Complexity theory and organizations

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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. [1] [2] [3] [4] It draws from research in the natural sciences that examines uncertainty and non-linearity. [5] 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. [6] : 74 

Contents

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. Organizations have complex structures 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. [7] [8]

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, [9] interdependence, space of possibilities, co-evolution, [10] [11] [12] chaos, [13] [14] [11] [12] and self-similarity. [7] [15] [11] [12]

CAS are contrasted with ordered and chaotic systems by the relationship that exists between the system and the agents which act within it. [13] In an ordered system the level of constraint means that all agent behavior is limited to the rules of the system. In a chaotic system, the agents are unconstrained and susceptible to statistical and other analyses. In a CAS, the system and the agents co-evolve; the system lightly constrains agent behavior, 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. [7] [13] [14] [11] [12]

Organizational environments can be viewed as complex adaptive systems where coevolution generally occurs near the edge of chaos, and it should maintain a balance between flexibility and stability to avoid organizational failure. [16] [13] [4] [10] [11] [12] As a response to coping with turbulent environments; businesses bring out flexibility, creativity, [17] agility, and innovation near the edge of chaos; provided the organizational structure has sufficient decentralized, non-hierarchical network structures. [16] [13] [4] [11]

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. Some 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." [18] Complexity Theory, KM, and OL are all complementary and co-dependent. [18] “KM and OL each lack a theory of how cognition happens in human social systems – complexity theory offers this missing piece”. [18]

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. [19] : 23  This approaches advocates forming a "culture of trust" that "welcomes outsiders, embraces new ideas, and promotes cooperation." [19] : 35 

Recommendations for managers

Complexity Theory implies approaches that focus on flatter, more flexible organizations, rather than top-down, command-and-control styles of management. [6] : 84  [4] [16]

Practical examples

A typical example for an organization behaving as CAS is Wikipedia itself, [20] which is collaborated and managed by a loosely organized management structure [20] that is composed of a complex mix of human–computer interactions. [21] [22] [23] By managing behavior, and not only mere content, Wikipedia uses simple rules to produce a complex, evolving knowledge base that has largely replaced older sources in popular use.[ citation needed ]

Other examples include:

This new macro level state may create difficulty for an observer in explaining and describing the collective behavior in terms of its constituent parts, as a result of the complex dynamic networks of interactions, outlined earlier. [7]

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.

Strategy is a general plan to achieve one or more long-term or overall goals under conditions of uncertainty. In the sense of the "art of the general", which included several subsets of skills including military tactics, siegecraft, logistics etc., the term came into use in the 6th century C.E. in Eastern Roman terminology, and was translated into Western vernacular languages only in the 18th century. From then until the 20th century, the word "strategy" came to denote "a comprehensive way to try to pursue political ends, including the threat or actual use of force, in a dialectic of wills" in a military conflict, in which both adversaries interact.

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">Self-organization</span> Process of creating order by local interactions

Self-organization, also called spontaneous order in the social sciences, is a process where some form of overall order arises from local interactions between parts of an initially disordered system. The process can be spontaneous when sufficient energy is available, not needing control by any external agent. It is often triggered by seemingly random fluctuations, amplified by positive feedback. The resulting organization is wholly decentralized, distributed over all the components of the system. As such, the organization is typically robust and able to survive or self-repair substantial perturbation. Chaos theory discusses self-organization in terms of islands of predictability in a sea of chaotic unpredictability.

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">Edge of chaos</span> Transition space between order and disorder

The edge of chaos is a transition space between order and disorder that is hypothesized to exist within a wide variety of systems. This transition zone is a region of bounded instability that engenders a constant dynamic interplay between order and disorder.

<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">Social complexity</span> Conceptual framework

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.

<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.

A complex adaptive system is a system that is complex in that it is a dynamic network of interactions, but the behavior of the ensemble may not be predictable according to the behavior of the components. It is adaptive in that the individual and collective behavior mutate and self-organize corresponding to the change-initiating micro-event or collection of events. It is a "complex macroscopic collection" of relatively "similar and partially connected micro-structures" formed in order to adapt to the changing environment and increase their survivability as a macro-structure. The Complex Adaptive Systems approach builds on replicator dynamics.

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.

Complexity economics is the application of complexity science to the problems of economics. It relaxes several common assumptions in economics, including general equilibrium theory. While it does not reject the existence of an equilibrium, it sees such equilibria as "a special case of nonequilibrium", and as an emergent property resulting from complex interactions between economic agents. The complexity science approach has also been applied to computational economics.

Agent-based computational economics (ACE) is the area of computational economics that studies economic processes, including whole economies, as dynamic systems of interacting agents. As such, it falls in the paradigm of complex adaptive systems. In corresponding agent-based models, the "agents" are "computational objects modeled as interacting according to rules" over space and time, not real people. The rules are formulated to model behavior and social interactions based on incentives and information. Such rules could also be the result of optimization, realized through use of AI methods.

Business agility refers to rapid, continuous, and systematic evolutionary adaptation and entrepreneurial innovation directed at gaining and maintaining competitive advantage. Business agility can be sustained by maintaining and adapting the goods and services offered to meet with customer demands, adjusting to the marketplace changes in a business environment, and taking advantage of available human resources.

The healthcare error proliferation model is an adaptation of James Reason’s Swiss Cheese Model designed to illustrate the complexity inherent in the contemporary healthcare delivery system and the attribution of human error within these systems. The healthcare error proliferation model explains the etiology of error and the sequence of events typically leading to adverse outcomes. This model emphasizes the role organizational and external cultures contribute to error identification, prevention, mitigation, and defense construction.

<span class="mw-page-title-main">Ralph D. Stacey</span>

Ralph Douglas Stacey was a British organizational theorist and Professor of Management at Hertfordshire Business School, University of Hertfordshire, in the UK and one of the pioneers of enquiring into the implications of the natural sciences of complexity for understanding human organisations and their management. He is best known for his writings on the theory of organisations as complex responsive processes of relating.

Integrated modification methodology (IMM) is a procedure encompassing an open set of scientific techniques for morphologically analyzing the built environment in a multiscale manner and evaluating its performance in actual states or under specific design scenarios.

<span class="mw-page-title-main">Raymond-Alain Thietart</span> French business school professor (born 1944)

Raymond-Alain Thietart is a French business school professor. He is the author of eight books on strategy and management and over a hundred articles in the same field. His research and teaching focus on organization theory and strategic management.

References

  1. ↑ M. Eisenhardt, Kathleen; McKelvey, Bill (2011). Section 29, Complexity Theory and Corporate Strategy, from book - The SAGE Handbook of Complexity and Management edited by Peter Allen, Steve Maguire, Bill McKelvey. SAGE, 2011. p.  506. ISBN   9781446209745 . Retrieved 23 August 2020.
  2. ↑ Forgues, Bérnard; Thietart, Alain (2011). Section 2, Complexity science and organization, from book - The SAGE Handbook of Complexity and Management edited by Peter Allen, Steve Maguire, Bill McKelvey. SAGE, 2011. p.  53. ISBN   9781446209745 . Retrieved 23 August 2020.
  3. ↑ Mak, Wai Ming (2012). Section 7, Rethinking Business Strategy with Complexity Theory, from book - Systems Theory and Practice in the Knowledge Age, edited by Gillian Ragsdell, Daune West, Jennifer Wilby. Springer Science & Business Media, 2012. p.  321. doi:10.1007/978-1-4615-0601-0_37. ISBN   9781461506010 . Retrieved 23 August 2020.
  4. 1 2 3 4 L. Levy, David. "Applications and Limitations of Complexity Theory in Organization Theory and Strategy" (PDF). umb.edu. Retrieved 23 August 2020.
  5. ↑ Grobman, Gary M. (2005). "Complexity Theory: a new way to look at organizational change" (PDF). Public Administration Quarterly. 29 (3). Archived from the original (PDF) on 7 August 2016. Retrieved 10 July 2016.
  6. 1 2 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.
  7. 1 2 3 4 "Insights from Complexity Theory: Understanding Organisations better". by Assoc. Prof. Amit Gupta, Student contributor - S. Anish, IIM Bangalore. Retrieved 1 June 2012.
  8. ↑ "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 1 June 2012.
  9. ↑ "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. uow.edu.au. Archived from the original (PDF) on 5 September 2012. Retrieved 25 August 2012.
  10. 1 2 Kauffman, Stuart (15 January 1992). "Coevolution in Complex Adaptive Systems". Santa Fe Institute. Retrieved 24 August 2020.
  11. 1 2 3 4 5 6 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.
  12. 1 2 3 4 5 Terra, L. A. A.; Passador, J. L. (2019). "The nature of social organization of production: From firms to complex dynamics". Systems Research and Behavioral Science. 36 (4): 514–531. doi:10.1002/sres.2567. S2CID   149946425.
  13. 1 2 3 4 5 Berreby, David (1 April 1996). "Between Chaos and Order: What Complexity Theory Can Teach Business". strategy-business.com. Retrieved 23 August 2020.
  14. 1 2 Birkinshaw, Julian (11 November 2013). "Managing Complexity Is the Epic Battle Between Emergence and Entropy". Harvard Business Review. Retrieved 23 August 2020.
  15. ↑ "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. Archived from the original (PDF) on 12 May 2013. Retrieved 1 June 2012.
  16. 1 2 3 B. Porter, Terry. "Coevolution as a research framework for organizations and the natural environment" (PDF). University of Maine. Retrieved 23 August 2020.
  17. ↑ A Lambert, Philip (June 2018). "The Order-Chaos Dynamic of Creativity". University of New Brunswick. Retrieved 24 August 2020.
  18. 1 2 3 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 20 March 2016.
  19. 1 2 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. S2CID   47040850.
  20. 1 2 Faucher, Jean-Baptiste. "A Complex Adaptive Organization Under the Lens of the LIFE Model:The Case of Wikipedia". Egosnet.org. Retrieved 25 August 2012.
  21. ↑ "The Internet Analyzed as a Complex Adaptive System". Archived from the original on 29 May 2019. Retrieved 25 August 2012.
  22. ↑ "Cyberspace: The Ultimate Complex Adaptive System" (PDF). The International C2 Journal. Retrieved 25 August 2012. by Paul W. Phister Jr
  23. ↑ "Complex Adaptive Systems" (PDF). mit.edu. 2001. Retrieved 25 August 2012. by Serena Chan, Research Seminar in Engineering Systems
  24. ↑ "Toward a Complex Adaptive Intelligence Community The Wiki and the Blog". D. Calvin Andrus. cia.gov. Archived from the original on 13 June 2007. Retrieved 25 August 2012.

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