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An Ideal Model for the Growth of Knowledge in Research Programs

Published online by Cambridge University Press:  01 April 2022

Aharon Kantorovich
Aharon Kantorovich*
Affiliation:
Tel-Aviv University
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Abstract

In this paper a model is presented for the growth of knowledge in a dynamic scientific system. A system which is in some respects an idealization of a Lakatosian research program. The kinematics of the system is described in terms of two probabilistic variables, one of which is related to the evolution of its theoretical component and the other—to the growth of the empirical component. It is shown that when the empirical growth is faster than the theoretical growth the posterior probability of the theoretical component increases. Thus, empirical progressiveness of a research program, as explicated in this model, is accompanied by an increase in the degree of confirmation. In such a case the system grows in a Popperian-like spirit, while learning from experience in a Bayesian manner.

Type
Research Article
Information
Philosophy of Science , Volume 45 , Issue 2 , June 1978 , pp. 250 - 272
Copyright
Copyright © Philosophy of Science Association 1978

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Footnotes

†

I wish to express my thanks to the University of Melbourne for its Fellowship support during the period of which the first version of this paper was written. The first version of this paper was read at the Annual Conference of the Australasian association for the History and Philosophy of Science, in August 1974. I would like to thank Harold Lindman, Manfred von Thun and the referees of Philosophy of Science for their valuable comments.

References

[1] Carnap, R. Logical Foundations of Probability. Chicago: University of Chicago Press, 1950.Google Scholar
[2] Edwards, W., Lindman, H., and Savage, L. “Bayesian Statistical Inference for Psychological Research,” Psychological Review, 70(1963): 193–242.10.1037/h0044139CrossRefGoogle Scholar
[3] Hacking, I. “Slightly More Realistic Personal Probability,” Philosophy of Science, 35(1967): 311–325.10.1086/288169CrossRefGoogle Scholar
[4] Hesse, M. The Structure of Scientific Inference. London: Macmillan, 1974.10.1525/9780520313316CrossRefGoogle Scholar
[5] Keynes, M. A Treatise on Probability. London: Macmillan, 1921.Google Scholar
[6] Lakatos, I. “Falsification and the Methodology of Scientific Research Programmes.” In Criticism and the Growth of Knowledge, I. Lakatos and A. Musgrave Eds. Cambridge: Cambridge University Press, 1970. Pp. 91–196.10.1017/CBO9781139171434.009CrossRefGoogle Scholar
[7] Popper, K. “Truth, Rationality, and the Growth of Scientific Knowledge.” In [8], pp 215–250.Google Scholar
[8] Popper, K. Conjectures and Refutations. London: Routledge and Kegan Paul, 1969.Google Scholar
[9] Salmon, W. The Foundations of Scientific Inference. Pittsburgh: University of Pittsburg Press, 1967.10.2307/j.ctt5hjqm2CrossRefGoogle Scholar
[10] Watanabe, S. “Pattern Recognition as an Inductive Process.” In Methodologies of Pattern Recognition S. Watanabe ed., Pp. 521–534.10.1016/B978-1-4832-3093-1.50033-XCrossRefGoogle Scholar