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Profile: Jon Williamson (University of Kent at Canterbury)
  1. Jon Williamson, Causality and Probability in the Biomedical Sciences.
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  2. Jon Williamson, Enough of Enough.
    This is a short critique of sufficiency views.
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  3. Jon Williamson, How Bad is Death?
    (Canadian Journal of Philosophy 37 (2007), pp. 111-127) A popular view about why death is bad for the one who dies is that death deprives its subject of the good things in life. This is the “deprivation account” of the evil of death. There is another view about death that seems incompatible with the deprivation account: the view that a person’s death is less bad if she has lived a good life. I give some arguments against this view and defend (...)
     
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  4. Jon Williamson, Randomness is Unpredictability.
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  5. Jon Williamson, Causality: Metaphysics and Methods.
    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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  6. Jon Williamson, From Bayesian Epistemology to Inductive Logic.
    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 (i) (...)
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  7. Jon Williamson, Keywords.
    Machamer, Darden and Craver: ‘Mechanisms are entities and activities organized such that they are productive of regular changes from start or set-up to finish or termination conditions.’ (Machamer, Darden and Craver 2000 p3.) Glennan: ‘A mechanism for a behavior is a complex system that produces that behavior by the interaction of a number of parts, where the interactions between parts can be characterized by direct, invariant, change-relating generalizations.’ (Glennan 2002b pS344.) Bechtel and Abrahamsen: ‘A mechanism is a structure performing a (...)
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  8. Jon Williamson, Probability Logic.
    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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  9. Jon Williamson, Recursive Bayesian Nets for Prediction, Explanation and Control in Cancer Science.
    this paper we argue that the formalism can also be applied to modelling the hierarchical structure of physical 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 are vital for prediction, explanation and control respectively, a recursive Bayesian net can be applied to all these tasks. We show how a Recursive Bayesian Net can be used to model mechanisms in cancer science. (...)
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  10. Jon Williamson, The Philosophy of Science and its Relation to Machine Learning.
    In this chapter I discuss connections between machine learning and the philosophy of science. First I consider the relationship between the two disciplines. There is a clear analogy between hypothesis choice in science and model selection in machine learning. While this analogy has been invoked to argue that the two disciplines are essentially doing the same thing and should merge, I maintain that the disciplines are distinct but related and that there is a dynamic interaction operating between the two: a (...)
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  11. Jon Williamson, Why Look at Causality in the Sciences? A Manifesto.
    This introduction to the volume begins with a manifesto that puts forward two theses: first, that the sciences are the best place to turn in order to understand causality; second, that scientifically-informed philosophical investigation can bring something to the sciences too. Next, the chapter goes through the various parts of the volume, drawing out relevant background and themes of the chapters in those parts. Finally, the chapter discusses the progeny of the papers and identifies some next steps for research into (...)
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  12. Jon Williamson, Abstract.
    by living organisms to process chemical comchemical reactions. Understanding metabolism is an impounds in order to take energy and eliminate portant problem for biology, pharmacology (in particular..
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  13. Jon Williamson, Aggregating Judgements by Merging Evidence.
    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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  14. Jon Williamson, A Note on Probabilistic Logics and Probabilistic Networks.
    ϕ1, . . . , ϕn |≈ ψ? Here ϕ1, . . . , ϕn, ψ are premisses of some formal language, such as a propositional language or a predicate language. |≈ is an entailment relation: the entailment holds if all models of the premisses also satisfy the conclusion, where the logic provides some suitable notion of ‘model’ and ‘satisfy’. Proof theory is normally invoked to answer a question of this form: one tries to prove the conclusion from the premisses (...)
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  15. Jon Williamson, Bayesian Nets and Causality.
    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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  16. Jon Williamson, Bayesian Networks for Logical Reasoning.
    By identifying and pursuing analogies between causal and logical influence I show how the Bayesian network formalism can be applied to reasoning about logical deductions.
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  17. Jon Williamson, Causality.
    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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  18. Jon Williamson, Editorial.
    How is probability related to logic? Should probability and logic be combined? If so, how? Bayesianism tells us we ought to reason probabilistically. In that sense, probability theory is logic. How then does probability theory relate to classical logic and the various non-classical logics that also stake a claim on normative reasoning? Is probability theory to be preferred over other logics or vice versa? Is probability theory to be used in some situations, and the other logics in other situations? Or (...)
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  19. Jon Williamson, Epistemic Complexity From an Objective Bayesian Perspective.
    Evidence can be complex in various ways: e.g., it may exhibit structural complexity, containing information about causal, hierarchical or logical structure as well as empirical data, or it may exhibit combinatorial complexity, containing a complex combination of kinds of information. This paper examines evidential complexity from the point of view of Bayesian epistemology, asking: how should complex evidence impact on an agent’s degrees of belief? The paper presents a high-level overview of an objective Bayesian answer: it presents the objective Bayesian (...)
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  20. Jon Williamson, Evidential Probability, Objective Bayesianism, Non-Monotonicity and System P.
    This paper is a comparison of how first-order Kyburgian Evidential Probability (EP), second-order EP, and objective Bayesian epistemology compare as to the KLM system-P rules for consequence relations and the monotonic / non-monotonic divide.
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  21. Jon Williamson, Intervention, Underdetermination, and Theory Generation.
    We consider the use of intervention data for eliminating the underdetermination in statistical modelling, and for guiding extensions of the statistical models. The leading example is factor analysis, a major statistical tool in the social sciences. We first relate indeterminacy in factor analysis to the problem of underdetermination. Then we draw a parallel between factor analysis models and Bayesian networks with hidden nodes, which allows us to clarify the use of intervention data for dealing with indeterminacy. It will be shown (...)
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  22. Jon Williamson, Learning Causal Relationships.
    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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  23. Jon Williamson, Maximising Entropy Efficiently.
    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/. September 18, 2002. </div><div class="catsCon" id="ecats-con-WILMEE">No categories</div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://www.kent.ac.uk/secl/philosophy/jw/2002/maxenteffic.pdf" target='_blank' >Direct download</a>  <div id="ml-WILMEE" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('WILMEE','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-WILMEE" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('WILMEE')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-WILMEE"></span></div></div></li> <li id='eWILMOB-2' onclick="ee('click','WILMOB-2')" onmouseover="ee('over','WILMOB-2')" onmouseout="ee('out','WILMOB-2')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/WILMOB-2"><span class='name'>Jon Williamson</span>, <span class='articleTitle'>Motivating Objective Bayesianism: From Empirical Constraints to Objective Probabilities.</span></a><span class='pubInfo'></span></span><div class="extras"><div class="abstract">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. </div><div class="catsCon" id="ecats-con-WILMOB-2">No categories</div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://www.kent.ac.uk/secl/philosophy/jw/2005/motivating.pdf" target='_blank' >Direct download</a>  <div id="ml-WILMOB-2" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('WILMOB-2','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-WILMOB-2" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('WILMOB-2')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-WILMOB-2"></span></div></div></li> <li id='eWILMTO-2' onclick="ee('click','WILMTO-2')" onmouseover="ee('over','WILMTO-2')" onmouseout="ee('out','WILMTO-2')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/WILMTO-2"><span class='name'>Jon Williamson</span>, <span class='articleTitle'>Mechanistic Theories of Causality.</span></a><span class='pubInfo'></span></span><div class="extras"><div class="abstract">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. </div><div class="catsCon" id="ecats-con-WILMTO-2">No categories</div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/apps/internet-group-chat.png"><div id="tr-WILMTO-2" title="Translate to English" class="yui-skin-sam ldiv" style="cursor:pointer" onclick="translateEntry('WILMTO-2')">Translate to English</div> | <img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://www.kent.ac.uk/secl/philosophy/jw/2010/MechanisticCausality.pdf" target='_blank' >Direct download</a>  <div id="ml-WILMTO-2" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('WILMTO-2','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-WILMTO-2" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('WILMTO-2')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-WILMTO-2"></span></div></div></li> <li id='eWILOBN' onclick="ee('click','WILOBN')" onmouseover="ee('over','WILOBN')" onmouseout="ee('out','WILOBN')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/WILOBN"><span class='name'>Jon Williamson</span>, <span class='articleTitle'>Objective Bayesian Nets.</span></a><span class='pubInfo'></span></span><div class="extras"><div class="abstract">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. </div><div class="catsCon" id="ecats-con-WILOBN"><div><a class='catName' href='/browse/bayesian-reasoning-misc' rel='section'>Bayesian Reasoning, Misc</a><span class='catIn'> in </span><a class='catArea href='/browse/philosophy-of-probability' rel='section'>Philosophy of Probability</a></div></div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://www.kent.ac.uk/secl/philosophy/jw/2005/obnets.pdf" target='_blank' >Direct download</a>  <div id="ml-WILOBN" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('WILOBN','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-WILOBN" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('WILOBN')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-WILOBN"></span></div></div></li> <li id='eWILOBN-2' onclick="ee('click','WILOBN-2')" onmouseover="ee('over','WILOBN-2')" onmouseout="ee('out','WILOBN-2')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/WILOBN-2"><span class='name'>Jon Williamson</span>, <span class='articleTitle'>Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer.</span></a><span class='pubInfo'></span></span><div class="extras"><div class="abstract">Cancer treatment decisions should be based on all available evidence. But this evidence is complex and varied: it includes not only the patient’s symptoms and expert knowledge of the relevant causal processes, but also clinical databases relating to past patients, databases of observations made at the molecular level, and evidence encapsulated in scientific papers and medical informatics systems. Objective Bayesian nets offer a principled path to knowledge integration, and we show in this chapter how they can be applied to integrate<span id="WILOBN-2-absexp"> (<span class="ll" onclick='$("WILOBN-2-abstract2").show();$("WILOBN-2-absexp").hide()'>...</span>)</span><span id="WILOBN-2-abstract2" style="display:none"> various kinds of evidence in the cancer domain. This is important from the systems biology perspective, which needs to integrate data that concern different levels of analysis, and is also important from the point of view of medical informatics. (<span class="ll" onclick='$("WILOBN-2-abstract2").hide();$("WILOBN-2-absexp").show();'>shrink</span>)</span></div><div class="catsCon" id="ecats-con-WILOBN-2"><div><a class='catName' href='/browse/bayesian-reasoning-misc' rel='section'>Bayesian Reasoning, Misc</a><span class='catIn'> in </span><a class='catArea href='/browse/philosophy-of-probability' rel='section'>Philosophy of Probability</a></div></div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://www.kent.ac.uk/secl/philosophy/jw/2006/ObnetsPrognosis.pdf" target='_blank' >Direct download</a>  <div id="ml-WILOBN-2" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('WILOBN-2','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-WILOBN-2" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('WILOBN-2')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-WILOBN-2"></span></div></div></li> <li id='eWILOBP' onclick="ee('click','WILOBP')" onmouseover="ee('over','WILOBP')" onmouseout="ee('out','WILOBP')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/WILOBP"><span class='name'>Jon Williamson</span>, <span class='articleTitle'>Objective Bayesian Probabilistic Logic.</span></a><span class='pubInfo'></span></span><div class="extras"><div class="abstract">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<span id="WILOBP-absexp"> (<span class="ll" onclick='$("WILOBP-abstract2").show();$("WILOBP-absexp").hide()'>...</span>)</span><span id="WILOBP-abstract2" style="display:none"> is introduced and this machinery is applied to provide a calculus for probabilistic logic that meshes with the objective Bayesian semantics. (<span class="ll" onclick='$("WILOBP-abstract2").hide();$("WILOBP-absexp").show();'>shrink</span>)</span></div><div class="catsCon" id="ecats-con-WILOBP"><div><a class='catName' href='/browse/bayesian-reasoning-misc' rel='section'>Bayesian Reasoning, Misc</a><span class='catIn'> in </span><a class='catArea href='/browse/philosophy-of-probability' rel='section'>Philosophy of Probability</a></div></div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://www.kent.ac.uk/secl/philosophy/jw/2008/obprogic.pdf" target='_blank' >Direct download</a>  <div id="ml-WILOBP" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('WILOBP','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-WILOBP" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('WILOBP')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-WILOBP"></span></div></div></li> <li id='eWILPOP-3' onclick="ee('click','WILPOP-3')" onmouseover="ee('over','WILPOP-3')" onmouseout="ee('out','WILPOP-3')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/WILPOP-3"><span class='name'>Jon Williamson</span>, <span class='articleTitle'>Philosophies of Probability: Objective Bayesianism and its Challenges.</span></a><span class='pubInfo'></span></span><div class="extras"><div class="abstract">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. </div><div class="catsCon" id="ecats-con-WILPOP-3"><div><a class='catName' href='/browse/bayesian-reasoning-misc' rel='section'>Bayesian Reasoning, Misc</a><span class='catIn'> in </span><a class='catArea' href='/browse/philosophy-of-probability' rel='section'>Philosophy of Probability</a></div> </div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://www.kent.ac.uk/secl/philosophy/jw/2004/philprob.pdf" target='_blank' >Direct download</a>  <div id="ml-WILPOP-3" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('WILPOP-3','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-WILPOP-3" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('WILPOP-3')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-WILPOP-3"></span></div></div></li> <li id='eWILSIO' onclick="ee('click','WILSIO')" onmouseover="ee('over','WILSIO')" onmouseout="ee('out','WILSIO')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/WILSIO"><span class='name'>Jon Williamson</span>, <span class='articleTitle'>Special Issue on Combining Probability and Logic Introduction.</span></a><span class='pubInfo'></span></span><div class="extras"><div class="abstract">This volume arose out of an international, interdisciplinary academic network on Probabilistic Logic and Probabilistic Networks involving four of us (Haenni, Romeijn, Wheeler and Williamson), called Progicnet and funded by the Leverhulme Trust from 2006–8. Many of the papers in this volume were presented at an associated conference, the Third Workshop on Combining Probability and Logic (Progic 2007), held at the University of Kent on 5–7 September 2007. The papers in this volume concern either the special focus on the connection<span id="WILSIO-absexp"> (<span class="ll" onclick='$("WILSIO-abstract2").show();$("WILSIO-absexp").hide()'>...</span>)</span><span id="WILSIO-abstract2" style="display:none"> between probabilistic logic and probabilistic networks or the more general question of the links between probability and logic. Here we introduce probabilistic logic, probabilistic networks, current and future directions of research and also the themes of the papers that follow. (<span class="ll" onclick='$("WILSIO-abstract2").hide();$("WILSIO-absexp").show();'>shrink</span>)</span></div><div class="catsCon" id="ecats-con-WILSIO"><div><a class='catName' href='/browse/philosophy-of-probability' rel='section'>Philosophy of Probability</a></div></div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://www.kent.ac.uk/secl/philosophy/jw/2007/progic_editorial.pdf" target='_blank' >Direct download</a>  <div id="ml-WILSIO" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('WILSIO','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-WILSIO" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('WILSIO')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-WILSIO"></span></div></div></li> <li id='eCLAMAT-3' onclick="ee('click','CLAMAT-3')" onmouseover="ee('over','CLAMAT-3')" onmouseout="ee('out','CLAMAT-3')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/CLAMAT-3"><span class='name'>Brendan Clarke</span>, <span class='name'>Donald Gillies</span>, <span class='name'>Phyllis Illari</span>, <span class='name'>Federica Russo</span> & <span class='name'>Jon Williamson</span> (forthcoming). <span class='articleTitle'>Mechanisms and the Evidence Hierarchy.</span></a><span class='pubInfo'> <em class='pubName'>Topoi</em>:1-22.</span></span><div class="extras"><div class="abstract">Evidence-based medicine (EBM) makes use of explicit procedures for grading evidence for causal claims. Normally, these procedures categorise evidence of correlation produced by statistical trials as better evidence for a causal claim than evidence of mechanisms produced by other methods. We argue, in contrast, that evidence of mechanisms needs to be viewed as complementary to, rather than inferior to, evidence of correlation. In this paper we first set out the case for treating evidence of mechanisms alongside evidence of correlation in<span id="CLAMAT-3-absexp"> (<span class="ll" onclick='$("CLAMAT-3-abstract2").show();$("CLAMAT-3-absexp").hide()'>...</span>)</span><span id="CLAMAT-3-abstract2" style="display:none"> explicit protocols for evaluating evidence. Next we provide case studies which exemplify the ways in which evidence of mechanisms complements evidence of correlation in practice. Finally, we put forward some general considerations as to how the two sorts of evidence can be more closely integrated by EBM. (<span class="ll" onclick='$("CLAMAT-3-abstract2").hide();$("CLAMAT-3-absexp").show();'>shrink</span>)</span></div><div class="catsCon" id="ecats-con-CLAMAT-3"><div><a class='catName' href='/browse/value-theory' rel='section'>Value Theory</a></div> </div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://link.springer.com/10.1007/s11245-013-9220-9" target='_blank' >Direct download</a> (<a href='/rec/CLAMAT-3'>4 more</a>)  <div id="ml-CLAMAT-3" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('CLAMAT-3','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-CLAMAT-3" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('CLAMAT-3')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-CLAMAT-3"></span></div></div></li> <li id='eCLAMMW' onclick="ee('click','CLAMMW')" onmouseover="ee('over','CLAMMW')" onmouseout="ee('out','CLAMMW')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/CLAMMW"><span class='name'>Brendan Clarke</span>, <span class='name'>Bert Leuridan</span> & <span class='name'>Jon Williamson</span> (2013). <span class='articleTitle'>Modelling Mechanisms with Causal Cycles.</span></a><span class='pubInfo'> <em class='pubName'>Synthese</em>:1-31.</span></span><div class="extras"><div class="abstract">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<span id="CLAMMW-absexp"> (<span class="ll" onclick='$("CLAMMW-abstract2").show();$("CLAMMW-absexp").hide()'>...</span>)</span><span id="CLAMMW-abstract2" style="display:none"> nature of mechanisms. Like the standard Bayesian net formalism, it models causal relationships using directed acyclic graphs. Given this appeal to acyclicity, causal cycles pose a prima facie problem for the RBN approach. This paper argues that the problem is a significant one given the ubiquity of causal cycles in mechanisms, but that the problem can be solved by combining two sorts of solution strategy in a judicious way. (<span class="ll" onclick='$("CLAMMW-abstract2").hide();$("CLAMMW-absexp").show();'>shrink</span>)</span></div><div class="catsCon" id="ecats-con-CLAMMW"><div><a class='catName' href='/browse/bayesian-reasoning-misc' rel='section'>Bayesian Reasoning, Misc</a><span class='catIn'> in </span><a class='catArea' href='/browse/philosophy-of-probability' rel='section'>Philosophy of Probability</a></div> </div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://link.springer.com/10.1007/s11229-013-0360-7" target='_blank' >Direct download</a> (<a href='/rec/CLAMMW'>4 more</a>)  <div id="ml-CLAMMW" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('CLAMMW','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-CLAMMW" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('CLAMMW')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-CLAMMW"></span></div></div></li> <li id='eILLIDO' onclick="ee('click','ILLIDO')" onmouseover="ee('over','ILLIDO')" onmouseout="ee('out','ILLIDO')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/ILLIDO"><span class='name'>Phyllis Illari</span> & <span class='name'>Jon Williamson</span> (2013). <span class='articleTitle'>In Defence of Activities.</span></a><span class='pubInfo'> <em class='pubName'>Journal for General Philosophy of Science</em> 44 (1):69-83.</span></span><div class="extras"><div class="abstract">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<span id="ILLIDO-absexp"> (<span class="ll" onclick='$("ILLIDO-abstract2").show();$("ILLIDO-absexp").hide()'>...</span>)</span><span id="ILLIDO-abstract2" style="display:none"> ontology are explicitly articulated. We distinguish three such desiderata. The first is a more permissive descriptive ontology of science, the second a more reductive ontology prioritising understanding, and the third a more reductive ontology prioritising minimalism. We compare MDC’s entities-activities ontology to its closest rival, the entities-capacities ontology, and argue that the entities-activities ontology does better on all three desiderata. (<span class="ll" onclick='$("ILLIDO-abstract2").hide();$("ILLIDO-absexp").show();'>shrink</span>)</span></div><div class="catsCon" id="ecats-con-ILLIDO"><div><a class='catName' href='/browse/science-logic-and-mathematics' rel='section'>Science, Logic, and Mathematics</a></div> </div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://link.springer.com/content/pdf/10.1007%2Fs10838-013-9217-5" target='_blank' >Direct download</a> (<a href='/rec/ILLIDO'>6 more</a>)  <div id="ml-ILLIDO" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('ILLIDO','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-ILLIDO" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('ILLIDO')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-ILLIDO"></span></div></div></li> <li id='eLANOBA' onclick="ee('click','LANOBA')" onmouseover="ee('over','LANOBA')" onmouseout="ee('out','LANOBA')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/LANOBA"><span class='name'>Jürgen Landes</span> & <span class='name'>Jon Williamson</span>, <span class='articleTitle'>Objective Bayesianism and the Maximum Entropy Principle.</span></a><span class='pubInfo'></span></span><div class="extras"><div class="abstract">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<span id="LANOBA-absexp"> (<span class="ll" onclick='$("LANOBA-abstract2").show();$("LANOBA-absexp").hide()'>...</span>)</span><span id="LANOBA-abstract2" style="display:none"> are usually justified in different ways. In this paper we show that the three norms can all be subsumed under a single justification in terms of minimising worst-case expected loss. This, in turn, is equivalent to maximising a generalised notion of entropy. We suggest that requiring language invariance, in addition to minimising worst-case expected loss, motivates maximisation of standard entropy as opposed to maximisation of other instances of generalised entropy. Our argument also provides a qualified justification for updating degrees of belief by Bayesian conditionalisation. However, conditional probabilities play a less central part in the objective Bayesian account than they do under the subjective view of Bayesianism, leading to a reduced role for Bayes’ Theorem. (<span class="ll" onclick='$("LANOBA-abstract2").hide();$("LANOBA-absexp").show();'>shrink</span>)</span></div><div class="catsCon" id="ecats-con-LANOBA"><div><a class='catName' href='/browse/conditionalization' rel='section'>Conditionalization</a><span class='catIn'> in </span><a class='catArea' href='/browse/philosophy-of-probability' rel='section'>Philosophy of Probability</a></div> <div><a class='catName' href='/browse/indifference-principles' rel='section'>Indifference Principles</a><span class='catIn'> in </span><a class='catArea' href='/browse/philosophy-of-probability' rel='section'>Philosophy of Probability</a></div> <div><a class='catName' href='/browse/updating-principles' rel='section'>Updating Principles</a><span class='catIn'> in </span><a class='catArea' href='/browse/philosophy-of-probability' rel='section'>Philosophy of Probability</a></div> </div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://kar.kent.ac.uk/35197/" target='_blank' >Direct download</a> (<a href='/rec/LANOBA'>2 more</a>)  <div id="ml-LANOBA" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('LANOBA','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-LANOBA" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('LANOBA')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-LANOBA"></span></div></div></li> <li id='eWILHCC-3' onclick="ee('click','WILHCC-3')" onmouseover="ee('over','WILHCC-3')" onmouseout="ee('out','WILHCC-3')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/WILHCC-3"><span class='name'>Jon Williamson</span> (2013). <span class='articleTitle'>How Can Causal Explanations Explain?</span></a><span class='pubInfo'> <em class='pubName'>Erkenntnis</em> 78 (2):257-275.</span></span><div class="extras"><div class="abstract">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. </div><div class="catsCon" id="ecats-con-WILHCC-3">No categories</div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://link.springer.com/content/pdf/10.1007%2Fs10670-013-9512-x.pdf" target='_blank' >Direct download</a> (<a href='/rec/WILHCC-3'>6 more</a>)  <div id="ml-WILHCC-3" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('WILHCC-3','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-WILHCC-3" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('WILHCC-3')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-WILHCC-3"></span></div></div></li> <li id='eWILHUD' onclick="ee('click','WILHUD')" onmouseover="ee('over','WILHUD')" onmouseout="ee('out','WILHUD')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/WILHUD"><span class='name'>Jon Williamson</span> (2013). <span class='articleTitle'>How Uncertain Do We Need to Be?</span></a><span class='pubInfo'> <em class='pubName'>Erkenntnis</em>:1-23.</span></span><div class="extras"><div class="abstract">Expert probability forecasts can be useful for decision making (Sect. 1). 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 (Sect. 2). Bayesian epistemology provides the means to halt this escalator, by tying expressions of uncertainty to the propositions expressible in an agent’s language (Sect. 3). But Bayesian epistemology comes in<span id="WILHUD-absexp"> (<span class="ll" onclick='$("WILHUD-abstract2").show();$("WILHUD-absexp").hide()'>...</span>)</span><span id="WILHUD-abstract2" style="display:none"> three main varieties. Strictly subjective Bayesianism and empirically-based subjective Bayesianism have difficulty in justifying the use of a forecaster’s probabilities for decision making (Sect. 4). On the other hand, objective Bayesianism can justify the use of these probabilities, at least when the probabilities are consistent with the agent’s evidence (Sect. 5). Hence objective Bayesianism offers the most promise overall for explaining how testimony of uncertainty can be useful for decision making. Interestingly, the objective Bayesian analysis provided in Sect. 5 can also be used to justify a version of the Principle of Reflection (Sect. 6). (<span class="ll" onclick='$("WILHUD-abstract2").hide();$("WILHUD-absexp").show();'>shrink</span>)</span></div><div class="catsCon" id="ecats-con-WILHUD"><div><a class='catName' href='/browse/philosophy-of-probability' rel='section'>Philosophy of Probability</a></div> </div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://link.springer.com/content/pdf/10.1007%2Fs10670-013-9516-6.pdf" target='_blank' >Direct download</a> (<a href='/rec/WILHUD'>6 more</a>)  <div id="ml-WILHUD" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('WILHUD','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-WILHUD" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('WILHUD')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-WILHUD"></span></div></div></li> <li id='eWILWFA' onclick="ee('click','WILWFA')" onmouseover="ee('over','WILWFA')" onmouseout="ee('out','WILWFA')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/WILWFA"><span class='name'>Jon Williamson</span> (2013). <span class='articleTitle'>Why Frequentists and Bayesians Need Each Other.</span></a><span class='pubInfo'> <em class='pubName'>Erkenntnis</em> 78 (2):293-318.</span></span><div class="extras"><div class="abstract">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. </div><div class="catsCon" id="ecats-con-WILWFA"><div><a class='catName' href='/browse/philosophy-of-statistics' rel='section'>Philosophy of Statistics</a><span class='catIn'> in </span><a class='catArea' href='/browse/philosophy-of-probability' rel='section'>Philosophy of Probability</a></div> </div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://www.kent.ac.uk/secl/philosophy/jw/2011/bridges.pdf" target='_blank' >Direct download</a> (<a href='/rec/WILWFA'>8 more</a>)  <div id="ml-WILWFA" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('WILWFA','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-WILWFA" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('WILWFA')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-WILWFA"></span></div></div></li> <li id='eILLWIA' onclick="ee('click','ILLWIA')" onmouseover="ee('over','ILLWIA')" onmouseout="ee('out','ILLWIA')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/ILLWIA"><span class='name'>Phyllis Illari</span> & <span class='name'>Jon Williamson</span> (2012). <span class='articleTitle'>What is a Mechanism? Thinking About Mechanisms Across the Sciences.</span></a><span class='pubInfo'> <em class='pubName'>European Journal for Philosophy of Science</em> 2 (1):119-135.</span></span><div class="extras"><div class="abstract">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. </div><div class="catsCon" id="ecats-con-ILLWIA"><div><a class='catName' href='/browse/science-logic-and-mathematics' rel='section'>Science, Logic, and Mathematics</a></div> </div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://www.springerlink.com/content/f2h42456437766r0/" target='_blank' >Direct download</a> (<a href='/rec/ILLWIA'>7 more</a>)  <div id="ml-ILLWIA" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('ILLWIA','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-ILLWIA" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('ILLWIA')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-ILLWIA"></span></div></div></li> <li id='eRUSETI' onclick="ee('click','RUSETI')" onmouseover="ee('over','RUSETI')" onmouseout="ee('out','RUSETI')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/RUSETI"><span class='name'>Federica Russo</span> & <span class='name'>Jon Williamson</span> (2012). <span class='articleTitle'>EnviroGenomarkers: The Interplay Between Mechanisms and Difference Making in Establishing Causal Claims.</span></a><span class='pubInfo'> <em class='pubName'>Medicine Studies</em> 3 (4):249-262.</span></span><div class="extras"><div class="abstract">According to Russo and Williamson (Int Stud Philos Sci 21(2):157–170, 2007, Hist Philos Life Sci 33:389–396, 2011a, Philos Sci 1(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<span id="RUSETI-absexp"> (<span class="ll" onclick='$("RUSETI-abstract2").show();$("RUSETI-absexp").hide()'>...</span>)</span><span id="RUSETI-abstract2" style="display:none"> primacy to evidence of difference making over evidence of mechanisms and are flawed because the two sorts of evidence are required and they should be treated on a par. An alternative approach gives primacy to evidence of mechanism over evidence of difference making. In this paper, we argue that this alternative approach is equally flawed, again because both sorts of evidence need to be treated on a par. As an illustration of this parity, we explain how scientists working in the ‘EnviroGenomarkers’ project constantly make use of the two evidential components in a dynamic and intertwined way. We argue that such an interplay is needed not only for causal assessment but also for policy purposes. (<span class="ll" onclick='$("RUSETI-abstract2").hide();$("RUSETI-absexp").show();'>shrink</span>)</span></div><div class="catsCon" id="ecats-con-RUSETI"><div><a class='catName' href='/browse/epistemology' rel='section'>Epistemology</a></div> <div><a class='catName' href='/browse/medical-ethics' rel='section'>Medical Ethics</a><span class='catIn'> in </span><a class='catArea' href='/browse/applied-ethics' rel='section'>Applied Ethics</a></div> </div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://link.springer.com/content/pdf/10.1007%2Fs12376-012-0079-7" target='_blank' >Direct download</a> (<a href='/rec/RUSETI'>7 more</a>)  <div id="ml-RUSETI" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('RUSETI','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-RUSETI" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('RUSETI')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-RUSETI"></span></div></div></li> <li id='eCASMFP' onclick="ee('click','CASMFP')" onmouseover="ee('over','CASMFP')" onmouseout="ee('out','CASMFP')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/CASMFP"><span class='name'>Lorenzo Casini</span>, <span class='name'>Phyllis Mckay Illari</span>, <span class='name'>Federica Russo</span> & <span class='name'>Jon Williamson</span> (2011). <span class='articleTitle'>Models for Prediction, Explanation and Control.</span></a><span class='pubInfo'> <em class='pubName'>Theoria</em> 26 (1):5-33.</span></span><div class="extras"><div class="abstract">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<span id="CASMFP-absexp"> (<span class="ll" onclick='$("CASMFP-abstract2").show();$("CASMFP-absexp").hide()'>...</span>)</span><span id="CASMFP-abstract2" style="display:none"> how a simple two-level RBN can be used tomodel a mechanism in cancer science. The higher level of our model contains variables at the clinical level, while the lower level maps the structure of the cell’s mechanism for apoptosis. (<span class="ll" onclick='$("CASMFP-abstract2").hide();$("CASMFP-absexp").show();'>shrink</span>)</span></div><div class="catsCon" id="ecats-con-CASMFP"><div><a class='catName' href='/browse/science-logic-and-mathematics' rel='section'>Science, Logic, and Mathematics</a></div> </div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://www.pdcnet.org/collection/show?id=theoria_2011_0026_0001_0005_0033&file_type=pdf" target='_blank' >Direct download</a> (<a href='/rec/CASMFP'>3 more</a>)  <div id="ml-CASMFP" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('CASMFP','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-CASMFP" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('CASMFP')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-CASMFP"></span></div></div></li> <li id='eDARITA' onclick="ee('click','DARITA')" onmouseover="ee('over','DARITA')" onmouseout="ee('out','DARITA')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/DARITA"><span class='name'>George Darby</span> & <span class='name'>Jon Williamson</span> (2011). <span class='articleTitle'>Imaging Technology and the Philosophy of Causality.</span></a><span class='pubInfo'> <em class='pubName'>Philosophy and Technology</em> 24 (2):115-136.</span></span><div class="extras"><div class="abstract">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 difference<span id="DARITA-absexp"> (<span class="ll" onclick='$("DARITA-abstract2").show();$("DARITA-absexp").hide()'>...</span>)</span><span id="DARITA-abstract2" style="display:none"> making, cannot account for the need for the other kind of evidence. Weber (Int Stud Philos Sci 23(2):277–295, 2009) has suggested to the contrary that Giere’s probabilistic analysis of causality survives this criticism. In this paper, we look in detail at the case of medical imaging technology, which, we argue, supports the thesis of Russo and Williamson, and we respond to Weber’s suggestion, arguing that Giere’s account does not survive the criticism. (<span class="ll" onclick='$("DARITA-abstract2").hide();$("DARITA-absexp").show();'>shrink</span>)</span></div><div class="catsCon" id="ecats-con-DARITA"><div><a class='catName' href='/browse/formal-epistemology' rel='section'>Formal Epistemology</a><span class='catIn'> in </span><a class='catArea' href='/browse/epistemology' rel='section'>Epistemology</a></div> <div><a class='catName' href='/browse/observation-misc' rel='section'>Observation, Misc</a><span class='catIn'> in </span><a class='catArea' href='/browse/general-philosophy-of-science' rel='section'>General Philosophy of Science</a></div> <div><a class='catName' href='/browse/theories-of-causation-misc' rel='section'>Theories of Causation, Misc</a><span class='catIn'> in </span><a class='catArea' href='/browse/metaphysics' rel='section'>Metaphysics</a></div> </div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://www.springerlink.com/content/376j74k283161p4r/" target='_blank' >Direct download</a> (<a href='/rec/DARITA'>4 more</a>)  <div id="ml-DARITA" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('DARITA','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-DARITA" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('DARITA')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-DARITA"></span></div></div></li> <li id='eHAEPLA-2' onclick="ee('click','HAEPLA-2')" onmouseover="ee('over','HAEPLA-2')" onmouseout="ee('out','HAEPLA-2')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/HAEPLA-2"><span class='name'>Rolf Haenni</span>, <span class='name'>Jan-Willem Romeijn</span>, <span class='name'>Gregory Wheeler</span> & <span class='name'>Jon Williamson</span> (2011). <span class='pub_name'><span class='articleTitle'>Probabilistic Logics and Probabilistic Networks.</span></span></a><span class='pubInfo'> Synthese Library.</span></span><div class="extras"><div class="abstract">Additionally, the text shows how to develop computationally feasible methods to mesh with this framework. </div><div class="catsCon" id="ecats-con-HAEPLA-2"><div><a class='catName' href='/browse/bayesian-reasoning-misc' rel='section'>Bayesian Reasoning, Misc</a><span class='catIn'> in </span><a class='catArea href='/browse/philosophy-of-probability' rel='section'>Philosophy of Probability</a></div><div><a class='catName' href='/browse/imprecise-credences' rel='section'>Imprecise Credences</a><span class='catIn'> in </span><a class='catArea href='/browse/philosophy-of-probability' rel='section'>Philosophy of Probability</a></div><div><a class='catName' href='/browse/logical-probability' rel='section'>Logical Probability</a><span class='catIn'> in </span><a class='catArea href='/browse/philosophy-of-probability' rel='section'>Philosophy of Probability</a></div></div><div class="options"><div class='affiliateLinks'><span class='price_used bargain'><a class='price_used bargain' target="_blank" rel="nofollow" href="http://www.amazon.com/gp/offer-listing/9400700075?SubscriptionId=1CYYSXRPEAM0Q99H1WR2&tag=philp-20&linkCode=xm2&camp=2025&creative=386001&creativeASIN=9400700075&condition=used">$24.08 used</a></span>   <span class='price_new bargain'><a class='price_new bargain' target="_blank" rel="nofollow" href="http://www.amazon.com/gp/offer-listing/9400700075?SubscriptionId=1CYYSXRPEAM0Q99H1WR2&tag=philp-20&linkCode=xm2&camp=2025&creative=386001&creativeASIN=9400700075&condition=new">$35.00 new</a></span>   <span class='price_amazon bargain'><a class='price_amazon bargain' target="_blank" 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bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-HAEPLA-2" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('HAEPLA-2')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-HAEPLA-2"></span></div></div></li> <li id='eILLCIT' onclick="ee('click','ILLCIT')" onmouseover="ee('over','ILLCIT')" onmouseout="ee('out','ILLCIT')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/ILLCIT"><span class='name'>Phyllis McKay Illari</span>, <span class='name'>Federica Russo</span> & <span class='name'>Jon Williamson</span> (eds.) (2011). <span class='pub_name'><span class='articleTitle'>Causality in the Sciences.</span></span></a><span class='pubInfo'> Oxford University Press.</span></span><div class="extras"><div class="abstract">The book tackles these questions as well as others concerning the use of causality in the sciences. </div><div class="catsCon" id="ecats-con-ILLCIT">No categories</div><div class="options"><div class='affiliateLinks'><span class='price_used bargain'><a class='price_used bargain' target="_blank" rel="nofollow" href="http://www.amazon.com/gp/offer-listing/0199574138?SubscriptionId=1CYYSXRPEAM0Q99H1WR2&tag=philp-20&linkCode=xm2&camp=2025&creative=386001&creativeASIN=0199574138&condition=used">$102.34 used</a></span>   <span class='price_new'><a class='price_new' target="_blank" rel="nofollow" href="http://www.amazon.com/gp/offer-listing/0199574138?SubscriptionId=1CYYSXRPEAM0Q99H1WR2&tag=philp-20&linkCode=xm2&camp=2025&creative=386001&creativeASIN=0199574138&condition=new">$134.64 new</a></span>   <span class='price_amazon'><a class='price_amazon' target="_blank" rel="nofollow" href="http://www.amazon.com/Causality-Sciences-Phyllis-McKay-Illari/dp/0199574138?SubscriptionId=1CYYSXRPEAM0Q99H1WR2&tag=philp-20&linkCode=xm2&camp=2025&creative=165953&creativeASIN=0199574138">$140.55 direct from Amazon</a></span>   <a href="http://www.amazon.com/Causality-Sciences-Phyllis-McKay-Illari/dp/0199574138%3FSubscriptionId%3D1CYYSXRPEAM0Q99H1WR2%26tag%3Dphilp-20%26linkCode%3Dxm2%26camp%3D2025%26creative%3D165953%26creativeASIN%3D0199574138">Amazon page</a></div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://books.google.com/books?id=aa8r4vNriUsC&printsec=front_cover" target='_blank' >Direct download</a>  <div id="ml-ILLCIT" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('ILLCIT','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-ILLCIT" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('ILLCIT')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-ILLCIT"></span></div></div></li> <li id='eILLWLA' onclick="ee('click','ILLWLA')" onmouseover="ee('over','ILLWLA')" onmouseout="ee('out','ILLWLA')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/ILLWLA"><span class='name'>Phyllis McKay Illari</span>, <span class='name'>Federica Russo</span> & <span class='name'>Jon Williamson</span> (2011). <span class='articleTitle'>Why Look at Causality in the Sciences?</span></a><span class='pubInfo'> In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), <em><a href="http://philpapers.org/rec/MCKCIT">Causality in the Sciences</a></em>. Oup Oxford.</span></span><div class="extras"><div class="catsCon" id="ecats-con-ILLWLA">No categories</div><div class="options"><div id="ml-ILLWLA" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('ILLWLA','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-ILLWLA" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('ILLWLA')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-ILLWLA"></span></div></div></li> <li id='eWILMAR-2' onclick="ee('click','WILMAR-2')" onmouseover="ee('over','WILMAR-2')" onmouseout="ee('out','WILMAR-2')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/WILMAR-2"><span class='name'>Phyllis McKay Illari</span> & <span class='name'>Jon Williamson</span> (2011). <span class='articleTitle'>Mechanisms Are Real and Local.</span></a><span class='pubInfo'> In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), <em><a href="http://philpapers.org/rec/MCKCIT">Causality in the Sciences</a></em>. Oup Oxford.</span></span><div class="extras"><div class="catsCon" id="ecats-con-WILMAR-2">No categories</div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://www.kent.ac.uk/secl/philosophy/jw/2009/real-local.pdf" target='_blank' >Direct download</a>  <div id="ml-WILMAR-2" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('WILMAR-2','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-WILMAR-2" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('WILMAR-2')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-WILMAR-2"></span></div></div></li> <li id='eMCKCIT' onclick="ee('click','MCKCIT')" onmouseover="ee('over','MCKCIT')" onmouseout="ee('out','MCKCIT')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/MCKCIT"><span class='name'>Phyllis McKay Illari</span>, <span class='name'>Federica Russo</span> & <span class='name'>Jon Williamson</span> (eds.) (2011). <span class='pub_name'><span class='articleTitle'>Causality in the Sciences.</span></span></a><span class='pubInfo'> Oxford University Press.</span></span><div class="extras"><div class="abstract">There is a need for integrated thinking about causality, probability and mechanisms in scientific methodology. Causality and probability are long-established central concepts in the sciences, with a corresponding philosophical literature examining their problems. On the other hand, the philosophical literature examining mechanisms is not long-established, and there is no clear idea of how mechanisms relate to causality and probability. But we need some idea if we are to understand causal inference in the sciences: a panoply of disciplines, ranging from epidemiology<span id="MCKCIT-absexp"> (<span class="ll" onclick='$("MCKCIT-abstract2").show();$("MCKCIT-absexp").hide()'>...</span>)</span><span id="MCKCIT-abstract2" style="display:none"> to biology, from econometrics to physics, routinely make use of probability, statistics, theory and mechanisms to infer causal relationships. -/- These disciplines have developed very different methods, where causality and probability often seem to have different understandings, and where the mechanisms involved often look very different. This variegated situation raises the question of whether the different sciences are really using different concepts, or whether progress in understanding the tools of causal inference in some sciences can lead to progress in other sciences. The book tackles these questions as well as others concerning the use of causality in the sciences. (<span class="ll" onclick='$("MCKCIT-abstract2").hide();$("MCKCIT-absexp").show();'>shrink</span>)</span></div><div class="catsCon" id="ecats-con-MCKCIT">No categories</div><div class="options"><div id="ml-MCKCIT" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('MCKCIT','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-MCKCIT" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('MCKCIT')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-MCKCIT"></span></div></div></li> <li id='eRUSECA-3' onclick="ee('click','RUSECA-3')" onmouseover="ee('over','RUSECA-3')" onmouseout="ee('out','RUSECA-3')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/RUSECA-3"><span class='name'>Federica Russo</span> & <span class='name'>Jon Williamson</span> (2011). <span class='articleTitle'>Epistemic Causality and Evidence-Based Medicine.</span></a><span class='pubInfo'> <em class='pubName'>History and Philosophy of the Life Sciences</em> 33 (4).</span></span><div class="extras"><div class="catsCon" id="ecats-con-RUSECA-3"><div><a class='catName' href='/browse/philosophy-of-medicine' rel='section'>Philosophy of Medicine</a><span class='catIn'> in </span><a class='catArea' href='/browse/philosophy-of-science-misc' rel='section'>Philosophy of Science, Misc</a></div> </div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://philsci-archive.pitt.edu/8351/1/epcause-medicine.pdf" target='_blank' >Direct download</a> (<a href='/rec/RUSECA-3'>2 more</a>)  <div id="ml-RUSECA-3" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('RUSECA-3','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-RUSECA-3" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('RUSECA-3')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-RUSECA-3"></span></div></div></li> <li id='eRUSGVS' onclick="ee('click','RUSGVS')" onmouseover="ee('over','RUSGVS')" onmouseout="ee('out','RUSGVS')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/RUSGVS"><span class='name'>Federica Russo</span> & <span class='name'>Jon Williamson</span> (2011). <span class='articleTitle'>Generic Versus Single-Case Causality: The Case of Autopsy.</span> <span class='hint'>[REVIEW]</span></a><span class='pubInfo'> <em class='pubName'>European Journal for Philosophy of Science</em> 1 (1):47-69.</span></span><div class="extras"><div class="abstract">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<span id="RUSGVS-absexp"> (<span class="ll" onclick='$("RUSGVS-abstract2").show();$("RUSGVS-absexp").hide()'>...</span>)</span><span id="RUSGVS-abstract2" style="display:none"> second question, categorised according to whether single-case relations are reduced to generic, generic relations are reduced to single-case, or the levels are independent. We then explore causal inference in autopsy. This is an interesting case study, we argue, because it refutes all three epistemologies and all three metaphysics. We close by sketching an account of causality that survives autopsy—the epistemic theory. (<span class="ll" onclick='$("RUSGVS-abstract2").hide();$("RUSGVS-absexp").show();'>shrink</span>)</span></div><div class="catsCon" id="ecats-con-RUSGVS"><div><a class='catName' href='/browse/philosophy-of-medicine-miscellaneous' rel='section'>Philosophy of Medicine, Miscellaneous</a><span class='catIn'> in </span><a class='catArea' href='/browse/philosophy-of-science-misc' rel='section'>Philosophy of Science, Misc</a></div> </div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://www.springerlink.com/content/g887341776467351/" target='_blank' >Direct download</a> (<a href='/rec/RUSGVS'>8 more</a>)  <div id="ml-RUSGVS" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('RUSGVS','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-RUSGVS" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('RUSGVS')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-RUSGVS"></span></div></div></li> <li id='eWHEEPA' onclick="ee('click','WHEEPA')" onmouseover="ee('over','WHEEPA')" onmouseout="ee('out','WHEEPA')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/WHEEPA"><span class='name'>Gregory Wheeler</span> & <span class='name'>Jon Williamson</span> (2011). <span class='articleTitle'>Evidential Probability and Objective Bayesian Epistemology.</span></a><span class='pubInfo'> In Prasanta S. Bandyopadhyay & Malcolm Forster (eds.), <em><a href="http://philpapers.org/rec/BANHOT">Handbook of the Philosophy of Statistics</a></em>. Elsevier.</span></span><div class="extras"><div class="abstract">In this chapter we draw connections between two seemingly opposing approaches to probability and statistics: evidential probability on the one hand and objective Bayesian epistemology on the other. </div><div class="catsCon" id="ecats-con-WHEEPA"><div><a class='catName' href='/browse/bayesian-reasoning-misc' rel='section'>Bayesian Reasoning, Misc</a><span class='catIn'> in </span><a class='catArea' href='/browse/philosophy-of-probability' rel='section'>Philosophy of Probability</a></div> <div><a class='catName' href='/browse/formal-epistemology-misc' rel='section'>Formal Epistemology, Misc</a><span class='catIn'> in </span><a class='catArea' href='/browse/epistemology' rel='section'>Epistemology</a></div> </div><div class="options"><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://www.kent.ac.uk/secl/philosophy/jw/2008/EP-OBE.pdf" target='_blank' >Direct download</a>  <div id="ml-WHEEPA" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('WHEEPA','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-WHEEPA" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('WHEEPA')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-WHEEPA"></span></div></div></li> <li id='eWILAOB-2' onclick="ee('click','WILAOB-2')" onmouseover="ee('over','WILAOB-2')" onmouseout="ee('out','WILAOB-2')" class='entry'><span class="citation"><a href="http://philpapers.org/rec/WILAOB-2"><span class='name'>Jon Williamson</span> (2011). <span class='articleTitle'>An Objective Bayesian Account of Confirmation. In.</span></a><span class='pubInfo'> In Dennis Dieks, Wenceslao Gonzalo, Thomas Uebel, Stephan Hartmann & Marcel Weber (eds.), <em><a href="http://philpapers.org/rec/DIEEPA">Explanation, Prediction, and Confirmation</a></em>. Springer. 53--81.</span></span><div class="extras"><div class="catsCon" id="ecats-con-WILAOB-2"><div><a class='catName' href='/browse/bayesian-reasoning-misc' rel='section'>Bayesian Reasoning, Misc</a><span class='catIn'> in </span><a class='catArea' href='/browse/philosophy-of-probability' rel='section'>Philosophy of Probability</a></div> </div><div class="options"><div class='affiliateLinks'><span class='price_new bargain'><a class='price_new bargain' target="_blank" rel="nofollow" href="http://www.amazon.com/gp/offer-listing/9400711794?SubscriptionId=1CYYSXRPEAM0Q99H1WR2&tag=philp-20&linkCode=xm2&camp=2025&creative=386001&creativeASIN=9400711794&condition=new">$117.95 new</a></span>   <span class='price_amazon bargain'><a class='price_amazon bargain' target="_blank" rel="nofollow" href="http://www.amazon.com/Explanation-Prediction-Confirmation-Philosophy-Perspective/dp/9400711794?SubscriptionId=1CYYSXRPEAM0Q99H1WR2&tag=philp-20&linkCode=xm2&camp=2025&creative=165953&creativeASIN=9400711794">$145.17 direct from Amazon</a></span>   <span class='price_used bargain'><a class='price_used bargain' target="_blank" rel="nofollow" href="http://www.amazon.com/gp/offer-listing/9400711794?SubscriptionId=1CYYSXRPEAM0Q99H1WR2&tag=philp-20&linkCode=xm2&camp=2025&creative=386001&creativeASIN=9400711794&condition=used">$152.19 used</a></span>   (collection)   <a href="http://www.amazon.com/Explanation-Prediction-Confirmation-Philosophy-Perspective/dp/9400711794%3FSubscriptionId%3D1CYYSXRPEAM0Q99H1WR2%26tag%3Dphilp-20%26linkCode%3Dxm2%26camp%3D2025%26creative%3D165953%26creativeASIN%3D9400711794">Amazon page</a></div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/go-down.png"><a rel="nofollow" href="http://www.kent.ac.uk/secl/philosophy/jw/2010/confirmation.pdf" target='_blank' >Direct download</a> (<a href='/rec/WILAOB-2'>3 more</a>)  <div id="ml-WILAOB-2" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/places/folder.png"><span title="File in your personal bibliography" class="ll" onclick="showLists('WILAOB-2','')">My bibliography<img src="/philpapers/raw/subind.gif"></span>  <div id="la-WILAOB-2" title="Export to another format" class="yui-skin-sam ldiv"> </div><img class="texticon" src="/assets/raw/icons/tango-full/16x16/actions/document-save.png"><span class="ll" onclick="showExports('WILAOB-2')">Export citation<img src="/philpapers/raw/subind.gif"></span>  <span class="eMsg" id="msg-WILAOB-2"></span></div></div></li> </ol> </div> <div id='prevNextHtml' class='centered'><center><table><td><span class='prevNext'><img border='0' src='/philpapers/raw/icons/back-g.png'></td><td>1 — 50 / 90</td><td><span class='prevNext'><span title='Next page' class='clickable' onclick='goToNextPage()'><img border='0' src='/philpapers/raw/icons/forward.png'></span></span></td></table></center></div> </td> <td class="side_td"> <form name="expform"> <div class="sideBox"> <div class="sideBoxH">BibTeX / EndNote / RIS / etc</div> <div class="sideBoxC"> Export this page: <div style='margin-top:5px'> <select name="expf" id="expf" onChange="$('expLimit').show()"> <option value=''>Choose a format..</option> <option value='htm'>Formatted text</option><option value='txt'>Plain text</option><option value='bib'>BibTeX</option><option value='zot'>Zotero</option><option value='enw'>EndNote</option><option value='ris'>Reference Manager</option></select> <div id='expLimit' style="display:none"> Limit to <input type="text" id="expLimitI" size="3" value="500"> items. <input type="button" value="Export" onclick=" if ($F('expf')) { $('ap-format').value=$F('expf'); $('ap-limit').value=$F('expLimitI'); refreshWith($('allparams')); } else { alert('You must first choose a format.') } "> </div> </div> </div> </div> </form> <form id="moreOptions" name="more"> <div class="sideBox"> <div class="sideBoxH">Restrictions</div> <div class="sideBoxC"> <input class='checkbox' type='checkbox' name='proOnly' id='proOnly' onClick="createCookie('proOnly',this.checked ? 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