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AI-Empowered Modeling and Simulation for Complex Systems
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AI-Empowered Modeling and Simulation for Complex Systems

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Complex Systems and Cybernetics".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 4291

Special Issue Editors

Dr. Hang Xiong

E-Mail Website
Guest Editor
Marco Agriculture Research Institute, Huazhong Agricultural University, Wuhan 430070, China
Interests: computational social science; digital economy
Dr. Quan Bai

E-Mail Website
Guest Editor
School of Information and Communication Technology, University of Tasmania, Hobart, TAS 7001, Australia
Interests: multi-agent systemsmachine learningdata miningartificial intelligence
Prof. Dr. Peng Lv

E-Mail Website
Guest Editor
School of Public Administration, Central South University, Changsha 410083, China
Interests: computational social science; social simulation; agent-based modeling
Dr. Zhou He

E-Mail Website
Guest Editor
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100043, China
Interests: decision science; Industry Internet; supply chain management; blockchain and fintech; agent-based modeling and simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI has emerged as a transformative force revolutionizing various fields, including modeling and simulation of complex systems. Complex systems, such as ecological networks, financial markets, and social dynamics, pose significant challenges in understanding, predicting, and optimizing processes within these systems. AI techniques, particularly those applied in large language models, have shown remarkable capabilities in handling the inherent complexity and nonlinearity of complex systems. Utilizing AI for modeling and simulation, researchers can develop more accurate predictions and gain deeper insights into the behavior of these systems. Furthermore, AI algorithms can optimize system performance, identify critical factors, and even suggest interventions to improve system outcomes.

This Special Issue provides a timely platform for the readers of Systems to showcase their latest advancements in AI-based modeling and simulation techniques for complex systems and their applications. It aims to bring together diverse perspectives, innovative methodologies, and practical applications, fostering collaborations and knowledge exchange among the community. By highlighting the latest research and developments, this Special Issue will contribute significantly to advancing the field of AI-empowered modeling and simulation, leading to better understanding, prediction, and optimization of complex systems. Moreover, the Special Issue is a valuable resource for academics, industry professionals, and policymakers, providing insights and guidance on how AI can be effectively leveraged to address real-world challenges associated with complex systems.

We welcome submissions from a wide range of domains, including but not limited to the topics in the following part:

  • Large language models in simulation;
  • AI-driven system modeling;
  • Complex system dynamics simulation;
  • Hybrid AI–physics modeling;
  • Deep learning in modeling and simulation;
  • Reinforcement learning for system control;
  • Natural language processing in system analysis;
  • Predictive modeling with AI;
  • AI-enhanced simulation accuracy;
  • Real-time simulation with AI
  • AI for multi-scale modeling;
  • Data-driven modeling techniques;
  • AI in risk assessment and prediction;
  • Agent-based modeling with AI;
  • AI for complex network modeling.

Dr. Hang Xiong
Dr. Quan Bai
Prof. Dr. Peng Lv
Dr. Zhou He
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI-driven system modeling
  • complex system dynamics simulation
  • predictive modeling with AI
  • data-driven modeling techniques
  • AI in risk assessment and prediction
  • agent-based modeling with AI
  • AI for complex network modeling etc.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

30 pages, 7055 KiB  
Article
A Hybrid AI Framework for Enhanced Stock Movement Prediction: Integrating ARIMA, RNN, and LightGBM Models
by Adel Alarbi, Wagdi Khalifa and Ahmad Alzubi
Systems 2025, 13(3), 162; https://doi.org/10.3390/systems13030162 - 26 Feb 2025
Viewed by 834
Abstract
Forecasting stock market movements is a critical yet challenging endeavor due to the inherent nonlinearity, chaotic behavior, and dynamic nature of financial markets. This study proposes the Autoregressive Integrated Moving Average Ensemble Recurrent Light Gradient Boosting Machine (AR-ERLM), an innovative model designed to [...] Read more.
Forecasting stock market movements is a critical yet challenging endeavor due to the inherent nonlinearity, chaotic behavior, and dynamic nature of financial markets. This study proposes the Autoregressive Integrated Moving Average Ensemble Recurrent Light Gradient Boosting Machine (AR-ERLM), an innovative model designed to enhance the precision and reliability of stock movement predictions. The AR-ERLM integrates ARIMA for identifying linear dependencies, RNN for capturing temporal dynamics, and LightGBM for managing large-scale datasets and non-linear relationships. Using datasets from Netflix, Amazon, and Meta platforms, the model incorporates technical indicators and Google Trends data to construct a comprehensive feature space. Experimental results reveal that the AR-ERLM outperforms benchmark models such as GA-XGBoost, Conv-LSTM, and ANN. For the Netflix dataset, the AR-ERLM achieved an RMSE of 2.35, MSE of 5.54, and MAE of 1.58, surpassing other models in minimizing prediction errors. Moreover, the model demonstrates robust adaptability to real-time data and consistently superior performance across multiple metrics. The findings emphasize AR-ERLM’s potential to enhance predictive accuracy, mitigating overfitting and reducing computational overhead. These implications are crucial for financial institutions and investors seeking reliable tools for risk assessment and decision-making. The study sets the foundation for integrating advanced AI models into financial forecasting, encouraging future exploration of hybrid optimization techniques to further refine predictive capabilities. Full article
(This article belongs to the Special Issue AI-Empowered Modeling and Simulation for Complex Systems)
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28 pages, 1347 KiB  
Article
Intelligent Assessment of Personal Credit Risk Based on Machine Learning
by Chuansheng Wang and Hang Yu
Systems 2025, 13(2), 112; https://doi.org/10.3390/systems13020112 - 12 Feb 2025
Viewed by 1000
Abstract
In the 21st-century global economy, the rapid growth of the finance industry, particularly in personal credit, fuels economic growth and market prosperity. However, the rapid expansion of personal credit business has brought explosive growth in the amount of data, which puts forward higher [...] Read more.
In the 21st-century global economy, the rapid growth of the finance industry, particularly in personal credit, fuels economic growth and market prosperity. However, the rapid expansion of personal credit business has brought explosive growth in the amount of data, which puts forward higher requirements for the risk management of financial institutions. To solve this problem, this paper constructs an intelligent evaluation model of personal credit risk under the background of big data. Firstly, based on the forest optimization feature selection algorithm, combined with initialization based on chi-square check, adaptive global seeding, and greedy search strategies, key risk factors are accurately identified from high-dimensional data. Then, the XGBoost algorithm is used to evaluate the credit risk level of customers, and the traditional Sparrow Search Algorithm is improved by using Tent chaotic mapping, sine and cosine search, reverse learning, and Cauchy mutation strategy to improve the optimization performance of algorithm parameters. Finally, using the Lending Club dataset for empirical analysis, the experiment shows that the model improves the accuracy of personal credit risk assessment and enhances the ability of risk control. Full article
(This article belongs to the Special Issue AI-Empowered Modeling and Simulation for Complex Systems)
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28 pages, 1451 KiB  
Article
LLM-AIDSim: LLM-Enhanced Agent-Based Influence Diffusion Simulation in Social Networks
by Lan Zhang, Yuxuan Hu, Weihua Li, Quan Bai and Parma Nand
Systems 2025, 13(1), 29; https://doi.org/10.3390/systems13010029 - 3 Jan 2025
Viewed by 1567
Abstract
This paper introduces an LLM-Enhanced Agent-Based Influence Diffusion Simulation (LLM-AIDSim) framework that integrates large language models (LLMs) into agent-based modelling to simulate influence diffusion in social networks. The proposed framework enhances traditional influence diffusion models by allowing agents to generate language-level responses, providing [...] Read more.
This paper introduces an LLM-Enhanced Agent-Based Influence Diffusion Simulation (LLM-AIDSim) framework that integrates large language models (LLMs) into agent-based modelling to simulate influence diffusion in social networks. The proposed framework enhances traditional influence diffusion models by allowing agents to generate language-level responses, providing deeper insights into user agent interactions. Our framework addresses the limitations of probabilistic models by simulating realistic, context-aware user behaviours in response to public statements. Using real-world news topics, we demonstrate the effectiveness of LLM-AIDSim in simulating topic evolution and tracking user discourse, validating its ability to replicate key aspects of real-world information propagation. Our experimental results highlight the role of influence diffusion in shaping collective discussions, revealing that, over time, diffusion narrows the focus of conversations around a few dominant topics. We further analyse regional differences in topic clustering and diffusion behaviours across three cities, Sydney, Auckland, and Hobart, revealing how demographics, income, and education levels influence topic dominance. This work underscores the potential of LLM-AIDSim as a decision-support tool for strategic communication, enabling organizations to anticipate and understand public sentiment trends. Full article
(This article belongs to the Special Issue AI-Empowered Modeling and Simulation for Complex Systems)
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