Learning to Infer Structures of Network Games
This addresses a key limitation in economics and social sciences where utility functions are often unknown, enabling more practical applications of network game models.
The paper tackles the problem of inferring network structure from observed equilibrium actions in network games without requiring knowledge of the utility function, and demonstrates effectiveness with superior performance over existing methods on synthetic and real-world data.
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.