Journal of Systems Science & Complexity

Scope & Guideline

Connecting Disciplines to Illuminate Complex Systems.

Introduction

Welcome to the Journal of Systems Science & Complexity information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of Journal of Systems Science & Complexity, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN1009-6124
PublisherSPRINGER HEIDELBERG
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2006 to 2024
AbbreviationJ SYST SCI COMPLEX / J. Syst. Sci. Complex.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressTIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY

Aims and Scopes

The Journal of Systems Science & Complexity focuses on the integration of systems science principles with complex systems analysis, emphasizing both theoretical and applied aspects. The journal provides a platform for researchers to share innovative methodologies and findings that advance the understanding of complex systems across various disciplines.
  1. Systems Theory and Control:
    Research articles focusing on the development and application of systems theory and control strategies, including nonlinear control, adaptive control, and event-triggered control mechanisms.
  2. Complex Networks and Multi-Agent Systems:
    Studies exploring the dynamics, stability, and control of complex networks and multi-agent systems, often highlighting cooperative behaviors and distributed algorithms.
  3. Statistical Methods and Data Analysis:
    Papers that employ advanced statistical techniques to analyze complex data, including high-dimensional data, time series analysis, and machine learning approaches.
  4. Optimization Techniques:
    Research on optimization methodologies applied to complex systems, including resource allocation, stochastic optimization, and game theory.
  5. Mathematical Modeling:
    Development of mathematical models to describe various phenomena in complex systems, such as differential equations, stochastic processes, and control systems.
  6. Applications in Engineering and Economics:
    Practical applications of systems science and complexity theories in fields such as engineering, economics, finance, and environmental science.
The Journal of Systems Science & Complexity is witnessing a shift towards several emerging themes that reflect the current trends in systems science and complexity research. This section outlines these trending areas, showcasing their relevance and potential impact.
  1. Physics-Informed Neural Networks (PINNs):
    A growing trend in utilizing physics-informed neural networks for solving differential equations and modeling complex systems, reflecting an intersection of machine learning and physics.
  2. Event-Triggered and Adaptive Control Systems:
    An increasing focus on event-triggered control strategies and adaptive systems that can respond dynamically to changing environments or system states, enhancing efficiency and robustness.
  3. Data-Driven Approaches in Control and Optimization:
    Research that emphasizes data-driven methods for control, optimization, and system identification, showcasing the importance of big data and machine learning in systems science.
  4. Game Theory and Cooperative Strategies:
    A rising interest in applying game theory to multi-agent systems and complex networks, particularly in understanding cooperative behaviors and strategic interactions.
  5. Sustainable Systems and Risk Management:
    Emerging themes related to sustainability, particularly in resource allocation and risk management within complex systems, reflecting global challenges such as climate change and economic stability.

Declining or Waning

As the field evolves, certain themes within the Journal of Systems Science & Complexity appear to be losing prominence. This section highlights these waning areas of research based on recent publication trends.
  1. Traditional Control Theory:
    While control theory remains a core focus, traditional methods without innovative adaptations or integrations with complex systems are being overshadowed by more advanced approaches, such as adaptive and event-triggered control.
  2. Basic Statistical Methods:
    Basic statistical analysis techniques are less frequently published, as the journal increasingly favors complex, high-dimensional, and cutting-edge statistical methodologies.
  3. Static Optimization Problems:
    Static optimization problems that do not incorporate dynamic or complex system elements are becoming less common, as the journal shifts towards dynamic and adaptive optimization frameworks.
  4. Deterministic Models without Complexity Considerations:
    Deterministic models that do not account for uncertainty or complex interactions are seeing a decline, as the research community increasingly emphasizes the importance of stochastic modeling and uncertainty quantification.

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