Moving Beyond Causes: Optimality Models and Scientific Explanation.

A prominent approach to scientific explanation and modeling claims that for a model to provide an explanation it must accurately represent at least some of the actual causes in the event's causal history. In this paper, I argue that many optimality explanations present a serious challenge to this ca...

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Published in:Nous (0029-4624) Vol. 49; no. 3; pp. 589 - 616
Main Author: Rice, Collin
Format: Article
Published: Wiley-Blackwell Sep2015
Subjects:
Online Access:View this record in EBSCOhost
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        atl: Moving Beyond Causes: Optimality Models and Scientific Explanation.
      aug:
        au: Rice, Collin
        affil: Lycoming College
      su:
        Causal models
        Explanation
        Mathematical optimization
        Mathematical models
        Science
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          Causal models
          Explanation
          Mathematical optimization
          Mathematical models
          Science
      ab: A prominent approach to scientific explanation and modeling claims that for a model to provide an explanation it must accurately represent at least some of the actual causes in the event's causal history. In this paper, I argue that many optimality explanations present a serious challenge to this causal approach. I contend that many optimality models provide highly idealized equilibrium explanations that do not accurately represent the causes of their target system(s). Furthermore, in many contexts, it is in virtue of their independence of causes that optimality models are able to provide a better explanation than competing causal models. Consequently, our account of explanation and modeling must expand beyond the causal approach.
      pubtype: Academic Journal
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      src: R
    language: English
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