Hard Problems in the Philosophy of Science: Idealization and Commensurability

This page was last edited on 05/19/02 by Malcolm R Forster

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Publication Data

Forster, M. R. (2000): "The Hard Problems in the Philosophy of Science" in R. Nola and H. Sankey (eds.) After Popper, Kuhn & Feyerabend: Issues in Theories of Scientific Method, Australasian Studies in History and Philosophy of Science, Kluwer.


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In the 1960s, Kuhn maintained that there is no standard higher of rationality than the assent of the relevant community. Realists have seek to evaluate the rationality of science relative to a highest standard possible—namely the truth, or approximate truth, of our best theories. Given that the realist view of rationality is controversial, it seems that a more secure reply to Kuhn should be based on a less controversial objective of science—namely, the goal of predictive accuracy. Not only does this yield a more secure reply to Kuhn, but it also provides the foundation on which any realist arguments should be based. In order to make this case, it is necessary to introduce a three-way distinction between theories, models, and predictive hypotheses, and then ask some hard questions about how the methods of science can actually achieve their goals. As one example of the success of such a program, I explain how the truth of models can sometimes lower their predictive accuracy. As a second example, I describe how one can define progress across paradigms in terms of predictive accuracy. These are examples of hard problems in the philosophy of science, which fall outside the scope of social psychology.


  1. Introduction
  2. Theories, Models and Predictive Hypotheses
  3. Kuhn’s Picture of Science
  4. Hempel’s Criticism of Kuhn and Popper
  5. The Problem of Idealization
  6. Kuhnian Commensurability
  7. The Objectivity of Science