Choice Is a Tricky Thing: Integrating Sophisticated Choice Models With Learning Processes to Better Account for Complex Choice Behavior

Two very different paradigms have been used to experimentally study decision-making: in the descriptive paradigm, all the information concerning available actions and their possible outcomes is described to the decision maker; in the experiential paradigm, everything about the outcomes produced by each action is learned from trial by trial experience. Different phenomena have been discovered within each paradigm and consequently different theories have evolved to account for these disparate findings. This article accomplishes four goals: (a) demonstrate the shortcomings of the most commonly used class of choice models, (b) combine basic learning with more sophisticated decision-making models so that the combination is capable of addressing the phenomena of both paradigms, (c) demonstrate that previous learning and choice models are special cases of this integrated model, and (d) empirically evaluate the ability of this integrated model to successfully explain both sets of findings. Based on a variety of statistical tests and model comparison techniques, the empirical results demonstrate both the need for the new model as well as its superiority to the commonly used class of choice and learning model combinations.