Convergence of Time-Delayed Opinion Dynamics with Complex Interaction Types

In opinion dynamics, time delays in agent-to-agent interactions are ubiquitous, which can significantly affect the dynamical processes rooted in agents’ opinion exchange, decision-making, and feedback mechanisms. However, a thorough comprehension of quantitative impacts of time delays on opinion evolution, considering diverse interaction types and system dynamics, remains to be developed. In this paper, we conduct a systematic investigation into the convergence and the associated rate of time-delayed opinion dynamics with diverse interaction types in both discrete-time and continuous-time systems. For the discrete-time system considered in this paper, we first establish sufficient conditions for convergence over signed interaction networks, showing that convergence depends solely on network topology and is independent of the delay magnitude. Subsequently, we examine the influence of random and other interaction types on the convergence rate and discover that time delays tend to decelerate this rate. Regarding the continuous-time system considered in this paper, we derive the feasible domain of the delay that ensures the convergence of opinion dynamics, revealing that, unlike the discrete-time scenario, large time delays can instigate the divergence of opinions. Specifically, we prove that for both random and other interaction types, small delays can expedite the convergence of continuous-time system. Finally, we present simulation examples to demonstrate the effectiveness and robustness of our research findings.