Philosophy

profile image for Professor Jon Williamson

Professor Jon Williamson

Professor of Reasoning, Inference and Scientific Method

 

Office: GAN3b

About

Jon Williamson has research interests in the following areas: the philosophy of causality; the foundations of probability; logics and reasoning; and the use of causality, probability and logics in AI. His books Bayesian Nets and Causality and In Defence of Objective Bayesianism develop the view that causality and probability are features of the way we represent the world, not a part of the world itself.

See http://blogs.kent.ac.uk/jonw/ for current work.

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Publications

Also view these in the Kent Academic Repository

Book
Williamson, J. (2017). Lectures on inductive logic. [Online]. Oxford, UK: Oxford University Press. Available at: https://global.oup.com/academic/product/lectures-on-inductive-logic-9780199666478.
Haenni, R. et al. (2011). Probabilistic Logics and Probabilistic Networks. [Online]. Berlin: Springer. Available at: http://www.springer.com/philosophy/epistemology+and+philosophy+of+science/book/978-94-007-0007-9.
Williamson, J. (2010). In Defence of Objective Bayesianism. Oxford: Oxford University Press.
Williamson, J. (2004). Bayesian Nets and Causality: Philosophical and Computational Foundations. Oxford: Oxford University Press.
Edited book
Illari, P., Russo, F. and Williamson, J. eds. (2011). Causality in the Sciences. Oxford: Oxford University Press.
Russo, F. and Williamson, J. eds. (2007). Causality and Probability in the Sciences. London: College Publications.
Corfield, D. and Williamson, J. eds. (2001). Foundations of Bayesianism. Dordrecht: Kluwer Academic Publishers.
Article
Williamson, J. (2017). Models in Systems Medicine. Disputatio.
Williamson, J. (2015). Deliberation, Judgement and the Nature of Evidence. Economics and Philosophy [Online] 31:27-65. Available at: http://dx.doi.org/10.1017/S026626711400039X.
Hawthorne, J. et al. (2015). The Principal Principle Implies the Principle of Indifference. The British Journal for the Philosophy of Science [Online] 68:123-131. Available at: http://dx.doi.org/10.1093/bjps/axv030.
Landes, J. and Williamson, J. (2015). Justifying Objective Bayesianism on Predicate Languages. Entropy [Online] 17:2459-2543. Available at: http://doi.org/10.3390/e17042459.
Williamson, J. (2014). How Uncertain Do We Need to Be? Erkenntnis [Online] 79:1249-1271. Available at: http://dx.doi.org/10.1007/s10670-013-9516-6.
Clarke, B. et al. (2014). Mechanisms and the Evidence Hierarchy. Topoi [Online] 33:339-360. Available at: http://dx.doi.org/10.1007/s11245-013-9220-9.
Clarke, B., Leuridan, B. and Williamson, J. (2014). Modelling Mechanisms with Causal Cycles. Synthese [Online] 191:1651-1681. Available at: http://dx.doi.org/10.1007/s11229-013-0360-7.
Williamson, J. (2013). How can Causal explanations Explain? Erkenntnis [Online] 78:257-275. Available at: http://dx.doi.org/10.1007/s10670-013-9512-x.
Clarke, B. et al. (2013). The Evidence that Evidence-based Medicine Omits. Preventative Medicine [Online] 57:745-747. Available at: http://dx.doi.org/10.1016/j.ypmed.2012.10.020.
Williamson, J. (2013). From Bayesian Epistemology to Inductive Logic. Journal of Applied Logic [Online] 11:468-486. Available at: http://dx.doi.org/10.1016/j.jal.2013.03.006.
Williamson, J. (2013). Why Frequentists and Bayesians Need Each Other. Erkenntnis [Online] 78:293-318. Available at: http://dx.doi.org/10.1007/s10670-011-9317-8.
Illari, P. and Williamson, J. (2012). What is a Mechanism? Thinking about Mechanisms across the Sciences. European Journal for Philosophy of Science [Online] 2:119-135. Available at: http://dx.doi.org/10.1007/s13194-011-0038-2.
Russo, F. and Williamson, J. (2012). EnviroGenomarkers: The Interplay between Mechanisms and Difference Making in Establishing Causal Claims. Medicine Studies [Online] 3:249-262. Available at: http://dx.doi.org/10.1007/s12376-012-0079-7.
Casini, L. et al. (2011). Models for Prediction, Explanation and Control: Recursive Bayesian Networks. Theoria [Online] 26:5-33. Available at: http://www.ehu.es/ojs/index.php/THEORIA/article/view/1192/825.
Russo, F. and Williamson, J. (2011). Epistemic Causality and Evidence-Based Medicine. History and Philosophy of the Life Sciences [Online] 33:563-582. Available at: http://www.hpls-szn.com/articles.asp?id=146&book=31.
Williamson, J. (2011). Mechanistic Theories of Causality. Philosophy Compass [Online] 6:421-447. Available at: http://dx.doi.org/10.1111/j.1747-9991.2011.00400.x.
Williamson, J. (2011). Objective Bayesianism, Bayesian Conditionalisation and Voluntarism. Synthese [Online] 178:67-85. Available at: http://dx.doi.org/10.1007/s11229-009-9515-y.
Osimani, B., Russo, F. and Williamson, J. (2011). Scientific Evidence and the Law: An Objective Bayesian Formalisation of the Precautionary Principle in Pharmaceutical Regulation. Journal of Philosophy, Science and Law [Online] 11. Available at: http://www.miami.edu/ethics/jpsl/.
Russo, F. and Williamson, J. (2011). Generic versus Single-Case Causality: The Case of Autopsy. European Journal for Philosophy of Science [Online] 1:47-69. Available at: http://dx.doi.org/10.1007/s13194-010-0012-4.
Darby, G. and Williamson, J. (2011). Imaging Technology and the Philosophy of Causality. Philosophy & Technology [Online] 24:115-136. Available at: http://dx.doi.org/10.1007/s13347-010-0010-7.
McKay Illari, P. and Williamson, J. (2010). Function and Organization: Comparing the Mechanisms of Protein Synthesis and Natural Selection. Studies in History and Philosophy of Science Part C [Online] 41:279-291. Available at: http://dx.doi.org/10.1016/j.shpsc.2010.07.001.
Williamson, J. (2009). Aggregating Judgements by Merging Evidence. Journal of Logic and Computation [Online] 19:461-473. Available at: http://dx.doi.org/10.1093/logcom/exn011.
Williamson, J. (2008). Objective Bayesian Probabilistic Logic. Journal of Algorithms [Online] 63:167-183. Available at: http://dx.doi.org/10.1016/j.jalgor.2008.07.001 .
Williamson, J. (2008). A Note on Probabilistic Logics and Probabilistic Networks. The Reasoner [Online] 2:4-5. Available at: http://www.thereasoner.org/.
Williamson, J. (2007). Inductive Influence. British Journal for the Philosophy of Science [Online] 58:689-708. Available at: http://dx.doi.org/10.1093/bjps/axm032.
Russo, F. and Williamson, J. (2007). Interpreting Causality in the Health Sciences. International Studies in the Philosophy of Science [Online] 21:157-170. Available at: http://www.informaworld.com/smpp/content~content=a781489193~db=all~order=page.
Williamson, J. (2006). Causal Pluralism versus Epistemic Causality. Philosophica 77:69-96.
Williamson, J. (2006). From Bayesianism to the Epistemic View of Mathematics: Remarks motivated by Richard Jeffrey's 'Subjective Probability: The Real thing'. Philosophia Mathematica [Online] 14:365-369. Available at: http://philmat.oxfordjournals.org/cgi/content/extract/14/3/365.
Williamson, J. (2006). Dispositional versus Epistemic Causality. Minds and Machines 16:259-276.
Williamson, J. (2006). Combining Probability and Logic: Introduction. Journal of Logic, Language and Information 15:1-3.
Williams, M. and Williamson, J. (2006). Combining Argumentation and Bayesian Nets for Breast Cancer Prognosis. Journal of Logic, Language and Information [Online] 15:155-178. Available at: http://dx.doi.org/10.1007/s10849-005-9010-x.
Williamson, J. (2004). A Dynamic Interaction between Machine Learning and the Philosophy of Science. Minds and Machines 14:539-549.
Williamson, J. and Gabbay, D. (2003). Special issue on Combining Probability and Logic Landes, J. and Williamson, J. eds. Journal of Applied Logic [Online] 1:135-138. Available at: http://dx.doi.org/10.1016/S1570-8683(03)00009-0.
Williamson, J. (2003). Bayesianism and Language Change. Journal of Logic, Language and Information 12:53-97.
Williamson, J. (2002). Maximising Entropy Efficiently. Electronic Transactions in Artificial Intelligence [Online] 7. Available at: http://www.ida.liu.se/ext/etai/received/machi/mi19.html.
Williamson, J. (1996). Social inequalities and mental health: Developing services and developing knowledge. Journal of Community & Applied Social Psychology [Online] 6:311-316. Available at: http://dx.doi.org/1052-9284/96/050311-06.
Williamson, J. and Watson, G. (1991). Sexual Inequality and Clinical-Psychology Training in Britain - Survey Report. Feminism & Psychology 1:78-88.
Williamson, J. and Watson, G. (1991). Sexual Inequality and Clinical-Psychology Training in Britain - Workshop Report . Feminism & Psychology 1:101-109.
Vickers, E., Williamson, J. and Watson, G. (1991). Looking Back - Reflections on Clinical-Psychology Training. Feminism & Psychology 1:69-73.
Williamson, J. and Watson, G. (1991). Oppression, Inequality and Invisible Lesbians -a Reply. Feminism & Psychology 1:429-429.
Williamson, J. and Watson, G. (1991). Clinical-Psychology Training - Training in Oppression. Feminism & Psychology 1:55-57.
Book section
Wallmann, C. and Williamson, J. (2017). Four approaches to the reference class problem. in: Hofer-Szabó, G. and Wroński, L. eds. Making it Formally Explicit: Probability, Causality and Indeterminism. Springer, pp. 61-81. Available at: http://dx.doi.org/10.1007/978-3-319-55486-0_4.
Wilde, M. and Williamson, J. (2016). Bayesianism and Information. in: The Routledge Handbook of Philosophy of Information. Abingdon: Routledge, pp. 180-187. Available at: https://www.routledge.com/The-Routledge-Handbook-of-Philosophy-of-Information/Floridi/p/book/9781138796935.
Wilde, M. and Williamson, J. (2016). Evidence and Epistemic Causality. in: Wiedermann, W. and von Eye, A. eds. Statistics and Causality: Methods for Applied Empirical Research. Wiley, pp. 31-41.
Wilde, M. and Williamson, J. (2016). Models in medicine. in: The Routledge Companion to Philosophy of Medicine. Abingdon, Oxfordshire: Routledge, pp. 271-284.
Wheeler, G. and Williamson, J. (2011). Evidential Probability and Objective Bayesian Epistemology. in: Bandyopadhyay, P. S. and Forster, M. R. eds. Philosophy of statistics. Oxford: Elsevier Science & Technology/ North Holland, pp. 307-331. Available at: http://www.elsevier.com/wps/find/bookdescription.cws_home/BS_HPHS/description.
Williamson, J. (2011). An Objective Bayesian Account of Confirmation. in: Dieks, D. et al. eds. Explanation, Prediction, and Confirmation. New Trends and Old Ones Reconsidered. Dordrecht: Springer, pp. 53-81. Available at: http://dx.doi.org/10.1007/978-94-007-1180-8.
Williamson, J. (2011). Mechanisms are Real and Local. in: Illari, P., Russo, F. and Williamson, J. eds. Causality in the Sciences. Oxford: Oxford University Press, pp. 818-844.
Williamson, J. (2010). Epistemic Complexity from an Objective Bayesian Perspective. in: Carsetti, A. ed. Causality, Meaningful Complexity and Embodied Cognition. Dordrecht: Springer, pp. 231-246. Available at: http://dx.doi.org/10.1007/978-90-481-3529-5_13.
Williamson, J. (2009). Philosophies of probability. in: Gabbay, D. M., Thagard, P. and Woods, J. eds. Philosophy of Mathematics. Oxford: Elsevier Science & Technology/ North Holland, pp. 493-533.
Williamson, J. (2009). The Philosophy of Science and its relation to Machine Learning. in: Gaber, M. M. ed. Scientific Data Mining and Knowledge Discovery: Principles and Foundations. Berlin: Springer, pp. 77-89.
Williamson, J. (2009). Probabilistic Theories of Causality. in: Beebee, H., Hitchcock, C. and Menzies, P. eds. The Oxford Handbook of Causation. Oxford: Oxford University Press, pp. 185-212.
Haenni, R. et al. (2008). Possible Semantics for a Common Framework of Probabilistic Logics. in: Huynh, V. -N. et al. eds. Interval / Probabilistic Uncertainty and Non-Classical Logics. Berlin: Springer , pp. 268-279.
Nagl, S., Williams, M. and Williamson, J. (2008). Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer. in: Holmes, D. and Jain, L. C. eds. Innovations in Bayesian Networks: Theory and Applications. Berlin: Springer, pp. 131-168.
Russo, F. and Williamson, J. (2007). Interpreting Probability in Causal Models for Cancer. in: Russo, F. and Williamson, J. eds. Causality and Probability in the Sciences. London: College Publications.
Williamson, J. (2007). Motivating Objective Bayesianism: From Empirical Constraints to Objective Probabilities. in: Harper, W. L. and Wheeler, G. R. eds. Probability and Inference: Essays in Honour of Henry E. Kyburg Jr. London, UK: College Publications, pp. 155-183.
Williamson, J. (2007). Causality. in: Gabbay, D. M. and Guenthner, F. eds. Handbook of Philosophical Logic. Springer, pp. 89-120. Available at: http://www.springer.com/philosophy/logic/book/978-1-4020-6323-7.
Williamson, J. (2005). Objective Bayesian Nets. in: Artemov, S., Barringer, H. and Garcez, A. d'A. eds. We Will Show Them: Essays in Honour of Dov Gabbay. London: College Publications, pp. 713-730.
Williamson, J. and Gabbay, D. (2004). Recursive Causality in Bayesian Networks and Self-Fibring Networks. in: Gillies, D. ed. Laws and Models in Science. London: King's College Publications, pp. 173-221.
Williamson, J. (2002). Probability Logic. in: Gabbay, D. M. ed. Handbook of the Logic of Inference and Argument: The Turn Toward the Practical. Amsterdam: Elsevier Science & Technology, pp. 397-424.
Williamson, J. (2001). Foundations for Bayesian Networks. in: Corfield, D. and Williamson, J. eds. Foundations of Bayesianism. Dordrecht: Kluwer Academic Publishers Group, pp. 75-115. Available at: http://bookshop.blackwell.co.uk/jsp/id/Foundations_of_Bayesianism/9781402002236.
Williamson, J. (2001). Bayesian Networks for Logical Reasoning. in: Proceedings of the AAAI Fall Symposium on using Uncertainty within Computation. AAAI Press, pp. 136-143. Available at: http://www.aaai.org/Papers/Symposia/Fall/2001/FS-01-04/FS01-04-021.pdf .
Corfield, D. and Williamson, J. (2001). Introduction: Bayesianism into the 21st Century. in: Corfield, D. and Williamson, J. eds. Foundations of Bayesianism. Dordrecht: Kluwer Academic Publishers Group, pp. 1-16.
Review
Williamson, J. (2003). Review of Lorenzo Magnani: 'Abduction, Reason and Science: Processes of Discovery and Explanation'. British Journal for the Philosophy of Science 54:353-358.
Internet publication
Williamson, J. (2002). Learning Causal Relationships [Online paper (technical report) in Causality: Metaphysics and Methods project (2002 - 2004)]. Available at: http://www.lse.ac.uk/CPNSS/pdf/DP_withCover_Causality/CTR02-02-C.pdf.
Conference or workshop item
Landes, J. and Williamson, J. (2016). Objective Bayesian nets from consistent datasets. in: 35TH INTERNATIONAL WORKSHOP ON BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING. AIP, p. . Available at: http://doi.org/10.1063/1.4959048.
Total publications in KAR: 73 [See all in KAR]
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Teaching

Jon Williamson teaches modules on logic.

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Philosophy, School of European Culture and Languages, University of Kent, Canterbury, Kent, CT2 7NF

Enquiries: +44 (0)1227 827159 or email Philosophy

Last Updated: 25/08/2015