基于Agent模型的竞争资源分配的统计力学
Statistical Mechanics of Competitive Resource Allocation using Agent-based Models
摘要：
在大多数情况下，需求超过了现有资源，这就引起了竞争、互动和学习。在这篇文章中，我们回顾了广泛的多智能体竞争模型（El Farol Bar问题，少数人博弈，Kolkata Paise餐厅问题，稳定婚姻问题，停车位问题等）以及用于分析理解它们的方法。我们强调从统计力学的概念和工具的力量，以充分理解和解释集体现象，如相变和长记忆，以及代理异质性和物理无序之间的映射。由于这些方法可以应用于任何大规模的竞争性资源分配模型的异构自适应代理与非线性交互，他们提供了一个前瞻性的统一范式，许多科学学科。
英文摘要：
Demand outstrips available resources in most situations, which gives rise to competition, interaction and learning. In this article, we review a broad spectrum of multi-agent models of competition (El Farol Bar problem, Minority Game, Kolkata Paise Restaurant problem, Stable marriage problem, Parking space problem and others) and the methods used to understand them analytically. We emphasize the power of concepts and tools from statistical mechanics to understand and explain fully collective phenomena such as phase transitions and long memory, and the mapping between agent heterogeneity and physical disorder. As these methods can be applied to any large-scale model of competitive resource allocation made up of heterogeneous adaptive agent with non-linear interaction, they provide a prospective unifying paradigm for many scientific disciplines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
