具有二次向量值支付函数和弱Pareto改进的双智能体非合作动力系统

IF 2.4 Q2 AUTOMATION & CONTROL SYSTEMS
Zehui Guo;Tomohisa Hayakawa
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引用次数: 0

摘要

给出了具有向量值支付函数的非合作动态系统的弱Pareto改进和弱Pareto改进陷阱的定义。具体而言,我们开发了一种满足可持续预算约束的激励机制。我们假设有一个系统管理者,她被授权设计激励函数,以使她所选择的社会福利最大化。具体来说,社会最大状态是基于所有收益函数的加权和。结果表明,根据设计参数的选择,我们可以观察到,即使系统轨迹是弱帕累托改进,一些智能体也可能陷入弱帕累托改进。我们的结果是标量值支付函数结果的广义版本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two-Agent Noncooperative Dynamical Systems With Quadratic Vector-Valued Payoff Functions and Weak Pareto Improvement
Definition of the weak Pareto improvement and the trap of weak Pareto improvement are given for noncooperative dynamical systems with vector-valued payoff functions. Specifically, we develop an incentive mechanism that satisfies sustainable budget constraint. We assume that there is a system manager who is authorized to design the incentive functions in order to maximize the social welfare of her choice. Specifically, the socially maximum state is predicated on the weighted sum of all the payoff functions. It turns out that depending on the choice of the design parameters, we may observe that some of the agents can be trapped in a weak Pareto improvement even though the system trajectory is weakly Pareto improving. Our results is a generalized version of the results for scalar-valued payoff functions.
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来源期刊
IEEE Control Systems Letters
IEEE Control Systems Letters Mathematics-Control and Optimization
CiteScore
4.40
自引率
13.30%
发文量
471
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