Whereas some people regard models of risky decision making as if they were statistical summaries of data collected for some other purpose, I think of models as theories that can be tested by experiments. I argue that comparing theories by means of global indices of fit is not a fruitful way to evaluate theories of risky decision making. I argue instead for experimental science. That is, test critical properties, which are theorems of one model that are violated by a rival model. Recent studies illustrate how conclusions based on fit can be overturned by critical tests. Elsewhere, I have warned against drawing
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Michael H. Birnbaum (2011) studied this question.
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