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Computational sociology

Sociologists increasingly draw upon computationally intensive methods to analyse and model social phenomena. Using computer simulations, artificial intelligence, text mining, complex statistical methods, and new analytic approaches like social network analysis and social sequence analysis, computational sociology develops and tests theories of complex social processes through bottom-up modelling of social interactions.

Although the subject matter and methodologies in social science differ from those in natural science or computer science, several of the approaches used in contemporary social simulation originated from fields such as physics and artificial intelligence. By the same token, some of the approaches that originated in computational sociology have been imported into the natural sciences, such as measures of network centrality from the fields of social network analysis and network science. In relevant literature, computational sociology is often related to the study of social complexity. Social complexity concepts such as complex systems, non-linear interconnection among macro and micro process, and emergence, have entered the vocabulary of computational sociology. A practical and well-known example is the construction of a computational model in the form of an " artificial society ", by which researchers can analyse the structure of a social system.