一句话:揭示标准策略模型下互模拟不保持Nash均衡,并提出三种保持Nash均衡的策略模型。
博弈论被广泛用于分析并发与多智能体系统:进程视为玩家,计算运行视为博弈过程,策略决定智能体行为,通常建模为从状态序列到动作的函数。本文发现,在这一标准策略模型下,互模拟不能保持Nash均衡的存在性,因此行为等价且满足相同时序逻辑公式的两个并发博弈可能在博弈论解上根本不同。作者先用激励性例子说明该问题,然后提出三种策略模型,使Nash均衡存在性在互模拟下保持;并利用其中部分模型为策略推理逻辑提供新的语义基础,同时研究标准策略模型下互模拟仍可保持Nash均衡的受限场景。
Game theory provides a well-established framework for the analysis of concurrent and multi-agent systems. The basic idea is that concurrent processes (agents) can be understood as corresponding to players in a game; plays represent the possible computation runs of the system; and strategies define the behaviour of agents. Typically, strategies are modelled as functions from sequences of system states to player actions. Analysing a system in such a setting involves computing the set of (Nash) equilibria in the concurrent game. However, we show that, with respect to the above model of strategies (arguably, the "standard" model in the computer science literature), bisimilarity does not preserve the existence of Nash equilibria. Thus, two concurrent games which are behaviourally equivalent from a semantic perspective, and which from a logical perspective satisfy the same temporal logic formulae, may nevertheless have fundamentally different properties (solutions) from a game theoretic perspective. Our aim in this paper is to explore the issues raised by this discovery. After illustrating the issue by way of a motivating example, we present three models of strategies with respect to which the existence of Nash equilibria is preserved under bisimilarity. We use some of these models of strategies to provide new semantic foundations for logics for strategic reasoning, and investigate restricted scenarios where bisimilarity can be shown to preserve the existence of Nash equilibria with respect to the conventional model of strategies in the computer science literature.
@article{paperbot753,
title = {Nash Equilibrium and Bisimulation Invariance},
author = {Julian Gutierrez and Paul Harrenstein and Giuseppe Perelli and Michael Wooldridge},
journal = {Logical Methods in Computer Science},
volume = {Volume 15, Issue 3},
year = {2019},
doi = {10.23638/lmcs-15(3:32)2019}
}