Optimality explanations: a new approach
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[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Despite its importance, philosophers have found it difficult to say precisely what constitutes a scientific explanation. One of the most prominent approaches is the causal approach, which claims that explanation is a matter of citing the causes that led to the event to be explained. Recently, however, considering cases in which scientists provide explanations with highly idealized mathematical models has complicated the issue. For example, optimality models are widely used in economics and biology. These models mathematically represent the constraints and tradeoffs involved in a design problem in order to deduce the design that will optimize some quantity—e.g. utility or fitness. Then, with the help of various idealizations, these models are often used to explain phenomena by showing that the optimal design is the equilibrium point of the system. In the dissertation, I provide a novel approach to understanding the explanations of these models by analyzing the features of physical systems that they aim to capture and the assumptions required for them to explain phenomena. Next, I use this framework to analyze biological optimality explanations and argue that biological optimality explanations present a serious challenge to the causal approach to explanation. My thesis is that optimality explanations are often preferable precisely because they are independent of the causes of their target a population. As a result, our account of explanation must expand beyond the causal approach. Additionally, I argue that optimality models can produce scientific understanding without providing a satisfactory explanation of any real-world biological phenomena.
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