An accumulation of preference: Two alternative dynamic models for understanding transport choices

Interest in behavioural realism has gradually led to the introduction of alternatives to random utility models (RUMs) as a paradigm for representing choice behaviour, with notable interest, for example, in random regret minimisation (RRM). These more general models continue to rely on a framework where a single value function is calculated for each alternative in each choice setting, and the choice probabilities are calculated by comparing these value functions across alternatives. By contrast, research in mathematical psychology has used a more dynamic approach, where the preference value of each alternative updates over time in a given situation while the decision maker is deliberating about the choice to make. These accumulator models are well suited to accommodating a variety of context effects, and have been shown to give good performance for data collected in laboratory-based settings. The present paper considers two such accumulator models, namely decision field theory (DFT) and the multi-attribute linear ballistic accumulator (MLBA), and addresses limitations that have prevented their use in travel behaviour research. The methodological additions include the ability to capture the influence of socio-demographics, the presence of underlying preferences for specific alternatives, and/or the representation of attributes that have opposite effects on choice probabilities. We develop what we believe to be the first in-depth simultaneous comparison of DFT and MLBA with typical discrete choice models, and test both DFT and MLBA on a revealed preference dataset. We find that each model outperforms typical RUM and RRM implementations for both in-sample estimation and out-of-sample prediction, including in a large scale simulation experiment.