Mathematics· 2026Q2
AI-Farol: Co-Evolutionary Dynamics in a Multi-Agent Two-Sided Learning Framework
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- Q2SCImago
- 2026year
Short summary
This paper introduces AI-Farol, a novel framework where a learning AI-powered bar dynamically adjusts pricing to manage attendance, while agents learn to adapt their attendance strategies based on partial information, creating a co-evolutionary system.
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Key points
- The bar is modeled as an active AI mechanism designer, not a passive constraint.
- Partial observability is introduced, where agents only see subsets of past attendees.
- Agents use AI-based learning to adapt attendance strategies under incomplete information.
- The bar uses policy learning to optimize dynamic pricing based on revenue, utilization, and sustainability.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Mathematics, 2026 · DOI ↗
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Management Science and Operations ResearchDecision Sciences