探索系统利润与乘车司机收入平等之间的权衡

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Evan Yifan Xu, Pan Xu
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引用次数: 0

摘要

本文考虑了性别、年龄和种族等人口因素的影响,研究了乘客歧视性取消订单导致的共享单车司机收入不平等问题。我们研究了收入不平等(被称为公平目标)与系统效率(被称为利润目标)之间的权衡问题。为了解决这个问题,我们提出了一个在线两方匹配模型,该模型根据已知分布捕捉乘客的先后到达。该模型结合了司机与乘客类型之间的接受率概念,而接受率是根据人口统计特征定义的。具体来说,我们分析了乘客接受或取消指定司机的概率,反映了不同乘客和司机类型之间的接受程度。我们构建了一个双目标线性程序作为有效基准,并提出了两种基于 LP 的参数化在线算法。我们对在线竞争比率进行了严格分析,以说明我们的算法在实现公平与利润平衡方面的灵活性和效率。此外,我们还展示了基于真实世界和合成数据集的实验结果,验证了我们研究中提出的理论预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the Tradeoff Between System Profit and Income Equality Among Ride-hailing Drivers
This paper examines the income inequality among rideshare drivers resulting from discriminatory cancellations by riders, considering the impact of demographic factors such as gender, age, and race. We investigate the tradeoff between income inequality, referred to as the fairness objective, and system efficiency, known as the profit objective. To address this issue, we propose an online bipartite-matching model that captures the sequential arrival of riders according to a known distribution. The model incorporates the notion of acceptance rates between driver-rider types, which are defined based on demographic characteristics. Specifically, we analyze the probabilities of riders accepting or canceling their assigned drivers, reflecting the level of acceptance between different rider and driver types. We construct a bi-objective linear program as a valid benchmark and propose two LP-based parameterized online algorithms. Rigorous analysis of online competitive ratios is conducted to illustrate the flexibility and efficiency of our algorithms in achieving a balance between fairness and profit. Furthermore, we present experimental results based on real-world and synthetic datasets, validating the theoretical predictions put forth in our study.
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来源期刊
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research 工程技术-计算机:人工智能
CiteScore
9.60
自引率
4.00%
发文量
98
审稿时长
4 months
期刊介绍: JAIR(ISSN 1076 - 9757) covers all areas of artificial intelligence (AI), publishing refereed research articles, survey articles, and technical notes. Established in 1993 as one of the first electronic scientific journals, JAIR is indexed by INSPEC, Science Citation Index, and MathSciNet. JAIR reviews papers within approximately three months of submission and publishes accepted articles on the internet immediately upon receiving the final versions. JAIR articles are published for free distribution on the internet by the AI Access Foundation, and for purchase in bound volumes by AAAI Press.
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