进化泊松游戏用于控制大规模群体行为
Evolutionary Poisson Games for Controlling Large Population Behaviors
AI总结：
本文提出进化泊松游戏框架，用于控制复杂网络中异质用户的行为，通过设计机制实现系统目标，以网络安全中的病毒防护挑战为例进行验证。
摘要：
新兴工程应用如众包和（误）信息传播涉及复杂网络中异质用户或智能体的大量群体，他们战略地做出动态决策。本文建立进化泊松游戏框架，以捕捉智能体的随机、动态和异质相互作用，并设计机制以控制其行为以实现系统目标。我们使用网络安全中的病毒防护挑战来激发该框架，其中网络中的每个用户可以选择是否采用软件。我们引入了进化泊松稳定的均衡概念，并展示了其存在性和唯一性。利用随机逼近技术与群体动力学技术开发了在线算法，并证明它们收敛于控制器问题的最优解。数值示例用于说明和验证我们的结果。
英文摘要：
Emerging applications in engineering such as crowd-sourcing and (mis)information propagation involve a large population of heterogeneous users or agents in a complex network who strategically make dynamic decisions. In this work, we establish an evolutionary Poisson game framework to capture the random, dynamic and heterogeneous interactions of agents in a holistic fashion, and design mechanisms to control their behaviors to achieve a system-wide objective. We use the antivirus protection challenge in cyber security to motivate the framework, where each user in the network can choose whether or not to adopt the software. We introduce the notion of evolutionary Poisson stable equilibrium for the game, and show its existence and uniqueness. Online algorithms are developed using the techniques of stochastic approximation coupled with the population dynamics, and they are shown to converge to the optimal solution of the controller problem. Numerical examples are used to illustrate and corroborate our results.
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