Trade the System Efficiency for the Income Equality of Drivers in Rideshare
Trade the System Efficiency for the Income Equality of Drivers in Rideshare
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DOI：
10.24963/ijcai.2020/580
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Yifan Xu;Pan Xu
中科院分区：
文献类型：
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作者：
Yifan Xu;Pan Xu
一些科学研究报告称，基于性别、年龄、种族等人口因素，拼车司机之间存在收入差距。本文研究了由于乘客的歧视性取消而导致的拼车司机之间的收入不平等，以及收入不平等（称为公平目标）与系统效率（称为利润目标）之间的权衡。我们提出了一个在线双方匹配模型，假设乘客按照事先已知的分布顺序到达。我们模型的亮点是任何一对司机-乘客类型之间的接受率概念，其中类型是根据人口统计因素定义的。特别地，我们假设每个骑手都可以接受或取消分配给她的司机，每种情况都以一定的概率发生，反映了骑手类型对司机类型的接受程度。我们构造了一个双目标线性规划作为有效基准，并提出了两种基于lp的参数化在线算法。通过严格的在线竞争比分析，证明了我们的在线算法在平衡公平和利润两个相互冲突的目标方面的灵活性和效率。并给出了实际数据集的实验结果，证实了我们的理论预测。
Several scientific studies have reported the existence of the income gap among rideshare drivers based on demographic factors such as gender, age, race, etc. In this paper, we study the income inequality among rideshare drivers due to discriminative cancellations from riders, and the tradeoff between the income inequality (called fairness objective) with the system efficiency (called profit objective). We proposed an online bipartite-matching model where riders are assumed to arrive sequentially following a distribution known in advance. The highlight of our model is the concept of acceptance rate between any pair of driver-rider types, where types are defined based on demographic factors. Specially, we assume each rider can accept or cancel the driver assigned to her, each occurs with a certain probability which reflects the acceptance degree from the rider type towards the driver type. We construct a bi-objective linear program as a valid benchmark and propose two LP-based parameterized online algorithms. Rigorous online competitive ratio analysis is offered to demonstrate the flexibility and efficiency of our online algorithms in balancing the two conflicting goals, promotions of fairness and profit. Experimental results on a real-world dataset are provided as well, which confirm our theoretical predictions.