  This paper introduces the Heterogeneous Aggregate Valence Analysis (HAVAN)
model, a novel class of discrete choice models. We adopt the term "valence'' to
encompass any latent quantity used to model consumer decision-making (e.g.,
utility, regret, etc.). Diverging from traditional models that parameterize
heterogeneous preferences across various product attributes, HAVAN models
(pronounced "haven") instead directly characterize alternative-specific
heterogeneous preferences. This innovative perspective on consumer
heterogeneity affords unprecedented flexibility and significantly reduces
simulation burdens commonly associated with mixed logit models. In a simulation
experiment, the HAVAN model demonstrates superior predictive performance
compared to state-of-the-art artificial neural networks. This finding
underscores the potential for HAVAN models to improve discrete choice modeling
capabilities.
