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Optimal Signaling Mechanisms in Unobservable Queues

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  • David Lingenbrink

    (School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853)

  • Krishnamurthy Iyer

    (School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853)

Abstract

We consider the problem of optimal information sharing in an unobservable single-server queue offering service at a fixed price to a Poisson arrival of delay-sensitive customers. The service provider observes the queue and may share state information with arriving customers. The customers, who are Bayesian and strategic, incorporate this information into their beliefs before deciding whether to join the queue. We pose the following question: Which signaling mechanism should the service provider adopt to maximize her expected revenue? We formulate this problem as an infinite linear program in the queue’s steady-state distribution and establish that, in general, the optimal signaling mechanism requires the service provider to strategically conceal information in order to incentivize customers to join. In particular, we show that a binary signaling mechanism with a threshold structure is optimal. Finally, we prove that coupled with an optimal fixed price, the optimal signaling mechanism generates the same expected revenue as the optimal state-dependent pricing mechanism. This suggests that in settings where state-dependent pricing is infeasible, signaling can be effective in achieving the optimal revenue. Our work contributes to the literature on dynamic Bayesian persuasion and provides many interesting directions for extensions.

Suggested Citation

  • David Lingenbrink & Krishnamurthy Iyer, 2019. "Optimal Signaling Mechanisms in Unobservable Queues," Operations Research, INFORMS, vol. 67(5), pages 1397-1416, September.
  • Handle: RePEc:inm:oropre:v:67:y:2019:i:5:p:1397-1416
    DOI: opre.2018.1819
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    References listed on IDEAS

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    5. Ehud Lehrer & Dimitry Shaiderman, 2022. "Markovian Persuasion with Stochastic Revelations," Papers 2204.08659, arXiv.org, revised May 2022.
    6. Jerry Anunrojwong & Krishnamurthy Iyer & Vahideh Manshadi, 2023. "Information Design for Congested Social Services: Optimal Need-Based Persuasion," Management Science, INFORMS, vol. 69(7), pages 3778-3796, July.
    7. Ehud Lehrer & Dimitry Shaiderman, 2021. "Markovian Persuasion," Papers 2111.14365, arXiv.org.
    8. Zhongbin Wang & Yunan Liu & Lei Fang, 2022. "Pay to activate service in vacation queues," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2609-2627, June.
    9. Kimon Drakopoulos & Shobhit Jain & Ramandeep Randhawa, 2021. "Persuading Customers to Buy Early: The Value of Personalized Information Provisioning," Management Science, INFORMS, vol. 67(2), pages 828-853, February.
    10. Babichenko, Yakov & Talgam-Cohen, Inbal & Xu, Haifeng & Zabarnyi, Konstantin, 2022. "Regret-minimizing Bayesian persuasion," Games and Economic Behavior, Elsevier, vol. 136(C), pages 226-248.
    11. Harish Guda & Milind Dawande & Ganesh Janakiraman, 2023. "The economics of process transparency," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1812-1829, June.
    12. Francis de Véricourt, & Huseyin Gurkan, & Shouqiang Wang,, 2020. "Informing the public about a pandemic," ESMT Research Working Papers ESMT-20-03, ESMT European School of Management and Technology, revised 11 Feb 2021.
    13. Jianfu Wang & Ming Hu, 2020. "Efficient Inaccuracy: User-Generated Information Sharing in a Queue," Management Science, INFORMS, vol. 66(10), pages 4648-4666, October.
    14. Saed Alizamir & Francis de Véricourt & Shouqiang Wang, 2020. "Warning Against Recurring Risks: An Information Design Approach," Management Science, INFORMS, vol. 66(10), pages 4612-4629, October.
    15. Shivam Gupta & Wei Chen & Milind Dawande & Ganesh Janakiraman, 2023. "Three Years, Two Papers, One Course Off: Optimal Nonmonetary Reward Policies," Management Science, INFORMS, vol. 69(5), pages 2852-2869, May.
    16. Deepanshu Vasal, 2020. "Dynamic information design," Papers 2005.07267, arXiv.org.
    17. Gur, Yonatan & Macnamara, Gregory & Saban, Daniela, 2020. "On the Disclosure of Promotion Value in Platforms with Learning Sellers," Research Papers 3865, Stanford University, Graduate School of Business.
    18. Gabi Hanukov & Michael Hassoun & Oren Musicant, 2021. "On the Benefits of Providing Timely Information in Ticket Queues with Balking and Calling Times," Mathematics, MDPI, vol. 9(21), pages 1-16, October.
    19. Yonatan Gur & Gregory Macnamara & Ilan Morgenstern & Daniela Saban, 2019. "Information Disclosure and Promotion Policy Design for Platforms," Papers 1911.09256, arXiv.org, revised Dec 2022.
    20. Pengfei Guo & Moshe Haviv & Zhenwei Luo & Yulan Wang, 2022. "Optimal queue length information disclosure when service quality is uncertain," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 1912-1927, May.
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    22. Nasimeh Heydaribeni & Ketan Savla, 2021. "Information Design for a Non-atomic Service Scheduling Game," Papers 2110.00090, arXiv.org.

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