Abstract
(I) Synchronic norms of theory choice, a traditional concern in scientific methodology, restrict the theories one can choose in light of given information. (II) Diachronic norms of theory change, as studied in belief revision, restrict how one should change one’s current beliefs in light of new information. (III) Learning norms concern how best to arrive at true beliefs. In this paper, we undertake to forge some rigorous logical relations between the three topics. Concerning (III), we explicate inductive truth conduciveness in terms of optimally direct convergence to the truth, where optimal directness is explicated in terms of reversals and cycles of opinion prior to convergence. Concerning (I), we explicate Ockham’s razor and related principles of choice in terms of the information topology of the empirical problem context and show that the principles are necessary for reversal or cycle optimal convergence to the truth. Concerning (II), we weaken the standard principles of agm belief revision theory in intuitive ways that are also necessary (and in some cases, sufficient) for reversal or cycle optimal convergence. Then we show that some of our weakened principles of change entail corresponding principles of choice, completing the triangle of relations between (I), (II), and (III).
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References
Baker, A., Simplicity, in E. N. Zalta, (ed.), The Stanford Encyclopedia of Philosophy, fall 2013 edn., 2013.
Baltag, A., N. Gierasimczuk, and S. Smets, On the solvability of inductive problems: a study in epistemic topology (forthcoming), in Proceedings of the fifteenth conference on Theoretical Aspects of Rationality and Knowledge, 2015.
Baltag, A., N. Gierasimczuk, and S. Smets, Truth-tracking by belief revision (to appear), Studia Logica, 2018.
Carlucci, L., and J. Case, On the necessity of U-Shaped learning, Topics in Cognitive Science 5(1):56–88, 2013.
Carlucci, L., J. Case, S. Jain, and F. Stephan, Non U-shaped vacillatory and team learning, in Algorithmic Learning Theory, Springer, Berlin, 2005, pp. 241–255.
Carnap, R., On inductive logic, Philosophy of Science 12(2):72, 1945.
Case, J., and C. Smith, Comparison of identification criteria for machine inductive inference, Theoretical Computer Science 25(2):193–220, 1983.
de Brecht, M., and A. Yamamoto, Interpreting learners as realizers for \({\Sigma }_2^0\) -measurable functions, (Manuscript), 2009.
Douglas, H., Inductive risk and values in science, Philosophy of Science 67(4):559–579, 2000.
Gärdenfors, P., Knowledge in Flux, MIT Press, Cambridge, 1988.
Glymour, C., Theory and Evidence, Princeton University Press, Princeton, 1980.
Gold, E. M., Language identification in the limit, Information and Control 10(5):447–474, 1967.
Hempel, C., Valuation and objectivity in science, 1983. Reprinted in J. Fetzer, (ed.), The philosophy of Carl G. Hempel, 2001.
Jain, S., D. N. Osherson, J. S. Royer, and A. Sharma, Systems that Learn: An Introduction to Learning Theory, MIT Press, Cambridge, 1999.
Kelly, K. T., The Logic of Reliable Inquiry, Oxford University Press, Oxford, 1996.
Kelly, K. T., Justification as truth-finding efficiency: How Ockham’s razor works, Minds and Machines 14(4):485–505, 2004.
Kelly, K. T., A topological theory of learning and simplicity, Manuscript, 2005.
Kelly, K. T., How simplicity helps you find the truth without pointing at it, in Induction, Algorithmic Learning Theory, and Philosophy, Springer, Berlin, 2007, pp. 111–143.
Kelly, K. T., A new solution to the puzzle of simplicity, Philosophy of Science 74(5):561–573, 2007.
Kelly, K. T., Ockham’s razor, empirical complexity, and truth-finding efficiency, Theoretical Computer Science 383(2):270–289, 2007.
Kelly, K. T., Simplicity, truth, and the unending game of science, in T. Raesch, J. van Benthem, S. Bold, B. Loewe, (eds.), Infinite Games: Foundations of the Formal Sciences V, College Press, New York, 2007.
Kelly, K. T., Ockham’s Razor, Truth, and Information, Elsevier, Dordrecht, 2008.
Kelly, K. T., Simplicity, truth and probability, in P. S. Bandyopadhyay, and M. Forster, (eds.), Handbook of the Philosophy of Science. Volume 7: Philosophy of Statistics, North Holland, Amsterdam, 2011.
Kelly, K. T., K. Genin, and H. Lin, Simplicity, truth, and topology, Manuscript, 2014.
Kelly, K. T., and C. Glymour, Why probability does not capture the logic of scientific justification, in C. Hitchcock, (ed.), Debates in the Philosophy of Science, Blackwell, New York, 2004, pp. 94–114.
Laudan, L., Science and Values, vol. 87, Cambridge Univ Press, Cambridge, 1984.
Lin, Hanti, and Kevin T Kelly, Propositional reasoning that tracks probabilistic reasoning, Journal of Philosophical Logic 41(6):957–981, 2012.
Luo, W., and O. Schulte, Mind change efficient learning, Information and Computation 204(6):989–1011, 2006.
Martin, E., and D. N. Osherson, Elements of Scientific Inquiry, MIT Press, Cambridge, 1998.
Morrison, M., Unifying Scientific Theories: Physical Concepts and Mathematical Structures, Cambridge University Press, Cambridge, 2007.
Osherson, D. N., M. Stob, and S. Weinstein, Systems that Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists, The MIT Press, Cambridge, 1986.
Popper, K. R., The Logic of Scientific Discovery, Hutchinson, London, 1959.
Putnam, H., Trial and error predicates and the solution to a problem of Mostowski, Journal of Symbolic Logic 30:49–57, 1965.
Rott, H., Two dogmas of belief revision, The Journal of Philosophy 97(9):503–522, 2000.
Schurz, G., Abductive belief revision in science, in Belief Revision Meets Philosophy of Science, Springer, Berlin, 2011, pp. 77–104.
Sharma, A., F. Stephan, and Y. Ventsov, Generalized notions of mind change complexity, in Proceedings of the Tenth Annual Conference on Computational Learning Theory, ACM, 1997, pp. 96–108.
Acknowledgements
In Spring 2000, John Case suggested to the second author to consider the consequences of U-shaped learning for Ockham’s razor. We are indebted to Thomas Icard for suggesting connections between our topological conception of simplicity and related work in the semantics of provability logic, which proved to be very fruitful. We are indebted to Hanti Lin for the Maxwell example. We are indebted to Alexandru Baltag, Nina Gierasimczuk, and Sonja Smets, for comments and discussions, for informing us of related work by Debrecht and Yamamoto [8], and for sharing their related results with us, particularly Proposition 10.2.
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Presented by Jacek Malinowski
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Genin, K., Kelly, K.T. Theory Choice, Theory Change, and Inductive Truth-Conduciveness. Stud Logica 107, 949–989 (2019). https://doi.org/10.1007/s11225-018-9809-5
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DOI: https://doi.org/10.1007/s11225-018-9809-5