Alexander, J McKenzie (2013) Preferential attachment and the search for successful theories. Philosophy of science . ISSN 0031-8248 (In Press)
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.
|Additional Information:||© Philosophy of Science Association|
|Library of Congress subject classification:||B Philosophy. Psychology. Religion > B Philosophy (General)|
|Sets:||Departments > Philosophy, Logic and Scientific Method|
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