Online market platforms play an increasingly powerful role in the economy. An empirical phenomenon is that platforms, such as Amazon, Apple, and DoorDash, also enter their own marketplaces, imitating successful products developed by third-party sellers. We formulate a Stackelberg model, where the platform acts as the leader by committing to an entry policy: when will it enter and compete on a product? We study this model through a theoretical and computational framework. We begin with a single seller, and consider different kinds of policies for entry. We characterize the seller's optimal explore-exploit strategy via a Gittins-index policy, and give an algorithm to compute the platform's optimal entry policy. We then consider multiple sellers, to account for competition and information spillover. Here, the Gittins-index characterization fails, and we employ deep reinforcement learning to examine seller equilibrium behavior. Our findings highlight the incentives that drive platform entry and seller innovation, consistent with empirical evidence from markets such as Amazon and Google Play, with implications for regulatory efforts to preserve innovation and market diversity.

翻译：在线市场平台在经济中扮演着日益重要的角色。一个经验现象是，诸如亚马逊、苹果和DoorDash等平台也会进入其自身的市场，模仿第三方卖家开发的成功产品。我们构建了一个斯塔克尔伯格模型，其中平台作为领导者，通过承诺一个进入策略来决定：何时进入并在某个产品上展开竞争？我们通过理论和计算框架来研究这一模型。我们从单一卖家情形出发，考虑不同类型的进入策略。我们通过吉廷斯指数策略刻画了卖家的最优探索-利用策略，并给出了一种算法来计算平台的最优进入策略。随后，我们考虑多个卖家的情况，以纳入竞争和信息溢出效应。在此情形下，吉廷斯指数刻画不再适用，我们采用深度强化学习来考察卖家的均衡行为。我们的研究结果揭示了驱动平台进入和卖家创新的激励因素，这与来自亚马逊和Google Play等市场的经验证据一致，并对旨在维护创新和市场多样性的监管努力具有启示意义。