In this paper, we consider a discrete-time Stackelberg mean field game with a leader and an infinite number of followers. The leader and the followers each observe types privately that evolve as conditionally independent controlled Markov processes. The leader commits to a dynamic policy and the followers best respond to that policy and each other. Knowing that the followers would play a mean field game based on her policy, the leader chooses a policy that maximizes her reward. We refer to the resulting outcome as a Stackelberg mean field equilibrium (SMFE). In this paper, we provide a master equation of this game that allows one to compute all SMFE. Based on our framework, we consider two numerical examples. First, we consider an epidemic model where the followers get infected based on the mean field population. The leader chooses subsidies for a vaccine to maximize social welfare and minimize vaccination costs. In the second example, we consider a technology adoption game where the followers decide to adopt a technology or a product and the leader decides the cost of one product that maximizes his returns, which are proportional to the people adopting that technology
翻译:在本文中,我们考虑的是与一个领导者和为数众多追随者分时间的Stackelberg不同时间的野外游戏。领导者和追随者各自观察作为有条件独立控制的Markov进程而演变的私人类型。领导者承诺采取动态政策,追随者对政策做出最佳反应。知道追随者将依据其政策玩一种卑鄙的野外游戏,领导者选择了使她得到最大奖赏的政策。我们把由此产生的结果称为Stackelberg中平均野外平衡(SMFE ) 。在本文中,我们给出了这一游戏的总方程式,允许一个人计算所有SMFE。根据我们的框架,我们考虑两个数字例子。首先,我们考虑了一个流行病模式,在这种模式下,追随者根据平均的野外人口受到感染。领导人选择了疫苗补贴,以最大限度地提高社会福利和降低接种费用。在第二个例子中,我们考虑的是技术采纳者决定采用某种技术或产品,而领导者决定一种产品的成本最大化,这与采用这种技术的人成比例。