  A central design problem in game theoretic analysis is the estimation of the
players' utilities. In many real-world interactive situations of human decision
making, including human driving, the utilities are multi-objective in nature;
therefore, estimating the parameters of aggregation, i.e., mapping of
multi-objective utilities to a scalar value, becomes an essential part of game
construction. However, estimating this parameter from observational data
introduces several challenges due to a host of unobservable factors, including
the underlying modality of aggregation and the possibly boundedly rational
behaviour model that generated the observation. Based on the concept of
rationalisability, we develop algorithms for estimating multi-objective
aggregation parameters for two common aggregation methods, weighted and
satisficing aggregation, and for both strategic and non-strategic reasoning
models. Based on three different datasets, we provide insights into how human
drivers aggregate the utilities of safety and progress, as well as the
situational dependence of the aggregation process. Additionally, we show that
irrespective of the specific solution concept used for solving the games, a
data-driven estimation of utility aggregation significantly improves the
predictive accuracy of behaviour models with respect to observed human
behaviour.
