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.

翻译：博弈论分析中的一个核心设计问题是估计博弈参与者的效用。在包括人类驾驶在内的许多现实世界交互性人类决策情境中，效用本质上具有多目标特性；因此，估计聚合参数（即将多目标效用映射为标量值）成为博弈构建的关键组成部分。然而，由于存在包括潜在聚合模式与生成观测数据的有限理性行为模型在内的大量不可观测因素，从观测数据中估计该参数面临诸多挑战。基于理性化概念，我们针对两种常见聚合方法（加权聚合与满意聚合）以及战略性与非战略性推理模型，开发了用于估计多目标聚合参数的算法。基于三个不同的数据集，我们揭示了人类驾驶员如何聚合安全性与进展性效用，以及聚合过程的情境依赖性。此外，研究表明，无论用于求解博弈的具体解概念如何，基于数据驱动的效用聚合估计都能显著提升行为模型对观测人类行为的预测准确性。