论文标题
随机Stackelberg游戏的顺序分解
Sequential decomposition of stochastic Stackelberg games
论文作者
论文摘要
在本文中，我们考虑了一个离散的随机Stackelberg游戏，其中有一个领导者和多个追随者。追随者和领导者共同有条件独立的私人类型，以行动和以前的状态为条件，以受控的马尔可夫流程发展。目的是计算领导者致力于动态策略的游戏随机stackelberg均衡。每个追随者的策略是对领导者策略和其他追随者策略的最佳反应，而每个领导者的策略是最佳的，鉴于追随者的表现最好。通常，计算这种平衡涉及求解整个游戏的定点方程。在本文中，我们提出了一种向后的递归算法，该算法每次$ t $求解较小的定点方程来计算此类策略。基于此算法，我们计算安全示例的随机stackelberg平衡，以及在〜\ cite {el17}（beeps）中使用的动态信息设计示例。
In this paper, we consider a discrete-time stochastic Stackelberg game with a single leader and multiple followers. Both the followers and the leader together have conditionally independent private types, conditioned on action and previous state, that evolve as controlled Markov processes. The objective is to compute the stochastic Stackelberg equilibrium of the game where the leader commits to a dynamic strategy. Each follower's strategy is the best response to the leader's strategies and other followers' strategies while the each leader's strategy is optimum given the followers play the best response. In general, computing such equilibrium involves solving a fixed-point equation for the whole game. In this paper, we present a backward recursive algorithm that computes such strategies by solving smaller fixed-point equations for each time $t$. Based on this algorithm, we compute stochastic Stackelberg equilibrium of a security example and a dynamics information design example used in~\cite{El17} (beeps).
