Persuading AI Agents in a Queueing Game of Socially Scarce Resources Acquisition

Hongyi Liu () and Qiaochu He ()
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Hongyi Liu: Southern University of Science and Technology, Colloge of Business
Qiaochu He: Southern University of Science and Technology, Colloge of Business

A chapter in AI, Society and Digital Transformation, 2026, pp 262-275 from Springer

Abstract: Abstract Queueing models serve as effective proxies for the allocation of socially scarce resources, where customers sequentially receive limited services. To address this, service providers aiming to maximize social welfare can use quality-related signals to deter low-priority customers from queueing, thereby reserving access for high-priority counterparts. We conduct a behavioral economics experiment between the service provider (the environment) and customers (AI agents, or “suspects”). Our proposed framework integrates human-algorithm interaction in a transparent “white-box” setting. To explain the experimental results, we construct a theoretical model of information design to regulate scarce services allocation. Our research emphasizes the interaction between AI decision-making and its environment, highlighting how AI can overcome behavioral inefficiencies (e.g., selfish queueing) to foster cooperation and improve welfare.

Keywords: socially scarce resources; queueing games; Q-learning; information design (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-032-13116-4_21

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DOI: 10.1007/978-3-032-13116-4_21

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