Description-based and experience-based decisions: individual analysis

We analyze behavior in two basic classes of decision tasks: description-based and experience-based. In particular, we compare the prediction power of a number of decision learning models in both kinds of tasks. Unlike most previous studies, we focus on individual, rather than aggregate, behavioral characteristics. We carry out an experiment involving a battery of both description- and experience-based choices between two mixed binary prospects made by each of the participants, and employ a number of formal models for explaining and predicting participants' choices: Prospect theory (PT) (Kahneman & Tversky, 1979); Expectancy-Valence model (EVL) (Busemeyer & Stout, 2002); and three combinations of these well-established models. We document that the PT and the EVL models are best for predicting people's decisions in description- and experience-based tasks, respectively, which is not surprising as these two models are designed specially for these kinds of tasks. Furthermore, we find that models involving linear weighting of gains and losses perform better in both kinds of tasks, from the point of view of generalizability and individual parameter consistency. We therefore, conclude that, overall, when both prospects are mixed, the assumption of diminishing sensitivity does not improve models' prediction power for individual decision-makers. Finally, for some of the models' parameters, we document consistency at the individual level between description- and experience-based tasks.