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

In: AI, Society and Digital Transformation

Author

Listed:
  • Hongyi Liu

    (Southern University of Science and Technology, Colloge of Business)

  • Qiaochu He

    (Southern University of Science and Technology, Colloge of Business)

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.

Suggested Citation

  • Hongyi Liu & Qiaochu He, 2026. "Persuading AI Agents in a Queueing Game of Socially Scarce Resources Acquisition," Lecture Notes in Operations Research, in: Xiaolei Xie & Kejia Hu & Guiping Hu & Weiwei Chen & Robin Qiu (ed.), AI, Society and Digital Transformation, pages 262-275, Springer.
    • Handle: RePEc:spr:lnopch:978-3-032-13116-4_21
    DOI: 10.1007/978-3-032-13116-4_21
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