Interdisciplinary Modeling and Analysis of Complex Systems
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D: Statistics and Operational Research".
Deadline for manuscript submissions: 20 April 2026 | Viewed by 283
Special Issue Editors
Interests: complex systems; optimization; machine learning; multi-agent systems
Interests: signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Complex systems—characterized by emergent behavior, nonlinear dynamics, and intricate interdependencies—underpin critical challenges across global health, climate resilience, socio-technical networks, financial ecosystems, and biological processes. Understanding their structure, evolution, and resilience is essential for scientific advancement and sustainable societal solutions.
Traditional single-discipline approaches often fail to capture the multi-scale, adaptive nature of complex systems. The system modeling and system simulation of these systems demand the integration of theories and tools from mathematics, computer science, physics, engineering, economics, and social sciences. Novel synergies—such as merging complex networks theory with game theory or embedding AI agents within multi-agent systems frameworks—are vital for decoding emergent patterns and predicting systemic behaviors.
This Special Issue of Mathematics aims to bridge disciplinary silos by curating cutting-edge research at the intersection of computational mathematics and domain-specific complexity. We seek to establish a platform where methodological innovation meets real-world application, advancing rigorous, transferable frameworks for complex system analysis that transcend conventional boundaries.
We invite original research and reviews focusing on interdisciplinary methodologies for complex systems. Contributions may address (but are not limited to) the following areas:
- Cross-domain applications of complex networks, dynamical systems, or stochastic processes;
- Multi-agent systems enriched by cognitive science, behavioral economics, or AI agents;
- Hybrid system modeling (e.g., agent-based + network-based + data-driven);
- Co-design of models with domain experts (e.g., ecologists, epidemiologists, urban planners);
- Scalable system simulation for high-dimensional, adaptive systems;
- Uncertainty quantification, sensitivity analysis, and validation of cross-disciplinary models.
Prof. Dr. Qing Cai
Guest Editor
Dr. Xu Zhang
Guest Editor Assistant
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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
- system modeling
- system simulation
- complex networks
- complex systems
- multi-agent systems
- AI agents
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