Complex Systems

Complex systems theory, sometimes referred to as complexity theory, does not originate from a single discipline and hence does not have a singular definition. In order to approach a useful theory of complex systems, we can identify shared definitions and typically attributed qualities in order to attain an overview, if not quite a consensus. This entry provides a working definition of a complex system, describes the structure of systems generally, and goes on to identify and explain the core concepts inherent in the study of complex systems and the theories that have been advanced by scholars in the various disciplines from which this field emerged. The entry concludes with a detailed discussion of the most widely recognized theoretical influences and movements related to and coming out the study of complex systems.

A complex system is made of multiple components, which interact with each other over time, resulting in patterns of behavior at the system level and at other scales that cannot be predicted through a simple causal understanding of the individual parts and their functions. Furthermore, the organization of components and the relationships of parts to each other at individual and collective scales in terms of flows and interactions are as important as the components themselves for cognition of system behavior. In other words, typically reductive approaches that are used to break down “complicated” problems into smaller parts, which are resolved separately, are unlikely to produce results in the study of complex systems or to unveil traceable causal chains resulting in collective dynamics.

Although this is an extremely generalized description, it will be expanded in order to demonstrate how this statement incorporates possibilities of emergent behaviors, self-organization, unpredictability, feedback loops, and sensitivity to initial conditions and in some cases outcomes based on learning, memory, and interaction with environments, similar to evolution. There is a clear distinction between “complex” systems and “complicated” systems. A number of extremely complicated systems exist, such as airplanes and large infrastructure projects such as dams. The aim and characteristics of these complicated systems are that they are predictable and linear: The same outcome is always expected for the same input, with any deviations from this treated as errors to be corrected. Complex systems, on the other hand, are typically nonlinear, and while it may be possible to identify the patterns of behavior, the system is not predictable in all aspects.

Many real-world systems demonstrate some or all behaviors attributed to complex systems. Examples include ant colonies, the biosphere and ecosystem, the immune system, social or political groups, the global economy, and cities as both artifacts and living adaptive systems. Theories from complex systems have gained popularity over the past few decades, partially due to the failure of existing theories to explain or predict real-world phenomena such as the global financial crisis of 2007–2008. They are also providing useful approaches to function and learn across different disciplines, such as physics, biology, economics, ecology, mathematics, computation, and economics, through identification of similar or overlapping phenomena. Complex systems theory emphasizes the impossibility of a single perspective or language to describe all the qualities of the system or a single entity to control all aspects of the system. It creates the need to reconsider the logic of reductionism, the absolutes of positivism, and the causality of determinism. While frames of reference and subjectivity from relativism are part of complex systems, lack of validity intrinsic to absolute relativism is not accepted as the default outcome. Instead, [Page 173]systems of relations between objects, as acknowledged in G. F. W. Leibniz’s relationist description of space and time and Joseph Kaipayil’s reference to relational particulars, appear to provide a more useful operating framework.

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