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Summary
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This study introduces a framework to improve parameter inference in dynamical systems by strategically influencing network dynamics. Optimized control strategies accelerate the estimation of unknown influences in networked opinion models.

Keywords:
complex networksnetwork controlnetwork inferencevoting dynamics

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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.