Complex systems are networks made of a number of components that interact with each other, typically in a nonlinear fashion. Complex systems may arise and evolve through self-organisation, such that they are neither completely regular nor completely random, permitting the development of emergent behavior at macroscopic scales.
– Sayama, H. (2015). Introduction to the modeling and analysis of complex systems
Complex systems contain linear and nonlinear interacting components. These systems evolve and organise themselves to a point where they are neither completely random nor regular. How these systems self-organise leads to patterns emerging when we examine the system at a macroscopic level. The interacting components make complex systems difficult to analyse through conventional statistics alone. Statistical methods such as regression, analysis of variance or correlations matrices may not capture the multiple multi-levelled interactions. Researchers can use complex system science to study the ‘‘structural and dynamical properties of a system’’ to describe these systems of interdependent components for both ‘‘general’’ and ‘‘cross-disciplinary implications and applications.’‘1 Sayama (2015)1 describes the seven topic clusters under complex systems, namely, Game Theory, Collective Behaviour, Networks, Evolution and Adaptation, Pattern Formation, Systems Theory, and Nonlinear Dynamics.1
Topic clusters that constitute complex systems. This image is available in the book by Sayama and from Wikipedia.
Nonlinear Systems
A Nonlinear System is subject to irreversibility, such that given some change in the inputs to the system, undoing the change does not necessarily return the system to its start, whereas all linear systems are reversible. Furthermore, nonlinear systems can be subject to discontinuous or catastrophic state changes, which is not possible in linear systems.
– Fieguth, P. (2017). An introduction to complex systems. Complex Systems and Archaeology, 10, 978-83.
Characteristics of a Complex System
Across these clusters, two core concepts exist; emergence and self-orgnisation.
Emergence
The first concept is emergence. Emergence is defined as ‘‘a nontrivial relationship between the properties of a system at microscopic and macroscopic scales.’’ 2 The definition has been debated, yet, consistently, emergence has been described as observing the different scales of a system, which means that what we may observe at one level can be fundamentally different at a sub or microscopic level.
Further Information
Emergence occurs when micro and macro-level interactions produce properties varying from the system rules. Behaviours can be considered a complex system where there are multi-causal influences causing interactions with different system elements. These interactions can utilise a feedback loop where the subsystems can display the behaviour patterns. These subsystems are not just the brain as the decision-making centre but a collection of systems involving the body, experiences, environment, and system constraints.3
Behaviours are evolving and in a constant state of self-organisation and changing with respect to time.4 An example of emergence is characterising the outcomes of an individual’s search for information. At the individual level, one may observe that individuals seek information from their physician and use it to speak to another physician for a second opinion.5 The individual may return to their first physician to discuss their condition. This simple task of going between physicians may be helpful to the individual and seems harmless at the individual level. However, if one observes from a different level, such as from the community level, the increased use by one individual may reduce access to a physician for another individual. If the second individual desires to source information from their physician, they may not be getting timely access to their information source. How will they then act? While a hypothetical untested scenario, emergence would be observing tasks to see the effect of low-level interactions on different system levels. It should be noted that while speculative, access to a healthcare source of information is known to be a predictor of an individual’s health information-seeking behaviour.6, 7
Self-Organisation
The second concept is **self-organisation **, which is related to time. A system has self-organising behaviour when emergent properties are being examined over time. A definition for self-organisation is a ‘‘dynamical process by which a system spontaneously forms the nontrivial macroscopic structure and/or behaviours over time.’‘1 That is, order and structure in the system can form from the individual interactions. The lower-level interactions create emergent properties at the system level. When these system-level properties are observed over time, they organise to improve stability and reduce chaos.
Further Information
The common example of self-organisation is in biology and birds flocking together. While their individual movement seems erratic at first, their collective movement has purpose and direction. The birds may move following each other with no clear leader, yet all the birds together may migrate from one point to another. This collective behaviour is also known as swarm behaviour.
What are some real-world examples of a complex system?
What are some examples where we may see complex systems in the pharmacy?
References
Sayama, H. (2015). Introduction to the modeling and analysis of complex systems. Open SUNY Textbooks.
Bar-Yam, Y., McKay, S. R., & Christian, W. (1998). Dynamics of complex systems (Studies in nonlinearity). Computers in Physics, 12(4), 335-336.
Chiel, H. J., & Beer, R. D. (1997). The brain has a body: adaptive behavior emerges from interactions of nervous system, body and environment. Trends in neurosciences, 20(12), 553-557.
Connell, J. P., DiMercurio, A., Corbetta, D., Vonk, J., & Shackelford, T. (2017). Dynamic systems theory. Encyclopedia of Animal Cognition and Behavior, 1-8.
Anker, A. E., Reinhart, A. M., & Feeley, T. H. (2011). Health information seeking: a review of measures and methods. Patient education and counseling, 82(3), 346-354.
Health Literacy And Its Association With The Use Of Information Sources And With Barriers To Information Seeking In Clinic-Based Pregnant Women
Shieh, C., Mays, R., McDaniel, A., & Yu, J. (2009). Health literacy and its association with the use of information sources and with barriers to information seeking in clinic-based pregnant women. Health care for women international, 30(11), 971-988.
Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the national academy of sciences, 99(suppl_3), 7280-7287.
Key Points
Complex systems contain linear and nonlinear interacting components.
Self-Organisation: A system has self-organising behaviour when emergent properties are being examined over time.
Emergence is what may be observed at one level can be fundamentally different at a sub or microscopic level.
