Hostname: page-component-745bb68f8f-b6zl4 Total loading time: 0 Render date: 2025-01-09T00:43:54.327Z Has data issue: false hasContentIssue false
  • English
  • Français

50 Years of Successful Predictive Modeling Should Be Enough: Lessons for Philosophy of Science

Published online by Cambridge University Press:  01 January 2022

Michael A. Bishop  and
J. D. Trout
J. D. Trout*
Affiliation:
Iowa State University and Loyola University, Chicago
*
Send requests for reprints to the authors. Bishop: Department of Philosophy and Religious Studies, 402 Catt Hall, Iowa State University, Ames, IA 50011; [email protected]; Trout: Department of Philosophy, 6525 N. Sheridan Rd., Loyola University, Chicago, IL 60626; email: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

Our aim in this paper is to bring the woefully neglected literature on predictive modeling to bear on some central questions in the philosophy of science. The lesson of this literature is straightforward: For a very wide range of prediction problems, statistical prediction rules (SPRs), often rules that are very easy to implement, make predictions than are as reliable as, and typically more reliable than, human experts. We will argue that the success of SPRs forces us to reconsider our views about what is involved in understanding, explanation, good reasoning, and about how we ought to do philosophy of science.

Type
Research Article
Information
Philosophy of Science , Volume 69 , Issue S3 , September 2002 , pp. S197 - S208
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.)

References

Achinstein, Peter (1983), The Nature of Explanation. New York: Oxford University Press.Google Scholar
Ashenfelter, Orley, Ashmore, David, and Lalonde, Robert (1995), “Bordeaux Wine Vintage Quality and the Weather”, Bordeaux Wine Vintage Quality and the Weather 8:7–14.Google Scholar
Bishop, Michael (1999), “Semantic Flexibility in Scientific Practice: A Study of Newton’s Optics”, Semantic Flexibility in Scientific Practice: A Study of Newton’s Optics 32:210–232.Google Scholar
Brewer William, Clark Chinn, and Samarapungavan, Ala (1998), “Explanation in Scientists and Children”, Explanation in Scientists and Children 8:119–136.Google Scholar
Dawes, Robyn (1982), “The Robust Beauty of Improper Linear Models in Decision-Making” in Kahneman, Daniel, Slovic, Paul, and Tversky, Amos (eds.), Judgment under Uncertainty: Heuristics and Biases. Cambridge: Cambridge University Press, 391–407.CrossRefGoogle Scholar
Dawes, Robyn (1994), House of Cards: Psychology and Psychotherapy Built on Myth. New York: The Free Press.Google Scholar
Dawes, Robyn, and Corrigan, Bernard (1974), “Linear Models in Decision Making”, Linear Models in Decision Making 81:95–106.Google Scholar
Dawes, Robyn, Faust, David, and Meehl, Paul (1989), “Clinical Versus Actuarial Judgment”, Clinical Versus Actuarial Judgment 243:1668–1674.Google ScholarPubMed
Einhorn, Hillel J., and Hogarth, Robin M. (1975), “Unit Weighting Schemas for Decision Making”, Unit Weighting Schemas for Decision Making 13:172–192.Google Scholar
Faust, David, and Meehl, Paul (1992), “Using Scientific Methods to Resolve Enduring Questions within the History and Philosophy of Science: Some Illustrations”, Using Scientific Methods to Resolve Enduring Questions within the History and Philosophy of Science: Some Illustrations 23:195–211.Google Scholar
Faust, David, and Ziskin, Jay (1988), “The Expert Witness in Psychology and Psychiatry”, The Expert Witness in Psychology and Psychiatry 241:1143–1144.Google ScholarPubMed
Fischhoff, Baruch, Slovic, Paul, and Lichtenstein, Sarah (1977), “Knowing with Certainty: The Appropriateness of Extreme Confidence”, Knowing with Certainty: The Appropriateness of Extreme Confidence 3:552–564.Google Scholar
Friedman, Michael ([1974] 1988), “Explanation and Scientific Understanding.” Reprinted in J. C. Pitt (ed.), Theories of Explanation. New York: Oxford University Press, 188–198. Originally published in Journal of Philosophy 71:5–19.CrossRefGoogle Scholar
Gilovich, Thomas (1991), How We Know What Isn’ t So. New York: The Free Press.Google Scholar
Goldberg, Lewis (1968), “Simple Models of Simple Processes? Some Research on Clinical Judgments”, Simple Models of Simple Processes? Some Research on Clinical Judgments 23:483–496.Google ScholarPubMed
Grove, William M., and Meehl, Paul E. (1996), “Comparative Efficiency of Informal (Subjective, Impressionistic) and Formal (Mechanical, Algorithmic) Prediction Procedures: The Clinical-Statistical Controversy”, Comparative Efficiency of Informal (Subjective, Impressionistic) and Formal (Mechanical, Algorithmic) Prediction Procedures: The Clinical-Statistical Controversy 2:293–323.Google Scholar
Henrion, Max, and Fischhoff, Baruch (1986), “Assessing Uncertainty in Physical Constants”, Assessing Uncertainty in Physical Constants 54:791–798.Google Scholar
Kahneman, Daniel, and Lovallo, Dan (1993), “Timid Choices and Bold Forecasts: A Cognitive Perspective on Risk Taking”, Timid Choices and Bold Forecasts: A Cognitive Perspective on Risk Taking 39:17–31.Google Scholar
Kitcher, Philip ([1981] 1988), “Explanatory Unification.” Reprinted in J. C. Pitt (ed.), Theories of Explanation. New York: Oxford University Press, 167–187. Originally published in Philosophy of Science 48:507–531.CrossRefGoogle Scholar
Leli, Dano A., and Filskov, Susan B. (1984), “Clinical Detection of Intellectual Deterioration Associated with Brain Damage”, Clinical Detection of Intellectual Deterioration Associated with Brain Damage 40:1435–1441.Google ScholarPubMed
Lovie, Alexander D., and Lovie, Patricia (1986), “The Flat Maximum Effect and Linear Scoring Models for Prediction”, The Flat Maximum Effect and Linear Scoring Models for Prediction 5:159–168.Google Scholar
Meehl, Paul (1954), Clinical Versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence. Minneapolis: University of Minnesota Press.CrossRefGoogle Scholar
Meehl, Paul (1986), “Causes and Effects of My Disturbing Little Book”, Causes and Effects of My Disturbing Little Book 50:370–375.Google ScholarPubMed
Miller, Richard W. (1987), Fact and Method. Princeton: Princeton University Press.Google Scholar
Passell, Peter (1990), “Wine Equation Puts Some Noses Out of Joint”, The New York Times, March 4, p. 1.Google Scholar
Peirce, Charles S. ([1878] 1982), “Doctrine of Chances”, in Writings of Charles Sanders Peirce: A Chronological Edition, Vol. 3. Bloomington: Indiana University Press, 276–289.Google Scholar
Salmon, Wesley (1998), “The Importance of Scientific Understanding”, in Causality and Explanation. New York: Oxford University Press, 79–91.CrossRefGoogle Scholar
Swets, John A., Dawes, Robyn, and Monahan, John (2000), “Psychological Science Can Improve Diagnostic Decisions”, Psychological Science Can Improve Diagnostic Decisions 1:1–26.Google ScholarPubMed
Taylor, Shelly (1989), Positive Illusions: Creative Self-Deception and the Healthy Mind. New York: Basic Books.Google Scholar
Trout, J. D. (1994), “A Realistic Look Backward”, A Realistic Look Backward 25:37–64.Google Scholar
Trout, J. D. (1998), Measuring the Intentional World: Realism, Naturalism, and Quantitative Methods in the Behavioral Sciences. New York: Oxford University Press.CrossRefGoogle Scholar
Trout, J. D. (2002), “Scientific Explanation and the Sense of Understanding”, Scientific Explanation and the Sense of Understanding 69:212–234.Google Scholar
Tversky, Amos, and Kahneman, Daniel (1986), “Rational Choice and the Framing of Decisions”, Rational Choice and the Framing of Decisions 59:S251–S278.Google Scholar
Woodward, James ([1984] 1993), “A Theory of Singular Causal Explanation.” Reprinted in D. H. Ruben (ed.), Explanation. New York: Oxford University Press, 246–274. Originally published in Erkenntnis 21:231–262.Google Scholar