We study the behavior of an economic platform (e.g., Amazon, Uber Eats, Instacart) under shocks, such as COVID-19 lockdowns, and the effect of different regulation considerations imposed on a platform. To this end, we develop a multi-agent Gym environment of a platform economy in a dynamic, multi-period setting, with the possible occurrence of economic shocks. Buyers and sellers are modeled as economically-motivated agents, choosing whether or not to pay corresponding fees to use the platform. We formulate the platform's problem as a partially observable Markov decision process, and use deep reinforcement learning to model its fee setting and matching behavior. We consider two major types of regulation frameworks: (1) taxation policies and (2) platform fee restrictions, and offer extensive simulated experiments to characterize regulatory tradeoffs under optimal platform responses. Our results show that while many interventions are ineffective with a sophisticated platform actor, we identify a particular kind of regulation -- fixing fees to optimal, pre-shock fees while still allowing a platform to choose how to match buyer demands to sellers -- as promoting the efficiency, seller diversity, and resilience of the overall economic system.
翻译:我们研究了经济平台(如亚马逊、乌伯饮食、Instacart)在冲击(如COVID-19锁闭)下的行为,以及在平台上实施的不同监管考虑因素的影响。为此,我们在动态的、多期的环境下,开发平台经济的多试剂健身环境,并有可能发生经济冲击。买方和卖方以经济动机的代理商为模型,选择是否支付使用平台的相应费用。我们把平台的问题设计成一个部分可观测的Markov决策程序,并利用深度强化学习来模拟其定价和匹配行为。我们考虑了两大类监管框架:(1)税收政策和(2)平台收费限制,并提供广泛的模拟实验,以在最佳平台对策下确定监管的权衡。我们的结果表明,虽然许多干预措施与精密的平台参与者是无效的,但我们确定了一种特殊的监管类型 -- 将费用定为最佳、冲击前收费,同时允许平台选择如何满足买方对卖方的需求 -- 即促进整个经济体系的效率、卖方的多样性和复原力。