Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-26T09:44:16.243Z Has data issue: false hasContentIssue false
  • English
  • Français

What Is the Point of Confirmation?

Published online by Cambridge University Press:  01 January 2022

Franz Huber
Get access Rights & Permissions [Opens in a new window]

Abstract

Philosophically, one of the most important questions in the enterprise termed confirmation theory is this: Why should one stick to well confirmed theories rather than to any other theories? This paper discusses the answers to this question one gets from absolute and incremental Bayesian confirmation theory. According to absolute confirmation, one should accept “absolutely well confirmed” theories, because absolute confirmation takes one to true theories. An examination of two popular measures of incremental confirmation suggests the view that one should stick to incrementally well confirmed theories, because incremental confirmation takes one to (the most) informative (among all) true theories. However, incremental confirmation does not further this goal in general. I close by presenting a necessary and sufficient condition for revealing the confirmational structure in almost every world when presented separating data.

Type
Decision Theory
Information
Philosophy of Science , Volume 72 , Issue 5: Proceedings of the 2004 Biennial Meeting of The Philosophy of Science Association. Part I: Contributed Papers , December 2005 , pp. 1146 - 1159
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

‡

I have benefited from discussions with Luc Bovens, Branden Fitelson, Jim Joyce, Sherri Roush, and Gerhard Schurz. My research was supported by the Alexander von Humboldt Foundation, the Federal Ministry of Education and Research, and the Program for the Investment in the Future (ZIP) of the German Government through a Sofja Kovalevskaja Award.

References

Bar-Hillel, Yehoshua (1952), “Semantic Information and Its Measures”, in Foerster, Heinz von, Mead, Margaret, and Teuber, Hans Lukas (eds.), Transactions of the Tenth Conference on Cybernetics. New York: Josiah Macy Jr. Foundation, 33–48.Google Scholar
Bar-Hillel, Yehoshua (1955), “An Examination of Information Theory”, An Examination of Information Theory 22:86–105.Google Scholar
Bar-Hillel, Yehoshua, and Carnap, Rudolf (1953), “Semantic Information”, Semantic Information 4:147–157.Google Scholar
Carnap, Rudolf (1962), Logical Foundations of Probability. Chicago: University of Chicago Press.Google Scholar
Carnap, Rudolf, and Bar-Hillel, Yehoshua (1952), An Outline of a Theory of Semantic Information. Technical Report 247. Cambridge, MA: MIT, Research Laboratory of Electronics.Google Scholar
Christensen, David (1999), “Measuring Confirmation”, Measuring Confirmation 96:437–461.Google Scholar
Earman, John (1992), Bayes or Bust? A Critical Examination of Bayesian Confirmation Theory. Cambridge, MA: MIT Press.Google Scholar
Fitelson, Branden (1999), “The Plurality of Bayesian Measures of Confirmation and the Problem of Measure Sensitivity”, The Plurality of Bayesian Measures of Confirmation and the Problem of Measure Sensitivity 66:S362–S378.Google Scholar
Fitelson, Branden (2001), “Studies in Bayesian Confirmation Theory.” PhD dissertation. Madison: University of Wisconsin.Google Scholar
Gaifman, Haim, and Snir, Marc (1982), “Probabilities over Rich Languages, Testing, and Randomness”, Probabilities over Rich Languages, Testing, and Randomness 47:495–548.Google Scholar
Hempel, Carl Gustav (1960), “Inductive Inconsistencies”, Inductive Inconsistencies 12:439–469.Google Scholar
Hempel, Carl Gustav (1962), “Deductive-Nomological vs. Statistical Explanation”, in Feigl, Herbert and Maxwell, Grover (eds.), Minnesota Studies in the Philosophy of Science, Vol. 3. Minneapolis: University of Minnesota Press, 98–169.Google Scholar
Hilpinen, Risto (1970), “On the Information Provided by Observations”, in Hintikka, Jaakko and Suppes, Patrick (eds.), Information and Inference. Dordrecht: Reidel, 97–122.CrossRefGoogle Scholar
Hintikka, Jaakko, and Pietarinen, Juhani (1966), “Semantic Information and Inductive Logic”, in Hintikka, Jaakko and Suppes, Patrick (eds.), Aspects of Inductive Logic. Amsterdam: North-Holland, 96–112.CrossRefGoogle Scholar
Huber, Franz (forthcoming), “Assessing Theories, Bayes Style”, Synthese.Google Scholar
Joyce, James M. (1999), The Foundations of Causal Decision Theory. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Kelly, Kevin T. (1996), The Logic of Reliable Inquiry. Oxford: Oxford University Press.Google Scholar
Levi, Isaac (1961), “Decision Theory and Confirmation”, Decision Theory and Confirmation 58:614–625.Google Scholar
Levi, Isaac (1963), “Corroboration and Rules of Acceptance”, Corroboration and Rules of Acceptance 13:307–313.Google Scholar
Levi, Isaac (1967), Gambling with Truth: An Essay on Induction and the Aims of Science. London: Routledge.Google Scholar
Milne, Peter (1996), “log[P(h|eb)/P(h/b)] Is the One True Measure of Confirmation”, log[P(h|eb)/P(h/b)] Is the One True Measure of Confirmation 63:21–26.Google Scholar