Information Design in Bayesian Routing Games
(2023) 62nd IEEE Conference on Decision and Control, CDC 2023 p.3945-3949- Abstract
We study optimal information provision in transportation networks when users are strategic and the network state is uncertain. An omniscient planner observes the network state and discloses information to the users with the goal of minimizing the expected travel time at the user equilibrium. Public signal policies, including full-information disclosure, are known to be inefficient in achieving optimality. For this reason, we focus on private signals and restrict without loss of generality the analysis to signals that coincide with path recommendations that satisfy obedience constraints, namely users have no incentive in deviating from the received recommendation according to their posterior belief. We first formulate the general problem... (More)
We study optimal information provision in transportation networks when users are strategic and the network state is uncertain. An omniscient planner observes the network state and discloses information to the users with the goal of minimizing the expected travel time at the user equilibrium. Public signal policies, including full-information disclosure, are known to be inefficient in achieving optimality. For this reason, we focus on private signals and restrict without loss of generality the analysis to signals that coincide with path recommendations that satisfy obedience constraints, namely users have no incentive in deviating from the received recommendation according to their posterior belief. We first formulate the general problem and analyze its properties for arbitrary network topologies and delay functions. Then, we consider the case of two parallel links with affine delay functions, and provide sufficient conditions under which optimality can be achieved by information design. Interestingly, we observe that the system benefits from uncer-tainty, namely it is easier for the planner to achieve optimality when the variance of the uncertain parameters is large. We then provide an example where optimality can be achieved even if the sufficient conditions for optimality are not met.
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- author
- Cianfanelli, Leonardo ; Ambrogio, Alexia and Como, Giacomo LU
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Bayesian routing games, Information design
- host publication
- Proceedings of the IEEE Conference on Decision and Control
- pages
- 5 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 62nd IEEE Conference on Decision and Control, CDC 2023
- conference location
- Singapore, Singapore
- conference dates
- 2023-12-13 - 2023-12-15
- external identifiers
-
- scopus:85184811066
- ISBN
- 9798350301243
- DOI
- 10.1109/CDC49753.2023.10384300
- language
- English
- LU publication?
- yes
- id
- 953e14a1-4ef6-4cad-bd0f-5e9185dc2d73
- date added to LUP
- 2024-02-26 16:13:40
- date last changed
- 2025-10-14 10:12:56
@inproceedings{953e14a1-4ef6-4cad-bd0f-5e9185dc2d73,
abstract = {{<p>We study optimal information provision in transportation networks when users are strategic and the network state is uncertain. An omniscient planner observes the network state and discloses information to the users with the goal of minimizing the expected travel time at the user equilibrium. Public signal policies, including full-information disclosure, are known to be inefficient in achieving optimality. For this reason, we focus on private signals and restrict without loss of generality the analysis to signals that coincide with path recommendations that satisfy obedience constraints, namely users have no incentive in deviating from the received recommendation according to their posterior belief. We first formulate the general problem and analyze its properties for arbitrary network topologies and delay functions. Then, we consider the case of two parallel links with affine delay functions, and provide sufficient conditions under which optimality can be achieved by information design. Interestingly, we observe that the system benefits from uncer-tainty, namely it is easier for the planner to achieve optimality when the variance of the uncertain parameters is large. We then provide an example where optimality can be achieved even if the sufficient conditions for optimality are not met.</p>}},
author = {{Cianfanelli, Leonardo and Ambrogio, Alexia and Como, Giacomo}},
booktitle = {{Proceedings of the IEEE Conference on Decision and Control}},
isbn = {{9798350301243}},
keywords = {{Bayesian routing games; Information design}},
language = {{eng}},
pages = {{3945--3949}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Information Design in Bayesian Routing Games}},
url = {{http://dx.doi.org/10.1109/CDC49753.2023.10384300}},
doi = {{10.1109/CDC49753.2023.10384300}},
year = {{2023}},
}