James Hawthorne University of Oklahoma
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About me
I'm a scientific realist and a mathematical instrumentalist with a fascination for figuring out what our best scientific theories imply about the nature of physical reality. As a logician my research primarily involves the investigation and development of probabilistic logics of evidential support, logics of belief and comparative plausibility, and logics for conditionals. Much of my work on probabilistic logic focuses on the development of a logical (non-subjectivist) version of Bayesian Confirmation Theory.
My works
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  1. James Hawthorne & Branden Fitelson, An Even Better Solution to the Paradox of the Ravens.
    Think of confirmation in the context of the Ravens Paradox this way. The likelihood ratio measure of incremental confirmation gives us, for an observed Black Raven and for an observed non-Black non-Raven, respectively, the following “full” likelihood ratios.
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  2. James Hawthorne (2014). A Primer on Rational Consequence Relations, Popper Functions, and Their Ranked Structures. Studia Logica 102 (4):731-749.
    Rational consequence relations and Popper functions provide logics for reasoning under uncertainty, the former purely qualitative, the latter probabilistic. But few researchers seem to be aware of the close connection between these two logics. I’ll show that Popper functions are probabilistic versions of rational consequence relations. I’ll not assume that the reader is familiar with either logic. I present them, and explicate the relationship between them, from the ground up. I’ll also present alternative axiomatizations for each logic, showing them to (...)
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  3. James Hawthorne (2011). Bayesian Confirmation Theory. In S. French & J. Saatsi (eds.), Continuum Companion to the Philosophy of Science. Continuum Press.
    Scientifi c theories and hypotheses make claims that go well beyond what we can immediately observe. How can we come to know whether such claims are true? The obvious approach is to see what a hypothesis says about the observationally accessible parts of the world. If it gets that wrong, then it must be false; if it gets that right, then it may have some claim to being true. Any sensible a empt to construct a logic that captures how we (...)
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  4. James Hawthorne (2011). Confirmation Theory. In Prasanta S. Bandyopadhyay & Malcolm Forster (eds.), Philosophy of Statistics, Handbook of the Philosophy of Science, Volume 7. Elsevier.
    Confirmation theory is the study of the logic by which scientific hypotheses may be confirmed or disconfirmed, or even refuted by evidence. A specific theory of confirmation is a proposal for such a logic. Presumably the epistemic evaluation of scientific hypotheses should largely depend on their empirical content – on what they say the evidentially accessible parts of the world are like, and on the extent to which they turn out to be right about that. Thus, all theories of confirmation (...)
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  5. James Hawthorne, Inductive Logic. The Stanford Encyclopedia of Philosophy.
    Sections 1 through 3 present all of the main ideas behind the probabilistic logic of evidential support. For most readers these three sections will suffice to provide an adequate understanding of the subject. Those readers who want to know more about how the logic applies when the implications of hypotheses about evidence claims (called likelihoods) are vague or imprecise may, after reading sections 1-3, skip to section 6. Sections 4 and 5 are for the more advanced reader who wants a (...)
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  6. Branden Fitelson & James Hawthorne (2010). How Bayesian Confirmation Theory Handles the Paradox of the Ravens. In. In Ellery Eells & James Fetzer (eds.), The Place of Probability in Science. Springer. 247--275.
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  7. Branden Fitelson & James Hawthorne (2010). Wason Task(s) and the Paradox of Confirmation. Philosophical Perspectives 24 (1):207-241.
  8. Branden Fitelson & James Hawthorne (2010). How Bayesian Confirmation Theory Handles the Paradox of the Ravens. In Ellery Eells & James Fetzer (eds.), The Place of Probability in Science. Springer. 247--275.
    The Paradox of the Ravens (a.k.a,, The Paradox of Confirmation) is indeed an old chestnut. A great many things have been written and said about this paradox and its implications for the logic of evidential support. The first part of this paper will provide a brief survey of the early history of the paradox. This will include the original formulation of the paradox and the early responses of Hempel, Goodman, and Quine. The second part of the paper will describe attempts (...)
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  9. James Hawthorne (2009). The Lockean Thesis and the Logic of Belief. In Franz Huber & Christoph Schmidt-Petri (eds.), Degrees of Belief. Synthese Library: Springer. 49--74.
    In a penetrating investigation of the relationship between belief and quantitative degrees of confidence (or degrees of belief) Richard Foley (1992) suggests the following thesis: ... it is epistemically rational for us to believe a proposition just in case it is epistemically rational for us to have a sufficiently high degree of confidence in it, sufficiently high to make our attitude towards it one of belief. Foley goes on to suggest that rational belief may be just rational degree of confidence (...)
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  10. James Hawthorne (2007). Nonmonotonic Conditionals That Behave Like Conditional Probabilities Above a Threshold. Journal of Applied Logic 5 (4):625-637.
    I’ll describe a range of systems for nonmonotonic conditionals that behave like conditional probabilities above a threshold. The rules that govern each system are probabilistically sound in that each rule holds when the conditionals are interpreted as conditional probabilities above a threshold level specific to that system. The well-known preferential and rational consequence relations turn out to be special cases in which the threshold level is 1. I’ll describe systems that employ weaker rules appropriate to thresholds lower than 1, and (...)
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  11. James Hawthorne & David Makinson (2007). The Quantitative/Qualitative Watershed for Rules of Uncertain Inference. Studia Logica 86 (2):247-297.
    We chart the ways in which closure properties of consequence relations for uncertain inference take on different forms according to whether the relations are generated in a quantitative or a qualitative manner. Among the main themes are: the identification of watershed conditions between probabilistically and qualitatively sound rules; failsafe and classicality transforms of qualitatively sound rules; non-Horn conditions satisfied by probabilistic consequence; representation and completeness problems; and threshold-sensitive conditions such as ‘preface’ and ‘lottery’ rules.
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  12. T. Szabo Gendler, J. Hawthorne & E. Paganini (2005). Recensioni/Reviews-Conceivability and Possibility. Epistemologia 28 (2).
     
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  13. James Hawthorne (2005). Degree-of-Belief and Degree-of-Support: Why Bayesians Need Both Notions. Mind 114 (454):277-320.
    I argue that Bayesians need two distinct notions of probability. We need the usual degree-of-belief notion that is central to the Bayesian account of rational decision. But Bayesians also need a separate notion of probability that represents the degree to which evidence supports hypotheses. Although degree-of-belief is well suited to the theory of rational decision, Bayesians have tried to apply it to the realm of hypothesis confirmation as well. This double duty leads to the problem of old evidence, a problem (...)
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  14. T. Szabo Gendler & J. Hawthorne (2004). Ity and Possibility (Oxford: Oxford University Press, 2002). Croatian Journal of Philosophy 4 (10-12):301.
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  15. James Hawthorne (2004). Three Models of Sequential Belief Updating on Uncertain Evidence. Journal of Philosophical Logic 33 (1):89-123.
    Jeffrey updating is a natural extension of Bayesian updating to cases where the evidence is uncertain. But, the resulting degrees of belief appear to be sensitive to the order in which the uncertain evidence is acquired, a rather un-Bayesian looking effect. This order dependence results from the way in which basic Jeffrey updating is usually extended to sequences of updates. The usual extension seems very natural, but there are other plausible ways to extend Bayesian updating that maintain order-independence. I will (...)
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  16. James Hawthorne & Branden Fitelson (2004). Discussion: Re‐Solving Irrelevant Conjunction with Probabilistic Independence. Philosophy of Science 71 (4):505-514.
    Naive deductivist accounts of confirmation have the undesirable consequence that if E confirms H, then E also confirms the conjunction H·X, for any X—even if X is completely irrelevant to E and H. Bayesian accounts of confirmation may appear to have the same problem. In a recent article in this journal Fitelson (2002) argued that existing Bayesian attempts to resolve of this problem are inadequate in several important respects. Fitelson then proposes a new‐and‐improved Bayesian account that overcomes the problem of (...)
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  17. Paul Teller, Stefano Gattei, Kent W. Staley, Eric Winsberg, James Hawthorne, Branden Fitelson, Patrick Maher, Peter Achinstein & Mathias Frisch (2004). 10. Selection, Drift, and the “Forces” of Evolution Selection, Drift, and the “Forces” of Evolution (Pp. 550-570). Philosophy of Science 71 (4).
     
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  18. James Hawthorne & Luc Bovens (1999). The Preface, the Lottery, and the Logic of Belief. Mind 108 (430):241-264.
    John Locke proposed a straightforward relationship between qualitative and quantitative doxastic notions: belief corresponds to a sufficiently high degree of confidence. Richard Foley has further developed this Lockean thesis and applied it to an analysis of the preface and lottery paradoxes. Following Foley's lead, we exploit various versions of these paradoxes to chart a precise relationship between belief and probabilistic degrees of confidence. The resolutions of these paradoxes emphasize distinct but complementary features of coherent belief. These features suggest principles that (...)
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  19. E. M. Hammer, J. Hawthorne, M. Kracht, E. Martino, J. M. Mendez, R. K. Meyer, L. S. Moss, A. Tzouvaras, J. van Benthem & F. Wolter (1998). De Rijke, M., 109 Di Maio, MC, 435 Doria, FA, 553 French, S., 603. Journal of Philosophical Logic 27 (661).
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  20. James Hawthorne (1998). On the Logic of Nonmonotonic Conditionals and Conditional Probabilities: Predicate Logic. [REVIEW] Journal of Philosophical Logic 27 (1):1-34.
    In a previous paper I described a range of nonmonotonic conditionals that behave like conditional probability functions at various levels of probabilistic support. These conditionals were defined as semantic relations on an object language for sentential logic. In this paper I extend the most prominent family of these conditionals to a language for predicate logic. My approach to quantifiers is closely related to Hartry Field's probabilistic semantics. Along the way I will show how Field's semantics differs from a substitutional interpretation (...)
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  21. James Hawthorne (1996). Mathematical Instrumentalism Meets the Conjunction Objection. Journal of Philosophical Logic 25 (4):363-397.
    Scientific realists often appeal to some version of the conjunction objection to argue that scientific instrumentalism fails to do justice to the full empirical import of scientific theories. Whereas the conjunction objection provides a powerful critique of scientific instrumentalism, I will show that mathematical instnrunentalism escapes the conjunction objection unscathed.
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  22. James Hawthorne (1996). On the Logic of Nonmonotonic Conditionals and Conditional Probabilities. Journal of Philosophical Logic 25 (2):185-218.
    I will describe the logics of a range of conditionals that behave like conditional probabilities at various levels of probabilistic support. Families of these conditionals will be characterized in terms of the rules that their members obey. I will show that for each conditional, →, in a given family, there is a probabilistic support level r and a conditional probability function P such that, for all sentences C and B, 'C → B' holds just in case P[B | C] ≥ (...)
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  23. James Hawthorne & Michael Silberstein (1995). For Whom the Bell Arguments Toll. Synthese 102 (1):99-138.
    We will formulate two Bell arguments. Together they show that if the probabilities given by quantum mechanics are approximately correct, then the properties exhibited by certain physical systems must be nontrivially dependent on thetypes of measurements performedand eithernonlocally connected orholistically related to distant events. Although a number of related arguments have appeared since John Bell's original paper (1964), they tend to be either highly technical or to lack full generality. The following arguments depend on the weakest of premises, and the (...)
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  24. James Hawthorne (1994). On the Nature of Bayesian Convergence. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1994:241 - 249.
    The objectivity of Bayesian induction relies on the ability of evidence to produce a convergence to agreement among agents who initially disagree about the plausibilities of hypotheses. I will describe three sorts of Bayesian convergence. The first reduces the objectivity of inductions about simple "occurrent events" to the objectivity of posterior probabilities for theoretical hypotheses. The second reveals that evidence will generally induce converge to agreement among agents on the posterior probabilities of theories only if the convergence is 0 or (...)
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  25. James Hawthorne (1993). Bayesian Induction IS Eliminative Induction. Philosophical Topics 21 (1):99-138.
    Eliminative induction is a method for finding the truth by using evidence to eliminate false competitors. It is often characterized as "induction by means of deduction"; the accumulating evidence eliminates false hypotheses by logically contradicting them, while the true hypothesis logically entails the evidence, or at least remains logically consistent with it. If enough evidence is available to eliminate all but the most implausible competitors of a hypothesis, then (and only then) will the hypothesis become highly confirmed. I will argue (...)
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  26. James Hawthorne (1989). Giving Up Judgment Empiricism: The Bayesian Epistemology of Bertrand Russell and Grover Maxwell. In C. Wade Savage & C. Anthony Anderson (eds.), ReReading Russell: Bertrand Russell's Metaphysics and Epistemology; Minnesota Studies in the Philosophy of Science, Volume 12. University of Minnesota Press.
    This essay is an attempt to gain better insight into Russell's positive account of inductive inference. I contend that Russell's postulates play only a supporting role in his overall account. At the center of Russell's positive view is a probabilistic, Bayesian model of inductive inference. Indeed, Russell and Maxwell actually held very similar Bayesian views. But the Bayesian component of Russell's view in Human Knowledge is sparse and easily overlooked. Maxwell was not aware of it when he developed his own (...)
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  27. James Hawthorne (1988). A Semantic Approach to Non-Monotonic Conditionals. In J. F. Lemmer & L. N. Kanal (eds.), Uncertainty in Artificial Intelligence 2. Elsevier.
    Any inferential system in which the addition of new premises can lead to the retraction of previous conclusions is a non-monotonic logic. Classical conditional probability provides the oldest and most widely respected example of non-monotonic inference. This paper presents a semantic theory for a unified approach to qualitative and quantitative non-monotonic logic. The qualitative logic is unlike most other non- monotonic logics developed for AI systems. It is closely related to classical (i.e., Bayesian) probability theory. The semantic theory for qualitative (...)
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  28. William H. Hanson & James Hawthorne (1985). Validity in Intensional Languages: A New Approach. Notre Dame Journal of Formal Logic 26 (1):9-35.
    Although the use of possible worlds in semantics has been very fruitful and is now widely accepted, there is a puzzle about the standard definition of validity in possible-worlds semantics that has received little notice and virtually no comment. A sentence of an intensional language is typically said to be valid just in case it is true at every world under every model on every model structure of the language. Each model structure contains a set of possible worlds, and models (...)
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  29. James Hawthorne, Voting in Search of the Public Good: The Probabilistic Logic of Majority Judgments.
    I argue for an epistemic conception of voting, a conception on which the purpose of the ballot is at least in some cases to identify which of several policy proposals will best promote the public good. To support this view I first briefly investigate several notions of the kind of public good that public policy should promote. Then I examine the probability logic of voting as embodied in two very robust versions of the Condorcet Jury Theorem and some related results. (...)
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