Scientific publications

We study an average-oriented opinion dynamics model where agents update their views through a discrete-time linear process over a social network. Within a multi-agent optimal control framework, we analyze and compare one-step-ahead, feedback, and hybrid control strategies. Each player minimizes an individual cost function while strategically influencing the networked evolution of opinions. Using the Hamilton-Jacobi-Bellman equation, we derive optimal controls and characterize the corresponding Nash equilibria. Numerical experiments compare the resulting opinion trajectories and costs, revealing clear trade-offs between control complexity and performance.