Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-12-04T09:23:27.026Z Has data issue: false hasContentIssue false
  • English
  • Français

Bayesian Networks and the Problem of Unreliable Instruments

Published online by Cambridge University Press:  01 January 2022

Luc Bovens  and
Stephan Hartmann
Luc Bovens
Affiliation:
University of Colorado at Boulder and University of Konstanz
Stephan Hartmann*
Affiliation:
University of Colorado at Boulder and University of Konstanz
*
Send reprint requests to Luc Bovens, University of Colorado at Boulder, Dept. of Philosophy, CB 232, Boulder, CO 80309 [email protected] or to Stephan Hartmann, University of Konstanz, Dept. of Philosophy, 78457 Konstanz [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

We appeal to the theory of Bayesian Networks to model different strategies for obtaining confirmation for a hypothesis from experimental test results provided by less than fully reliable instruments. In particular, we consider (i) repeated measurements of a single test consequence of the hypothesis, (ii) measurements of multiple test consequences of the hypothesis, (iii) theoretical support for the reliability of the instrument, and (iv) calibration procedures. We evaluate these strategies on their relative merits under idealized conditions and show some surprising repercussions on the variety-of-evidence thesis and the Duhem-Quine thesis.

Type
Research Article
Information
Philosophy of Science , Volume 69 , Issue 1 , March 2002 , pp. 29 - 72
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

‡

We are grateful for comments from J. McKenzie Alexander, David R. Cox, Robert Dodier, Malcolm Forster, Branden Fitelson, Allan Franklin, Patrick Maher, Iain Martel, František Matuš, Theo Kuipers, Richard Scheines, Kent Staley and an anonymous referee of this journal. The research was supported by the Alexander von Humboldt Foundation, the Federal Ministry of Education and Research, and the Program for Investment in the Future (ZIP) of the German Government, by the National Science Foundation, Science and Technology Studies (SES 00-80580) and by the Transcoop Program and the Feodor Lynen Program of the Alexander von Humboldt Foundation. Stephan Hartmann also thanks Jim Lennox and the Center for Philosophy of Science at the University of Pittsburgh for their hospitality.

References

Alexander, J. McKenzie (2001), Comments on Stephan Hartmann and Luc Bovens, “The Import of Auxiliary Theories of the Instruments: a Bayesian-Network Approach”, presented at the Pacific APA.Google Scholar
Bovens, Luc, and Olsson, Erik J. (2000), “Coherentism, Reliability and Bayesian Networks”, Coherentism, Reliability and Bayesian Networks 109:685–719.Google Scholar
Christensen, David (1999), “Measuring Confirmation”, Measuring Confirmation 96:437–61.Google Scholar
Dawid, A. Philip (1979), “Conditional Independence in Statistical Theory”, Conditional Independence in Statistical Theory A41:1–31.Google Scholar
Dodier, Robert (1999), Unified Prediction and Diagnosis in Engineering Systems by Means of Distributed Belief Systems. Ph.D. Dissertation—Department of Civil, Environmental and Architectural Engineering, Boulder, CO: University of Colorado.Google Scholar
Dorling, Jon (1996), “Further Illustrations of the Bayesian Solution of Duhem's Problem”,Google Scholar
Earman, John (1992), Bayes or Bust? A Critical Examination of Bayesian Confirmation Theory. Cambridge MA: MIT Press.Google Scholar
Eells, Ellery, and Fitelson, Branden (2002), “Symmetries and Asymmetries in Evidential Support”, Philosophical Studies (forthcoming).Google Scholar
Fitelson, Branden (1996), “Wayne, Horwich and Evidential Diversity”, Wayne, Horwich and Evidential Diversity 63:652–660.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 63:652–660.Google Scholar
Fitelson, Branden (2001), Studies in Bayesian Confirmation Theory. Ph.D. Dissertation in Philosophy, Madison, WI: University of Wisconsin.Google Scholar
Franklin, Allan (1986), The Neglect of Experiment. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Franklin, Allan, and Howson, Colin (1988), “It Probably is a Valid Experimental Result: a Bayesian Approach to the Epistemology of Experiment”, It Probably is a Valid Experimental Result: a Bayesian Approach to the Epistemology of Experiment 19:419–427.Google Scholar
Hartmann, Stephan, and Bovens, Luc (2001), “The Variety-of-Evidence Thesis and the Reliability of Instruments: A Bayesian-Network Approach”, (forthcoming)Google Scholar
Horwich, Paul (1982), Probability and Evidence. Princeton: Princeton University Press.Google Scholar
Howson, Colin, and Urbach, Peter ([1989] 1993), Scientific Reasoning—The Bayesian Approach. (2nd ed.) Chicago: Open Court.Google Scholar
Jensen, Finn V. (1996), An Introduction to Bayesian Networks. Berlin: Springer.Google Scholar
Jensen, Finn V. (2001), Bayesian Networks and Decision Graphs. Berlin: Springer.CrossRefGoogle Scholar
Kyburg, Henry Jr. (1983), “Recent Work in Inductive Logic”, in Lucey, Kenneth G. and Machan, Tibor R. (eds.), Recent Work in Philosophy. Totowa, NJ: Rowman and Allenheld.Google Scholar
Maher, Patrick (2001), ‘Comments on Stephan Hartmann and Luc Bovens, “The Variety-of-Evidence Thesis and the Reliability of Instruments: a Bayesian Network Approach'”, presented at the Central APA.Google Scholar
Neapolitan, Richard E. (1990), Probabilistic Reasoning in Expert Systems. New York: Wiley.Google Scholar
Nicholson, Ann E., and Brady, J.M. (1994), “Dynamic Belief Networks for Discrete Monitoring”, Dynamic Belief Networks for Discrete Monitoring 24:1593–1610.Google Scholar
Pearl, Judea (1988), Probabilistic Reasoning in Intelligent Systems. San Mateo, CA.: Morgan Kaufmann.Google Scholar
Spohn, Wolfgang (1980), “Stochastic Independence, Causal Independence, and Shieldability”, Stochastic Independence, Causal Independence, and Shieldability 9:73–99.Google Scholar
Staley, Kent W. (1996), “Novelty, Severity and History in the Testing of Hypothesis: the Case of the Top Quark”, Novelty, Severity and History in the Testing of Hypothesis: the Case of the Top Quark 93 (Proceedings) S248–55.Google Scholar
Staley, Kent W. (2000), “What Experiment Did We just Do? Counterfactual Error Statistics and Uncertainties about the Reference Class”, Talk presented at PSA 2000, Vancouver, BC, Canada.Google Scholar
Wayne, Andrew (1995), “Bayesianism and Diverse Evidence”, Bayesianism and Diverse Evidence 62:111–121.Google Scholar