Decisions from experience: How models of aggregate choices explain individual choices?

Decisions from Experience (DFE) involve situations where decision makers sample information before making a final choice. Trying clothes before choosing a garment and enquiring about jobs before opting for one are some examples involving such situations. In DFE research, conventionally, the final choice that is made after sampling information is aggregated over all participants and problems in a given dataset. However, this aggregation does not explain the individual choices made by participants. In this paper, we test the ability of computational models of aggregate choice to explain choices at the individual level. Top three DFE models of aggregate choices are evaluated on how these models account for individual choices. A Primed-Sampler (PS) model, a Natural-Mean Heuristic (NMH) model, and an Instance-Based Learning (IBL) model are calibrated to explain individual choices in the Technion Prediction Tournament (the largest publically available DFE dataset). Our results reveal that all the three DFE models of aggregate choices perform well to explain individual choices. Although the PS and NMH models perform slightly better than the IBL model; the IBL model is able to account for all individuals in the dataset compared to the PS and NMH models. We conclude by drawing implications for computational cognitive models in explaining individual choices in DFE research.