Editor's Note (5.4):
Why Emergence? Why Complexity?

Michael R. Lissack

As the fifth volume of Emergence draws to a close and with it my tenure as its editor, I felt it was important to review the mission of the journal and why those of us involved with it feel it is important to study questions of complexity and emergence.

Phil Anderson got us started:

A movement is underway toward joining together into a general subject all the various ideas about ways new properties emerge. We call this subject the science of complexity. Within this topic, ideas equal in depth and interest to those in physics come from some of the other sciences. This movement is overdue and healthy. On the other hand, one may well be apprehensive… We complexity enthusiasts (perish the thought that we be called complexity scientists!) are talking, at least for the most part, about specific, testable schemes and specific mechanisms and concepts. Occasionally we find that these schemes and concepts bridge subjects, but if we value our integrity, we do not attempt to force the integration. (Anderson, 1991: 11)

When we began the journal we noted:

Complexity theory research has allowed for new insights into many phenomena and for the development of new manners of discussing issues regarding management and organizations… The most productive applications of complexity insights have to do with new possibilities for innovation in organizations. These possibilities require new ways of thinking, but old models of thinking persist long after they are productive. New ways of thinking don't just happen; they require new models which have to be learned. Emergence is dedicated to helping both practicing managers and academics acquire, understand and examine these new mental models.

The articles we have printed and the many emergence- and complexityrelated activities we have been a part of have gone a long way toward a fruitful examination of these mental models. But, merely examining mental models is insufficient. The immediate challenge arises of what to do with the products of such examinations. Here complex systems theory seems to have tripped. The forced integration that Anderson feared has indeed often been forced. The traditional answers of how “complexity” can be applied contain their own deep echoes of linear thought in their search for simplicity. Both the holist tradition and the reductionist tradition study complexity, not for itself, but to reveal a putative simplicity—either the simplicity of a holistic ideal or the simplicity of reductionist components (Emmeche & Hoffmeyer, 1991). While this search for simplicity at the core of complexity may be innately appealing, it is confoundedly misleading. The simplicity so expressed may be descriptive of a subset or of the observer, but it misses the essential character of the system supposedly being described.

As Roger Bradbury (2000) put it:

Models of complex adaptive systems are more problematic. They challenge the idea, developed since Newton's time, that good models equals simple models equals predictive models… In the first place, complex adaptive systems are characterized by many different entities, most of which are more or less weakly interacting, and with interactions that are diffuse and non-linear…When we use the same ideas to analyze the modeling of these systems as we use to model simple physical systems, we come up with different results. We find that “getting things right” and “capturing the essentials” are themselves more complex processes than for simple systems. We find that the character of the modeled complex adaptive system is embedded in the fine detail of the many entities and their interactions, not in the gross pattern of a few strong linkages. We find, in fact, that we need to build a model complex adaptive system in order to model a complex adaptive system.

Thus the first lesson of complexity research: Complex systems can only be adequately described, modeled, or characterized by other complex systems—anything else is merely a label, a facet, or a situated event of the system in question.

This is especially true when we think about social systems. As Kurt Richardson (private correspondence) notes:

A major difference between social systems and natural systems is that natural systems generally don't behave differently just because we think about them differently. A good model is a good model because it reflects some essential aspects of reality to a degree that facilitates successful action. Social systems can be considered a non-trivial sum of all our models of them. Social reality is co-determined by our thoughts about social reality—this is not such a big deal (one might even go as far as to suggest that it is mostly an irrelevant concern) in physics.

The feedback effects of models, labels, and stories about complex social systems mean that simple characterizations will leave out much more than they will capture. Only with many such characterizations and their interweaving into complex stories can something actionable be understood.

When complex systems research supposedly reveals power laws, CA formulations, NK models, phase space attractors, managed chaos, or other mathematical or statistical properties, we need to recognize that these are themselves labels describing situated events or facets and are not the essential characteristic of the system. It may be interesting to know that Zipf's law can plot city size, but that tells you little about any given city or its many networks. It may be convenient to know that the social network of company G is scale free, but you really need to know who is a critical node and what the effect will be of removing some of the “at this time” critical people.

The complexity of complexity means that these labels are factors in a larger story; the skill to be learned from complexity research is to tease out enough labels and enough factors to tell a complex story about the complex system. The complex story need not be as complex as the system to convey enough of the system's character to educate the observer about system potentials. An understanding of the potentialities of the system rather than predictions of system state is the goal. Such understanding allows for considerations of robustness, interference, and constraints to have context and meaning.

This is the second lesson of complexity research: The object of the research itself is potentialities, robustness, interference, constraints, context, and meaning.

Complexity and emergence studies have also provided the formalisms necessary to underpin a reality often challenged by those who prefer what may be called the simplist view (the idea that an underlying or overarching simplicity is what matters)—as considerations of “scale” change, ontologies change as well. Here scale may mean size, dimensionality, orthogonality, ensemble size, connectivity, or perspective (inside, outside, and/or degree of connectedness). Dynamic ontologies not only have shifts in what is but also shifts in the methodologies, perspectives, and methods that allow for the labeling of what is and the recognition of the properties and functionalities of what is. Those who prefer the simplist view dismiss all this as “relativism.” Complexity and its math demonstrate that the relativism may hold in situ, but that what matters if a system is to be described or understood is integrative pluralism—the consideration of the ensemble of relativisms and the situations in which they might apply with an effort to integrate the ensemble into a whole. Emergence is the label for the shifts in understanding which relativisms appear applicable in which situations across the ensemble.

Complexity research is a quest for understanding—understanding complex systems. Charles Sanders Pierce called the methodology behind such a quest “abduction.” Hugo Letiche and I (forthcoming) have labeled its pursuit in human organizations the “quest for coherence.”

Emergence the journal has been an abductive inquiry into potential coherence in organizations. That inquiry continues as the journal takes on the next phase in its ontology. Beginning with the next issue we will be called Emergence: Complexity and Organisation. Our publisher shifts to Palgrave/Macmillan and we return to print format as well as electronic. The shift from organizations to organization was deliberate—the focus has broadened from the effects inside organizations to the very process of organization itself.

I turn the editorship of the journal over to a notable threesome: Peter Allen, David Snowden, and Jeffrey Goldstein. Notice the shift in ontology—I was but one, they are three. Three editors because the field of inquiry has broadened over five years, as has our audience. Emergence began when the future of complexity research was still in question; indeed, our second issue asked the question “Is this merely a fad?” Emergence or ECO continues with a robust answer of no, this is no fad, it is the beginning of a major shift in perspective. From the simple to the complex… from the label to the story… from relativism to pluralism… from ubiquity to in situ … That is the story of emergence.