Results for 'Williamson Jon'

997 found
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  1.  59
    In Defence of Objective Bayesianism.Jon Williamson - 2010 - Oxford University Press.
    Objective Bayesianism is a methodological theory that is currently applied in statistics, philosophy, artificial intelligence, physics and other sciences. This book develops the formal and philosophical foundations of the theory, at a level accessible to a graduate student with some familiarity with mathematical notation.
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  2. Bayesian Nets and Causality: Philosophical and Computational Foundations.Jon Williamson - 2004 - Oxford University Press.
    Bayesian nets are widely used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. This book, aimed at researchers and graduate students in computer science, mathematics and philosophy, brings together two important research topics: how to automate reasoning in artificial intelligence, and the nature of causality and probability in philosophy.
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  3.  48
    Establishing Causal Claims in Medicine.Jon Williamson - 2019 - International Studies in the Philosophy of Science 32 (1):33-61.
    Russo and Williamson put forward the following thesis: in order to establish a causal claim in medicine, one normally needs to establish both that the putative cause and putative effect are appropriately correlated and that there is some underlying mechanism that can account for this correlation. I argue that, although the Russo-Williamson thesis conflicts with the tenets of present-day evidence-based medicine, it offers a better causal epistemology than that provided by present-day EBM because it better explains two key (...)
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  4. Countable Additivity and Subjective Probability.Jon Williamson - 1999 - British Journal for the Philosophy of Science 50 (3):401-416.
    While there are several arguments on either side, it is far from clear as to whether or not countable additivity is an acceptable axiom of subjective probability. I focus here on de Finetti's central argument against countable additivity and provide a new Dutch book proof of the principle, To argue that if we accept the Dutch book foundations of subjective probability, countable additivity is an unavoidable constraint.
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  5. Generic Versus Single-Case Causality: The Case of Autopsy. [REVIEW]Jon Williamson - 2011 - European Journal for Philosophy of Science 1 (1):47-69.
    This paper addresses questions about how the levels of causality (generic and single-case causality) are related. One question is epistemological: can relationships at one level be evidence for relationships at the other level? We present three kinds of answer to this question, categorised according to whether inference is top-down, bottom-up, or the levels are independent. A second question is metaphysical: can relationships at one level be reduced to relationships at the other level? We present three kinds of answer to this (...)
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  6.  64
    Deliberation, Judgement and the Nature of Evidence.Jon Williamson - unknown
    A normative Bayesian theory of deliberation and judgement requires a procedure for merging the evidence of a collection of agents. In order to provide such a procedure, one needs to ask what the evidence is that grounds Bayesian probabilities. After finding fault with several views on the nature of evidence, it is argued that evidence is whatever is rationally taken for granted. This view is shown to have consequences for an account of merging evidence, and it is argued that standard (...)
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  7. Bayesian Nets and Causality.Jon Williamson - manuscript
    How should we reason with causal relationships? Much recent work on this question has been devoted to the theses (i) that Bayesian nets provide a calculus for causal reasoning and (ii) that we can learn causal relationships by the automated learning of Bayesian nets from observational data. The aim of this book is to..
     
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  8. Models for Prediction, Explanation and Control: Recursive Bayesian Networks.Jon Williamson - 2011 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 26 (1):5-33.
    The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show in particular (...)
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  9. Objective Bayesianism, Bayesian Conditionalisation and Voluntarism.Jon Williamson - 2011 - Synthese 178 (1):67-85.
    Objective Bayesianism has been criticised on the grounds that objective Bayesian updating, which on a finite outcome space appeals to the maximum entropy principle, differs from Bayesian conditionalisation. The main task of this paper is to show that this objection backfires: the difference between the two forms of updating reflects negatively on Bayesian conditionalisation rather than on objective Bayesian updating. The paper also reviews some existing criticisms and justifications of conditionalisation, arguing in particular that the diachronic Dutch book justification fails (...)
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  10. Lectures on Inductive Logic.Jon Williamson - 2017 - Oxford University Press.
    Inductive logic is a theory of how one should reason in the face of uncertainty. It has applications to decision making and artificial intelligence, as well as to scientific problems.
     
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  11. What is a Mechanism? Thinking About Mechanisms Across the Sciences.Phyllis Illari & Jon Williamson - 2012 - European Journal for Philosophy of Science 2 (1):119-135.
    After a decade of intense debate about mechanisms, there is still no consensus characterization. In this paper we argue for a characterization that applies widely to mechanisms across the sciences. We examine and defend our disagreements with the major current contenders for characterizations of mechanisms. Ultimately, we indicate that the major contenders can all sign up to our characterization.
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  12. Causal Pluralism Versus Epistemic Causality.Jon Williamson - 2006 - Philosophica 77 (1):69-96.
    It is tempting to analyse causality in terms of just one of the indicators of causal relationships, e.g., mechanisms, probabilistic dependencies or independencies, counterfactual conditionals or agency considerations. While such an analysis will surely shed light on some aspect of our concept of cause, it will fail to capture the whole, rather multifarious, notion. So one might instead plump for pluralism: a different analysis for a different occasion. But we do not seem to have lots of different concepts of cause (...)
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  13.  29
    Calibration for Epistemic Causality.Jon Williamson - forthcoming - Erkenntnis:1-20.
    The epistemic theory of causality is analogous to epistemic theories of probability. Most proponents of epistemic probability would argue that one's degrees of belief should be calibrated to chances, insofar as one has evidence of chances. The question arises as to whether causal beliefs should satisfy an analogous calibration norm. In this paper, I formulate a particular version of a norm requiring calibration to chances and argue that this norm is the most fundamental evidential norm for epistemic probability. I then (...)
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  14.  30
    Establishing the Teratogenicity of Zika and Evaluating Causal Criteria.Jon Williamson - forthcoming - Synthese:1-14.
    The teratogenicity of the Zika virus was considered established in 2016, and is an interesting case because three different sets of causal criteria were used to assess teratogenicity. This paper appeals to the thesis of Russo and Williamson to devise an epistemological framework that can be used to compare and evaluate sets of causal criteria. The framework can also be used to decide when enough criteria are satisfied to establish causality. Arguably, the three sets of causal criteria considered here (...)
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  15.  80
    How Can Causal Explanations Explain?Jon Williamson - 2013 - Erkenntnis 78 (2):257-275.
    The mechanistic and causal accounts of explanation are often conflated to yield a ‘causal-mechanical’ account. This paper prizes them apart and asks: if the mechanistic account is correct, how can causal explanations be explanatory? The answer to this question varies according to how causality itself is understood. It is argued that difference-making, mechanistic, dualist and inferentialist accounts of causality all struggle to yield explanatory causal explanations, but that an epistemic account of causality is more promising in this regard.
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  16. Why Frequentists and Bayesians Need Each Other.Jon Williamson - 2013 - Erkenntnis 78 (2):293-318.
    The orthodox view in statistics has it that frequentism and Bayesianism are diametrically opposed—two totally incompatible takes on the problem of statistical inference. This paper argues to the contrary that the two approaches are complementary and need to mesh if probabilistic reasoning is to be carried out correctly.
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  17.  75
    Motivating Objective Bayesianism: From Empirical Constraints to Objective Probabilities.Jon Williamson - manuscript
    Kyburg goes half-way towards objective Bayesianism. He accepts that frequencies constrain rational belief to an interval but stops short of isolating an optimal degree of belief within this interval. I examine the case for going the whole hog.
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  18.  52
    Mechanistic Theories of Causality Part I.Jon Williamson - 2011 - Philosophy Compass 6 (6):421-432.
    Part I of this paper introduces a range of mechanistic theories of causality, including process theories and the complex-systems theories, and some of the problems they face. Part II argues that while there is a decisive case against a purely mechanistic analysis, a viable theory of causality must incorporate mechanisms as an ingredient, and describes one way of providing an analysis of causality which reaps the rewards of the mechanistic approach without succumbing to its pitfalls.
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  19. Probabilistic Theories of Causality.Jon Williamson - 2009 - In Helen Beebee, Peter Menzies & Christopher Hitchcock (eds.), The Oxford Handbook of Causation. Oxford University Press. pp. 185--212.
    This chapter provides an overview of a range of probabilistic theories of causality, including those of Reichenbach, Good and Suppes, and the contemporary causal net approach. It discusses two key problems for probabilistic accounts: counterexamples to these theories and their failure to account for the relationship between causality and mechanisms. It is argued that to overcome the problems, an epistemic theory of causality is required.
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  20.  98
    Mechanistic Theories of Causality.Jon Williamson - unknown
    After introducing a range of mechanistic theories of causality and some of the problems they face, I argue that while there is a decisive case against a purely mechanistic analysis, a viable theory of causality must incorporate mechanisms as an ingredient. I describe one way of providing an analysis of causality which reaps the rewards of the mechanistic approach without succumbing to its pitfalls.
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  21. Dispositional Versus Epistemic Causality.Jon Williamson - 2006 - Minds and Machines 16 (3):259-276.
    I put forward several desiderata that a philosophical theory of causality should satisfy: it should account for the objectivity of causality, it should underpin formalisms for causal reasoning, it should admit a viable epistemology, it should be able to cope with the great variety of causal claims that are made, and it should be ontologically parsimonious. I argue that Nancy Cartwright’s dispositional account of causality goes part way towards meeting these criteria but is lacking in important respects. I go on (...)
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  22. Imaging Technology and the Philosophy of Causality.Jon Williamson - 2011 - Philosophy and Technology 24 (2):115-136.
    Russo and Williamson (Int Stud Philos Sci 21(2):157–170, 2007) put forward the thesis that, at least in the health sciences, to establish the claim that C is a cause of E, one normally needs evidence of an underlying mechanism linking C and E as well as evidence that C makes a difference to E. This epistemological thesis poses a problem for most current analyses of causality which, in virtue of analysing causality in terms of just one of mechanisms or (...)
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  23.  7
    Evidential Proximity, Independence, and the Evaluation of Carcinogenicity.Jon Williamson - 2019 - Journal of Evaluation in Clinical Practice 25 (6):955-961.
    This paper analyses the methods of the International Agency for Research on Cancer for evaluating the carcinogenicity of various agents. I identify two fundamental evidential principles that underpin these methods, which I call Evidential Proximity and Independence. I then show, by considering the 2018 evaluation of the carcinogenicity of styrene and styrene‐7,8‐oxide, that these principles have been implemented in a way that can lead to inconsistency. I suggest a way to resolve this problem: admit a general exception to Independence and (...)
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  24. Interpreting Causality in the Health Sciences.Federica Russo & Jon Williamson - 2007 - International Studies in the Philosophy of Science 21 (2):157 – 170.
    We argue that the health sciences make causal claims on the basis of evidence both of physical mechanisms, and of probabilistic dependencies. Consequently, an analysis of causality solely in terms of physical mechanisms or solely in terms of probabilistic relationships, does not do justice to the causal claims of these sciences. Yet there seems to be a single relation of cause in these sciences - pluralism about causality will not do either. Instead, we maintain, the health sciences require a theory (...)
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  25. Philosophies of Probability: Objective Bayesianism and its Challenges.Jon Williamson - manuscript
    This chapter presents an overview of the major interpretations of probability followed by an outline of the objective Bayesian interpretation and a discussion of the key challenges it faces. I discuss the ramifications of interpretations of probability and objective Bayesianism for the philosophy of mathematics in general.
     
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  26. From Bayesian Epistemology to Inductive Logic.Jon Williamson - 2013 - Journal of Applied Logic 11 (4):468-486.
    Inductive logic admits a variety of semantics (Haenni et al., 2011, Part 1). This paper develops semantics based on the norms of Bayesian epistemology (Williamson, 2010, Chapter 7). §1 introduces the semantics and then, in §2, the paper explores methods for drawing inferences in the resulting logic and compares the methods of this paper with the methods of Barnett and Paris (2008). §3 then evaluates this Bayesian inductive logic in the light of four traditional critiques of inductive logic, arguing (...)
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  27.  20
    Justifying the Principle of Indifference.Jon Williamson - 2018 - European Journal for Philosophy of Science 8 (3):559-586.
    This paper presents a new argument for the Principle of Indifference. This argument can be thought of in two ways: as a pragmatic argument, justifying the principle as needing to hold if one is to minimise worst-case expected loss, or as an epistemic argument, justifying the principle as needing to hold in order to minimise worst-case expected inaccuracy. The question arises as to which interpretation is preferable. I show that the epistemic argument contradicts Evidentialism and suggest that the relative plausibility (...)
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  28.  85
    How Uncertain Do We Need to Be?Jon Williamson - 2014 - Erkenntnis 79 (6):1249-1271.
    Expert probability forecasts can be useful for decision making . But levels of uncertainty escalate: however the forecaster expresses the uncertainty that attaches to a forecast, there are good reasons for her to express a further level of uncertainty, in the shape of either imprecision or higher order uncertainty . Bayesian epistemology provides the means to halt this escalator, by tying expressions of uncertainty to the propositions expressible in an agent’s language . But Bayesian epistemology comes in three main varieties. (...)
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  29.  25
    Philosophies of Probability.Jon Williamson - unknown
    This chapter presents an overview of the major interpretations of probability followed by an outline of the objective Bayesian interpretation and a discussion of the key challenges it faces. I discuss the ramifications of interpretations of probability and objective Bayesianism for the philosophy of mathematics in general.
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  30.  83
    Causality.Jon Williamson - 2007
    This chapter addresses two questions: what are causal relationships? how can one discover causal relationships? I provide a survey of the principal answers given to these questions, followed by an introduction to my own view, epistemic causality, and then a comparison of epistemic causality with accounts provided by Judea Pearl and Huw Price.
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  31. Inductive Influence.Jon Williamson - 2007 - British Journal for the Philosophy of Science 58 (4):689 - 708.
    Objective Bayesianism has been criticised for not allowing learning from experience: it is claimed that an agent must give degree of belief ½ to the next raven being black, however many other black ravens have been observed. I argue that this objection can be overcome by appealing to objective Bayesian nets, a formalism for representing objective Bayesian degrees of belief. Under this account, previous observations exert an inductive influence on the next observation. I show how this approach can be used (...)
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  32.  36
    Probability Logic.Jon Williamson - unknown
    Practical reasoning requires decision—making in the face of uncertainty. Xenelda has just left to go to work when she hears a burglar alarm. She doesn’t know whether it is hers but remembers that she left a window slightly open. Should she be worried? Her house may not be being burgled, since the wind or a power cut may have set the burglar alarm off, and even if it isn’t her alarm sounding she might conceivably be being burgled. Thus Xenelda can (...)
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  33.  53
    An Objective Bayesian Account of Confirmation.Jon Williamson - 2011 - In Dennis Dieks, Wenceslao Gonzalo, Thomas Uebel, Stephan Hartmann & Marcel Weber (eds.), Explanation, Prediction, and Confirmation. Springer. pp. 53--81.
  34. Modelling Mechanisms with Causal Cycles.Brendan Clarke, Bert Leuridan & Jon Williamson - 2014 - Synthese 191 (8):1-31.
    Mechanistic philosophy of science views a large part of scientific activity as engaged in modelling mechanisms. While science textbooks tend to offer qualitative models of mechanisms, there is increasing demand for models from which one can draw quantitative predictions and explanations. Casini et al. (Theoria 26(1):5–33, 2011) put forward the Recursive Bayesian Networks (RBN) formalism as well suited to this end. The RBN formalism is an extension of the standard Bayesian net formalism, an extension that allows for modelling the hierarchical (...)
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  35. Function and Organization: Comparing the Mechanisms of Protein Synthesis and Natural Selection.Phyllis McKay Illari & Jon Williamson - 2010 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 41 (3):279-291.
    In this paper, we compare the mechanisms of protein synthesis and natural selection. We identify three core elements of mechanistic explanation: functional individuation, hierarchical nestedness or decomposition, and organization. These are now well understood elements of mechanistic explanation in fields such as protein synthesis, and widely accepted in the mechanisms literature. But Skipper and Millstein have argued that natural selection is neither decomposable nor organized. This would mean that much of the current mechanisms literature does not apply to the mechanism (...)
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  36.  78
    Objective Bayesianism with Predicate Languages.Jon Williamson - 2008 - Synthese 163 (3):341-356.
    Objective Bayesian probability is often defined over rather simple domains, e.g., finite event spaces or propositional languages. This paper investigates the extension of objective Bayesianism to first-order logical languages. It is argued that the objective Bayesian should choose a probability function, from all those that satisfy constraints imposed by background knowledge, that is closest to a particular frequency-induced probability function which generalises the λ = 0 function of Carnap’s continuum of inductive methods.
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  37.  47
    Causality: Metaphysics and Methods.Jon Williamson - unknown
    How ought we learn causal relationships? While Popper advocated a hypothetico-deductive logic of causal discovery, inductive accounts are currently in vogue. Many inductive approaches depend on the causal Markov condition as a fundamental assumption. This condition, I maintain, is not universally valid, though it is justifiable as a default assumption. In which case the results of the inductive causal learning procedure must be tested before they can be accepted. This yields a synthesis of the hypothetico-deductive and inductive accounts, which forms (...)
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  38.  67
    Objective Bayesian Nets.Jon Williamson - manuscript
    I present a formalism that combines two methodologies: objective Bayesianism and Bayesian nets. According to objective Bayesianism, an agent’s degrees of belief (i) ought to satisfy the axioms of probability, (ii) ought to satisfy constraints imposed by background knowledge, and (iii) should otherwise be as non-committal as possible (i.e. have maximum entropy). Bayesian nets offer an efficient way of representing and updating probability functions. An objective Bayesian net is a Bayesian net representation of the maximum entropy probability function.
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  39.  19
    Recursive Causality in Bayesian Networks and Self-Fibring Networks.Jon Williamson & D. M. Gabbay - unknown
  40.  75
    Bayesianism and Language Change.Jon Williamson - 2003 - Journal of Logic, Language and Information 12 (1):53-97.
    Bayesian probability is normally defined over a fixed language or eventspace. But in practice language is susceptible to change, and thequestion naturally arises as to how Bayesian degrees of belief shouldchange as language changes. I argue here that this question poses aserious challenge to Bayesianism. The Bayesian may be able to meet thischallenge however, and I outline a practical method for changing degreesof belief over changes in finite propositional languages.
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  41.  71
    In Defence of Activities.Phyllis Illari & Jon Williamson - 2013 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 44 (1):69-83.
    In this paper, we examine what is to be said in defence of Machamer, Darden and Craver’s (MDC) controversial dualism about activities and entities (Machamer, Darden and Craver’s in Philos Sci 67:1–25, 2000). We explain why we believe the notion of an activity to be a novel, valuable one, and set about clearing away some initial objections that can lead to its being brushed aside unexamined. We argue that substantive debate about ontology can only be effective when desiderata for an (...)
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  42.  64
    Aggregating Judgements by Merging Evidence.Jon Williamson - 2009
    The theory of belief revision and merging has recently been applied to judgement aggregation. In this paper I argue that judgements are best aggregated by merging the evidence on which they are based, rather than by directly merging the judgements themselves. This leads to a threestep strategy for judgement aggregation. First, merge the evidence bases of the various agents using some method of belief merging. Second, determine which degrees of belief one should adopt on the basis of this merged evidence (...)
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  43.  76
    Objective Bayesian Probabilistic Logic.Jon Williamson - 2008
    This paper develops connections between objective Bayesian epistemology—which holds that the strengths of an agent’s beliefs should be representable by probabilities, should be calibrated with evidence of empirical probability, and should otherwise be equivocal—and probabilistic logic. After introducing objective Bayesian epistemology over propositional languages, the formalism is extended to handle predicate languages. A rather general probabilistic logic is formulated and then given a natural semantics in terms of objective Bayesian epistemology. The machinery of objective Bayesian nets and objective credal nets (...)
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  44.  81
    Epistemic Causality and Evidence-Based Medicine.Federica Russo & Jon Williamson - 2011 - History and Philosophy of the Life Sciences 33 (4).
    Causal claims in biomedical contexts are ubiquitous albeit they are not always made explicit. This paper addresses the question of what causal claims mean in the context of disease. It is argued that in medical contexts causality ought to be interpreted according to the epistemic theory. The epistemic theory offers an alternative to traditional accounts that cash out causation either in terms of “difference-making” relations or in terms of mechanisms. According to the epistemic approach, causal claims tell us about which (...)
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  45.  78
    Objective Bayesianism and the Maximum Entropy Principle.Jürgen Landes & Jon Williamson - unknown
    Objective Bayesian epistemology invokes three norms: the strengths of our beliefs should be probabilities, they should be calibrated to our evidence of physical probabilities, and they should otherwise equivocate sufficiently between the basic propositions that we can express. The three norms are sometimes explicated by appealing to the maximum entropy principle, which says that a belief function should be a probability function, from all those that are calibrated to evidence, that has maximum entropy. However, the three norms of objective Bayesianism (...)
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  46.  31
    Inductive Influence: Objective Bayesianism has Been Criticised for Not Allowing Learning From Experience: It is Claimed That an Agent Must Give Degree of Belief Formula to the Next Raven Being Black, However Many Other Black Ravens Have Been Observed. I Argue That This Objection Can Be Overcome by Appealing to Objective Bayesian Nets, a Formalism for Representing Objective Bayesian Degrees of Belief. Under This Account, Previous Observations Exert an Inductive Influence on the Next Observation. I Show How This Approach Can Be Used to Capture the Johnson–Carnap Continuum of Inductive Methods, as Well as the Nix–Paris Continuum, and Show How Inductive Influence Can Be Measured. [REVIEW]Jon Williamson - 2007 - British Journal for the Philosophy of Science 58 (4):689-708.
    Objective Bayesianism has been criticised for not allowing learning from experience: it is claimed that an agent must give degree of belief 12 to the next raven being black, however many other black ravens have been observed. I argue that this objection can be overcome by appealing to objective Bayesian nets, a formalism for representing objective Bayesian degrees of belief. Under this account, previous observations exert an inductive influence on the next observation. I show how this approach can be used (...)
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  47.  73
    EnviroGenomarkers: The Interplay Between Mechanisms and Difference Making in Establishing Causal Claims.Federica Russo & Jon Williamson - 2012 - Medicine Studies 3 (4):249-262.
    According to Russo and Williamson :157–170, 2007, Hist Philos Life Sci 33:389–396, 2011a, Philos Sci 1:47–69, 2011b), in order to establish a causal claim of the form, ‘C is a cause of E’, one typically needs evidence that there is an underlying mechanism between C and E as well as evidence that C makes a difference to E. This thesis has been used to argue that hierarchies of evidence, as championed by evidence-based movements, tend to give primacy to evidence (...)
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  48.  66
    Calibration and Convexity: Response to Gregory Wheeler.Jon Williamson - 2012 - British Journal for the Philosophy of Science 63 (4):851-857.
    This note responds to some criticisms of my recent book In Defence of Objective Bayesianism that were provided by Gregory Wheeler in his ‘Objective Bayesian Calibration and the Problem of Non-convex Evidence’.
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  49. Mechanisms Are Real and Local.Phyllis McKay Illari & Jon Williamson - 2011 - In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press.
    Mechanisms have become much-discussed, yet there is still no consensus on how to characterise them. In this paper, we start with something everyone is agreed on – that mechanisms explain – and investigate what constraints this imposes on our metaphysics of mechanisms. We examine two widely shared premises about how to understand mechanistic explanation: (1) that mechanistic explanation offers a welcome alternative to traditional laws-based explanation and (2) that there are two senses of mechanistic explanation that we call ‘epistemic explanation’ (...)
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  50. Maximising Entropy Efficiently.Jon Williamson - 2002
    Recommended citation: . . Link¨ oping Electronic Articles in Computer and Information Science, Vol. 7(2002): nr 0. http://www.ep.liu.se/ea/cis/2002/00/. 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