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The Metainductive Justification of Induction: The Pool of Strategies

Published online by Cambridge University Press:  01 January 2022

Abstract

This article poses a challenge to Schurz’s proposed metainductive justification of induction. It is argued that Schurz’s argument requires a notion of optimality that can deal with an expanding pool of prediction strategies.

Type
Logic, Formal Epistemology, and Decision Theory
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

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To contact the author, please write to: Munich Center for Mathematical Philosophy, LMU Munich; e-mail: tom.sterkenburg@lmu.de.

References

Arnold, E. 2010. “Can the Best-Alternative Justification Solve Hume’s Problem? On the Limits of a Promising Approach.” Philosophy of Science 77 (4): 584–93..CrossRefGoogle Scholar
Cesa-Bianchi, N., and Lugosi, G.. 2006. Prediction, Learning and Games. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Chernov, A., and Vovk, V. G.. 2009. “Prediction with Expert Evaluators’ Advice.” In Algorithmic Learning Theory: 20th International Conference, ALT 2009, Porto, Portugal, October 3–5, 2009; Proceedings, ed. Gavaldà, R., Lugosi, G., Zeugmann, T., and Zilles, S., 822. Berlin: Springer.CrossRefGoogle Scholar
Earman, J. 1992. Bayes or Bust? Cambridge, MA: MIT Press.Google Scholar
Freund, Y., Schapire, R. E., Singer, Y., and Warmuth, M. K.. 1997. “Using and Combining Predictors That Specialize.” In Proceedings of the Twenty-Ninth Annual ACM Symposium on the Theory of Computing: El Paso, Texas, May 4–6, 1997, 334–43, New York: Association for Computing Machinery.CrossRefGoogle Scholar
Howson, C. 2000. Hume’s Problem. New York: Oxford University Press.CrossRefGoogle Scholar
Lipton, P. 2004. Inference to the Best Explanation. 2nd ed. London: Routledge.Google Scholar
Mourtada, J., and Maillard, O.-A.. 2017. “Efficient Tracking of a Growing Number of Experts.” In International Conference on Algorithmic Learning Theory, 15–17 October 2017, Kyoto University, Kyoto, Japan, ed. Hanneke, S. and Reyzin, L., 517–39. Proceedings of Machine Learning Research 76. PMLR.Google Scholar
Salmon, W. C. 1967. The Foundations of Scientific Inference. Pittsburgh: University of Pittsburgh Press.CrossRefGoogle Scholar
Schurz, G. 2008. “The Meta-inductivist’s Winning Strategy in the Prediction Game: A New Approach to Hume’s Problem.” Philosophy of Science 75 (3): 278305..CrossRefGoogle Scholar
Schurz, G.. 2009. “Meta-induction and Social Epistemology: Computer Simulations of Prediction Games.” Episteme 6 (2): 200220..CrossRefGoogle Scholar
Schurz, G.. 2018. “Optimality Justifications: New Foundations for Foundation-Oriented Epistemology.” Synthese 195 (9): 3877–97..CrossRefGoogle Scholar
Schurz, G.. 2019. Hume’s Problem Solved: The Optimality of Meta-induction. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Sterkenburg, T. F. 2018. “Universal Prediction: A Philosophical Investigation.” PhD diss., University of Groningen.Google Scholar
Sterkenburg, T. F.. 2019. “The Meta-inductive Justification of Induction.” Episteme, forthcoming.CrossRefGoogle Scholar