Alexander, J. McKenzie (2013) Preferential attachment and the search for successful theories. Philosophy of Science, 80 (5). pp. 769-782. ISSN 0031-8248
Abstract
Multiarm bandit problems have been used to model the selection of competing scientific theories by boundedly rational agents. In this paper, I define a variable-arm bandit problem, which allows the set of scientific theories to vary over time. I show that Roth-Erev reinforcement learning, which solves multiarm bandit problems in the limit, cannot solve this problem in a reasonable time. However, social learning via preferential attachment combined with individual reinforcement learning which discounts the past, does.
| Item Type: | Article |
|---|---|
| Official URL: | http://www.press.uchicago.edu/ucp/journals/journal... |
| Additional Information: | © 2013 Philosophy of Science Association |
| Library of Congress subject classification: | B Philosophy. Psychology. Religion > B Philosophy (General) Q Science > Q Science (General) |
| Sets: | Departments > Philosophy, Logic and Scientific Method |
| Date Deposited: | 09 Aug 2012 07:29 |
| URL: | http://eprints.lse.ac.uk/45283/ |
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