Preferential Attachment and the Search for Successful Theories.

Multiarm bandit problems have been used to model the selection of competing scientific theories by boundedly rational agents. In this article, 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 so...

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Publicado en:Philosophy of Science Vol. 80; no. 5; pp. 769 - 783
Autor principal: Alexander, J. McKenzie
Formato: Artículo
Publicado: Cambridge University Press Dec2013
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Acceso en línea:Ver este registro en EBSCOhost
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        au: Alexander, J. McKenzie
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        Reinforcement learning
        Slot machines
        Probability theory
        Social learning
        Philosophy of science
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          Reinforcement learning
          Slot machines
          Probability theory
          Social learning
          Philosophy of science
      ab: Multiarm bandit problems have been used to model the selection of competing scientific theories by boundedly rational agents. In this article, 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.
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