sintonen & Jan Wolenski (eds.), Handbook of Epistemology. Dordrecht: Kluwer Academic Pub. 183--203.
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  30. Kevin T. Kelly (2004). Justification as Truth-Finding Efficiency: How Ockham's Razor Works. 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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  31. Kevin T. Kelly & Clark Glymour, Why Bayesian Confirmation Does Not Capture the Logic of Scientific Justification.
    Kevin T. Kelly and Clark Glymour. Why Bayesian Confirmation Does Not Capture the Logic of Scientific Justification.
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  32. Kevin T. Kelly, Learning Theory and Epistemology.
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  33. Kevin Kelly (2000). The Logic of Success. 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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  34. Kevin T. Kelly (1999). Iterated Belief Revision, Reliability, and Inductive Amnesia. 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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  35. Kevin T. Kelly (1998). New Directions in Sexual Ethics: Moral Theology and the Challenge of Aids. G. Champman.
  36. Kevin T. Kelly & Oliver Schulte, Church's Thesis and Hume's Problem.
    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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  37. Kevin T. Kelly, Oliver Schulte & Vincent Hendricks, Reliable Belief Revision.
    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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  38. Kevin T. Kelly, Oliver Schulte & Cory Juhl (1997). Learning Theory and the Philosophy of Science. 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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  39. Kevin Kelly (1996). The Logic of Reliable Inquiry. 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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  40. Kevin T. Kelly & Oliver Schulte (1995). The Computable Testability of Theories Making Uncomputable Predictions. Erkenntnis 43 (1):29 - 66.
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  41. Cory Juhl & Kevin T. Kelly (1994). Realism, Convergence, and Additivity. 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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  42. Kevin T. Kelly & Clark Glymour (1992). Inductive Inference From Theory Laden Data. Journal of Philosophical Logic 21 (4):391 - 444.
    Kevin T. Kelly and Clark Glymour. Inductive Inference from Theory-Laden Data.
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  43. Kevin T. Kelly, Cory Juhl & Clark Glymour, Reliability, Realism, and Relativism.
    Kevin T. Kelly, Cory Juhl and Clark Glymour. Reliability, Realism, and Relativism.
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  44. Kevin T. Kelly (1991). Reichenbach, Induction, and Discovery. 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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  45. Clark Glymour & Kevin Kelly (1990). Why You'll Never Know Whether Roger Penrose is a Computer. Behavioral and Brain Sciences 13 (4):666-667.
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  46. Kevin T. Kelly, General Characteristics of Inductive Inference Over Arbitrary Sets of Data Representations.
    Kevin T. Kelly. General Characteristics of Inductive Inference Over Arbitrary Sets of Data Representations.
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  47. Kevin T. Kelly & Clark Glymour (1990). Getting to the Truth Through Conceptual Revolutions. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1990:89 - 96.
    There is a popular view that the alleged meaning shifts resulting from scientific revolutions are somehow incompatible with the formulation of general norms for scientific inquiry. We construct methods that can be shown to be maximally reliable at getting to the truth when the truth changes in response to the state of the scientist or his society.
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  48. Kevin T. Kelly & Clark Glymour (1990). Theory Discovery From Data with Mixed Quantifiers. 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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  49. Kevin T. Kelly & Clark Glymour (1989). Convergence to the Truth and Nothing but the Truth. 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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