Complex systems, characterized by intricate interactions among numerous entities, give rise to emergent behaviors whose data-driven modeling and control are of utmost significance, especially when there is abundant observational data but the intervention cost is high. Traditional methods rely on precise dynamical models or require extensive intervention data, often falling short in real-world applications. To bridge this gap, we consider a specific setting of the complex systems control problem: how to control complex systems through a few online interactions on some intervenable nodes when abundant observational data from natural evolution is available. We introduce a two-stage model predictive complex system control framework, comprising an offline pre-training phase that leverages rich observational data to capture spontaneous evolutionary dynamics and an online fine-tuning phase that uses a variant of model predictive control to implement intervention actions. To address the high-dimensional nature of the state-action space in complex systems, we propose a novel approach employing action-extended graph neural networks to model the Markov decision process of complex systems and design a hierarchical action space for learning intervention actions. This approach performs well in three complex system control environments: Boids, Kuramoto, and Susceptible-Infectious-Susceptible (SIS) metapopulation. It offers accelerated convergence, robust generalization, and reduced intervention costs compared to the baseline algorithm. This work provides valuable insights into controlling complex systems with high-dimensional state-action spaces and limited intervention data, presenting promising applications for real-world challenges.
Skip Nav Destination
Article navigation
September 2024
Research Article|
September 19 2024
Model predictive complex system control from observational and interventional data
Special Collection:
Data-Driven Models and Analysis of Complex Systems
Muyun Mou
;
Muyun Mou
(Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing)
1
School of Systems Science, Beijing Normal University
, Beijing 100875, China
2
National Key Laboratory for Novel Software Technology, Nanjing University
, Nanjing 210008, China
Search for other works by this author on:
Yu Guo
;
Yu Guo
(Conceptualization, Validation, Writing – review & editing)
3
School of Reliability and Systems Engineering, Beihang University
, Beijing 100191, China
Search for other works by this author on:
Fanming Luo
;
Fanming Luo
(Methodology, Validation, Writing – review & editing)
2
National Key Laboratory for Novel Software Technology, Nanjing University
, Nanjing 210008, China
Search for other works by this author on:
Yang Yu
;
Yang Yu
(Conceptualization, Methodology, Resources)
2
National Key Laboratory for Novel Software Technology, Nanjing University
, Nanjing 210008, China
Search for other works by this author on:
Jiang Zhang
Jiang Zhang
a)
(Conceptualization, Funding acquisition, Methodology, Supervision, Writing – review & editing)
1
School of Systems Science, Beijing Normal University
, Beijing 100875, China
4
Swarma Research
, Beijing 100085, China
a)Author to whom correspondence should be addressed: [email protected]
Search for other works by this author on:
a)Author to whom correspondence should be addressed: [email protected]
Chaos 34, 093125 (2024)
Article history
Received:
December 31 2023
Accepted:
August 06 2024
Citation
Muyun Mou, Yu Guo, Fanming Luo, Yang Yu, Jiang Zhang; Model predictive complex system control from observational and interventional data. Chaos 1 September 2024; 34 (9): 093125. https://doi.org/10.1063/5.0195208
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
116
Views
Citing articles via
Response to music on the nonlinear dynamics of human fetal heart rate fluctuations: A recurrence plot analysis
José Javier Reyes-Lagos, Hugo Mendieta-Zerón, et al.
Synchronization in spiking neural networks with short and long connections and time delays
Lionel Kusch, Martin Breyton, et al.
Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology
Eugene Tan, Shannon Algar, et al.
Related Content
Reservoir computing with swarms
Chaos (March 2021)
Mobility and density induced amplitude death in metapopulation networks of coupled oscillators
Chaos (November 2014)
Photoinduced dichroism and its low-temperature characteristics in obliquely deposited amorphous As–Ge–Se–S thin films
J. Vac. Sci. Technol. A (March 2000)
Epidemic spreading on metapopulation networks including migration and demographics
Chaos (August 2018)
On epidemic spreading in metapopulation networks with time-varying contact patterns
Chaos (September 2023)