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Value of Information in Bayesian Routing Games

Author

Listed:
  • Manxi Wu

    (Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Saurabh Amin

    (Laboratory of Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Asuman E. Ozdaglar

    (Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

We study a routing game in an environment with multiple heterogeneous information systems and an uncertain state that affects edge costs of a congested network. Each information system sends a noisy signal about the state to its subscribed traveler population. Travelers make route choices based on their private beliefs about the state and other populations’ signals. The question then arises, “How does the presence of asymmetric and incomplete information affect the travelers’ equilibrium route choices and costs?” We develop a systematic approach to characterize the equilibrium structure and determine the effect of population sizes on the relative value of information (i.e., difference in expected traveler costs) between any two populations. This effect can be evaluated using a population-specific size threshold. One population enjoys a strictly positive value of information in comparison with the other if and only if its size is below the corresponding threshold. We also consider the situation when travelers may choose an information system based on its value and characterize the set of equilibrium adoption rates delineating the sizes of subscribed traveler populations. The resulting routing strategies are such that all travelers face an identical expected cost and no traveler has the incentive to change subscriptions.

Suggested Citation

  • Manxi Wu & Saurabh Amin & Asuman E. Ozdaglar, 2021. "Value of Information in Bayesian Routing Games," Operations Research, INFORMS, vol. 69(1), pages 148-163, January.
    • Handle: RePEc:inm:oropre:v:69:y:2021:i:1:p:148-163
    DOI: 10.1287/opre.2020.1999
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    References listed on IDEAS

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    Cited by:

    1. Furkan Sezer & Hossein Khazaei & Ceyhun Eksin, 2021. "Maximizing Social Welfare and Agreement via Information Design in Linear-Quadratic-Gaussian Games," Papers 2102.13047, arXiv.org, revised Feb 2023.
    2. Manxi Wu & Saurabh Amin & Asuman Ozdaglar, 2021. "Multi-agent Bayesian Learning with Best Response Dynamics: Convergence and Stability," Papers 2109.00719, arXiv.org.
    3. Frederic Koessler & Marco Scarsini & Tristan Tomala, 2021. "Correlated Equilibria in Large Anonymous Bayesian Games," Papers 2107.06312, arXiv.org, revised Sep 2023.
    4. Furkan Sezer & Ceyhun Eksin, 2022. "Information Preferences of Individual Agents in Linear-Quadratic-Gaussian Network Games," Papers 2203.13056, arXiv.org.
    5. Frederic Koessler & Marco Scarsini & Tristan Tomala, 2025. "Full Implementation via Information Design in Nonatomic Games," Papers 2502.05920, arXiv.org.
    6. Macault, Emilien & Scarsini, Marco & Tomala, Tristan, 2022. "Social learning in nonatomic routing games," Games and Economic Behavior, Elsevier, vol. 132(C), pages 221-233.

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