ADVANCES IN COMPLEX SYSTEMS
Scope & Guideline
Advancing Knowledge in Multidisciplinary Complexity
Introduction
Aims and Scopes
- Complex Network Analysis:
The journal emphasizes the study of complex networks, including the structural properties, dynamics, and implications of networked systems across different contexts, such as social, biological, and technological networks. - Agent-Based Modeling:
A significant focus is placed on agent-based modeling (ABM) as a methodology for simulating interactions within complex systems, allowing researchers to explore emergent phenomena and the behavior of heterogeneous agents. - Human Behavior and Social Dynamics:
Research often investigates the dynamics of human behavior, including opinion formation, social influence, and the effects of social structures on individual and collective actions within complex systems. - Interdisciplinary Applications:
The journal promotes interdisciplinary studies that apply complex systems theory to various fields, such as economics, sociology, biology, and environmental science, highlighting its broad applicability. - Mathematical and Computational Approaches:
It encourages the use of mathematical modeling and computational techniques for analyzing complex systems, including statistical methods and algorithmic strategies for understanding system dynamics.
Trending and Emerging
- Machine Learning in Complex Systems:
Recent papers highlight the integration of machine learning technologies in analyzing and addressing challenges within complex systems, particularly in humanitarian work, indicating a growing intersection between AI and complex systems research. - Socio-Technical Systems:
There is an increasing emphasis on understanding socio-technical systems, particularly in the context of mobility and human behavior, suggesting a trend towards exploring how technology and society interact and influence each other. - Epidemic Modeling and Public Health:
The journal has seen a surge in research related to epidemic modeling, particularly in light of the COVID-19 pandemic, reflecting a critical interest in understanding disease dynamics within complex networks. - Cultural and Social Complexity:
Emerging themes surrounding cultural complexity and social networks indicate a growing interest in how cultural dynamics and social interactions shape complex systems, particularly in historical and contemporary contexts. - Resilience and Sustainability:
There is a notable trend towards exploring resilience and sustainability within complex systems, focusing on how systems adapt and respond to various challenges, including environmental and social changes.
Declining or Waning
- Classical Game Theory Applications:
While game theory remains relevant, the specific applications of classical game theory models, such as the Prisoner's Dilemma, appear to be less frequent, possibly as researchers explore more complex dynamics that incorporate additional factors and agent interactions. - Traditional Statistical Analysis:
The reliance on traditional statistical analysis methods for understanding complex systems seems to be waning, as there is a trend towards more sophisticated modeling techniques that better capture the intricacies of complex interactions. - Static Network Models:
There is a noticeable reduction in studies focusing solely on static network models, as researchers increasingly emphasize dynamic and multiplex networks that reflect real-world complexities more accurately.
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