fractal) Methodologically, social complexity is theory-neutral, meaning that it accommodates both local and global approaches to sociological research. by the researcher according to the level of description or explanation demanded by the research hypotheses. At the most localized level of analysis,
ethnographic,
participant- or non-participant observation,
content analysis and other
qualitative research methods may be appropriate. More recently, highly sophisticated
quantitative research methodologies are being developed and used in sociology at both local and global
levels of analysis. Such methods include (but are not limited to)
bifurcation diagrams,
network analysis,
non-linear modeling, and
computational models including
cellular automata programming,
sociocybernetics and other methods of
social simulation.
Complex social network analysis Complex
social network analysis is used to study the dynamics of large, complex social networks.
Dynamic network analysis brings together traditional
social network analysis,
link analysis and
multi-agent systems within
network science and
network theory. Through the use of key concepts and methods in
social network analysis,
agent-based modeling, theoretical
physics, and modern
mathematics (particularly
graph theory and
fractal geometry), this method of inquiry brought insights into the dynamics and structure of social systems. New computational methods of localized social network analysis are coming out of the work of
Duncan Watts,
Albert-László Barabási,
Nicholas A. Christakis,
Kathleen Carley and others. New methods of global network analysis are emerging from the work of
John Urry and the sociological study of globalization, linked to the work of
Manuel Castells and the later work of
Immanuel Wallerstein. Since the late 1990s, Wallerstein increasingly makes use of complexity theory, particularly the work of
Ilya Prigogine. Dynamic social network analysis is linked to a variety of methodological traditions, above and beyond
systems thinking, including
graph theory, traditional
social network analysis in sociology, and
mathematical sociology. It also links to
mathematical chaos and
complex dynamics through the work of
Duncan Watts and
Steven Strogatz, as well as fractal geometry through
Albert-László Barabási and his work on
scale-free networks.
Computational sociology The development of
computational sociology involves such scholars as
Nigel Gilbert,
Klaus G. Troitzsch,
Joshua M. Epstein, and others. The foci of methods in this field include
social simulation and
data-mining, both of which are sub-areas of computational sociology. Social simulation uses computers to create an artificial laboratory for the study of complex social systems;
data-mining uses machine intelligence to search for non-trivial patterns of relations in large, complex, real-world databases. The emerging methods of
socionics are a variant of computational sociology. Computational sociology is influenced by a number of micro-sociological areas as well as the macro-level traditions of systems science and systems thinking. The micro-level influences of
symbolic interaction,
exchange, and
rational choice, along with the micro-level focus of computational political scientists, such as
Robert Axelrod, helped to develop computational sociology's
bottom-up,
agent-based approach to modeling complex systems. This is what
Joshua M. Epstein calls
generative science. Sociocybernetics is directly tied to
systems thought inside and outside of sociology, specifically in the area of second-order cybernetics. ==Areas of application==