3.2 Complexity Science « Deliverable 2.4: EISB Models and Tools

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Human life is frequently described as becoming more and more complex, and rightly so. It seems that the terms “complex” or “complexity” appear everywhere. In some part, this is because life really is complex, but this conclusion is also driven by the fact that over the last few decades, much has been learned about the nature of complexity and the role that complexity plays in our lives¹. Complexity science has become a broad ranging subject appreciated in a variety of ways and illustrated in many books such as²,³,⁴,⁵,⁶,⁷,⁸, just to point some of the most referenced.

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Nowadays, it is viewed as a source of concepts for enabling the trans-disciplinary exploration of complex organizations in the networked economy and society, and for explaining the dynamics of networked systems at different levels of description (from the micro- to the macro-level). It offers a powerful set of methods for explaining non-linear, emergent behaviour in systems. Being such interdisciplinary, complexity science is approached differently by three major schools⁹,¹⁰:

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• European: which follows the theories of Nicolis Prigogine¹¹, as well as other recognized authors (e.g. Mainzer¹² ). It focuses on the discovery that certain levels of negentropy in physical systems at the “edge of chaos”, cause aggregate “dissipative structures” to emerge from a stochastic “soup” of microstates. According to the European school, theses structure evidence predictability, thus providing scientific explanations to complexity at the edge of chaos. It is a mathematical intensive school;

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• American: which focuses on how new order arises in biological and social systems. Represented by the “The Santa Fe Institute ”, it studies the so-called “edge of chaos” where order emerges when heterogeneous agents – such as biomolecules, organisms, people or social systems – are motivated, by a drive for improved fitness or learning, to initiate connections with other agents. Micro-systems operating at the vicinity of the edge exhibit creativity and produce new and novel behaviours at the whole system¹³;

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• Econophysics: Where the focus is on how the order creation actually unfolds once the forces of emergent order creation by self-organizing agents are set in motion. Key parts of this third aspect are fractal structures, power laws and scale-free theory.

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The science of complexity is, therefore, the study of emergent order in what are very disorderly systems. Spirals in whirlpools, funnels in tornadoes, flocks of birds, schools of fish, are all examples of orderly behaviour in systems that are neither centrally planned nor centrally controlled. In a sense, complex systems innovate by producing spontaneous, systemic bouts of novelty out of which new patterns of behaviour emerge. Understanding its influence on the performance of organizations could lead to major gains in driving businesses¹⁴.

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Complexity science introduces a new way of studying. It posits simple causes for complex effects, with rules that typically determine how a set of artificial agents will behave in their virtual environment over time, including their interaction with other agents. Unlike traditional science, it does not predict an outcome for every state; instead, it uses feedback and learning algorithms to enable complex adaptive systems (CAS) to adapt to its environment over time. The application of these rules to a large population leads to emergent behaviour that may bear some resemblance to real-world phenomena¹⁵. Advocates of complexity theory see it as a means of simplifying complex systems. However, due to the absence of a single identifiable complexity theory, the actual practice is not simple. Instead, a number of theories concerned with complex systems gather under the general banner of complexity research¹⁶.

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Complex networks are referred to as “adaptive” or “dynamic”, because they are constantly changing their interrelationships based upon the needs of individual agents and environmental impacts¹⁷. Figure 3 2 summarizes most of what has been described in this section concerning complex systems and networks: on the left their natural behaviour is illustrated, from multiple components dynamically interacting and giving rise to complex structures exhibiting common behaviour across multiple disciplines; on the right side, one could observe the phenomena of emergence; and on the centre, it illustrates the grouping of simpler systems into larger complex ones.

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It is possible to examine complexity on a discipline-by-discipline basis, and according to Manson (¹⁸ ), breaking complexity research into three major divisions affords a more coherent understanding of complexity theory. He divides it into Algorithmic complexity, in the form of mathematical complexity theory and information theory, for describing the complexity of internal system characteristics; Deterministic complexity dealing with chaos and catastrophe theory, for studying how the interaction of two or three key agents can create sudden discontinuities in stable systems; and Aggregate complexity concerning how individual elements work together to create systems with complex behaviour.

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Complexity Science, namely the research available under aggregate complexity has been important when applied to economics and organizational management, and can also be relevant to the study of enterprise and systems interoperability. In fact authors as in Merali McKelvey (¹⁹ ) believe that the information systems researchers of the 21^st century should do a paradigm shift towards a more dynamical theoretical and methodological framework better suited to support idiosyncratic aspects of IS, business and organizational domains. In that sense, aggregate complexity can be the answer, since:

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It contains mature concepts and methodologies focused on accessing the holism and synergy resulting from the interaction of system components, as occurs in Enterprise networks;

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If we consider that a system is a very broad concept that can enclose Software, Services, Enterprises, and even Networks, then it can be closely related to many of the EISB areas, understanding the interactions between:
- information models and schemas;
- business processes;
- software applications and services;
- ontologies;
- clouds of services and enterprise ecosystems;
- people;
- etc.

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Aggregate complexity illustrates how relationships are more important than attributes in defining the nature of components²⁰. The domain of Complexity addresses a number of inter-related concepts, which we summarise in Table 3‑5. In Section 3.2.1 we attempt to identify the relationships between these concepts and issues of enterprise interoperability. In Section 3.2.2 we go on to identify formal and descriptive methods which can be adopted in EI applications and which therefore must be associated with the EISB through its links with neighbouring domains.

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