Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-29T18:07:10.221Z Has data issue: false hasContentIssue false

MODES OF CONVERGENCE TO THE TRUTH: STEPS TOWARD A BETTER EPISTEMOLOGY OF INDUCTION

Published online by Cambridge University Press:  03 January 2022

HANTI LIN*
Affiliation:
PHILOSOPHY DEPARTMENT UNIVERSITY OF CALIFORNIA, DAVIS DAVIS, CA95616, USAE-mail: [email protected]

Abstract

Evaluative studies of inductive inferences have been pursued extensively with mathematical rigor in many disciplines, such as statistics, econometrics, computer science, and formal epistemology. Attempts have been made in those disciplines to justify many different kinds of inductive inferences, to varying extents. But somehow those disciplines have said almost nothing to justify a most familiar kind of induction, an example of which is this: “We’ve seen this many ravens and they all are black, so all ravens are black.” This is enumerative induction in its full strength. For it does not settle with a weaker conclusion (such as “the ravens observed in the future will all be black”); nor does it proceed with any additional premise (such as the statistical IID assumption). The goal of this paper is to take some initial steps toward a justification for the full version of enumerative induction, against counterinduction, and against the skeptical policy. The idea is to explore various epistemic ideals, mathematically defined as different modes of convergence to the truth, and look for one that is weak enough to be achievable and strong enough to justify a norm that governs both the long run and the short run. So the proposal is learning-theoretic in essence, but a Bayesian version is developed as well.

Type
Research Article
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of The Association for Symbolic Logic

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

BIBLIOGRAPHY

Baltag, A., Gierasimczuk, N., & Smets, S. (2015). On the solvability of inductive problems: A study in epistemic topology. In Ramanujam, R., editor. Proceedings of the 15th Conference on Theoretical Aspects of Rationality and Knowledge (TARK-2015). ILLC Prepublication Series PP-2015-13. ACM.Google Scholar
Carlucci, L., & Case, J. (2013). On the necessity of U-shaped learning. Topics in Cognitive Science, 5(1), 5688.CrossRefGoogle ScholarPubMed
Carlucci, L., Case, J., Jain, S., & Stephan, F. (2005). Non U-shaped vacillatory and team learning. In Algorithmic Learning Theory. Berlin–Heidelberg: Springer, pp. 241255.CrossRefGoogle Scholar
Carnap, R. (1963). Replies and systematic expositions. In Schilpp, P. A., editor. The Philosophy of Rudolf Carnap. La Salle, IL: Open Court.Google Scholar
Carnap, R. (1955). Statistical and Inductive Probability (leaflet). Brooklyn, NY: Galois Institute of Mathematics and Art.Google Scholar
de Finetti, B. (1974). Theory of Probability. John Wiley & Sons.Google Scholar
Diaconis, P., & Freedman, D. (1986). On the consistency of Bayes estimates. The Annals of Statistics, 14(1), 126.Google Scholar
Diaconis, P., & Freedman, D. (1986). Rejoinder: On the consistency of Bayes estimates. The Annals of Statistics, 14(1), 6367.Google Scholar
Forster, M., & Sober, E. (1994). How to tell when simpler, more unified, or less Ad Hoc theories will provide more accurate predictions. The British Journal for the Philosophy of Science, 45(1), 135.CrossRefGoogle Scholar
Genin, K. (2018). The Topology of Statistical Inquiry. Ph.D. Dissertation, Carnegie Mellon University.Google Scholar
Gold, E. M. (1965). Limiting recursion. Journal of Symbolic Logic, 30(1), 2748.CrossRefGoogle Scholar
Gold, E. M. (1967). Language identification in the limit. Information and Control, 10(5), 447474.CrossRefGoogle Scholar
Glymour, C. (2015). Thinking things through: an introduction to philosophical issues and achievements, 2nd edition. Cambridge, MA: MIT Press.Google Scholar
Henderson, L. (2020). The problem of induction. In Zalta, E. N., editor. Stanford Encyclopedia of Philosophy (Spring 2020 Edition), https://plato.stanford.edu/archives/spr2020/entries/induction-problem/.Google Scholar
Hintikka, J. (1966). A two-dimensional continuum of inductive methods. In Hintikka, J. & Suppes, P., editors. Aspects of Inductive Logic. Amsterdam: North-Holland.Google Scholar
Hintikka, J., & Niiniluoto, I. (1980). An axiomatic foundation for the logic of inductive generalization. In Jeffrey, R., editor. Studies in Inductive Logic and Probability, vol. 2. Berkeley and Los Angeles: University of California Press.Google Scholar
Hume, D. (1777). Enquiries Concerning Human Understanding and Concerning the Principles of Morals, reprinted and edited with introduction, comparative table of contents, and analytical index by Selby-Bigge, L. A. (1975), and the third edition with text revised and notes by Nidditch, P. H. Oxford: Clarendon Press.CrossRefGoogle Scholar
Imbens, G. W., & Rubin, D. B. (2015). Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press.CrossRefGoogle Scholar
Jaynes, E. T. (1968). Prior probabilities. IEEE Transactions on Systems Science and Cybernetics, 4(3), 227241.CrossRefGoogle Scholar
Joyce, J. M. (2011). The development of subjective Bayesianism. In Gabbay, D. M., Hartmann, S., & Woods, J., editors. Handbook of the History of Logic: Inductive Logic, vol. 10, pp. 415475.CrossRefGoogle Scholar
Kelly, K. T. (1996). The Logic of Reliable Inquiry. Oxford University Press.Google Scholar
Kelly, K. T. (2001). The logic of success. The British Journal for the Philosophy of Science, Special Millennium Issue 51, 639666.CrossRefGoogle Scholar
Kelly, K. T., & Glymour, C. (2004). Why probability does not capture the logic of scientific justification. In Hitchcock, C., editor. Contemporary Debates in the Philosophy of Science. London: Blackwell.Google Scholar
Kelly, T. K., Genin, K., & Lin, H. (2016). Realism, rhetoric, and reliability. Synthese, 193(4), 11911223.CrossRefGoogle Scholar
Lange, S., & Zeugmann, T. (1992). Types of monotonic language learning and their characterization. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory. ACM.Google Scholar
Lin, H. (2019). The hard problem of theory choice: A case study on causal inference and its faithfulness assumption. Philosophy of Science, 86, 967980.CrossRefGoogle Scholar
Lin, H., & Zhang, J. (2020). On learning causal structures from non-experimental data without any faithfulness assumption. Proceedings of Machine Learning Research, 117, 554582.Google Scholar
Mukouchi, Y. (1992). Characterization of finite identification. In Jantke, K. P., editor. Analogical and Inductive Inference, Lecture Notes in Computer Science, Vol. 642. Berlin–Heidelberg: Springer.Google Scholar
Osherson, D., Micheal, S., & Weinstein, S. (1986). Systems that Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists. MIT Press.Google Scholar
Oxtoby, J. C. (1996). Measure and Category: A Survey of the Analogies Between Topological and Measure Spaces (second edition). New York: Springer.Google Scholar
Pearl, J. (2009). Causality: Models, Reasoning, and Inference (second edition). New York: Cambridge University Press.CrossRefGoogle Scholar
Plato (1961). Meno, translated by Guthrie, W. K.C. In Hamilton, E. & Cairns, H., editors. The Collected Dialogues of Plato: Including the Letters. Princeton: Princeton University Press, pp. 353384.Google Scholar
Popper, C. (1959). The Logic of Scientific Discovery. London: Hutchinson & Co.Google Scholar
Putnam, H. (1963). Degree of confirmation and inductive logic. In Schilpp, P. A., editor. The Philosophy of Rudolf Carnap. La Salle, IL: Open Court.Google Scholar
Putnam, H. (1965). Trial and error predicates and a solution to a problem of Mostowski. Journal of Symbolic Logic, 30(1), 4957.CrossRefGoogle Scholar
Reichenbach, H. (1938). Experience and Prediction: An Analysis of the Foundation and the Structure of Knowledge. Chicago: University of Chicago Press.Google Scholar
Salmon, W. C. (1967). The Foundations of Scientific Inference. Pittsburgh, PA: University of Pittsburgh Press.CrossRefGoogle Scholar
Savage, L. J. (1972). The Foundations of Statistics (second edition). New York: Dover Publications, Inc.Google Scholar
Schulte, O. (1999). Means-ends epistemology. British Journal for the Philosophy of Science, 79(1), 132.CrossRefGoogle Scholar
Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (second edition). Cambridge, MA: The MIT Press.Google Scholar
van Fraassen, B. (1980). The Scientific Image. New York: Oxford University Press.CrossRefGoogle Scholar
Vickers, S. (1989). Topology via Logic. Cambridge: Cambridge University Press.Google Scholar
Williamson, J. (2010). In defense of objective Bayesianism. Oxford: Oxford University Press.CrossRefGoogle Scholar