有入侵者穿行的信息可变密集静态人群的折扣平均场博弈模型
Discounted Mean-Field Game model of a dense static crowd with variable information crossed by an intruder
AI总结：
针对行人认知差异影响密集人群被入侵者穿行时动力学的问题，本研究在极简平均场博弈模型中加入折扣因子$\gamma$，复现了实验全部行为，补充了解析结果与模型修正机制。
摘要：
Bonnemain等人2023年发表于《物理评论E》第107卷024612页的研究已证实，密集人群被入侵者穿行时展现的预期模式可通过一个极简平均场博弈（Mean-Field Games, MFG）模型成功描述。但实验表明，行人的认知差异会显著改变人群动力学行为。本研究表明，仅添加单个参数折扣因子$\gamma$（对时间上较远的事件赋予更低权重），就足以复现实验中观察到的全部行为类型。我们将折扣MFG模型与实验数据进行了对比，同时提供了新的解析结果，并阐释了引入$\gamma$对模型的修正机制。
英文摘要：
It was demonstrated in [Bonnemain et al., Phys. Rev. E 107, 024612 (2023)] that the anticipation pattern displayed by a dense crowd crossed by an intruder can be successfully described by a minimal Mean-Field Games model. However, experiments show that changes in the pedestrian knowledge significantly modify the dynamics of the crowd. Here, we show that the addition of a single parameter, the discount factor $γ$, which gives a lower weight to events distant in time, is sufficient to observe the whole variety of behaviors observed in the experiments. We present a comparison between the discounted MFG and the experimental data, also providing new analytic results and insight about how the introduction of $γ$ modifies the model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
