Optimal Intervention in Non-Binary Super-Modular Games
(2023) In IEEE Control Systems Letters 7. p.2353-2358- Abstract
We study intervention design problems for general finite non-binary super-modular games. The considered interventions consist in constraining or incentivizing the players to play actions above designed lower bounds, with a cost for the system planner that is a separable increasing function of such bounds. We study the intervention of minimum cost for which a best response learning algorithm leads the system to its greatest Nash equilibrium. We show that, if the utility functions are unimodal, then the optimal intervention problem can be reformulated in terms of improvement paths, leading to a low complexity distributed iterative algorithm for its solution.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/73da4c20-708f-48da-83d3-83da520ab363
- author
- Messina, Sebastiano ; Como, Giacomo LU ; Durand, Stephane and Fagnani, Fabio
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- equilibrium selection, Network games, network intervention, optimal targeting, super-modular games
- in
- IEEE Control Systems Letters
- volume
- 7
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85162701534
- ISSN
- 2475-1456
- DOI
- 10.1109/LCSYS.2023.3285708
- language
- English
- LU publication?
- yes
- id
- 73da4c20-708f-48da-83d3-83da520ab363
- date added to LUP
- 2023-10-09 12:06:34
- date last changed
- 2023-10-13 11:51:53
@article{73da4c20-708f-48da-83d3-83da520ab363, abstract = {{<p>We study intervention design problems for general finite non-binary super-modular games. The considered interventions consist in constraining or incentivizing the players to play actions above designed lower bounds, with a cost for the system planner that is a separable increasing function of such bounds. We study the intervention of minimum cost for which a best response learning algorithm leads the system to its greatest Nash equilibrium. We show that, if the utility functions are unimodal, then the optimal intervention problem can be reformulated in terms of improvement paths, leading to a low complexity distributed iterative algorithm for its solution.</p>}}, author = {{Messina, Sebastiano and Como, Giacomo and Durand, Stephane and Fagnani, Fabio}}, issn = {{2475-1456}}, keywords = {{equilibrium selection; Network games; network intervention; optimal targeting; super-modular games}}, language = {{eng}}, pages = {{2353--2358}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{IEEE Control Systems Letters}}, title = {{Optimal Intervention in Non-Binary Super-Modular Games}}, url = {{http://dx.doi.org/10.1109/LCSYS.2023.3285708}}, doi = {{10.1109/LCSYS.2023.3285708}}, volume = {{7}}, year = {{2023}}, }