Hostname: page-component-758b78586c-fxcbs Total loading time: 0 Render date: 2023-11-28T06:31:48.541Z Has data issue: false Feature Flags: { "corePageComponentGetUserInfoFromSharedSession": true, "coreDisableEcommerce": false, "useRatesEcommerce": true } hasContentIssue false
  • English
  • Français

A Pragmatist’s Guide to Epistemic Utility

Published online by Cambridge University Press:  01 January 2022

Benjamin Anders Levinstein
Get access Rights & Permissions [Opens in a new window]

Abstract

We use a theorem from M. J. Schervish to explore the relationship between accuracy and practical success. If an agent is pragmatically rational, she will quantify the expected loss of her credence with a strictly proper scoring rule. Which scoring rule is right for her will depend on the sorts of decisions she expects to face. We relate this pragmatic conception of inaccuracy to the purely epistemic one popular among epistemic utility theorists.

Type
Research Article
Information
Philosophy of Science , Volume 84 , Issue 4 , October 2017 , pp. 613 - 638
Copyright
Copyright © The Philosophy of Science Association

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.)

Footnotes

†

Thanks to Seamus Bradley, Catrin Campbell-Moore, Greg Gandenberger, James Joyce, Richard Pettigrew, Patricia Rich, and audiences in Bristol and Munich. I was supported by the European Research Council (ERC) starting grant Epistemic Utility Theory: Foundations and Applications during some of the work on this article. I received funding from the ERC under the European Union's Horizon 2020 research and innovation program (grant 669751).

References

Bronfman, A. 2009. “A Gap in Joyce’s Argument for Probabilism.” Unpublished manuscript, University of Michigan.Google Scholar
D’Agostino, M., and Sinigaglia, C.. 2010. “Epistemic Accuracy and Subjective Probability.” In EPSA Epistemology and Methodology of Science: Launch of the European Philosophy of Science Association, ed. Suárez, M., Dorato, M., and Rédei, M., 95–105. Dordrecht: Springer.Google Scholar
DeGroot, M. H., and Eriksson, E.. 1985. “Probability Forecasting, Stochastic Dominance, and the Lorenz Curve.” In Bayesian Statistics 2: Proceedings of the Second Valencia International Meeting, ed. Bernardo, J., DeGroot, M., Lindley, D., and Smith, A., 99–118. Amsterdam: Elsevier.Google Scholar
DeGroot, M. H., and Fienberg, S. E.. 1982. “Assessing Probability Assessors: Calibration and Refinement.” In Statistical Decision Theory and Related Topics III, Vol. 1, ed. S. S. Gupta and J. O. Berger. New York: Academic Press.Google Scholar
DeGroot, M. H., and Fienberg, S. E. 1983. “The Comparison and Evaluation of Forecasters.” In “Proceedings of the 1982 I.O.S. Annual Conference on Practical Bayesian Statistics,” special issue, Statistician 32 (1–2): 12–22.CrossRefGoogle Scholar
Gibbard, A. 2007. “Rational Credence and the Value of Truth.” In Oxford Studies in Epistemology, Vol. 2, ed. T. Gendler and J. Hawthorne, 143–64. Oxford: Oxford University Press.Google Scholar
Good, I. 1967. “On the Principle of Total Evidence.” British Journal for the Philosophy of Science 17:319–22.CrossRefGoogle Scholar
Greaves, H., and Wallace, D.. 2006. “Justifying Conditionalization: Conditionalization Maximizes Expected Epistemic Utility.” Mind 115 (632): 607–32.CrossRefGoogle Scholar
Jose, V. R. 2007. “A Characterization for the Spherical Scoring Rule.” Theory and Decision 66:263–81.Google Scholar
Joyce, J. M. 1998. “A Nonpragmatic Vindication of Probabilism.” Philosophy of Science 65:575–603.CrossRefGoogle Scholar
Joyce, J. M. 1999. The Foundations of Causal Decision Theory. Cambridge Studies in Probability, Induction, and Decision Theory. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Joyce, J. M. 2009. “Accuracy and Coherence: Prospects for an Alethic Epistemology of Partial Belief.” In Degrees of Belief, Vol. 342, ed. F. Huber and C. Schmidt-Petri, 263–97. Dordrecht: Springer.CrossRefGoogle Scholar
Konek, J., and Levinstein, B. A.. 2017. “The Foundations of Epistemic Decision Theory.” Mind, forthcoming.CrossRefGoogle Scholar
Leitgeb, H., and Pettigrew, R.. 2010a. “An Objective Justification of Bayesianism I: Measuring Inaccuracy.” Philosophy of Science 77:201–35.Google Scholar
Leitgeb, H., and Pettigrew, R. 2010b. “An Objective Justification of Bayesianism II: The Consequences of Minimizing Inaccuracy.” Philosophy of Science 77:236–72.Google Scholar
Myrvold, W. C. 2012. “Epistemic Value and the Value of Learning.” Synthese 187 (2): 547–68.CrossRefGoogle Scholar
Pettigrew, R. 2013. “A New Epistemic Utility Argument for the Principal Principle.” Episteme 10 (1): 19–35.CrossRefGoogle Scholar
Pettigrew, R. 2016a. Accuracy and the Laws of Credence. Oxford: Oxford University Press.CrossRefGoogle Scholar
Pettigrew, R. 2016b. “Accuracy, Risk, and the Principle of Indifference.” Philosophy and Phenomenological Research 92 (1): 35–59.CrossRefGoogle Scholar
Pettigrew, R. 2016c. “Jamesian Epistemology Formalised: An Explication of ‘The Will to Believe.’” Episteme 13 (3): 253–68.CrossRefGoogle Scholar
Popper, K. 1959. The Logic of Scientific Discovery. New York: Basic.Google Scholar
Rényi, A. 1955. “On a New Axiomatic Theory of Probability.” Acta Mathematica Academiae Hungarica 6 (3): 286–335.Google Scholar
Rosenkrantz, R. 1981. Foundations and Applications of Inductive Probability. Atascadero, CA: Ridgeview.Google Scholar
Schervish, M. 1989. “A General Method for Comparing Probability Assessors.” Annals of Statistics 17:1856–79.CrossRefGoogle Scholar