Abstract
The theory of fast and frugal heuristics, developed in a new book called Simple Heuristics that make Us Smart (Gigerenzer, Todd, and the ABC Research Group, in press), includes two requirements for rational decision making. One is that decision rules are bounded in their rationality –- that rules are frugal in what they take into account, and therefore fast in their operation. The second is that the rules are ecologically adapted to the environment, which means that they `fit to reality.' The main purpose of this article is to apply these ideas to learning rules–-methods for constructing, selecting, or evaluating competing hypotheses in science, and to the methodology of machine learning, of which connectionist learning is a special case. The bad news is that ecological validity is particularly difficult to implement and difficult to understand. The good news is that it builds an important bridge from normative psychology and machine learning to recent work in the philosophy of science, which considers predictive accuracy to be a primary goal of science.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Akaike, H. (1973), ‘Information Theory and an Extension of the Maximum Likelihood Principle’ in B. N. Petro and F. Csaki, eds., 2nd International Symposium on Information Theory. Budapest: Akademiai Kiado, pp. 267–281.
Akaike, H. (1974), ‘A New Look at the Statistical Model Identification’ IEEE Transactions on Automatic Control Ac-19, pp. 716–723.
Arntzenius, F. (1995), ‘A Heuristic for Conceptual Change’ Philosophy of Science 62, pp. 357–369.
Bertrand, J. (1907), Calcul des Probabilites, Paris: Hermann et Fils.
Dennett, D. (1984), ‘Cognitive Wheels: The Frame Problem of AI’ in C. Hookway, ed., Minds, Machines and Evolution, Cambridge: Cambridge University Press, pp. 129–151.
DeVito, S. (1997), ‘A Gruesome Problem for the Curve Fitting Solution’ British Journal for the Philosophy of Science 48, pp. 391–396.
Earman, J. (1992), Bayes or Bust? A Critical Examination of Bayesian Confirmation Theory, Cambridge: The MIT Press.
Forster, M. R. (1995a), ‘The Golfer's Dilemma: A Reply to Kukla on Curve-Fitting’ British Journal for the Philosophy of Science 46, pp. 348–360.
Forster, M. R. (1995b), ‘Bayes and Bust: The Problem of Simplicity for a Probabilist's Approach to Confirmation’ British Journal for the Philosophy of Science 46, pp. 399–424.
Forster, M. R. (1999), ‘Model Selection in Science: The Problem of Language Variance’ British Journal for the Philosophy of Science 50, pp. 83–102.
Forster, M. R. (in press, a), ‘The New Science of Simplicity’ in H. A. Keuzenkamp, M. McAleer and A. Zellner, eds., Simplicity, Inference and Econometric Modelling, Cambridge: Cambridge University Press.
Forster, M. R. (in press, b), ‘The Problem of Idealization and Other Hard Problems in the Philosophy of Science’ in R. Nola and H. Sankey, eds., After Popper, Kuhn, and Feyerabend: Issues in Theories of Scientific Method, Australasian Studies in History and Philosophy of Science series, Kluwer.
Forster, M. R. (in press, c), ‘Key Concepts in Model Selection: Performance and Generalizability’ Journal of Mathematical Psychology.
Forster, M. R. and Sober, E. (1994), ‘How to tell when Simpler, More Unified, or Less ad hoc Theories will provide more Accurate Predictions’ British Journal for the Philosophy of Science 45, pp. 1–35.
Gigerenzer, G. and Todd, P.M. (in press), ‘Fast and Frugal Heuristics: The Adaptive Toolbok’ in G. Gigerenzer, P.M. Todd, and the ABC Research Group, Simple Heuristics that make Us Smart, New York: Oxford University Press.
Gigerenzer, G., Todd, P. M. and the ABC Research Group, (in press), Simple Heuristics that make Us Smart, New York: Oxford University Press.
Goldstein, D. G. and Gigerenzer, G. (in press), ‘The Recognition Heuristic: How Ignorance makes Us Smart’ in G. Gigerenzer, P.M. Todd, and the ABC Research Group, Simple Heuristics that make Us Smart, New York: Oxford University Press.
Goodman, N. (1965), Fact, Fiction and Forecast, 2nd ed., Cambridge,MA: Harvard University Press.
Humphreys, P. and Freedman, D. (1996), ‘The Grand Leap’ British Journal for the Philosophy of Science 47, pp. 113–123.
Jaynes, E. T. (1971), ‘The well-posed Problem’ in Godambe, V. P. and Sprott, D. A., eds., Foundations of Statistics, Toronto: Holt, Rinehart and Winston, pp. 342–354.
Kieseppä, I. A. (1997), ‘Akaike information criterion, curve-fitting, and the philosophical problem of simplicity’ British Journal for the Philosophy of Science 48, pp. 21–48.
Korb, K. B. and Wallace, C. S. (1997), ‘In Search of the Philosopher's Stone: Remarks on Humphreys and Freedman's Critique of Causal Discovery’ British Journal for the Philosophy of Science 48, pp. 543–553.
Kuhn, T. (1970), The Structure of Scientific Revolution, 2nd ed., Chicago: University of Chicago Press.
Kukla, A. (1995), ‘Forster and Sober on the Curve-Fitting Problem’ British Journal for the Philosophy of Science 46, pp. 248–259.
Langely, P, Simon, H. A., Bradshaw, G. L. and Zytkow, J. M. (1987), Scientific Discovery: Computational Explorations of the Creative Process, Cambridge, MA: The MIT Press.
Levi, I. (1973), Gambling with Truth; An Essay on Induction and the Aims of Science, Cambridge, MA: The MIT Press.
Levi, I. (1984), Decisions and Revisions, Cambridge: Cambridge University Press.
Maher, P. (1993), Betting on Theories, Cambridge: Cambridge University Press.
Marr, D. (1982), Vision, New York: W. H. Freeman and Co.
Newton, I. (1687), Principia Mathematica, Motte's Translation as revised by F. Cajori (1934), Berkeley: University of California Press.
Rumelhart, D. E., McClelland, J., and the PDP Research Group (1986), Parallel Distributed Processing, Vol 1 and 2, Cambridge, MA: The MIT Press.
Sober, E. (1998), ‘Black Box Inference: When Should Intervening Variables be Postulated?’ British Journal for the Philosophy of Science 49, pp. 469–498.
Spirtes, P., Glymour, C. and Scheines, R. (1993), Causation, Prediction and Search, New York: Springer.
Spirtes, P., Glymour, C. and Scheines, R. (1997), ‘Reply to Humphreys and Freedman's Review of Causation, Prediction, and Search’ British Journal for the Philosophy of Science 48, pp. 555–568.
Staddon, J. E. R. (1983), Adaptive Behavior and Learning, Cambridge: Cambridge University Press.
van Fraassen, B. (1980), The Scientific Image, Oxford: Oxford University Press.
Urmson, J. O. (1953), ‘The Interpretation of the Moral Philosophy of J. S. Mill’ Philosophical Quarterly 3, pp. 33–39.
Wolpert, D. H. (1992), ‘On the Connection between In-Sample Testing and Generalization Error’ Complex Systems 6, pp. 47–94.
Wolpert, D. H. (1995), ‘The Relationship between PAC, the Statistical Physics Framework, the Bayesian Framework, and the VC Framework’ in D. H. Wolpert, ed., The Mathematics of Generalization, Reading, MA: Addison-Wesley, pp. 117–214.
Wolpert, D. H. (1996), ‘The Lack of a priori Distinctions between Learning Algorithms’ Neural Computation 8, pp. 1341–1390.
Zucchini, W. (in press), ‘An Introduction to Model Selection’ Journal of Mathematical Psychology.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Forster, M.R. How do Simple Rules `Fit to Reality' in a Complex World?. Minds and Machines 9, 543–564 (1999). https://doi.org/10.1023/A:1008304819398
Issue Date:
DOI: https://doi.org/10.1023/A:1008304819398