AI-Farol：多智能体双边学习框架中的协同进化动力学
AI-Farol: Co-Evolutionary Dynamics in a Multi-Agent Two-Sided Learning Framework
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中文总结 AI 辅助
本研究将El Farol Bar游戏的酒吧建模为AI驱动的策略参与者，扩展框架至部分可观测性与动态定价，构建双边协同进化学习系统，为复杂自适应系统的相关问题提供见解。
中文摘要 AI 辅助
El Farol Bar游戏是不确定性下协调问题的经典模型，传统上将该场所视为被动约束。本研究通过将酒吧建模为具备AI驱动学习能力的策略型参与者，重新构想该问题。我们在两个主要方向扩展了原框架：第一，引入部分可观测性，即智能体仅观测过去参与者的子集；第二，将酒吧从被动容量阈值转变为主动机制设计者，其调整定价政策以平衡收入、利用率和可持续性约束。智能体采用基于AI的学习形成信念并在不完全信息下调整出勤策略，而酒吧则应用策略学习优化动态定价。由此产生的双边学习系统将协调过程构建为有限理性智能体与自适应制度之间的协同进化过程，为复杂自适应系统中的拥堵管理、资源分配和机制设计提供见解。
英文摘要
The El Farol Bar game is a classical model of coordination under uncertainty that traditionally treats the venue as a passive constraint. In this work, we reconceptualize the problem by modeling the bar as a strategic player endowed with AI-driven learning capabilities. We extend the original framework in two principal directions: first, by introducing partial observability, whereby agents observe only subsets of past attendees; and second, by transforming the bar from a passive capacity threshold into an active mechanism designer that adjusts pricing policies to balance revenue, utilization, and sustainability constraints. Agents employ AI-based learning to form beliefs and adapt attendance strategies under incomplete information, while the bar applies policy learning to optimize dynamic pricing. The resulting two-sided learning system frames coordination as a co-evolutionary process between boundedly rational agents and an adaptive institution, offering insights into congestion management, resource allocation, and mechanism design in complex adaptive systems.
