Satisfaction and Regret in Stackelberg Games

Langford White, Duong Nguyen, Hung Nguyen
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Abstract

This paper introduces the new concept of (follower) satisfaction in Stackelberg games and compares the standard Stackelberg game with its satisfaction version. Simulation results are presented which suggest that the follower adopting satisfaction generally increases leader utility. This important new result is proven for the case where leader strategies to commit to are restricted to be deterministic (pure strategies). The paper then addresses the application of regret based algorithms to the Stackelberg problem. Although it is known that the follower adopts a no-regret position in a Stackelberg solution, this is not generally the case for the leader. The report examines the convergence behaviour of unconditional and conditional regret matching (RM) algorithms in the Stackelberg setting. The paper shows that, in the examples considered, that these algorithms either converge to any pure Nash equilibria for the simultaneous move game, or to some mixed strategies which do not have the "no-regret" property. In one case, convergence of the conditional RM algorithm over both players to a solution "close" to the Stackelberg case was observed. The paper argues that further research in this area, in particular when applied in the satisfaction setting could be fruitful.
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斯塔克尔伯格博弈中的满意与遗憾
本文在斯泰尔伯格博弈中引入了(追随者)满意度这一新概念,并将标准斯泰尔伯格博弈与其满意度版本进行了比较。本文给出的模拟结果表明,追随者采取满意策略通常会增加领导者的效用。这一重要的新结果是在领导者的承诺策略被限制为确定性策略(纯策略)的情况下证明的。然后,本文讨论了基于遗憾的算法在斯塔克尔伯格问题中的应用。虽然众所周知,追随者会采取无遗憾的立场来解决斯塔克尔伯格问题,但领导者一般不会这样做。本报告研究了无条件和有条件遗憾匹配(RM)算法在 Stackelberg 环境中的收敛行为。论文表明,在所考虑的例子中,这些算法要么收敛到同时移动博弈的任何纯纳什均衡,要么收敛到某些不具有 "无遗憾 "属性的混合战略。在一种情况下,观察到条件 RM 算法收敛于两个棋手的解,"接近 "斯塔克尔伯格情况。本文认为,在这一领域的进一步研究,特别是在满意设置中的应用,可能会取得丰硕成果。
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