From Complexity to Emergent Self-Organization
Scott Page is one of the major guides through the world of complex systems. He notes that complex systems not only contain many parts (complicated) they also contain parts that are interwoven with one another (Miller and Page, 2007). It is in this interweaving that we find the Lens of Essence being applied to a system—for the interweaving yields a structure that holds the system together and enables it to be agile and adaptive:
“While complex systems can be fragile, they can also exhibit an unusual degree of robustness to less radical changes in their component parts. The behavior of many complex systems emerges from the activities of lower-level components. Typically, this emergence is the result of a very powerful organizing force that can overcome a variety of change to the lower-level components.” (Miller and Page, 2007, p. 9)
Self-Organizing
Ilya Prigogine (1984), one of the other (and earlier) guides to the world of complex systems, won a Nobel Prize through his observation of this adaptive process in complex systems. He considered these systems to be “self-organizing.” There is no central control unit in these systems. Much as in the case of flocking birds and swarming fish, there is no one lead bird or fish; rather, there is an emergent interdependence of all members of the flock of birds or school of fish.
The actions taken by any one member of the system (be it an organization, flock or school) are strongly influenced by actions taken by the member next to it. This “neighbor” effect is very powerful and strongly influences the dynamics of most biological systems (including human systems). Our establishment of polystatic baselines and predictions is strongly influenced by the baselines and predictions established by our neighbors. It is not only the leading part in a system that can provide direction. In recent years, an analytic tool called Agent-Based Modeling (Wilensky and Rand, 2015) has emerged that is based primarily on the recognition and study of this neighborhood effect.
There are many benefits associated with this neighboring effect. First, there is no need for designating or empowering a leader. This saves time and resources. Second, there is the potential for greater agility. Influence and information flow through the system, unimpeded by formal hierarchy or chains of command. I bring in several concepts introduced in our first essay on essentials when considering several other important benefits of self-organization and the neighborhood effect. These concepts concern the process of Allostasis as identified by Peter Sterling (2020) and the delay functions that System dynamic theorists such as Donella Meadows (2008) suggest operates in all systems.
When we rely on information received from our neighbor, then the delay in the transmission of this information is much quicker than if it comes from a more distant source (such as a leadership command center). Furthermore, if allostasis rather than homeostasis is operating in most dynamic systems–as we considered in the essay on Essentials—then the ability to predict what is about to occur is critical in the ongoing adjustment of each member of the system to an ever-shifting environment. The neighborhood effect greatly aides this predictive capacity, for the predictive power of any one member of the system is much greater if it is predicting the behavior of its neighbor (proximal prediction) than if it is seeking to predict the behavior of some distant entity (be it a leader or some other member of the system) (distal prediction).
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