  Several non-linear functions and machine learning methods have been developed
for flexible specification of the systematic utility in discrete choice models.
However, they lack interpretability, do not ensure monotonicity conditions, and
restrict substitution patterns. We address the first two challenges by
modelling the systematic utility using the Choquet Integral (CI) function and
the last one by embedding CI into the multinomial probit (MNP) choice
probability kernel. We also extend the MNP-CI model to account for attribute
cut-offs that enable a modeller to approximately mimic the semi-compensatory
behaviour using the traditional choice experiment data. The MNP-CI model is
estimated using a constrained maximum likelihood approach, and its statistical
properties are validated through a comprehensive Monte Carlo study. The
CI-based choice model is empirically advantageous as it captures interaction
effects while maintaining monotonicity. It also provides information on the
complementarity between pairs of attributes coupled with their importance
ranking as a by-product of the estimation. These insights could potentially
assist policymakers in making policies to improve the preference level for an
alternative. These advantages of the MNP-CI model with attribute cut-offs are
illustrated in an empirical application to understand New Yorkers' preferences
towards mobility-on-demand services.
