Value of Information in Bayesian Routing Games
Name
1808.10590.pdf
Description
Submitted version
Size
610.25 KB
Format
Adobe PDF
Checksum (MD5)
aa40e8558668ebc36dd09920503ebb40
Author(s)
Wu, Manxi
Amin, Saurabh
Ozdaglar, Asuman E
Date Issued
December 2020
Journal
Operations Research
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Manxi Wu, Saurabh Amin, Asuman E. Ozdaglar, Value of Information in Bayesian Routing Games. Operations Research 69(1):148-163 (2020) Copyright: © 2020 INFORMS
Version
Original manuscript
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.
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1287/OPRE.2020.1999
