Complexity Theory

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David Bent:

"What do the authors mean by a complexity worldview? In a nutshell, they are saying the world is:

  • Systemic: the world cannot be understood through taking apart he bits and understanding them separately. Factors work together synergistically, that is, the whole is different from the sum of the parts. We live as part of patterns of relationships.
  • Path-dependent: history matters and the sequence of events is a key factor in giving shape to the future.
  • Sensitive to context: one size does not fit all, and the way change happens and the way the future emerges is dependent on the detail and particular events and patterns of relationships and particular features in the local situation. By generalising, we risk throwing out the very information that sheds light on why things happen and what might happen next.
  • Emergent, uncertain, but not random: although the future does not follow smoothly from the past, neither is what happens random. The world is neither chaotic not predictable but somewhere in between.
  • Episodic: things are becoming, developing, and changing but change happens in fits and starts. The intriguing thing about the world is that on the surface patterns of relationships and structures can seem almost stable for long periods of time, although micro-changes may be going on under the surface. And then radical change can happen suddenly and new patterns of relationships can self-organise and some completely new features that could not have been predicted may emerge.”


Source: Bent cites the author of this book: Embracing Complexity: Strategic Perspectives for an Age of Turbulence by Jean G. Boulton, Peter M. Allen, Cliff Bowman.

Self-reference, Self- description and Strange Loops

Helmut Willke:

"For very general constructivist epistemological reasons, we have to assume that complex living or social systems are self-referential systems12. Selfreferentiality means that a system operates in such a way that each operation refers back to the system itself, and therefore, so to speak, only makes sense in the context of this system. Since the human brain is undoubtedly a selfreferential system, all human observation is based on self-referentiality.

Therefore, it seems epistemologically unavoidable to conclude that systems can be observed and described only by taking into account that they operate self-referentially. More precisely: only by assuming self-referentiality in the standing operating procedures of social (or other) systems can an observer avoid creating an oversimplified description of a social system, and thus fail to reallize the internal complexity of the observed entity.

It seems that social scientists are now beginning to learn this lesson and so follow the lead of all those scientists - from sub-atomic researchers to biologists to astronomers - who have realised that they are bound to think in terms of systems and system-references.

To observe in terms of systems, means to create or reconstruct boundaries in order to demarcate the units of observation. These units make sense as units, if they themselves recreate or reproduce their units and their unity. It is easy to see that right here the notion of selfreferentiality is central. For it means that in all its operations a system is bound to refer back to its own identity on the level of system, of structure and of it's elements - which means that it operates as if it were a system.

There is an impressive amount of interdisciplinary evidence (e.g. from the theory of "autopoiesis", from "second order cybernetics", from black-box theory, or from cognitive sciences) for the assumption that self-referentiality is necessary and functional for a system to cope with its own complexity. Of course, in the first place, any system is a problem for itself, and this problem spells out: continuation and selfreproduction in time and space. This is the somewhat paradoxical foundation of complex systems: that they can become operative as systems only in the form of complex systems (that is: as systems with a high internal complexity) and that, on the other hand, this very complexity continually threatens to overwhelm and disorganize the system. Indeed, the most general explanation of the possibility of the evolution of systems today, is to base operative cycles or the cyclical structuring of events16, on a recurrent recombination of order and disorder, on the recurrent processing of differences in identical (closed) circuits or on a recurrent recombination of complexityproduction and complexity reduction.

In order to cope with its own complexity, a system "uses a simplified model of itself to orient its own operations"19. It uses a selfdescription, a sort of internal blueprint, to inform itself and thus control the validity of possible operations. Without this self-description a living or social system simply would not be able to decide which of the myriads of contingent operations fit into its own self-reproducing procedures and which would go awry.

Any self-description is necessarily based on self-observation, that is, on gaining information about the system's own functioning - and this, of course, within the functioning of that very system. This implies that any selfobservation and self-description as functions of the system within the system necessarily produce simplified models of the system. For we not only know by now, that any good regulator of a system must be a model of that system20; we also know from Gödel's work, that the complexity of a system cannot be represented in full within that system21.

On the one hand, we now see, self-descriptions are necessary for the selfreproduction of living and social systems in that they organize the systems' internal complexity. On the other hand, self-descriptions depend on selective reductions, and in the case of social systems that selectivity shows some degree of contingency, because the selection patterns are not fixed, but they are contingent on optional identities.

Again this gives us a glimpse at the constituting paradox of complex social systems. It is the paradox of the necessity of selfsimplification and the contingency of selfsimplification, or summarized, the paradox of the necessity of contingency. For example, this means that any self-description - based on self-simplification - is open to debate, because other self-descriptions are possible. But there can be no debate on the necessity of some selfdescription, because without it, the system would be without orientation to itself.

Paradox is just one side of organizing complexity. This side is well captured in Piaget's phrase: "L'intelligence ... organise le monde en s'organisant ellememe.", because, then, of course, any intelligent brain can turn around that phrase. And, for example, Spencer Brown has done so by saying that the Universe has created physicists in order to be able to observe itself. So paradox is the one dark side.

The other dark side of complex systems is the possibility of strange loops. A "strange loop" or "tangled hierarchy" occurs "when what you presume are clean hierarchical levels take you by surprise and fold back in a hierarchyviolating way."25 For example, the famous Escher-lithograph "Drawing Hands" constitutes a tangled hierarchy which can only be solved by adding an external level, that of the draawing artist. Another example would be the Secret Service of a State examining its own secret services." (


The shift towards complexity thinking

Kingsley Dennis:

"The 1990s saw the social sciences engaging with complexity in terms of books, articles, conferences, and workshops, leading some to label this more modern incursion into the social and cultural sciences as the complexity turn (Urry, 2005b). This turn resulted from a gradual shift in discourse, over several decades, away from mechanistic Newtonian epistemologies towards systemic thinking (Capra, 1985). The systems thinking to emerge in the 1950s came out of cybernetics and was characterised by being open and sustained through flows of energy, rather than the earlier forms of closed systems. And systems thinking, the language of process over structure, began to be informed through new discoveries in the natural sciences. Discourses in the social sciences too began to be more transdisciplinary as solutions to social phenomena were sought from more and varied sources. It became necessary to find ways to understand and evaluate increasing patterns of conflict, unpredictability, flows, dynamic equilibrium, breakdowns, breakthroughs, and transnational relations. Approaches that proposed linear analysis and closed systems thinking became increasingly unsatisfying in providing means to interpret accelerating global flows, as well as more mobile social interrelations. Social science found itself increasingly lacking in its ability to analyse patterns of non-causality, where small anomalies or impacts can result in large-scale shifts; where multiple actors/parts can create emergent ‘whole’ effects greater than the sum of its parts; where phases of equilibrium are maintained not through stability but dynamic instability or ‘order through chaos’; where contradictions work as part of a system; and when decentralised and bottom-up processes are increasingly becoming more effective against top-down hierarchical structures. Thus, the complexity sciences at this time emerged as a potentially significant tool for social science to better grasp and contend with these issues.

According to a major report from the Gulbenkian Commission:

Perhaps we are witnessing the end of a type of rationality that is no longer appropriate to our time. The accent we call for is one placed on the complex, the temporal, and the unstable, which corresponds today to a transdisciplinary movement gaining in vigour. (GCRSS, 1996: 79)

Complexity science not only resonates well with traditions of the social sciences, it also helps to bridge the gap between the natural and the social sciences, between disciplines and fields of knowledge. It encourages, and in some way demands, a shift to systemic thinking. Complexity also urges a break from mechanistic, linear, and causal methods of analysis towards viewing interdependence and interrelation rather than linearity and exclusion. Processes, flows, feedback cycles, fluctuations, networks, order from chaos, and dynamism are all features of the complexity sciences." (