 In other contexts, interpreting people’s behavior as generally expected-utility maximizing requires undue violence to the need for generality in theory construction. We get better prediction using fewer case-specific restrictions if we suppose that subjects are maximizing according to one or (typically) more of several alternatives (which will not be described here because they are not directly about game theory): cumulative prospect theory  (Tversky and Kahneman 1992),  or alpha-nu utility theory  (Chew and MacCrimmon 1979),  or rank-dependent utility theory  (Quiggin 1982,  Yaari 1987). (The last alternative in fact denotes a family of alternative specifications. One of these, the specification of  Prelec (1998),  has emerged in an accumulating mass of empirical estimations as the statistically most common human utility function.)  Harrison and Rutstrom (2008)  show how to design and code maximum likelihood mixture models, which allow an empirical modeler to apply a range of these decision functions to a single set of choice data. The resulting analysis identifies the proportion of the total choice set best explained by each model in the mixture.  Andersen et al (2014)  take this approach to the current state of the art, demonstrating the empirical value of including a model of non-maximizing psychological processes in a mixture along with maximizing economic models. This new effective flexibility with respect to the decision modeling that can be deployed in empirical applications of game theory relieves most pressure to seek adjustments in the game theoretic structures themselves. Thus it fits well with the interpretation of game theory as part of the behavioral scientist’s mathematical toolkit, rather than as a first-order empirical model of human psychology.