Three systems paradigms

Systems have been studied from a variety of different perspectives. Over time the types of systems in focus have also varied, which has led to many different systems theories. Three paradigms can be identified in this development of systems research, as outlined below (for a full elaboration of the paradigms, see Stähle, 1998, pp. 13-98).

The first presentation of a concrete system with scientifically verified laws was Isaac Newton’s model of the solar system as put forward in his Principia (1687/ 1972), which created the foundation for the first systems paradigm. Since then, the Newtonian model has been applied to almost all scientific research. The Newtonian perspective to studying systems is characterised by linear thinking, cause-and-effect thinking, determinism, predictability, universal laws, principles and regularities, as well as preservation and quantification. The research conducted under this paradigm aims to explain and define natural laws and principles and to predict events conforming to the formulated theories. Ultimately, this perspective resulted in a theory that considered systems as machine-like entities following predetermined laws.

The second paradigm started from von Bertalanffy and his understanding of open systems. Systems were now no longer seen as machines but living organisms, and the perspective shifted from closeness to openness. While a closed, mechanistic system had just one optimal way of achieving its goal, an open system has access to multiple avenues. Open systems are flexible, self-regulating, and depend on their environment for survival. They are constantly striving towards equilibrium, as instability is hazardous to the system. Feedback is crucial: the system needs input, throughput, and output in order to maintain its stability (Von Bertalanffy, 1950). This research tradition has gained substantial ground since the 1950s and is still very popular today. Although the open systems view originates in biology, this hypothesis is theoretically grounded in physics: all open systems thinking stems from the second law of thermodynamics, which says that all systems, when left to themselves, are destined for disorder and disintegration. Since the system’s survival is thought to require this steady state, maintaining stability is the primary focus of open systems thinking.

The third paradigm concentrates on dynamic, chaotic systems that are capable of self-organisation. While the focus was earlier on systemic order, the research emphasis has now shifted to disorder and to the relationship between chaos and the emergence of order. The starting point for the new emerging paradigm was Edward

Lorenz’s chaos research and the famous “butterfly effect” (Lorenz, 1963), but the broadest theoretical contributions have come from two sources. First, Belgian physical chemist Ilya Prigogine published his studies on non-equilibrium statistical mechanics in 1962 and on dissipative systems in 1967. His studies provided systems research with a completely new perspective on how systems reorganise unpredict-ably and without external control. Prigogine does not contradict the second law of thermodynamics, but instead shows its limitations and argues that most systems are capable of self-organisation (see e.g., Prigogine, 1980). The other revolutionary approach was the autopoiesis theory put forward by Chilean biologists Humberto Maturana and Francisco Varela in the early 1970s. They introduced the term autopoiesis to describe how each living system reproduces its nucleus and struggles for self-renewal (see e.g., Maturana & Varela, 1980, 1992).

The third systems paradigm marked a fundamental break in the understanding of systems. The relationship of individual and system (every individual is always part of a system) and the internal dynamics of systems (selforganisation requires a chaotic state) were now seen in a new way. Theoretically, all these changes profoundly altered the starting point for systems sciences. This radical paradigm shift in the 1970s brought to light the extreme complexity of systems and the significance of chaos for the self-renewal and transformation of systems. This evolution towards quantum physics opened up a broader theoretical perspective with its emphasis on discontinuity, non-determinism, and non-locality.

These three systems paradigms highlight diverse characteristics and dimensions of systems. Although they date back to different eras, all three continue to have relevance today. Nonetheless they do differ in terms of their explanatory power. The third systems paradigm has particular explanatory power in today’s volatile world where ecosystems and connectivity are created by the internet and virtual platforms. This does not mean to say it has universal applicability, however. We still have problems that can be well-defined and systems that are controllable and relatively stable. Furthermore, most real-life systems consist of various subsystems that can be closed, open, or dynamic. For instance, business organisations are usually system holograms, that is, simultaneously comprising of mechanistic, organic, and dynamic subsystems (Stâhle & Gronroos, 2000; Stâhle, Stâhle, & Pôyhônen, 2003). Recently, however, the dynamic systems paradigm has gained increasing prominence because of its substantial explanatory power, which at once has demonstrated the limitations of the other system paradigms (see Table 12.1).