论文标题
在骑车市场中用于平台竞争的随机通用NASH平衡模型
A stochastic generalized Nash equilibrium model for platforms competition in the ride-hail market
论文作者
论文摘要
乘车市场中不确定性的存在使按需平台的定价策略变得复杂，这些平台相互竞争以提供出行服务，同时努力最大化其利润。将这个问题视为随机广义的NASH平衡问题（SGNEP），我们设计了一个分布式的，随机的平衡寻求使用Tikhonov正则化算法，以找到最佳的定价策略。值得注意的是，提出的迭代方案不需要增加（可能是无限）的随机变量样品来执行随机近似，从而使其从实际角度吸引人。此外，我们表明该算法仅在单调性假设下返回NASH平衡，并仔细选择步长序列，通过利用手头的SGNEP的特定结构获得。我们终于在按需乘车市场的数值实例上证实了我们的结果。
The presence of uncertainties in the ride-hailing market complicates the pricing strategies of on-demand platforms that compete each other to offer a mobility service while striving to maximize their profit. Looking at this problem as a stochastic generalized Nash equilibrium problem (SGNEP), we design a distributed, stochastic equilibrium seeking algorithm with Tikhonov regularization to find an optimal pricing strategy. Remarkably, the proposed iterative scheme does not require an increasing (possibly infinite) number of samples of the random variable to perform the stochastic approximation, thus making it appealing from a practical perspective. Moreover, we show that the algorithm returns a Nash equilibrium under mere monotonicity assumption and a careful choice of the step size sequence, obtained by exploiting the specific structure of the SGNEP at hand. We finally corroborate our results on a numerical instance of the on-demand ride-hailing market.
