Generalized Bounded Rationality and Robust Multicommodity Network Design
提出一种基于路段感知误差的广义有限理性模型，描述驾驶员在无拥堵网络中的满意决策行为，并将其应用于鲁棒多商品网络设计问题，通过蒙特卡洛方法估计参数并设计割平面算法，以危险品运输为例验证方法。
Often, network users are not perfectly rational, especially when they are satisficing—rather than optimizing—decision makers and each individual’s perception of the decision environment reflects personal preferences or perception errors due to lack of information. While the assumption of satisficing drivers has been used in modeling route choice behavior, this research uses a link-based perception error model to describe driver’s uncertain behavior, without assuming stochasticity. In congestion-free networks, we show that the perception error model is more general than the existing bounded rationality models with satisficing drivers with special cases when the two approaches yield the same results; that is, satisficing under accurate perception is equivalent to optimizing under inaccurate perception. This motivates us to define generalized bounded rationality in route choice behavior modeling. The proposed modeling framework is general enough to capture link-specific cost-perception of drivers. We use a Monte Carlo method to estimate modeling parameter values to guarantee a certain coverage probability in comparison with the random utility model. We demonstrate how the notion of generalized bounded rationality can be used in robust multicommodity network design problems and devise a cutting plane algorithm. We illustrate our approaches in the context of hazardous materials transportation. The electronic companion is available at https://doi.org/10.1287/opre.2017.1621 .