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Knowledge, congestion, and economics: Parameter uncertainty in Naor’s model

Author

Listed:
  • Ying Chen

    (University of Texas at Austin)

  • John J. Hasenbein

    (University of Texas at Austin)

Abstract

This paper studies an extension of Naor’s model in which there is parameter uncertainty. In particular, the arrival rate is known, to customers and system managers, only through its distribution. For the observable case, the relationship between the optimal individual threshold and the thresholds for a social optimizer or revenue maximizer does not change from the classical model with a known arrival rate. However, in the unobservable case, it is shown that the decisions of the social optimizer and revenue maximizer no longer coincide. Furthermore, in the unobservable case with arrival rate uncertainty, the social optimizer induces a lower expected arrival rate than the revenue maximizer. This stands in contrast to the observable case, in which the social optimizer prefers a more congested system.

Suggested Citation

  • Ying Chen & John J. Hasenbein, 2020. "Knowledge, congestion, and economics: Parameter uncertainty in Naor’s model," Queueing Systems: Theory and Applications, Springer, vol. 96(1), pages 83-99, October.
    • Handle: RePEc:spr:queues:v:96:y:2020:i:1:d:10.1007_s11134-020-09667-7
    DOI: 10.1007/s11134-020-09667-7
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    References listed on IDEAS

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    Cited by:

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    3. van Eekelen, Wouter, 2023. "Distributionally robust views on queues and related stochastic models," Other publications TiSEM 9b99fc05-9d68-48eb-ae8c-9, Tilburg University, School of Economics and Management.

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