Systemic Complexity Thinking
From the report: Digital Nexus of Post-Automobility
Dennis and Ury:
"Complexity theory, as a development from chaos theory, has emerged as an approach for analysing non-linear processes. Specifically, how components of a system through their dynamic interaction ‘spontaneously’ develop collective properties or patterns that are not implicit within, or at least not implicit in the same way, within individual components (see Urry 2003: chap 2 for a fuller account). Complexity investigates emergent properties, certain regularities of behaviour that somehow transcend the ingredients that make them up. Complexity thinking transforms scientific understanding of far-from-equilibrium structures, of irreversible processes, and of non-Euclidean mobile spaces. It emphasises the nature of strong interactions occurring between the parts of systems, with often the absence of a central hierarchical structure that ‘governs’ and produces outcomes. These outcomes are both uncertain and irreversible.
One of the most influential developments to have contributed to a self-organising systemic theory, and to the complexity sciences, is Prigogine’s theory of ‘dissipative structures’. His work (1980; Prigogine and Stengers, 1985; Prigogine, 1997) on dissipative systems showed that open systems existed far-from-equilibrium and sustained themselves through maintaining energy flows, thus shifting from entropic reactions to negentropic ones. Systems in states far-from-equilibrium, it seemed, increased in complexity through an internal generation of networks by using a through-flow of energy from an external environment, while constantly maintaining a dynamic state of stable order. How the open-system responds shows whether it adapts to new circumstances in terms of growth, or whether it collapses. A nonequilibrium system, Prigogine observed, ‘may evolve spontaneously to a state of increased complexity’ (1997: 64). This increase in the complexity of a system is a successful utilisation of the energy influx that, at times, is created by the disturbance itself. Complexity then appears to be a process whereby open-systems self-organise their dynamic interior networks in response to its environment.
The pluralistic character of the complexity sciences enables it to function within various disciplines since ‘there is as yet no single science of complexity but, rather, a number of different strands comprising what might be called the complexity sciences’ (Griffin, Shaw and Stacey, 2000: 85). The interdisciplinary approach of complexity thinking is central to framing how various components of a system through their interaction ‘spontaneously’ develop collective properties or patterns. However, because of the complex interdependencies of systems it is almost impossible to predict what would be the appropriate means of effecting change. There are just so many unintended consequences across time and space of economic, social and political innovation; and these consequences themselves engender further adaptive and evolving system consequences. And not all changes.
Abbott argues that while change is the normal order of things and indeed many assessments of contemporary social life emphasise the increasingly accelerating nature of profound changes, there are certain systems that get stabilised for very long periods of time (2001). They are path dependent. Causation flows from contingent events to general processes, from small causes to large system effects, from historically or geographically remote locations to the general (Arthur 1994; Mahoney 2000). Path-dependence means that the ordering of processes through time significantly influences the non-linear ways in which they eventually turn out decades or even centuries later. Path dependence is a process model in which systems develop irreversibly through a ‘lock-in’ but with only certain small causes being necessary to prompt or tip the initiation of the original ‘path’. Such small causes are mostly unpredictable, difficult to foresee although in hindsight they appear explicable in terms of how they tipped the system into path dependent patterns. Lock-ins mean that institutions matter a great deal as to how systems develop over the long time. Institutions, using this term very broadly, can produce a long term irreversibility that is: ‘both more predictable and more difficult to reverse’ (North 1990: 104)."