论文标题
将系统效率交换为乘车司机的收入平等
Trading the System Efficiency for the Income Equality of Drivers in Rideshare
论文作者
论文摘要
一些科学研究报告了基于性别，年龄，种族等人口因素的乘车驱动因素之间的收入差距。在本文中，我们研究了骑手的歧视取消乘车驱动因素的收入不平等，而收入不平等（称为公平目标）之间的折衷（称为利润目标）（称为利润目标）（称为利润目标）。我们提出了一个在线双方匹配模型，该模型假定骑手在提前已知的发行版之后依次到达。我们模型的亮点是任何一对驾驶员类型之间的接受率的概念，其中根据人口统计学因素定义了类型。特别是，我们假设每个骑手都可以接受或取消分配给她的驱动程序，每种驾驶员都以一定概率发生，该概率反映了从骑手类型到驾驶员类型的接受度。我们将双目标线性程序构建为有效的基准测试，并提出了两个基于LP的在线算法。提供了严格的在线竞争比分析，以证明我们在线算法在平衡两个矛盾的目标，促进公平和利润方面的灵活性和效率。还提供了现实数据集的实验结果，这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.
