Learning an Unknown Network State in Routing Games
Name
1-s2.0-S2405896319320324-main.pdf
Description
Published version
Size
613.11 KB
Format
Adobe PDF
Checksum (MD5)
3eca8f390884034a3a45c6702a93b318
Author(s)
Wu, Manxi
Amin, Saurabh
Date Issued
December 2019
Journal
IFAC-PapersOnLine
Publisher
Elsevier BV
Citation
Wu, Manxi, and Saurabh Amin. "Learning an Unknown Network State in Routing Games." IFAC-PapersOnLine, 52, 20 (2019): 345-350. © 2019 IFAC-PapersOnLine. All rights reserved.
Version
Final published version
Abstract
We study learning dynamics induced by myopic travelers who repeatedly play a routing game on a transportation network with an unknown state. The state impacts cost functions of one or more edges of the network. In each stage, travelers choose their routes according to Wardrop equilibrium based on public belief of the state. This belief is broadcasted by an information system that observes the edge loads and realized costs on the used edges, and performs a Bayesian update to the prior stage's belief. We show that the sequence of public beliefs and edge load vectors generated by the repeated play converge almost surely. In any rest point, travelers have no incentive to deviate from the chosen routes and accurately learn the true costs on the used edges. However, the costs on edges that are not used may not be accurately learned. Thus, learning can be incomplete in that the edge load vector at rest point and complete information equilibrium can be different. We present some conditions for complete learning and illustrate situations when such an outcome is not guaranteed.
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.ifacol.2019.12.179
