Systems science is concerned with understanding systems, from life at simple level to other complex ever-evolving interactions across incredible spectra. This diverse spectrum ranges from mathematics to medicine, engineering, technology and science, spanning across formal, natural, social, applied and computational sciences with the aim of developing transformative interdisciplinary foundations and amalgamating thereof. It encompasses applications spanning across a variety of areas such as control theory, control systems, complex systems, computational systems, neural networks, social systems theory, systems biology, system dynamics, physical laws, intelligence systems, robotic systems, service systems, computer science, AI-driven systems, systems engineering, systems psychology in complex systems, nonlinear systems, stochastic processes, dynamical systems, chaotic systems, cybernetics, engineering theories, linguistics or systems theory, amongst the other ones. Furthermore, rigorous mathematical procedures and applications, as the driving force behind discovery and innovation, involve an extensive array of disciplines to highlight the multidisciplinary applications and universal character of mathematical science and modeling. The key aspects commonly highlighted in systems science signify and entail a holistic view, the importance of interaction between a system and its embedded environment and complex or subtle trajectories of dynamic behavior. With a focus on the advances in theory and applications of mathematics, science, engineering and medicine, it becomes a pivotal endeavor to sustain sets of system-of-systems interdisciplinary theory.
Given the breadth of the realms within those the topics addressed in a distinctive and / or merging pattern, the relationship between systems science and complex systems reveals the endeavors in conceptual analysis, detection of gaps, accuracy of expression, spotting conceptual links. While conceptual explanations are sought in alternative ways, philosophy including that of complex systems conveys that it can provide methods for the generation of new ideas, novel perspectives and critical thinking [Rovelli, C. (2018)], [Laplane, L. et al. (2019)]. Complex systems are those where large networks of components interact leading to complex collective behaviors, intricate processing of information as well as adaptation processes through evolution or learning. Complexity entails an entwining process of various fields with their constituents also being irreducibly entwined. Systems with the organized behavior arising without internal or external controllers are named as self-organizing systems. To make things more complicated, simple rules may yield complex behavior ways which are hard to predict. Within such configuration, such systems’ macroscopic behaviors are called emergent behaviors. A systems-oriented generalization should take into account the feedback loops’ presence, which are the micro components reacting to macro properties. Other elements to be considered are interaction among components belonging to different classes and subsystems’ layered interaction operating at various spatio-temporal scales which are known as the hierarchical organization.
Order, created out of disorder (entropy), flips over the usual turb of events where order decays and disorder surpass. Prediction of complex systems is compelling since slight changes may result in huge differences in outcomes. Long-term prediction due to sensitive dependence on initial conditions which require the future values based on the of observation of previous values and current values. With these aspects, “order in chaos” is a concept which emerges in universal properties overlapping with large sets of chaotic systems. Chaos theory, in this regard, is concerned with the behavior of non- linear dynamical systems under which circumstances that exhibit a phenomenon referred to as chaos is marked by sensitivity to initial conditions. Ergodicity, initial value sensitivity and unpredictability are among the key features of chaos theory [Karaca, Y. (2022)]. Life is said to exist at the edge of chaos, herewith a network becoming sufficiently complex signifies that it has a large number of nodes that control the other ones leading to the emerging of complex and self-organized behaviors. Life is innately inclined to get more complex regardless of any tendency of natural selection. For further details, please refer to...
Complex problems are characterized by nonlinearity, dynamic aspects, ambiguity, causality, chaos, nonlinearity, autopoiesis and transiency. Within this scope, each system entails its optimal solutions peculiar to its own distinctive parameters. The dissipative structure surrounding complexity transforms into work under progress to profoundly understand the world and the universe, transversing information into knowledge to create, maintain and expand the organized complexity of the systems. Complex systems and order, manifest multiple dynamical interactive components grounded on multiscale spatial and temporal fields, collaborating for the formation of an operational whole. Thus, universal order and complex universe require solutions and models to address the complexity challenge by self-organization, emergency, harmonization, adaptability and optimization. Alternative mathematical foundations and corresponding algorithmic models along with applicable and actionable solution-oriented schemes come to the foreground to address the related challenges of complexity and nonlinearity. Complex systems, wholes being more than the sum of their parts, are collections of dynamic components which upon interaction can produce novel system-level properties not directly explained based on components in isolation. Thus, systems science and complexity entail ever-evolving reliability, autonomy and performance. Highly analytic and numeric-based methods may simplify the problem to ensure analytical tractability. Yet, many natural systems and manufactured systems would not be analyzed without the multiple interacting frameworks.
Systems science and complex systems spectrum ranges from mathematics to medicine, engineering, technology and sciences. Thus, it can be aimed at tackling the systems distinctively as complex systems, dynamic systems, and many others by taking into consideration the parameters and needs of each system with a solution-oriented, tailored, precise and sophisticated approach. All these considerations and needs require the fostering the development of transformative interdisciplinary foundations and merging thereof. The applications can be observed in a variety of areas, while the profound analyses into systems sciences will enable understanding and realizing of the multiple layers of intelligence, as a thought-provoking vital subject matter owing to the requirement of systems science and complex systems considerations for perusing research and practice at different levels with different goals.
The overarching framework of systems science and complex systems may include scopes ranging across various disciplines some of which are biomedical engineering, bioinformatics, computational modeling as well as other related clinical and / or medical research applications. In view of the initial domains that include computational modeling as well as other related clinical/medical research applications, different but interrelated disciplines are to be considered so that the essential phenomena underlying the entire behaviors of systems science and complex systems at the base. Thus, elements like experimental evaluation and the way the proposed methodology are observed to be applicable to the distinctive discipline orientations.
Along with these points, the scopes involve the capabilities of predictability, estimation, interpretation, relying on the scope from mathematical sciences, computer sciences to applied sciences as well as engineering mathematics with nonlinear intelligence dynamics lie at the pedestal and intersection with different fields characterized by complex, chaotic, nonlinear, dynamic and transient components to reveal the significance of optimized approaches in real systems and related realms. Significant developments based on these critical points and perspectives, with mathematical modeling grounded in the integration of systems science and complex system dynamics, can pave the way for the future research towards the formulation, design, deployment, analysis and multi-criteria optimization, and extension of the predictability horizons of the applicable solutions to problems arising as formidable challenges in our ever-evolving and fast-changing global landscape. Through scientific perspectives, procedures and applications, as the driving force behind discovery and innovation, an extensive array of disciplines highlights the multidisciplinary applications and universal character of mathematical science and modeling. Across this line of thought, through the integrated solutions with the dynamic approach, critical multi-stage decision-making processes can be purported through the lenses of realistic, actionable and timely information-oriented optimal solutions to current challenging problems in a precise and tailored manner in systems sciences and complex systems. You may kindly resort to...
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