Extending the El Farol Bar Game with Partial Observability and Incentive Design
For researchers in multi-agent systems and mechanism design, this work provides a novel framework for modeling adaptive institutions in coordination problems, though it is incremental in nature.
This paper extends the El Farol Bar game by introducing partial observability for agents and modeling the bar as an active mechanism designer that uses AI-driven pricing to balance revenue, utilization, and sustainability, demonstrating a co-evolutionary learning framework for congestion management.
The El Farol Bar game is a classic model of coordination under uncertainty, traditionally treating the venue as a passive constraint. In this work, we re-conceptualize the problem by modeling the bar as a strategic player equipped with AI-driven learning capabilities. We extend the original framework to include partial observability, i.e., agents observe only subsets of past attendees, and transform 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 uses 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.