Hostname: page-component-848d4c4894-cjp7w Total loading time: 0 Render date: 2024-06-13T12:08:32.587Z Has data issue: false hasContentIssue false

Approximate Coherentism and Luck

Published online by Cambridge University Press:  01 January 2022

Abstract

Approximate coherentism suggests that imperfectly rational agents should hold approximately coherent credences. This norm is intended as a generalization of ordinary coherence. I argue that it may be unable to play this role by considering its application under learning experiences. While it is unclear how imperfect agents should revise their beliefs, I suggest a plausible route is through Bayesian updating. However, Bayesian updating can take an incoherent agent from relatively more coherent credences to relatively less coherent credences, depending on the data observed. Thus, comparative rationality judgments among incoherent agents are unduly sensitive to luck.

Type
Research Article
Copyright
Copyright 2021 by the Philosophy of Science Association. All rights reserved.

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

I would like to thank Glauber De Bona, Kenny Easwaran, Simon Huttegger, Calum McNamara, and Julia Staffel for very helpful feedback. I have also benefited from conversations with the participants of the 34th Annual Conference on Chance and Probability in Science in Boulder, CO. Special thanks are also due to the anonymous reviewers from Philosophy of Science for supporting this article and for their valuable comments.

References

Babic, B. 2019. “A Theory of Epistemic Risk.” Philosophy of Science 86 (3): 522–50.CrossRefGoogle Scholar
Brier, G. W. 1950. “Verification of Forecasts Expressed in Terms of Probability.” Monthly Weather Review 78 (1): 13.2.0.CO;2>CrossRefGoogle Scholar
Broome, J. 2007. “Wide or Narrow Scope?Mind 116 (462): 359–70.CrossRefGoogle Scholar
De Bona, G., and Staffel, J. 2017. “Graded Incoherence for Accuracy-Firsters.” Philosophy of Science 84 (2): 189213.CrossRefGoogle Scholar
De Bona, G., and Staffel, J. 2018. “Why Be (Approximately) Coherent?Analysis 78 (3): 405–15.CrossRefGoogle Scholar
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. 2013. Bayesian Data Analysis. 3rd ed. New York: CRC.CrossRefGoogle Scholar
Gilboa, I., and Schmeidler, D. 1993. “Updating Ambiguous Beliefs.” Journal of Economic Theory 59 (1): 3349.CrossRefGoogle Scholar
Gneiting, T., and Raftery, A. E. 2007. “Strictly Proper Scoring Rules, Prediction, and Estimation.” Journal of the American Statistical Association 102 (477): 359–78.CrossRefGoogle Scholar
Goldman, A. I. 2002. Pathways to Knowledge: Private and Public. Oxford: Oxford University Press.CrossRefGoogle Scholar
Greaves, H., and Wallace, D. 2006. “Justifying Conditionalization: Conditionalization Maximizes Expected Epistemic Utility.” Mind 115 (459): 607–32.CrossRefGoogle Scholar
Haldane, J. 1932. “A Note on Inverse Probability.” Mathematical Proceedings of the Cambridge Philosophical Society 28 (1): 5561.CrossRefGoogle Scholar
Hedden, B. 2015. “Time Slice Rationality.” Mind 124 (494): 449–91.CrossRefGoogle Scholar
Huttegger, S. M. 2014. “Learning Experiences and the Value of Knowledge.” Philosophical Studies 171 (2): 279–88.CrossRefGoogle Scholar
Huttegger, S. M. 2017. The Probabilistic Foundations of Rational Learning. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Jeffreys, H. 1946. “An Invariant Form for the Prior Probability in Estimation Problems.” Proceedings of the Royal Society of London A 186 (1007): 453–61.Google ScholarPubMed
Joyce, J. M. 1998. “A Nonpragmatic Vindication of Probabilism.” Philosophy of Science 65:575603.CrossRefGoogle Scholar
Joyce, J. M. 2009. “Accuracy and Coherence: Prospects for an Alethic Epistemology of Partial Belief.” In Degrees of Belief, ed. Huber, F. and Shmidt-Petri, C., 263300. Dordrecht: Springer.CrossRefGoogle Scholar
Kolodny, N. 2005. “Why Be Rational?Mind 114 (455): 509–63.CrossRefGoogle Scholar
Lindley, D. V., and Phillips, L. 1976. “Inference for a Bernoulli Process: A Bayesian View.” American Statistician 30 (3): 112–19.Google Scholar
Lipsey, R., and Lancaster, K. 1956. “The General Theory of Second Best.” Review of Economic Studies 24 (1): 1132.CrossRefGoogle Scholar
Moss, S. 2015. “Time-Slice Epistemology and Action under Indeterminacy.” In Oxford Studies in Epistemology, Vol. 5, ed. J. Hawthorne and T. S. Gendler. Oxford: Oxford University Press.CrossRefGoogle Scholar
Pettigrew, R. 2016. Accuracy and the Laws of Credence. Oxford: Oxford University Press.CrossRefGoogle Scholar
Pollock, J. L. 1987. “Epistemic Norms.” Synthese 71 (1): 6195.CrossRefGoogle Scholar
Robert, C. P. 2007. The Bayesian Choice: From Decision Theoretic Foundations to Computational Implementation. Dordrecht: Springer.Google Scholar
Seidenfeld, T., and Wasserman, L. 1993. “Dilation for Sets of Probabilities.” Annals of Statistics 21 (3): 1139–54.CrossRefGoogle Scholar
Shannon, C. E. 1948a. “A Mathematical Theory of Communication.” Pt. 1. Bell Systems Technical Journal 27 (3): 379423.CrossRefGoogle Scholar
Shannon, C. E. 1948b. “A Mathematical Theory of Communication.” Pt. 2. Bell Systems Technical Journal 27 (4): 623–66.CrossRefGoogle Scholar
Staffel, J. 2015. “Measuring the Overall Incoherence of Credence Functions.” Synthese 192 (5): 1467–93.CrossRefGoogle Scholar
Wedgwood, R. 2002. “Internalism Explained.” Philosophy and Phenomenological Research 65 (2): 349–69.CrossRefGoogle Scholar
Zabell, S. L. 1982. “W. E. Johnson’s Sufficientness Postulate.” Annals of Statistics 10 (4): 1090–99.CrossRefGoogle Scholar