Data-driven stochastic simulation leading to the allometric scaling laws in complex systems.
Phys Rev E
; 106(6-1): 064304, 2022 Dec.
Article
en En
| MEDLINE
| ID: mdl-36671187
We propose a data-driven stochastic method that allows the simulation of a complex system's long-term evolution. Given a large amount of historical data on trajectories in a multi-dimensional phase space, our method simulates the time evolution of a system based on a random selection of partial trajectories in the data without detailed knowledge of the system dynamics. We apply this method to a large data set of time evolution of approximately one million business firms for a quarter century. Accordingly, from simulations starting from arbitrary initial conditions, we obtain a stationary distribution in three-dimensional log-size phase space, which satisfies the allometric scaling laws of three variables. Furthermore, universal distributions of fluctuation around the scaling relations are consistent with the empirical data.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Simulación por Computador
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Phys Rev E
Año:
2022
Tipo del documento:
Article
País de afiliación:
Japón
Pais de publicación:
Estados Unidos