Social Sciences
“Man, the molecule of society, is the subject of social science.” - Henry Charles Carey
Social science includes the exploration of social interactions and their implications for the society. Not being either very predictive or quantitative, social science is acknowledged to be descriptive and qualitative. With the advent of abundance of multidimensional and longitudinal big data in contrast with the previous times with shortage of data, the centuries- old data issue regarding social sciences has started to fade away. Social processes are often said to be hard to understand mathematically since they have properties like being out-of- equilibrium, path-dependent, evolutionary and context-dependent, so high-dimensional analyses and interactions on multiple levels are required. When we think about systems in a formal sense, it is seen that their organization is coherent with interconnected sets of parts, which perform a function when they operate together.
Thus, it can be possible to introduce and implement a formal approach to systems thinking, providing complex systems analysis and drawing a conclusion from the lenses of an in-depth examples that can convert complex systems thinking into action both in a research context and practice area. Consequently, multifaceted data analysis and computational aspects of social sciences deal with another problem of the field, which is related to lack of repeatability and reproducibility. Through these elements, it is attempted to bridge the gap in the domain so that social processes can be understood based on mathematics-driven informed frameworks by paying attention to the aforementioned complex properties.
In the area of computational social science, as an interdisciplinary endeavor, uniting together important research across different strata of social sciences such as sociology, economics, linguistics, psychology and other related disciplines along with the contributions from computer science, data science, biology, mathematics, physics, management science, bioinformatics, virus propagation models, ecosystem dynamics, control, population dynamics, social media dynamics, and so forth using computational approaches and critical theoretical components can be addressed for the analysis of social and / or economic phenomena or structures in a more accurate and enlightening for researchers and professionals.
In economics, conventional way of thinking is constrained by “old physics” way by identifying the enduring laws so that complexity observed can be grasped. Yet, as economic systems with their difference due to being reflexive, physical laws may not relevant all the time since humans can create artificial structures negating the impact of a physical law. Thus, complexity and complex thinking will pave the way for a historical process by which the organized complexity of dissipative structures’ evolution can be understood. To illustrate, the applicability of complex systems theory in economics is evaluated and compared based on standard approaches based on limited optimization. However, the upper hand of knowing complex system systems within economic context is that the analysis is done from a network perspective instead of the production and utility function perspective. why it is necessary to approach economic. In this manner, it will be possible to establish new connections between different interacting elements. The methods and fields related include but are not limited to large-scale data, simulations, different computational approaches, complex systems, risk management, artificial intelligence, high-performance computing, text analytics, urban planning, financial networks, economic modeling, numerical optimization, image processing, computational linguistics and simulation-based statistical inference.
As for social life, complex adaptive systems based on computational models will be provided with accessible and reliable accounts so that some of the most intriguing theoretical and practical challenges that confront the social sciences will be enlightened focusing on their significant aspects such as self-organized criticality, emergence, diversity, adaptation, networks and feedback among others. The related part is directed toward the way complex adaptive systems can be explored through the employment of methods ranging from mathematics to computational models of adaptive components.
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