Decision field theory: An extension for real-world settings

Decision field theory (DFT) is a model originally developed in cognitive psychology to explain behavioural phenomena such as context effects and decision-making under time pressure. Given this focus, the model has primarily been used to explain choices observed under controlled laboratory settings, with little attention paid to generalisability. Recent work has improved the mathematical foundations of DFT, making it a tractable model that is easier to apply to a wider variety of choice contexts. In particular, the inclusion of attribute importance parameters has led to successful applications to multi-alternative multi-attribute choice settings, notably with stated preference data in transport. However, thus far, implementations to real-life behaviour (i.e., revealed preference, RP, data) have been limited. The aim of this paper is to extend DFT for larger and more real-world applications, where data may be more ‘noisy’ and prone to larger variances of the error term. A theoretical extension for the model is presented, relaxing the assumption of independent normal error terms to capture heteroskedasticity. We apply the new model specification to two large-scale revealed preference datasets, also incorporating a range of sociodemographic variables. The new ‘heteroskedastic’ DFT model substantially outperforms the original version of DFT, as well as choice models based on econometric theory, in both estimation and validation subsets.