Control Meets Inference: Using Network Control to Uncover the Behaviour of Opponents

Area of Science:

• Complex Systems
• Network Science
• Control Theory

Background:

• Inferring parameters in dynamical systems from observational data is crucial for real-world applications.
• Networked agent models, such as opinion dynamics, are widely used to study complex interactions.
• Understanding and predicting the influence of unknown controllers is a significant challenge.

Purpose of the Study:

• To propose a framework for strategically influencing dynamical processes to enhance parameter inferability.
• To investigate how an active controller can infer a passive controller's influence in a networked opinion model.
• To develop methods for accelerating the convergence of parameter estimates through strategic interaction.

Main Methods:

• Modeling networked agents with opinion dynamics under peer and controller influence.
• Developing a framework for an active controller to infer a passive controller's influence.
• Proposing and evaluating two heuristic algorithms for optimal influence allocation.
• Analyzing the impact of network structure (degree heterogeneity) on predictability.

Main Results:

• The proposed algorithms significantly accelerate the inference process by strategically interacting with network dynamics.
• Agents with higher degrees and larger opponent allocations are found to be more difficult to predict.
• Opponent's influence is harder to predict in more degree-heterogeneous social networks, even with strategic allocations.

Conclusions:

• Strategic influence deployment is effective in accelerating parameter inference in dynamical systems.
• Network topology, particularly degree heterogeneity, critically impacts the predictability of agent behavior and controller influence.
• The framework provides a novel approach to enhance observability in complex networked systems.