Micro-Process Models of Decision Making

Computational models are like the new kids in town for the field of decision making. This field is largely dominated by axiomatic utility theories (Bell, Raiffa, & Tversky, 1998; Luce, 2000) or simple heuristic rule models (Gigerenzer, Todd, & the ABC Research Group, 1999; Payne, Bettman, & Johnson, 1993). It is difficult for “the new kids” to break into this field for a very important reason: They just seem too complex in comparison. Computational models are constructed from a large number of elementary units that are tightly interconnected to form a complex dynamical system. So the question, “what does this extra complexity buy us?,” is raised. Computational theorists first have to prove that their models are worth the extra complexity. This chapter provides some answers to that challenge. First, the current state of decision research applied to preferences under uncertainty is reviewed. The evolution of the algebraic utility approach that has dominated the field of decision making is described, showing a steady progression away from a simple and intuitive principle of maximizing expected value. The development of utility theories into their current form has included modifications for the subjective assessment of objective value and probability, with the most recent work focusing on finer specification of the latter. The impetus for these modifications is then discussed; in particular, specific and pervasive “paradoxes” of human choice behavior are briefly reviewed. This section arrives at the conclusion that no single utility theory provides an accurate descriptive model of human choice behavior. Then, computational approaches to decision making are introduced, which seem more promising in their ability to capture robust trends in human choice behavior. This advantage is due to their common focus on the micro-mechanisms of the underlying deliberation process, rather than solely on the overt choice behavior driven by choice stimuli. A number of different approaches are introduced, providing a broad survey of the current corpus of computational models of decision making. The fourth section focuses on one particular model to offer a