A random utility maximisation model considering the information search process

Discrete choice analysis aims to understand and predict decision-makers’ behaviour, a goal that is crucial across several disciplines, including transportation. This type of analysis has relied predominantly on static representations of preferences, principally through the Random Utility Maximisation (RUM) model, due to its ease of implementation, economic interpretability, and statistical formality. However, this model assumes that individuals possess complete information about all attributes of alternatives and that they can process and recall this information instantaneously, which may not align with actual human behaviour. In contrast, the Decision Field Theory (DFT) model from mathematical psychology explicitly incorporates the repeated scrutiny of attributes and recall effects within the decision-making process, which enables it to model attention weights, but lacks microeconomic interpretability and clear statistical parameter identification. This paper introduces the RUM-DFT model, which seeks to integrate strengths of both approaches. Through Monte Carlo simulations, the proposed model is shown to be able to: (i) recover parameters related to the deliberation process, (ii) replicate the dynamic behaviour of utilities during deliberation as observed in practice, (iii) maintain economic interpretability by estimating coefficients that can be used to calculate the marginal indirect utilities, and (iv) highlight the pitfalls of using a RUM model that disregards the true dynamics of data generation process. The SwissMetro case study is employed also to evaluate the RUM-DFT model using a real-world dataset, demonstrating the viability and superior goodness-of-fit of the proposed model.