Bayesian strategy assessment in multi-attribute decision making

Behavioral Decision Research on multi-attribute decision making is plagued with the problem of drawing inferences from behavioral data on cognitive strategies. This bridging problem has been tackled by a range of methodical approaches, namely Structural Modeling (SM), Process Tracing (PT), and comparative model fitting. Whereas SM and PT have been criticized for a number of reasons, the comparative fitting approach has some theoretical advantages as long as the formal relation between theories and data is specified. A Bayesian method is developed that is able to assess, whether an empirical data vector was most likely generated by a 'Take The Best' heuristic (Gigerenzer et al., 1991), by an equal weight rule, or a compensatory strategy. Equations are derived for the two- and three-alternative cases, respectively, and a simulation study supports its validity. The classification also showed convergent validity with Process Tracing measures in an experiment. Potential extensions of the general approach to other applications in behavioral decision research are discussed. Copyright (C) 2003 John Wiley Sons, Ltd.