论文标题

在人口游戏中融合纳什均衡的消散工具

Dissipativity Tools for Convergence to Nash Equilibria in Population Games

论文作者

Arcak, Murat, Martins, Nuno C.

论文摘要

我们分析了一个非线性动力学模型的稳定性,该模型描述了有限种群集合的代理之间的非合作战略相互作用。每个代理商一次选择一种策略,并根据一项协议重复修改,该协议通常优先考虑其收益要么高于当前策略或超过人口平均水平的策略。该模型基于人口和进化游戏的公认研究,并具有两个子组件。第一个是回报动力学模型(PDM),它根据采用可用策略的每个人群的比例将收益归因于每种策略。第二个子组件是说明修订过程的进化动力学模型(EDM)。在我们的模型中,平衡的社会状态是对回报的最佳响应,可以看作是一种类似NASH的解决方案,当它在全球渐近稳定时具有预测价值(气体)。我们提出了一种系统的方法,该方法可以通过单独检查EDM和PDM是否满足适当定义的系统理论耗散性能来确定气体。我们的工作概括了基于适用于无内存PDM的合同性概念以及更一般的系统理论被动性条件的开创性方法。如示例所证明的那样,当PDM的收缩特性在人群之间不平等时,我们的方法提供的增加的灵活性特别有用。

We analyze the stability of a nonlinear dynamical model describing the noncooperative strategic interactions among the agents of a finite collection of populations. Each agent selects one strategy at a time and revises it repeatedly according to a protocol that typically prioritizes strategies whose payoffs are either higher than that of the current strategy or exceed the population average. The model is predicated on well-established research in population and evolutionary games, and has two sub-components. The first is the payoff dynamics model (PDM), which ascribes the payoff to each strategy according to the proportions of every population adopting the available strategies. The second sub-component is the evolutionary dynamics model (EDM) that accounts for the revision process. In our model, the social state at equilibrium is a best response to the payoff, and can be viewed as a Nash-like solution that has predictive value when it is globally asymptotically stable (GAS). We present a systematic methodology that ascertains GAS by checking separately whether the EDM and PDM satisfy appropriately defined system-theoretic dissipativity properties. Our work generalizes pioneering methods based on notions of contractivity applicable to memoryless PDMs, and more general system-theoretic passivity conditions. As demonstrated with examples, the added flexibility afforded by our approach is particularly useful when the contraction properties of the PDM are unequal across populations.

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