揭示人类驾驶中的多目标效用聚合
Revealed Multi-Objective Utility Aggregation in Human Driving
AI总结：
本文通过分析人类驾驶中的多目标效用聚合，提出基于理性化概念的算法，揭示了安全与进度效用的聚合机制及情境依赖性，并提升了行为模型的预测准确性。
摘要：
博弈论分析中的核心设计问题在于对玩家效用的估计。在许多涉及人类决策的现实互动情境中，包括人类驾驶，效用具有多目标的性质；因此，估计聚合参数，即将多目标效用映射为标量值的过程，成为游戏构建的重要组成部分。然而，从观测数据中估计此参数会带来若干挑战，因为存在许多不可观测因素，包括聚合的底层模态以及可能的有界理性行为模型所生成的观测。基于理性化概念，我们开发了用于估计两种常见聚合方法（加权和满意聚合）的多目标聚合参数的算法，并针对战略性和非战略性推理模型进行处理。基于三个不同的数据集，我们提供了关于人类驾驶员如何聚合安全性和进度效用以及聚合过程情境依赖性的见解。此外，我们还表明，无论用于解决游戏的具体解概念如何，基于数据的效用聚合估计显著提高了行为模型对观察到的人类行为的预测准确性。
英文摘要：
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.
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