Learning to infer structures of network games

Conference item

Strategic interactions between a group of individuals or organisations can be modelled as games played on networks, where a player’s payoff depends not only on their actions but also on those of their neighbours. Inferring the network structure from observed game outcomes (equilibrium actions) is an important problem with numerous potential applications in economics and social sciences. Existing methods mostly require the knowledge of the utility function associated with the game, which is often unrealistic to obtain in real-world scenarios. We adopt a transformer-like architecture which correctly accounts for the symmetries of the problem and learns a mapping from the equilibrium actions to the network structure of the game without explicit knowledge of the utility function. We test our method on three different types of network games using both synthetic and real-world data, and demonstrate its effectiveness in network structure inference and superior performance over existing methods.

Authors: Rossi, E; Monti, F; Leng, Y; Bronstein, MM; Dong, X

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Journal of Machine Learning Research
• Host title: Proceedings of the 39th International Conference on Machine Learning
• Pages: 18809-18827
• Series: Proceedings of Machine Learning Research
• Series number: 162
• Publication date: 2022-06-28
• Event title: 39th International Conference on Machine Learning (ICML 2022)
• Event location: Baltimore, Maryland, USA
• Event website: https://icml.cc/Conferences/2022
• Event start date: 2022-07-17
• Event end date: 2022-07-23
• ISSN: 2640-3498
• Language: English
• Keywords: FFR