2021/01/31 by Hong Chen, Ee Hou Yong
Engineering · Mathematics · Physics and Astronomy · Psychology · Social Sciences · #Artificial intelligence #Collective behavior #Complex Network Analysis Techniques #Complex network #Complex system #Computer science #Conformity #Control (management) #Control engineering #Control theory (sociology) #Engineering #Evolutionary Game Theory and Cooperation #Flocking (texture) #Mathematics #Opinion Dynamics and Social Influence #Psychology #Scaling #Simulation #Social psychology #physics.soc-ph
paper · pdf · doi:10.1103/physreve.104.014301
published as Phys. Rev. E 104, 014301 (2021)
arxiv created 2021/07/02 · openalex publication_date 2021/07/02 · arxiv updated 2021/07/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
To understand controlling a complex system, an estimation of the required effort needed to achieve control is vital. Previous works have addressed this issue by studying the scaling laws of energy cost in a general way with continuous-time linear dynamics. However, continuous-time linear dynamics is unable to capture conformity behavior, which is common in many complex social systems. Therefore, to understand controlling social systems with conformity, discrete-time modeling is used and the energy cost scaling laws are derived. The results are validated numerically with model and real networks. In addition, the energy costs needed for controlling systems with and without conformity are compared, and it was found that controlling networked systems with conformity features always requires less control energy. Finally, it is shown through simulations that heterogeneous scale-free networks are less controllable, requiring a higher number of minimum drivers. Since the conformity-based model relates to various complex systems, such as flocking, or evolutionary games, the results of this paper represent a step forward toward developing realistic control of complex social systems.