Interleaved Information Structures in Dynamic Games: A General Framework with Application to the Linear-Quadratic Case
This addresses a gap in noncooperative dynamic game theory for realistic settings with arbitrary interleaved information, though it is incremental as it builds on existing canonical structures.
The paper tackles the problem of computing Nash equilibria in dynamic games with interleaved information structures, where agents observe subsets of others at each timestep, by introducing a framework using Mathematical Program Networks and deriving Riccati-like equations for linear-quadratic cases.
A fundamental problem in noncooperative dynamic game theory is the computation of Nash equilibria under different information structures, which specify the information available to each agent during decision-making. Prior work has extensively studied equilibrium solutions for two canonical information structures: feedback, where agents observe the current state at each time, and open-loop, where agents only observe the initial state. However, these paradigms are often too restrictive to capture realistic settings exhibiting interleaved information structures, in which each agent observes only a subset of other agents at every timestep. To date, there is no systematic framework for modeling and solving dynamic games under arbitrary interleaved information structures. To this end, we make two main contributions. First, we introduce a method to model deterministic dynamic games with arbitrary interleaved information structures as Mathematical Program Networks (MPNs), where the network structure encodes the informational dependencies between agents. Second, for linear-quadratic (LQ) dynamic games, we leverage the MPN formulation to develop a systematic procedure for deriving Riccati-like equations that characterize Nash equilibria. Finally, we illustrate our approach through an example involving three agents exhibiting a cyclic information structure.