Autonomous vehicles will be an integral part of ride-sharing services in the future. This setting differs from traditional ride-sharing marketplaces because of the absence of the supply side (drivers). However, it has far-reaching consequences because in addition to pricing, players now have to make decisions on how to distribute fleets across network locations and re-balance vehicles in order to serve future demand. In this paper, we explore a duopoly setting in the ride-sharing marketplace where the players have fully autonomous fleets. Each ride-service provider (RSP)'s prices depend on the prices and the supply of the other player. We formulate their decision-making problems using a game-theoretic setup where each player seeks to find the optimal prices and supplies at each node while considering the decisions of the other player. This leads to a scenario where the players' optimization problems are coupled and it is challenging to find the equilibrium. We characterize the types of demand functions (e.g.: linear) for which this game admits an exact potential function and can be solved efficiently. For other types of demand functions, we propose an iterative algorithm to compute the equilibrium. We conclude by providing numerical insights into how different kinds of equilibria would play out in the market when the players are asymmetric. Our numerical evaluations also provide insights into how the regulator needs to consider network effects while deciding regulation in order to avoid unfavorable outcomes.
翻译:自动车辆将是未来搭车共享服务的一个组成部分。 这种环境与传统的搭车共享市场不同,因为没有供应方(驾驶员),因此与传统的搭车共享市场不同。然而,它具有深远的影响,因为除了定价之外,球员现在必须就如何在网络地点之间分配车队和重新平衡车辆作出决定,以满足未来需求。在本文件中,我们探索搭车共享市场中的双曲线环境,即球员拥有完全自主的车队。每个搭车服务提供商(RSP)的价格取决于其他球员的价格和供应情况。我们利用游戏理论设置来制定他们的决策问题,其中每个球员在考虑其他球员的决定时,寻求在每个节点找到最佳价格和供应。这导致一种情景,即球员的优化问题相互交织在一起,寻找平衡。我们描述的是各种需求功能的种类(例如:线性),因为每个游车供应商的价格取决于其他球员的价格和供应者的供应情况。我们建议用一个不迭代算法的算法来避免在每一个节点上找到最佳价格和供应者的最佳供应方,同时考虑其他节点。我们通过数字洞察来判断我们如何判断我们的市场规则,我们如何在数字上分析结果中进行。我们如何判断。我们如何判断。</s>