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KOLMOGOROV CONDITIONALIZERS CAN BE DUTCH BOOKED (IF AND ONLY IF THEY ARE EVIDENTIALLY UNCERTAIN)

Published online by Cambridge University Press:  21 October 2020

ALEXANDER MEEHAN
Affiliation:
DEPARTMENT OF PHILOSOPHY PRINCETON UNIVERSITY 1879 HALL PRINCETON, NJ08544, USAE-mail: alexandermeehan@princeton.eduE-mail: xueyinz@princeton.edu
SNOW ZHANG
Affiliation:
DEPARTMENT OF PHILOSOPHY PRINCETON UNIVERSITY 1879 HALL PRINCETON, NJ08544, USAE-mail: alexandermeehan@princeton.eduE-mail: xueyinz@princeton.edu
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Abstract

A vexing question in Bayesian epistemology is how an agent should update on evidence which she assigned zero prior credence. Some theorists have suggested that, in such cases, the agent should update by Kolmogorov conditionalization, a norm based on Kolmogorov’s theory of regular conditional distributions. However, it turns out that in some situations, a Kolmogorov conditionalizer will plan to always assign a posterior credence of zero to the evidence she learns. Intuitively, such a plan is irrational and easily Dutch bookable. In this paper, we propose a revised norm, Kolmogorov–Blackwell conditionalization, which avoids this problem. We prove a Dutch book theorem and converse Dutch book theorem for this revised norm, and relate our results to those of Rescorla (2018).

Type
Research Article
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of The Association for Symbolic Logic

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References

BIBLIOGRAPHY

Armendt, B. (1980). Is there a Dutch book argument for probability kinematics? Philosophy of Science, 47(4), 583588.CrossRefGoogle Scholar
Arntzenius, F. (2003). Some problems for conditionalization and reflection. The Journal of Philosophy, 100(7), 356370.CrossRefGoogle Scholar
Arntzenius, F., Elga, A., & Hawthorne, J. (2004). Bayesianism, infinite decisions, and binding. Mind, 113(450), 251283.CrossRefGoogle Scholar
Bacchus, F., Kyburg, H. E., & Thalos, M. (1990). Against conditionalization. Synthese, 85(3), 475506.CrossRefGoogle Scholar
Berti, P., & Rigo, P. (1999). Sufficient conditions for the existence of disintegrations. Journal of Theoretical Probability, 12(1), 7586.CrossRefGoogle Scholar
Berti, P., & Rigo, P (2002). On coherent conditional probabilities and disintegrations. Annals of Mathematics and Artificial Intelligence, 35(1), 7182.CrossRefGoogle Scholar
Billingsley, P. (1979). Probability and Measure. Hoboken, NJ: John Wiley & Sons.Google Scholar
Blackwell, D., & Dubins, L. E. (1975). On existence and non-existence of proper, regular, conditional distributions. The Annals of Probability, 3(5), 741752.CrossRefGoogle Scholar
Blackwell, D., & Ryll-Nardzewski, C. (1963). Non-existence of everywhere proper conditional distributions. The Annals of Mathematical Statistics, 34(1), 223225.CrossRefGoogle Scholar
Briggs, R. A., & Pettigrew, R. (2020). An accuracy-dominance argument for conditionalization. Noûs, 54(1), 162–181.CrossRefGoogle Scholar
Çınlar, E. (2011). Probability and Stochastics. Graduate Texts in Mathematics, Vol. 261. New York, NY: Springer.CrossRefGoogle Scholar
Diaconis, P., & Zabell, S. L. (1982). Updating subjective probability. Journal of the American Statistical Association, 77(380), 822830.CrossRefGoogle Scholar
Dubins, L. E. (1975). Finitely additive conditional probabilities, conglomerability and disintegrations. The Annals of Probability, 8999.Google Scholar
Dubins, L. E (1977). Measurable tail disintegrations of the haar integral are purely finitely additive. Proceedings of the American Mathematical Society, 62(1), 3436.Google Scholar
Dubins, L. E., & David, H. (1983). With respect to tail sigma fields, standard measures possess measurable disintegrations. Proceedings of the American Mathematical Society, 88(3), 416418.CrossRefGoogle Scholar
Easwaran, K. K. (2008). The foundations of conditional probability. Ph.D Thesis, University of California, Berkeley.Google Scholar
Easwaran, K. K (2013). Expected accuracy supports conditionalization—and conglomerability and reflection. Philosophy of Science, 80(1), 119142.CrossRefGoogle Scholar
Easwaran, K. K (2019). Conditional probabilities. In Pettigrew, R., and Weisberg, J., editors. The Open Handbook of Formal Epistemology. PhilPapers Foundation.Google Scholar
Gyenis, Z., Hofer-Szabó, G., & Rédei, M. (2017). Conditioning using conditional expectations: The Borel–Kolmogorov paradox. Synthese, 194(7), 25952630.CrossRefGoogle Scholar
Gyenis, Z., & Rédei, M. (2017). General properties of bayesian learning as statistical inference determined by conditional expectations. The Review of Symbolic Logic, 10(4), 719755.CrossRefGoogle Scholar
Hájek, A. (2003). What conditional probability could not be. Synthese, 137(3), 273323.CrossRefGoogle Scholar
Hájek, A. (2008). Dutch book arguments. In Anand, P., Pattanaik, P., and Puppe, C., editors. The Oxford Handbook of Rational and Social Choice. Oxford, UK: Oxford University Press, pp. 173193.Google Scholar
Hájek, A (2011). Conditional probability. In Bandyopadhyay, S. and Forster, M. R., editors. Handbook of the Philosophy of Science, Vol. 7. Philosophy of Statistics. Oxford: Elsevier.Google Scholar
Hervés-Beloso, C., & Monteiro, P. K. (2013). Information and sigma-algebras. Economic Theory, 54(2), 405418.CrossRefGoogle Scholar
Hewitt, E., & Savage, L. J. (1955). Symmetric measures on cartesian products. Transactions of the American Mathematical Society, 80(2), 470501.CrossRefGoogle Scholar
Hudson, R. L., & Moody, G. R. (1976). Locally normal symmetric states and an analogue of de finetti's theorem. Probability Theory and Related Fields, 33(4), 343351.Google Scholar
Huttegger, S. M. (2015). Merging of opinions and probability kinematics. The Review of Symbolic Logic, 8(4), 611648.CrossRefGoogle Scholar
Huttegger, S. M (2017). The Probabilistic Foundations of Rational Learning. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Jeffrey, R. (1965). The Logic of Decision. University of Chicago Press: Chicago.Google Scholar
Kolmogorov, A. N. (1933). Grundbegriffe der Wahrscheinlichkeitsrechnung. Berlin, Germany: Springer.CrossRefGoogle Scholar
Leitgeb, H., & Pettigrew, R. (2010). An objective justification of Bayesianism II: The consequences of minimizing inaccuracy. Philosophy of Science, 77(2), 236272.CrossRefGoogle Scholar
Lewis, D. (1999). Why conditionalize. In Eagle, A., editor. Philosophy of Probability: Contemporary Readings, Routledge, pp. 403407.Google Scholar
Mahtani, A. (2012). Diachronic Dutch book arguments. Philosophical Review, 121(3), 443450.CrossRefGoogle Scholar
Maitra, A., & Ramakrishnan, S. (1988). Factorization of measures and normal conditional distributions. Proceedings of the American Mathematical Society, 103(4), 12591267.CrossRefGoogle Scholar
McGee, V. (1994). Learning the impossible. In Probability and Conditionals: Belief Revision and Rational Decision, Cambridge University Press Cambridge. pp. 179199.Google Scholar
Meehan, A., & Zhang, S. (2020). Jeffrey meets Kolmogorov. Journal of Philosophical Logic, 49(5), 941979.CrossRefGoogle Scholar
Musiał, K. (1980). Projective limits of perfect measure spaces. Fundamenta Mathematicae, 110(3), 163189.CrossRefGoogle Scholar
Pettigrew, R. (2020). Bayesian Updating When What You Take Yourself to Learn Might Be False. Manuscript. Available from: https://philpapers.org/rec/PETBUW?fbclid=IwAR1F9UA6QVIut2LlAXvOKc1jrMdIVMYfr-YWkfR6gsNgMj9Y2JcNiRBsHV0.Google Scholar
Rescorla, M. (2015). Some epistemological ramifications of the Borel–Kolmogorov paradox. Synthese, 192(3), 735767.CrossRefGoogle Scholar
Rescorla, M (2018). A Dutch book theorem and converse Dutch book theorem for Kolmogorov conditionalization. The Review of Symbolic Logic, 11(4), 705735.CrossRefGoogle Scholar
Rescorla, M (2019). On the proper formulation of conditionalization. Synthese, 131.Google Scholar
Rescorla, M (2020). An improved Dutch book theorem for conditionalization. Erkenntnis, 129.Google Scholar
Schilling, R. L. (2005). Measures, Integrals and Martingales. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Seidenfeld, T., & Schervish, M. J. (1983). A conflict between finite additivity and avoiding Dutch book. Philosophy of Science, 50(3), 398412.CrossRefGoogle Scholar
Seidenfeld, T., Schervish, M. J, & Kadane, J. B. (2001). Improper regular conditional distributions. Annals of Probability, 29(4), 16121624.Google Scholar
Seidenfeld, T., Schervish, M. J., & Kadane, J. B. (2006). Correction: Improper regular conditional distributions. The Annals of Probability, 34(1), 423426.CrossRefGoogle Scholar
Shimony, A. (1955). Coherence and the axioms of confirmation. The Journal of Symbolic Logic, 20(1), 128.CrossRefGoogle Scholar
Skyrms, B. (1987). Dynamic coherence and probability kinematics. Philosophy of Science, 54(1), 120.CrossRefGoogle Scholar
Vineberg, S. (2016). Dutch book arguments. In Zalta, E. N., editor. The Stanford Encyclopedia of Philosophy (2016 edition). New York, NY: Metaphysics Research Lab, Stanford University, Spring.Google Scholar