{"ID":3050091,"CreatedAt":"2026-06-04T02:13:16.786527022Z","UpdatedAt":"2026-06-06T11:27:32.998563389Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04753","arxiv_id":"2606.04753","title":"Extending the El Farol Bar Game with Partial Observability and Incentive Design","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2606.04753","url_pdf":"https://arxiv.org/pdf/2606.04753v1","authors":"[\"Iosif Polenakis\",\"Kalliopi Kastampolidou\",\"Theodore Andronikos\"]","published":"2026-06-03T11:37:29Z","proceeding":"cs.GT","tasks":"[\"cs.GT\"]","methods":"[]","has_code":false}