Causality, Mechanisms & Scientific Discovery

Michael Wilde & Jon Williamson: Models in medicine, in H. Kincaid, J. Simon & M. Solomon (eds), The Routledge Companion to Philosophy of Medicine. Routledge, pp. 271-284, 2016.  ISBN: 978-1-13-884679-1

Michael Wilde & Jon Williamson: Evidence and Epistemic Causality, in A. von Eye & W. Wiedermann (eds), Statistics and Causality: methods for applied empirical research, pp. 31-41. Wiley, 2016.   ISBN: 978-1-118-94704-3

Brendan Clarke, Donald Gillies, Phyllis Illari, Federica Russo & Jon Williamson: Mechanisms and the Evidence Hierarchy, Topoi 33(2):339-360, 2014.  doi: 10.1007/s11245-013-9220-9

Brendan Clarke, Bert Leuridan & Jon Williamson: Modelling mechanisms with causal cycles, Synthese 191(8):1651-1681, 2014. . doi: 10.1007/s11229-013-0360-7.

Jon Williamson: How can causal explanations explain? Erkenntnis 78:257-275, 2013. doi: 10.1007/s10670-013-9512-x

Brendan Clarke, Donald Gillies, Phyllis Illari, Federica Russo & Jon Williamson: The evidence that evidence-based medicine omits, Preventative Medicine 57:745-747, 2013. doi: 10.1016/j.ypmed.2012.10.020

Phyllis McKay Illari and Jon Williamson: In defence of activities, Journal of General Philosophy of Science, 44(1):69-83, 2013. doi: 10.1007/s10838-013-9217-5.

Federica Russo & Jon Williamson: EnviroGenomarkers: the interplay between mechanisms and difference making in establishing causal claims, Medicine Studies: International Journal for the History, Philosophy and Ethics of Medicine & Allied Sciences, 3:249–262, 2012. .

Phyllis McKay Illari and Jon Williamson: What is a mechanism: thinking about mechanisms across the sciences, European Journal for Philosophy of Science 2:119-135, 2012;

Jon Williamson: Mechanistic theories of causality, Philosophy Compass 6(6): 421-432, 433-444, 445-447, 2011; Part 1: ; Part II: ; Teaching and learning guide: ; Local combined copy:

Phyllis McKay Illari and Jon Williamson: Mechanisms are real and local, in Phyllis McKay Illari, Federica Russo and Jon Williamson (eds): Causality in the Sciences, Oxford University Press, pp. 818-844, 2011;

Federica Russo and Jon Williamson: Epistemic causality and evidence-based medicine, History and Philosophy of the Life Sciences 33(4):563-582, 2011.

Lorenzo Casini, Phyllis McKay Illari, Federica Russo and Jon Williamson: Models for prediction, explanation and control: recursive Bayesian networks, Theoria 26(1):5-33, 2011.

Barbara Osimani, Federica Russo and Jon Williamson: Scientific evidence and the law: an objective Bayesian formalisation of the precautionary principle in pharmaceutical regulation, Journal of Philosophy, Science and Law 11, 2011;

George Darby and Jon Williamson: Imaging Technology and the Philosophy of Causality, Philosophy and Technology 24(2): 115-136, 2011.

Federica Russo and Jon Williamson: Generic versus single-case causality: the case of autopsy, European Journal for Philosophy of Science 1(1): 47-69, 2011.

Phyllis McKay Illari and Jon Williamson: Function and organization: comparing the mechanisms of protein synthesis and natural selection, Studies in History and Philosophy of Biological and Biomedical Sciences 41, pp. 279-291, 2010, doi 10.1016/j.shpsc.2010.07.001; ;

Jon Williamson: The philosophy of science and its relation to machine learning, in Mohamed Medhat Gaber (ed.): Scientific Data Mining and Knowledge Discovery: Principles and Foundations, Springer, pp. 77-89, 2010.

Lorenzo Casini, Phyllis McKay Illari, Federica Russo and Jon Williamson: Recursive Bayesian networks for prediction, explanation and control in cancer science: a position paper, Proceedings of the First International Conference on Bioinformatics, Valencia, 20-23 January 2010;

Jon Williamson: Probabilistic theories [of causality], in Helen Beebee, Chris Hitchcock & Peter Menzies (eds): The Oxford Handbook of Causation, Oxford University Press, pp. 185-212, 2009;

Jon Williamson: Causality, in Dov Gabbay & F. Guenthner (eds.): Handbook of Philosophical Logic, volume 14, Springer, pp. 95-126, 2007;

Federica Russo and Jon Williamson: Interpreting causality in the health sciences, International Studies in the Philosophy of Science 21(2): 157-170, 2007.

Federica Russo and Jon Williamson: Interpreting probability in causal models for cancer, in Federica Russo and Jon Williamson (eds): Causality and probability in the sciences, London: College Publications, 2007, pp. 217-241.

Jon Williamson: Causal pluralism versus epistemic causality, Philosophica 77(1), pp. 69-96, 2006;

Jon Williamson: Dispositional versus epistemic causality, Minds and Machines 16, pp. 259-276, 2006;

Jon Williamson & Dov Gabbay: Recursive Causality in Bayesian Networks and Self-Fibring Networks, in Donald Gillies (ed.): `Laws and models in science‘, London: King’s College Publications, 2005, pp. 173-221, with comments pp. 223-245.

Jon Williamson: A dynamic interaction between machine learning and the philosophy of science, Minds and Machines 14(4), 2004, pp. 539-549;

Jon Williamson: Learning causal relationships, Discussion Paper 02/02, LSE Centre for Natural and Social Sciences;


Probability, Logic & Formal Epistemology

Jim Hawthorne, Juergen Landes, Christian Wallmann & Jon Williamson: The Principal Principle implies the Principle of Indifference, British Journal for the Philosophy of Science. 68:123–131, 2017.    doi: 10.1093/bjps/axv030

Christian Wallmann & Jon Williamson: Four approaches to the reference class problem, in Gábor Hofer-Szabó & Leszek Wroński (eds), Making it Formally Explicit: Probability, Causality and Indeterminism, Springer, 2017, pp. 61-81.   ISBN 978-3-319-55486-0

Michael Wilde & Jon Williamson: Bayesianism and information, in L. Floridi (ed.), The Routledge Handbook of Philosophy of Information, pp. 180-187, Routledge 2016.  ISBN: 978-1-13-879693-5

Juergen Landes & Jon Williamson: Objective Bayesian nets from consistent datasets,  in Adom Giffin  & Kevin H. Knuth (eds), Proceedings of the 35th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Potsdam NY. American Institute of Physics Conference Proceedings 1757, 2016.  doi: 10.1063/1.4959048

Jon Williamson: Deliberation, Judgement and the Nature of Evidence, Economics and Philosophy 31(1): 27-65, 2015.   doi: 10.1017/S026626711400039X

Juergen Landes & Jon Williamson: Justifying Objective Bayesianism on Predicate LanguagesEntropy  17: 2459-2543, 2015;   doi: 10.3390/e17042459.

Jon Williamson: How uncertain do we need to be? Erkenntnis 79(6):1249-1271, 2014. Published version: . Local version: . Video: doi 10.1007/s10670-013-9516-6

Jürgen Landes & Jon Williamson: Objective Bayesianism and the Maximum Entropy Principle, Entropy 15(9): 3528-3591, 2013. doi:10.3390/e15093528

Jon Williamson: Why Frequentists and Bayesians Need Each Other, Erkenntnis 78:293-318, 2013. doi: 10.1007/s10670-011-9317-8.

Jon Williamson: From Bayesian epistemology to inductive logic, Journal of Applied Logic 11:468-486, 2013. doi: 10.1016/j.jal.2013.03.006

Jon Williamson: Review of ‘Reliable Reasoning’ by Gilbert Harman and Sanjeev Kulkarni, Mind 121:1073-1076, 2013. doi: 10.1093/mind/fzt006.

Jon Williamson: Calibration and Convexity: Response to Gregory Wheeler, British Journal for the Philosophy of Science 63:851-857, 2012. [Wheeler’s paper available here]

Jon Williamson: Inductive logic, The Reasoner 6(11):176-7, 2012.

Jon Williamson: Objective Bayesianism, Bayesian conditionalisation and voluntarism, Synthese, 178(1): 67-85, 2011;

Jon Williamson: An objective Bayesian account of confirmation, in Dennis Dieks, Wenceslao J. Gonzalez, Stephan Hartmann, Thomas Uebel, Marcel Weber (eds), `Explanation, Prediction, and Confirmation. New Trends and Old Ones Reconsidered’, The philosophy of science in a European perspective Volume 2, Springer, 2011, pp. 53-81;

Gregory Wheeler & Jon Williamson: Evidential probability and objective Bayesian epistemology, in Prasanta S. Bandyopadhyay & Malcolm R.Forster (eds): Philosophy of statistics, Handbook of the Philosophy of Science volume 7, Elsevier, pp. 307-331, 2011.

Jon Williamson: Bruno de Finetti: Philosophical lectures on probability, Philosophia Mathematica 18(1): 130-135, 2010;

Jon Williamson: Epistemic complexity from an objective Bayesian perspective, in A. Carsetti (ed.) `Causality, meaningful complexity and embodied cognition’, Springer, pp. 231-246, 2010;

Jan-Willem Romeijn, Rolf Haenni, Gregory Wheeler and Jon Williamson: Logical Relations in a Statistical Problem, in B. Lowe, E. Pacuit & J.W. Romeijn (eds): Foundations of the Formal Sciences VI, Reasoning about Probabilities and Probabilistic Reasoning, London: College Publications, pp. 49-79, 2009.

Jon Williamson: Aggregating judgements by merging evidence, Journal of Logic and Computation 19(3), pp. 461-473, 2009.

Jon Williamson: Philosophies of probability, in Andrew Irvine (ed.): Handbook of the Philosophy of Mathematics, Volume 4 of the Handbook of the Philosophy of Science, North-Holland, 2009, pp. 493–533;

Jon Williamson: Objective Bayesianism with predicate languages, Synthese 163(3), pp. 341-356, 2008;

Rolf Haenni, Jan-Willem Romeijn, Gregory Wheeler and Jon Williamson: Possible Semantics for a Common Framework of Probabilistic Logics, in V. N. Huynh (ed.): Interval / Probabilistic Uncertainty and Non-Classical Logics, Advances in Soft Computing Series, Springer 2008, pp. 268-279.

Jon Williamson: A note on probabilistic logics and probabilistic networks, The Reasoner 2(5), pp. 4-5, 2008.

Jon Williamson: Objective Bayesian probabilistic logic, Journal of Algorithms in Cognition, Informatics and Logic 63: 167-183, 2008.

Sylvia Nagl, Matt Williams and Jon Williamson: Objective Bayesian nets for systems modelling and prognosis in breast cancer, in Dawn Holmes and L.C. Jain (eds): `Innovations in Bayesian Networks: Theory and Applications’, Springer, 2008, pp. 131-167.

Jon Williamson: Inductive influence, British Journal for the Philosophy of Science 58, pp. 689-708, 2007;

Jon Williamson: Motivating objective Bayesianism: from empirical constraints to objective probabilities, in William L. Harper and Gregory R. Wheeler (eds.): Probability and Inference: Essays in Honor of Henry E. Kyburg Jr. London: College Publications, 2007, pp. 155-183;

Sylvia Nagl, Matt Williams, Nadjet El-Mehidi, Vivek Patkar and Jon Williamson: Objective Bayesian nets for integrating cancer knowledge: a systems biology approach, in Juho Rousu, Samuel Kaski and Esko Ukkonen (eds): Proceedings of the Workshop on Probabilistic Modeling and Machine Learning in Structural and Systems Biology (Tuusula, Finland, 17-18 June 2006), Helsinki University Printing House, 2006, pp. 44-49. Video.

Matt Williams and Jon Williamson: Combining argumentation and Bayesian nets for breast cancer prognosis, Journal of Logic, Language and Information 15: 155-178, 2006.

Jon Williamson: From Bayesianism to the Epistemic View of Mathematics: Remarks motivated by Richard Jeffrey’s ‘Subjective probability: the real thing’, Philosophia Mathematica 14(3), pp. 365-369, 2006;

Jon Williamson: Objective Bayesian nets, in S. Artemov, H. Barringer, A. S. d’Avila Garcez, L. C. Lamb, and J. Woods (eds.): We Will Show Them: Essays in Honour of Dov Gabbay, Vol 2., pp. 713-730, College Publications, 2005;

Jon Williamson: Bayesianism and language change, Journal of Logic, Language and Information, 12(1), 2003, pp. 53-97.

Jung-Wook Bang, Raphael Chaleil & Jon Williamson: Two-stage Bayesian networks for metabolic network prediction, in Peter Lucas (ed), Proceedings of the Workshop on Qualitative and Model-Based Reasoning in Biomedicine, 9th Conference on Artificial Intelligence in Medicine Europe, 18-22 October 2003, Cyprus, pp. 19-23;

Jon Williamson: Abduction and its distinctions , Review of Lorenzo Magnani [2001]: Abduction, reason and science: processes of discovery and explanation, British Journal for the Philosophy of Science 54(2), 2003, pp.353-358.

Jon Williamson: Maximising entropy efficiently, Electronic Transactions in Artificial Intelligence 6, 2002;

Jon Williamson: Probability logic, in Dov Gabbay, Ralph Johnson, Hans Jurgen Ohlbach & John Woods (eds)[2002]: Handbook of the Logic of Inference and Argument: The Turn Toward the Practical, Studies in Logic and Practical Reasoning Volume 1, Elsevier, pp. 397-424.

Jon Williamson & David Corfield: Bayesianism into the 21st century, in David Corfield & Jon Williamson (eds): `Foundations of Bayesianism‘, Kluwer Applied Logic Series, 2001, pp.1-16.

Jon Williamson: Bayesian networks for logical reasoning, in Carla Gomes & Toby Walsh (eds) [2001]: Proceedings of the AAAI Fall Symposium on using Uncertainty within Computation, AAAI Press Technical Report FS-01-04, pp. 136-143.

Jon Williamson: Foundations for Bayesian networks , in David Corfield & Jon Williamson (eds): Foundations of Bayesianism, Kluwer Applied Logic Series, 2001, pp. 75-115. Presented at Bayesianism 2000 (May 11-12 2000).

Jon Williamson: A probabilistic approach to diagnosis, Proceedings of the Eleventh International Workshop on Principles of Diagnosis (DX-00), Morelia, Michoacen, Mexico, June 8-11 2000.

Jon Williamson: Approximating discrete probability distributions with Bayesian networks, in Proceedings of the International Conference on Artificial Intelligence in Science and Technology, Hobart Tasmania, 16-20 December 2000.

Jon Williamson: Countable additivity and subjective probability, British Journal for the Philosophy of Science 50(3), 1999, pp. 401-416.