| Summary: | 碩士 === 國立陽明大學 === 心智哲學研究所 === 104 === This paper examines two competing norms of explanation for dynamical models. On the one hand, mechanists argue that dynamical models have explanatory power if and only if they describe the causal structure of the mechanism underlying the phenomenon to be explained. In view of this, Kaplan and Craver provide what they call the model-to-mechanism mapping (3M) constraint in order to evaluate whether a dynamical model describes the causal structure of a mechanism. On the other hand, predictivists argue that the predictive power of dynamical models is sufficient for them to be explanatory. In this paper, I will argue that if we adopt a norm of explanation that Douglas discusses, according to which an explanation is a cognitive tool that we use to generate predictions in order to revise the explanation, the two competing norms of explanation can be construed as different ways of explicating the norm that Douglas discusses. I will further demonstrate how this norm of explanation can better handle the objections to the mechanistic and the predictivistic norms of explanation.
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