Social complexity

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Social complexity: The infrastructure of train tracks through the Clapham Junction railway station, UK, is analogous to the complexity of the society served by the railroad. Clapham Junction - 49317028078.jpg
Social complexity: The infrastructure of train tracks through the Clapham Junction railway station, UK, is analogous to the complexity of the society served by the railroad.

In sociology, social complexity is a conceptual framework used in the analysis of society. In the sciences, contemporary definitions of complexity are found in systems theory, wherein the phenomenon being studied has many parts and many possible arrangements of the parts; simultaneously, what is complex and what is simple are relative and change in time. [1]

Contents

Contemporary usage of the term complexity specifically refers to sociologic theories of society as a complex adaptive system, however, social complexity and its emergent properties are recurring subjects throughout the historical development of social philosophy and the study of social change. [2]

Early theoreticians of sociology, such as Ferdinand Tönnies, Émile Durkheim, and Max Weber, Vilfredo Pareto and Georg Simmel, examined the exponential growth and interrelatedness of social encounters and social exchanges. The emphases on the interconnectivity among social relationships, and the emergence of new properties within society, is found in the social theory produced in the subfields of sociology. [3] Social complexity is a basis for the connection of the phenomena reported in microsociology and macrosociology, and thus provides an intellectual middle-range for sociologists to formulate and develop hypotheses. [4] [5] Methodologically, social complexity is theory-neutral, and includes the phenomena studied in microsociology and the phenomena studied in macrosociology. [2]

Theoretic background

In 1937, the sociologist Talcott Parsons continued the work of the early theoreticians of sociology with his work on action theory; [6] and by 1951, Parson had developed action theory into formal systems theory in The Social System (1951). [7] In the following decades, the synergy between general systems thinking and the development of social system theories is carried forward by Robert K. Merton in discussions of theories of the middle-range and social structure and agency. From the late 1970s until the early 1990s, sociological investigation concerned the properties of systems in which the strong correlation of sub-parts leads to the observation of autopoetic, self-organizing, dynamical, turbulent, and chaotic behaviours that arise from mathematical complexity, such as the work of Niklas Luhmann. [8]

One of the earliest usages of the term "complexity", in the social and behavioral sciences, to refer specifically to a complex system is found in the study of modern organizations and management studies. [9] However, particularly in management studies, the term often has been used in a metaphorical rather than in a qualitative or quantitative theoretical manner. [2] By the mid-1990s, the "complexity turn" [10] in social sciences begins as some of the same tools generally used in complexity science are incorporated into the social sciences. By 1998, the international, electronic periodical, Journal of Artificial Societies and Social Simulation , had been created. In the last several years, many publications have presented overviews of complexity theory within the field of sociology. Within this body of work, connections also are drawn to yet other theoretical traditions, including constructivist epistemology and the philosophical positions of phenomenology, postmodernism and critical realism.

Methodologies

Illustration of complexity (Penrose tiling fractal) Penrose tiling.gif
Illustration of complexity (Penrose tiling fractal)

Methodologically, social complexity is theory-neutral, meaning that it accommodates both local and global approaches to sociological research. [2] The very idea of social complexity arises out of the historical-comparative methods of early sociologists; obviously, this method is important in developing, defining, and refining the theoretical construct of social complexity. As complex social systems have many parts and there are many possible relationships between those parts, appropriate methodologies are typically determined to some degree by the research level of analysis differentiated [11] 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. [12] 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. [13] [14] [15] 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. [16] [17]

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. [17] Other important areas of influence include statistics, mathematical modeling and computer simulation.

Sociocybernetics

Sociocybernetics integrates sociology with second-order cybernetics and the work of Niklas Luhmann, along with the latest advances in complexity science. In terms of scholarly work, the focus of sociocybernetics has been primarily conceptual and only slightly methodological or empirical. [18] Sociocybernetics is directly tied to systems thought inside and outside of sociology, specifically in the area of second-order cybernetics.

Areas of application

In the first decade of the 21st century, the diversity of areas of application has grown [19] as more sophisticated methods have developed. Social complexity theory is applied in studies of social cooperation and public goods; [20] altruism; [21] education; [22] global civil society [23] collective action and social movements; [24] [25] social inequality; [26] workforce and unemployment; [27] [28] policy analysis; [29] [30] health care systems; [31] and innovation and social change, [32] [33] to name a few. A current international scientific research project, the Seshat: Global History Databank, was explicitly designed to analyze changes in social complexity from the Neolithic Revolution until the Industrial Revolution.

As a middle-range theoretical platform, social complexity can be applied to any research in which social interaction or the outcomes of such interactions can be observed, but particularly where they can be measured and expressed as continuous or discrete data points. One common criticism often cited regarding the usefulness of complexity science in sociology is the difficulty of obtaining adequate data. [34] Nonetheless, application of the concept of social complexity and the analysis of such complexity has begun and continues to be an ongoing field of inquiry in sociology. From childhood friendships and teen pregnancy [2] to criminology [35] and counter-terrorism, [36] theories of social complexity are being applied in almost all areas of sociological research.

In the area of communications research and informetrics, the concept of self-organizing systems appears in mid-1990s research related to scientific communications. [37] Scientometrics and bibliometrics are areas of research in which discrete data are available, as are several other areas of social communications research such as sociolinguistics. [2] Social complexity is also a concept used in semiotics. [38]

See also

Related Research Articles

Complexity characterises the behaviour of a system or model whose components interact in multiple ways and follow local rules, leading to non-linearity, randomness, collective dynamics, hierarchy, and emergence.

Systems theory is the transdisciplinary study of systems, i.e. cohesive groups of interrelated, interdependent components that can be natural or artificial. Every system has causal boundaries, is influenced by its context, defined by its structure, function and role, and expressed through its relations with other systems. A system is "more than the sum of its parts" by expressing synergy or emergent behavior.

<span class="mw-page-title-main">Social dynamics</span> Study of behavior of groups

Social dynamics is the study of the behavior of groups and of the interactions of individual group members, aiming to understand the emergence of complex social behaviors among microorganisms, plants and animals, including humans. It is related to sociobiology but also draws from physics and complex system sciences. In the last century, sociodynamics was viewed as part of psychology, as shown in the work: "Sociodynamics: an integrative theorem of power, authority, interfluence and love". In the 1990s, social dynamics began being viewed as a separate scientific discipline[By whom?]. An important paper in this respect is: "The Laws of Sociodynamics". Then, starting in the 2000s, sociodynamics took off as a discipline of its own, many papers were released in the field in this decade.

A complex system is a system composed of many components which may interact with each other. Examples of complex systems are Earth's global climate, organisms, the human brain, infrastructure such as power grid, transportation or communication systems, complex software and electronic systems, social and economic organizations, an ecosystem, a living cell, and ultimately the entire universe.

<span class="mw-page-title-main">Outline of academic disciplines</span> Overviews of and topical guides to academic disciplines

The following outline is provided as an overview of and topical guide to academic disciplines:

Social simulation is a research field that applies computational methods to study issues in the social sciences. The issues explored include problems in computational law, psychology, organizational behavior, sociology, political science, economics, anthropology, geography, engineering, archaeology and linguistics.

<span class="mw-page-title-main">Systems science</span> Study of the nature of systems

Systems science, also referred to as systems research, or, simply, systems, is a transdisciplinary field concerned with understanding systems—from simple to complex—in nature, society, cognition, engineering, technology and science itself. The field is diverse, spanning the formal, natural, social, and applied sciences.

<span class="mw-page-title-main">Computational sociology</span> Branch of the discipline of sociology

Computational sociology is a branch of sociology that uses computationally intensive methods to analyze and model social phenomena. Using computer simulations, artificial intelligence, complex statistical methods, and analytic approaches like social network analysis, computational sociology develops and tests theories of complex social processes through bottom-up modeling of social interactions.

An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents in order to understand the behavior of a system and what governs its outcomes. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to understand the stochasticity of these models. Particularly within ecology, ABMs are also called individual-based models (IBMs). A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used in many scientific domains including biology, ecology and social science. Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.

<span class="mw-page-title-main">Mathematical sociology</span> Interdisciplinary field of research

Mathematical sociology or the sociology of mathematics is an interdisciplinary field of research concerned both with the use of mathematics within sociological research as well as research into the relationships that exist between maths and society.

Dynamic network analysis (DNA) is an emergent scientific field that brings together traditional social network analysis (SNA), link analysis (LA), social simulation and multi-agent systems (MAS) within network science and network theory. Dynamic networks are a function of time to a set of graphs; for each time point there is a graph. This is akin to the definition of dynamical systems, in which the function is from time to an ambient space, where instead of ambient space time is translated to relationships between pairs of vertices.

Computer simulation is a prominent method in organizational studies and strategic management. While there are many uses for computer simulation, most academics in the fields of strategic management and organizational studies have used computer simulation to understand how organizations or firms operate. More recently, however, researchers have also started to apply computer simulation to understand organizational behaviour at a more micro-level, focusing on individual and interpersonal cognition and behavior such as team working.

<span class="mw-page-title-main">Douglas R. White</span> Social scientist

Douglas R. White was an American complexity researcher, social anthropologist, sociologist, and social network researcher at the University of California, Irvine.

Gerard de Zeeuw is a Dutch scientist and Emeritus professor Mathematical modelling of complex social systems at the University of Amsterdam in the Netherlands. He is known for his work on the theory and practice of action research, particularly on the "Problems of increasing competence", "Second order organisational research" and "Three phases of science: A methodological exploration".

<span class="mw-page-title-main">Loet Leydesdorff</span>

Louis André (Loet) Leydesdorff (21 August 1948, Batavia was a Dutch sociologist, cyberneticist, communication scientist and Professor in the Dynamics of Scientific Communication and Technological Innovation at the University of Amsterdam. He is known for his work in the sociology of communication and innovation, especially for his Triple helix model of innovation developed with Henry Etzkowitz in the 1990s.

<span class="mw-page-title-main">Nigel Gilbert</span>

Geoffrey Nigel Gilbert is a British sociologist and a pioneer in the use of agent-based models in the social sciences. He is the founder and director of the Centre for Research in Social Simulation, author of several books on computational social science, social simulation and social research and past editor of the Journal of Artificial Societies and Social Simulation (JASSS), the leading journal in the field.

<span class="mw-page-title-main">Kathleen Carley</span> American social scientist

Kathleen M. Carley is an American computational social scientist specializing in dynamic network analysis. She is a professor in the School of Computer Science in the Carnegie Mellon Institute for Software Research at Carnegie Mellon University and also holds appointments in the Tepper School of Business, the Heinz College, the Department of Engineering and Public Policy, and the Department of Social and Decision Sciences.

Cornelis Johannes "Cor" van Dijkum is a Dutch sociologist, consultant and academic at the Utrecht University, known for his contributions in the field of methodology for complex societal problems.

References

  1. ↑ Waldrop, M. Mitchell (1992.) Complexity: The Emerging Science at the Edge of Order and Chaos. New York, NY: Simon & Schuster.
  2. 1 2 3 4 5 6 Eve, Raymond, Sara Horsfall and Mary E. Lee (eds.) (1997). Chaos, Complexity and Sociology: Myths, Models, and Theories. Thousand Oaks, CA: Sage Publications.
  3. ↑ Giddens, Anthony (1979). Central problems in Social Theory: Action, Structure and Contradiction in Social Analysis. London: Macmillan.
  4. ↑ Freese, Lee (1980). "Formal Theorizing." Annual Review of Sociology, 6: 187–212 (August 1980).
  5. ↑ Cohen, B. P. (1989). Developing sociological knowledge: theory and method (2nd ed.). Chicago: Nelson–Hall.
  6. ↑ Parsons, Talcott (1937) and (1949). The Structure of Social Action: A Study in Social Theory with Special Reference to a Group of European Writers. New York, NY: The Free Press.
  7. ↑ Parsons, Talcott (1951). The Social System. New York, NY: The Free Press
  8. ↑ Luhmann, Niklas (1990.) Essays on Self-Reference, New York: Columbia University Press.
  9. ↑ Kiel, L. Douglas (1994). Managing Chaos and Complexity in Government: A New Paradigm for Managing Change, Innovation and Organizational Renewal. Jossey-Bass: San Francisco.
  10. ↑ Urry, John (2005). "The Complexity Turn." Theory, Culture and Society, 22(5): 1–14.
  11. ↑ Luhmann, Niklas (1982). The Differentiation of Society. New York, NY: Columbia University Press.
  12. ↑ Carley, Kathleen M. (2003), "Dynamic Network Analysis." Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers, Ronald Breiger, Kathleen Carley, and Philippa Pattison (eds.), National Research Council (Committee on Human Factors): Washington, D.C.: 133–145.
  13. ↑ Barabási, Albert-László (2003). Linked: The New Science of Networks. Cambridge, MA: Perseus Publishing.
  14. ↑ Freeman, Linton C. (2004). The Development of Social Network Analysis: A Study in the Sociology of Science. Vancouver Canada: Empirical Press.
  15. ↑ Watts, Duncan J. (2004). "The New Science of Networks." Annual Review of Sociology, 30: 243–270.
  16. ↑ Gilbert, Nigel and Klaus G. Troitzsch (2005). Simulation for Social Scientists, 2nd Edition. New York, NY: Open University Press.
  17. 1 2 Epstein, Joshua M. (2007). Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton, NJ: Princeton University Press.
  18. ↑ Geyer, Felix and Johannes van der Zouwen (1992). "Sociocybernetics." Handbook of Cybernetics, C.V. Negoita (ed.): 95–124. New York: Marcel Dekker.
  19. ↑ Saberi, Mohammad Karim, Alireza Isfandyari-Moghaddam and Sedigheh Mohamadesmaeil (2011). "Web Citations Analysis of the JASSS: the First Ten Years." Journal of Artificial Societies and Social Simulation, 14:(4), 22.
  20. ↑ Nowak, Martin and Roger Highfield (2011). Super Cooperators: Altruism, Evolution, and Why We Need Each Other to Succeed. New York, NY: Free Press.
  21. ↑ Hang, Ye, Fei Tan, Mei Ding, Yongmin Jia and Yefeng Chen (2011). "Sympathy and Punishment: Evolution of Cooperation in Public Goods Game." Journal of Artificial Societies and Social Simulation, 14(4): 20.
  22. ↑ Mason, Mark (2008). Complexity Theory and the Philosophy of Education. Hoboken, NJ: Wiley-Blackwell (Educational Philosophy and Theory Special Issues).
  23. ↑ Castellani, Brian. (2018). "The Defiance of Global Commitment: A Complex Social Psychology. Routledge complexity in social science series." doi:10.4324/9781351137140.
  24. ↑ Lohmann Susanne (1994). "Dynamics of Informational Cascades: The Monday Demonstrations in Leipzig, East Germany, 1989–1991," World Politics, 47: 42–101.
  25. ↑ Chesters, Graeme and Ian Welsh (2006). Complexity and Social Movements: Protest at the Edge of Chaos." London: Routledge (International Library of Sociology).
  26. ↑ Castellani, Brian et al. (2011). "Addressing the U.S. Financial/Housing Crisis: Pareto, Schelling and Social Mobility."Working Paper.
  27. ↑ Hedström, Peter and Yvonne Åberg (2011). "Social interaction and youth unemployment." Analytical Sociology and Social Mechanisms, Pierre Demeulenaere (ed.). Cambridge: Cambridge University Press.
  28. ↑ Yilmaz, Levent (2011). "Toward Multi-Level, Multi-Theoretical Model Portfolios for Scientific Enterprise Workforce Dynamics." Journal of Artificial Societies and Social Simulation, 14(4): 2.
  29. ↑ Jervis, Robert (1998). System Effects: Complexity in Political and Social Life. Princeton, NJ: Princeton University Press.
  30. ↑ Elliott, Euel and L. Douglas Kiel (eds.) (2000). Nonlinear Dynamics, Complexity and Public Policy. Hauppauge NY: Nova Science Publishers.
  31. ↑ Brian Castellani, Rajeev Rajaram, J. Galen Buckwalter, Michael Ball and Frederic Hafferty (2012). "Place and Health as Complex Systems: A Case Study and Empirical Test". SpringerBriefs in Public Health.
  32. ↑ Leydesdorff, Loet (2006). The Knowledge-Based Economy Modeled, Measured, Simulated. Boca Raton, FL: Universal-Publishers .
  33. ↑ Lane, D.; Pumain, D.; Leeuw, S.E. van der; West, G. (eds.) (2009). Complexity Perspectives in Innovation and Social Change. New York, NY: Springer (Methodos Series, Vol. 7).
  34. ↑ Stewart, Peter (2001). "Complexity Theories, Social Theory, and the Question of Social Complexity." Philosophy of the Social Sciences, 31(3): 323–360.
  35. ↑ Lee, Ju-Sung. (2001). "Evolving Drug Networks." Carnegie Mellon Center for Computational Analysis of Social and Organizational Systems (CASOS) Conference Presentation (unpublished).
  36. ↑ Carley, Kathleen (2003). "Destabilizing Terrorist Networks." Proceedings of the 8th International Command and Control Research and Technology Symposium. Conference held at the National Defense War College: Washington D.C., Evidence Based Research, Track 3. (Electronic Publication). Archived 2004-12-18 at the Wayback Machine
  37. ↑ Leydesdorff, Loet (1995). The Challenge of Scientometrics: The development, measurement, and self-organization of scientific communications. Leiden: DSWO Press, Leiden University.
  38. ↑ Dimitrov, Vladimir and Robert Woog (1997). "Studying Social Complexity: From Soft to Virtual Systems Methodology." Complex Systems, 11:(6).

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