Introduction

In this issue I noticed the abundance of editorials and editorial comment that already address, and present the contents of, the Special Edition, the Classic paper and warming comments on our earlier Volume. So, rather than add to the presentations of the papers here, I thought it might be more useful to raise some fundamental issues about complexity — ones that may provoke comments and papers in future editions.

The first one concerns the many different ways that complexity is considered, and the kind of responses that are implicitly or explicitly invoked. For example, my own personal definition of complexity is about systems that are capable of self-transformation — that is, new variables and parameters can appear within, and new attributes and capabilities can be perceived from without — emergence. However, for many scientists and engineers complexity often concerns the behavior of a system governed by nonlinear dynamical equations that can function in qualitatively different possible attractor basins. In this case new variables and parameters do not appear within the system, but new attributes and capabilities can be perceived from without, just as a sheet of paper can become an origami bird, vase or hat. The implication of both these approaches is that modeling can help understand what is going on, and that models can be used for some kind of exploration and learning. Obviously, for many people in the social and management areas however, such interpretations are far too ‘hard’ and complexity is about recognizing ‘complexity’ as a metaphor that shows us that organization and structures do not have to be hierarchical and top-down, but instead could be emergent forms that result from ‘bottom-up’ processes of interaction. This leads to discussions as to when top-down is better or worse than bottom-up, and in general the idea emerges that in a fast changing world, adaptive, learning behavior might be more easily obtained from the bottom-up approach.

Whilst this is a wildly oversimplified view of the hard-soft debate, it is true that this is a constant undercurrent to meetings and discussions about complexity. The latter soft approaches clearly do not tend to seek mathematical models or the use of formal representations of the situation to help understand and explore it. Instead they look to action research and to experimental interventions and reflection, perhaps collective or interactive, to create ideas, knowledge and a possible basis for decisions. The harder complexity people however, consider that this is not scientific and that it is necessary to create some formal representation of the situation in order to examine and explore its possible functioning. In this case, as well as marginal improvements, different possible attractor basins may suggest more radical innovations, and studies of the possible receptivity of a situation to some new element may also allow an understanding of a possible, qualitative change. The natural inclination of the ‘hard’ school is to use models to suggest and test new ideas, before trying them in the real world — where of course there may be many unexpected responses.

Really this raises the issue of whether one can understand a system by observing its external behavior (from data of flows, etc.) or whether there is a hidden part that is internal to the elements which cannot be observed from the data. Clearly, human motivation, purpose, beliefs and perceptions are not directly visible in normal data, but are absolutely vital in understanding what might happen in the event of some change. The answer then is that in all complex systems it is necessary to attempt to understand the inside and the outside of the system (and of its elements) in a connected way. Models that neglect the underlying goals and aims of the participants are clearly superficial and only describe the past, and totally miscalculate the response to some imposed change. On the other hand, organizations that can develop the collective system while/by harnessing these underlying forces may well be much stronger and more able to deal with change. In reality then, complexity goes beyond the hard-soft argument. In an organization involving a complicated supply chain, and many delicate logistical problems to deal with, it would be absurd to neglect information and hard modeling knowledge needed to make decisions about possible changes. Equally important however, would be obtaining some understanding of the impact of different possible contractual arrangement or issues of trust or distrust within such a system, factors not easily visible in the statistics of the flows. If axiology is the study of the values, aims and goals that people have, then clearly, complexity leads us to a view in which ontology coevolves with the coevolving epistemology and axiology of individuals. In other words the hard and the soft are coupled and their paths are intertwined. Studying one and neglecting the other will be a fairly fruitless exercise. It seems likely that wisdom is about having an understanding of how the inside and the outside coevolve, rather than acting on the basis of a short term view and current desires. Complexity science is therefore about wisdom rather than knowledge, and really requires us to reflect on the interaction over time of an external reality, some of which is hard (physical, legal, logistical) and some of which is soft (the epistemology and axiology within others) and our own internal goals, aims, perceptions and degree of autonomy.

Quite a programme!

Anyway, I found the papers in this issue most interesting and think that they take us an important step forward in our reflections on complexity.