AI-Farol: Co-Evolutionary Dynamics in a Multi-Agent Two-Sided Learning Framework
TL;DR
This work reconceptualizes the El Farol Bar game by modeling the bar as a strategic player endowed with AI-driven learning capabilities, offering insights into congestion management, resource allocation, and mechanism design in complex adaptive systems.
Abstract
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.