Complexity Theory and Organizations
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Complexity Theory and Organizations

Complexity theory and organizations, also called complexity strategy or complex adaptive organizations, is the use of the study of complexity systems in the field of strategic management and organizational studies.

Complexity theory is an interdisciplinary theory that grew out of systems theory in the 1960s.[1]:350 It draws from research in the natural sciences that examines uncertainty and non-linearity.[1] Complexity theory emphasizes interactions and the accompanying feedback loops that constantly change systems. While it proposes that systems are unpredictable, they are also constrained by order-generating rules.[2]:74

Complexity theory has been used in the fields of strategic management and organizational studies. Application areas include understanding how organizations or firms adapt to their environments and how they cope with conditions of uncertainty. The theory treats organizations and firms as collections of strategies and structures. The structure is complex; in that they are dynamic networks of interactions, and their relationships are not aggregations of the individual static entities. They are adaptive; in that the individual and collective behavior mutate and self-organize corresponding to a change-initiating micro-event or collection of events.[3][4]


Machine approach to organizations

In the early 20th century, organizational thinking primarily saw organizations as a machine, which meant large amounts of bureaucracy, hierarchy, and standardization. An organization was a determinist, closed system.[1]:356 This approach was rejected In the mid-1900s, as organizations became to be seen as dynamic systems in constant states of change.[1]:357-8

Planned approach to organizational change

From the 1950s to the 1980s, the dominant approach to organizational change was the planned approach. This focus tried to improve organizations by looking at operational practices and effectiveness through wide, participative change moments.[2]:74 Change was a process that organizations would deal with incrementally by focusing on one problem or goal at a time.[2]:76

Emergent approach to organizational change

From the 1980s onward, the emergent approach to organizational change has dominated. The emergent approach argues that change is continuous and unpredictable. Rather than happening through big events, change is happening all of the time through many small steps, and organizations do not have the luxury of dealing with one change at a time. This approach also takes into account the importance of power to organizational culture.

Many followers of this approach argue that organizations must be able to change themselves continuously, especially in fast moving sectors of the economy.[2]:75-6

The Punctuated Equilibrium model

The Punctuated Equilibrium model originated in the 1980s and is a complement to complexity theory. It argues that organizations evolve over time, with long periods of stability that are disrupted by short periods of large change. These disruptions set the stage for a new period in the life of the organization.[2]:76

Complexity theory in organizations

Beginning in the early 1990s, theorists began linking complexity theory to organizational change. Complexity Theory rejects the idea of organizations as a machine, as well as a planned approach to organizational change. Rather, it agrees with the emergent approach that power and constant change are crucial elements of organizational life.[2]:77 It can also be used to explain the often paradoxical nature of organizations.[1]:359

Key concepts

Complex adaptive systems

Organizations can be treated as complex adaptive systems (CAS) as they exhibit fundamental CAS principles like self-organization, complexity, emergence,[5] interdependence, space of possibilities, co-evolution, chaos, and self-similarity.[3][6][7]

CAS are contrasted with ordered and chaotic systems by the relationship that exists between the system and the agents which act within it. In an ordered system the level of constraint means that all agent behaviour is limited to the rules of the system. In a chaotic system the agents are unconstrained and susceptible to statistical and other analysis. In a CAS, the system and the agents co-evolve; the system lightly constrains agent behaviour, but the agents modify the system by their interaction with it. This self-organizing nature is an important characteristic of CAS; and its ability to learn to adapt, differentiate it from other self organizing systems.[3]

"The Edge of Chaos"

Organizational environments can be viewed as a system with coevolution[7]. According to CAS theories, each agent inside the environment tries to get a better payoff, while most of the time, the results are greatly influenced by what other agents do. That creates a dynamic equilibrium of coevolution with small, medium, or large changes in outcomes, according to power law. Small changes can sometimes lead to extraordinary improvements, and that is the reason why systems that are at the edge of chaos can defeat those not.[8] The best-run companies survive because they operate at the edge of chaos by relentlessly pursuing a path of continuous innovation, and, indeed, because they inject so much novelty and change into their normal operations, they constantly risk falling over the edge.[2]:81 An organization should maintain a balance between flexibility and stability to avoid failing.

Implications for organizational management

CAS approaches to strategy seek to understand the nature of system constraints and agent interaction and generally takes an evolutionary or naturalistic approach to strategy. More recently work by organizational scholars and their colleagues have added greatly to our understanding of how concepts from the complexity sciences can be used to understand strategy and organizations. Much of this later research integrates computer simulation and organizational studies.

Complexity theory and knowledge management

Complexity theory also relates to knowledge management (KM) and organizational learning (OL). "Complex systems are, by any other definition, learning organizations. Complexity theory is, therefore, on the verge of making a huge contribution to both KM and OL."[9] Complexity Theory, KM, and OL are all complimentary and co-dependent.[9] "KM and OL each lack a theory of how cognition happens in human social systems - complexity theory offers this missing piece".[9]

In 1997, a think tank called Knowledge Management Consortium International (KCMI) was formed in Washington, DC. The formation of the group acknowledged, "the profound connection between complexity theory and knowledge management".[10] Complexity theory offers new approaches to some of the questions that Peter Senge has posed in the field of KM. "In what has only recently become apparent, the issues Senge speaks of are precisely those that scholars and researchers of complexity theory have been dealing with for the past 15 years."[9]

Complexity theory and project management

Complexity theory is also being used to better understand new ways of doing project management, as traditional models have been found lacking to current challenges.[11]:23 This approaches advocates forming a "culture of trust" that "welcomes outsiders, embraces new ideas, and promotes cooperation."[11]:35

Recommendations for managers

Complexity Theory would advocate for approaches that focus on flatter, more flexible organizations, rather than top-down, command-and-control styles of management.[2]:84

Complexity theory also reveals that individual behaviors and choices are more important than executive plans in an organization. Instead, individuals are highly impacted by their interrelations with other individuals within the organization. Therefore, CAS suggests that managers should focus on self-organization instead of management control. Paying attention to small changes and interventions, and encouraging conflict and change are also necessary. This may seem to push the organization to an unstable situation, the organization actually can gain improvements from the healthy edge of chaos.[12] In contrast to the traditional perspective where managers fix problems, complexity theory would instead say that they should hold off and wait to see what happens on its own.[1]:373 Managers should instead seek to find the balance between chaos and stability to cultivate the greatest creativity and innovation. A workplace that is always stable will become predictable and stagnant, while too much chaos will also be unworkable. The trick is to find the place on the edge of chaos.[1]:374

Moreover, managers should increase the flow of information into the organization and encourage tension, rather than fight it. They should encourage questions and allow employees to forge their own path. Managers should see their own role as a participant, rather than an outside observer.[1]:376

Managers also need to understand the importance of relationships of individuals within the organization. Creating an environment of encouraging "care and connection" can help improve the organization's creativity, efficiency, and adaptability.[12]

For a non-technical introduction to complexity theory and its application to organizations see Douma & Schreuder (2017).

Additional examples

A typical example for an organization behaving as CAS, is Wikipedia[13] - collaborated and managed by a loosely organized management structure,[13] composed of a complex mix of human-computer interactions.[14][15][16] By managing behavior, and not only mere content, resource uses simple rules to produce a complex, evolving knowledge base which has largely replaced older sources in popular use.

Other examples include the complex global macroeconomic network within a country or group of countries; stock market and complex web of cross border holding companies; manufacturing businesses; and any human social group-based endeavour in a particular ideology and social system such as political parties, communities, geopolitical organisations, and terrorist networks of both hierarchical and leaderless nature.[17] This new macro level state may create difficulty for an observer in explaining and describing the collective behaviour in terms of its constituent parts; as a result of the complex dynamic networks of interactions, outlined earlier.[3]

See also


  1. ^ a b c d e f g h Grobman, Gary M. (2005). "Complexity Theory: a new way to look at organizational change" (PDF). Public Administration Quarterly. 29 (3). 
  2. ^ a b c d e f g h Burnes, Bernard (2005). "Complexity theories and organizational change". International Journal of Management Reviews. 7 (2): 73-90. doi:10.1111/j.1468-2370.2005.00107.x. 
  3. ^ a b c d "Insights from Complexity Theory: Understanding Organisations better". by Assoc. Prof. Amit Gupta, Student contributor - S. Anish , IIM Bangalore. Retrieved 2012. 
  4. ^ "Ten Principles of Complexity & Enabling Infrastructures" (PDF). by Professor Eve Mitleton-Kelly, Director Complexity Research Programme, London School of Economics. Archived from the original (PDF) on 29 December 2009. Retrieved 2012. 
  5. ^ "Complex Adaptive Systems as a Model for Evaluating Organisational Change Caused by the Introduction of Health Information Systems" (PDF). Kieren Diment, Ping Yu, Karin Garrety, Health Informatics Research Lab, Faculty of Informatics, University of Wollongong, School of Management, University of Wollongong, NSW. Retrieved 2012. 
  6. ^ "Page 3, Similar fundamental between CAS and organisations, from paper "Ten Principles of Complexity & Enabling Infrastructures"" (PDF). by Professor Eve Mitleton-Kelly, Director Complexity Research Programme, London School of Economics. Retrieved 2012. 
  7. ^ a b Terra, Leonardo Augusto Amaral; Passador, João Luiz (2016). "Symbiotic Dynamic: The Strategic Problem from the Perspective of Complexity". Systems Research and Behavioral Science. 33 (2): 235. doi:10.1002/sres.2379. 
  8. ^ Anderson, P. (1999). "Complexity theory and organization science". Organization Science. 10 (3): 216-232. doi:10.1287/orsc.10.3.216. 
  9. ^ a b c d McElroy, Mark W. (2000). "Integrating complexity theory, knowledge management and organizational learning". Journal of Knowledge Management. 4 (3): 195-203. doi:10.1108/13673270010377652. Retrieved 2016. 
  10. ^ McElroy, Mark W. (2000). "The New Knowledge Management". Knowledge And Innovation: Journal of the KMCI. Knowledge Management Consortium International, Inc. 1 (1): 43-67. 
  11. ^ a b Saynisch, Manfred (2010). "Beyond frontiers of traditional project management: An approach to evolutionary, self-organizational principles and the complexity theory--results of the research program". Project Management Journal. 41 (2): 21-37. doi:10.1002/pmj.20159. 
  12. ^ a b Lewin, R.; Parker, T. & Regine, B. (1998). "Complexity theory and the organization: Beyond the metaphor". Complexity. 3 (4): 36-40. doi:10.1002/(SICI)1099-0526(199803/04)3:4<36::AID-CPLX7>3.0.CO;2-I. 
  13. ^ a b "A Complex Adaptive Organization Under the Lens of the LIFE Model:The Case of Wikipedia". Retrieved 2012. 
  14. ^ "The Internet Analyzed as a Complex Adaptive System". Retrieved 2012. 
  15. ^ "Cyberspace: The Ultimate Complex Adaptive System" (PDF). The International C2 Journal. Retrieved 2012.  by Paul W. Phister Jr
  16. ^ "Complex Adaptive Systems" (PDF). 2001. Retrieved 2012.  by Serena Chan, Research Seminar in Engineering Systems
  17. ^ "Toward a Complex Adaptive Intelligence Community The Wiki and the Blog". D. Calvin Andrus. Retrieved 2012. 

Further reading

  • Axelrod, R. A., & Cohen, M. D., 2000. Harnessing Complexity: Organizational Implications of a Scientific Frontier. New York: The Free Press
  • Yaneer Bar-Yam (2005). Making Things Work: Solving Complex Problems in a Complex World. Cambridge, MA: Knowledge Press
  • Beautement, P. & Broenner, C. 2010. Complexity Demystified: A Guide for Practitioners. Axminster: Triarchy Press
  • Brown, S. L., & Eisenhardt, K. M. 1997. The Art of Continuous Change: Linking Complexity Theory and Time-paced Evolution in Relentlessly Shifting Organizations. Administrative Science Quarterly, 42: 1-34
  • Burns, S., & Stalker, G. M. 1961. The Management of Innovation. London: Tavistock Publications
  • Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. 2009. Optimal Structure, Market Dynamism, and the Strategy of Simple Rules. Administrative Science Quarterly, 54: 413-452
  • De Toni, A.F., Comello, L., 2010. Journey into Complexity. Udine: Lulu Publisher
  • Fonseca, J. (2001). Complexity and Innovation in Organizations. London: Routledge
  • Douma, S. & H. Schreuder, Economic Approaches to Organizations, 6th edition, Harlow: Pearson.
  • Gell-Mann, M. 1994. The Quark and the Jaguar: Adventures in the Simple and the Complex. New York: WH Freeman
  • Kauffman, S. 1993. The Origins of Order. New York, NY: Oxford University Press.
  • Levinthal, D. 1997. Adaptation on Rugged Landscapes. Management Science, 43: 934-950
  • Liang, T.Y. 2016. Complexity-Intelligence Strategy: A New Paradigmatic Shift. Singapore: World Scientific Publishing.
  • March, J. G. 1991. Exploration and Exploitation in Organizational Learning. Organization Science, 2(1): 71-87
  • McKelvey, B. 1999. Avoiding Complexity Catastrophe in Coevolutionary Pockets: Strategies for Rugged Landscapes. Organization Science, 10(3): 249-321
  • McMillan, E. 2004 Complexity, Organizations and Change. Routledge.ISBN 041531447X Hardback. ISBN 0-415-39502-X Paperback
  • Moffat, James. 2003. Complexity Theory and Network Centric Warfare.
  • Perrow, C. Complex Organizations: A Critical Essay Scott, Forseman & Co., Glenville, Illinois
  • Rivkin, J., W. 2000. Imitation of Complex Strategies. Management Science, 46(6): 824-844
  • Rivkin, J. and Siggelkow, N. 2003. Balancing Search and Stability: Interdependencies Among Elements of Organizational Design. Management Science, 49, pp. 290-311
  • Rudolph, J., & Repenning, N. 2002. Disaster Dynamics: Understanding the Role of Quantity in Organizational Collapse. Administrative Science Quarterly, 47: 1-30
  • Schilling, M. A. 2000. Toward a General Modular Systems Theory and its Applicability to Interfirm Product Modularity. Academy of Management Review, 25(2): 312-334
  • Siggelkow, S. 2002. Evolution toward Fit. Administrative Science Quarterly, 47, pp. 125-159
  • Simon, H. 1996 (1969; 1981) The Sciences of the Artificial (3rd Edition) MIT Press
  • Smith, Edward. 2006. Complexity, Networking, and Effects Based Approaches to Operations] by Edward
  • Snowden, D.J. Boone, M. 2007. "A Leader's Framework for Decision Making". Harvard Business Review, November 2007, pp. 69-76.
  • Weick, K. E. 1976. Educational Organizations as loosely coupled systems. Administrative Science Quarterly, 21(1): 1-19

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