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There is a difference between showing that a model is consistent with observed behavior and proving that a model implies this behavior: The latter is a much stronger result. Consider the finding that people often violate the predictions of expected utility theory. There are at least two ways to react to these violations. One is to rescue the basic framework of maximizing some function of utilities and probabilities by introducing additional adjustable parameters until the deviating behavior becomes consistent with the new theory. Cumulative prospect theory, for instance, represents such a repair program. It uses five adjustable parameters, which allow immense flexibility in finding parameter values that fit observed behavior. Yet this flexibility makes it hard to understand what behavior it predicts and what behavior it excludes. For instance, it is difficult to see that none of its parameter combinations are simultaneously consistent with gambling on low probability gains (e.g., people playing the lottery) and the Allais paradox behavior (Neilson & Stowe, 2002). That is, to account for each of these behaviors, one needs a different combination of parameters.
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