University of Stellenbosch, ZAF
The aim of this article is to investigate the implications of a general theory of complexity for social institutions and organizations, such as business corporations. Complexity theory has implications for the way we conceive of the structure of an organization, as well as for the way in which complex organizations should be managed. However, a preliminary warning is necessary: The lessons to be learned from the study of complexity are somewhat oblique. Any hope that a study of complex systems will uncover the way of running an organization is in vain. While we will not come up with a quick fix, the lessons are most certainly important.
The first half of the article will investigate what we can learn from a theory of complexity. Most of these insights are widely accepted, but it is useful to revisit them briefly. This general understanding of complex systems also provides the background to the second half of the article, in which I investigate what we cannot learn from complexity theory. The “negative” part of the article is at least as important as the “positive” part. There I will investigate the unavoidability of an ethical dimension to all decisions made in a complex environment.
I will not provide a detailed description of complexity here, but only summarize the general characteristics of complex systems as I see them.1
Certain systems may display some of these characteristics more prominently than others. These characteristics are not offered as a definition of complexity, but rather as a general, low-level, qualitative description. If we accept this description (which from the literature on complexity theory appears to be reasonable), we can investigate the implications it would have for social or organizational systems.
The notion of complexity has been applied to organizations in a number of different ways, and with varying degrees of rigor. I would like to emphasize two things. In the first place, the principles discussed here are of a very general nature. The contingent conditions at stake when investigating a specific case will be relevant, and may radically affect the importance of some of the implications. Despite this remark, I wish to stress, secondly, that this does not mean that the acknowledgment of the complexity of a situation allows us to be vague, nor does it imply a chaotic state of affairs. Complexity theory has important implications for the general framework we use to understand complex organizations, but within that (new) framework we must still be clear, as well as decisive.
These few implications of complexity theory for organizations are important, and can dramatically affect our understanding of complex organizations. They can be spelled out in much more detail, but as I insisted above, this will have to be done in the context of specific organizations and their contingent conditions. In order to do that, we should also be clear about what we cannot learn from a theory of complexity.
I hope to show that the implications of this negative part of the article are at least as important as those following from the positive part. Acknowledgment of the limitations of our knowledge lies at the root of the whole western tradition of Socratic philosophical reflection, but I am sure that the mere acknowledgment of limitations is not enough. On the one hand, it suppresses the challenge to shift the boundaries of our knowledge. On the other hand, it stops short of investigating the ramifications of this limitation. I want to argue that one important consequence is that we are forced to take up an ethical position.
What are the limits of a theory of complexity? Looking at the positive aspects we discussed above, you will notice that none is specific. They are all heuristic, in the sense that they provide a general set of guidelines or constraints. Perhaps the best way of putting it is to say that a theory of complexity cannot help us to take in specific positions, to make accurate predictions. This conclusion follows inevitably from the basic characteristics discussed above.
In order to predict the behavior of a system accurately, we need a detailed understanding of that system, i.e., a model. Since the nature of a complex system is the result of the relationships distributed all over the system, such a model will have to reflect all these relationships. Since they are nonlinear, no set of interactions can be represented by a set smaller than the set itself—superposition does not hold. This is one way of saying that complexity is not compressible. Moreover, we cannot accurately determine the boundaries of the system, because it is open. In order to model a system precisely, we therefore have to model each and every interaction in the system, each and every interaction with the environment—which is of course also complex—as well as each and every interaction in the history of the system. In short, we will have to model life, the universe and everything. There is no practical way of doing this.
Before I continue, two qualifications are required in order to prevent misunderstanding. The first is to re-emphasize that this is not the same as saying that complex systems are chaotic. Emergence is not a random or statistical phenomenon. Complex systems have structure, and, moreover, this structure is robust. Secondly, this does not imply that there is no point in developing formal models of complex systems. We can develop models on the basis of certain assumptions and limitations, just as with any scientific model.
Let me put the matter in slightly different terms. The prediction of complex behavior is only possible as a form of generalization. However, when we deal with a complex system, we can never escape the necessity of facing the particular nature of the system at any given moment. Since we do not know the boundaries of the system, we never know if we have taken enough into consideration. We have to make a selection of all the possible factors involved, but under nonlinear conditions we will never know if something that was left out because it appeared to be insignificant was indeed so.
What does this amount to in practice? It means that we have to make decisions without having a model or a method that can predict the exact outcome of those decisions. A theory of complexity cannot provide us with a method to predict the effects of our decisions, nor with a way to predict the future behavior of the system under consideration. Does this mean we should avoid decisions, hoping that they will make themselves? Most definitely not. We cannot avoid them. Without activity in the system, without the energy provided by engaging with the system, it would probably wither away into a state of equilibrium, another word for death. Not to make a decision is of course also a decision. What, then, are the nature of our decisions? Because we cannot base them on calculation only—calculation would eliminate the need for choice—we have to acknowledge that our decisions have an ethical nature.
I want to make clear how the notion of ethics is used here. I do not take it to mean being nice or being altruistic. It has nothing to do with middleclass values, nor can it be reduced to some interpretation of current social norms. I use the word in a rather lean sense: it refers to the inevitability of choices that cannot be backed up scientifically or objectively.
Why call it ethics? First, because the nature of the system or organization in question is determined by the collection of choices made in it. There are, of course, choices to be made on all scales: major ones, as well as all the seemingly insignificant small ones made all the time—and remember that the scale of the effect is not related to the scale of the cause. In a way, the history of the organization is nothing else but the collection of all these decisions. Secondly, since there is no final objective or calculable ground for our decisions, we cannot shift the responsibility for the decision on to something else—“Don't blame me, the genetic algorithm said we should sell!” We know that all of our choices to some extent, even if only in a small way, incorporate a step in the dark. Therefore we cannot but be responsible for them. This may have a pessimistic ring to it, but that need not be the case. An awareness of the contingency and provisionality of things is far better than a false sense of security. Such an awareness is also an integral part of the notion “adaptive.”
Of course, this does ultimately translate into a value system, but this system is not a given, something that is governed by a priori notions of good and bad. The system of values is itself a matter of choice. Our decisions are guided by some notion of what we think the organization should be—and it is in this “should” that the ethical dimension is contained. If an organization decides “The bottom line is our first priority,” then that is the kind of organization it would be: nothing comes in the way of money. The central issue here is that a system of values is exactly that. Values are not natural things that we can read off the face of nature; we choose them. It is not written in the stars that the bottom line is vital to the survival of a company, it comes with accepting a certain understanding of what a company should be under, say, capitalist conditions. Of course, it is not only the nature of the organization that is determined by choices, but also our nature as individuals. We are also the result of our choices. Thieves are not thieves when they are caught out, or found guilty under some legal system. Thieves are thieves when they steal.
A further implication of this “ethical” position needs to be spelled out. “Ethics” is part of all the different levels of activities in an organization.
These ethical components, related to the values and preferences of the members of the organization, are often referred to as merely “politics,” something separate to the organization's real operation and goals. The argument here is that the political aspects of the interactions in an organization are not something extraneous to the workings of that organization. It is not something that has to be dealt with in order to guarantee the proper working of the organization, it is integral to its proper working. The individual and collective values of members of the system cannot be separated from their functional roles. This point is probably instinctively accepted by most good managers. The fact of the matter is that this is the case, whether it is accepted by management as such or not.
To summarize the argument: The ethical position is not something imposed on an organization, something that is expected of it. It is an inevitable result of the inability of a theory of complexity to provide a complete description of all aspects of the system.6
It may appear at this stage as if I am arguing against any kind of calculation, that I am dismissing the importance of modeling complex systems. Nothing is further from the truth. The important point I want to make is that calculation will never be sufficient. The last thing this could mean is that calculation is unnecessary. On the contrary, we have to do all the calculation we possibly can. That is the first part of our responsibility as scientists and managers. Calculation and modeling will provide us with a great deal of vital information. It will just not provide us with all the information. Perhaps I am wrong here: it may become possible for some sophisticated model to provide all the information about a specific system. The problem would remain, however, that this information has to be interpreted.
All the models we construct—whether they are formal, mathematical models, or qualitative, descriptive models—have to be limited. We cannot model life, the universe, and everything. There may not be any explicit ethical component contained within the model itself, but ethics (in the sense in which I use the term) has already played its part when the limits of the model were determined, when the selection was made of what would be included in the frame of the investigation. The results produced by the model can never be interpreted independently of that frame. This is no revelation, it is something every scientist knows, or at least should know. Unfortunately, less scrupulous people, often the popularizers of some scientific idea or technique, extend the field of applicability of that idea way beyond the framework that gives it sense and meaning.
My position could be interpreted as an argument that contains some mystical or metaphysical component, slipped in under the name “ethics.” In order to forestall such an interpretation, I will digress briefly. It is often useful to distinguish between the notions “complex” and “complicated.” A jumbo jet is complicated, a mayonnaise is complex (at least for the French). A complicated system is something we can model accurately (at least in principle). Following this line of thought, one may argue that the notion “complex” is merely a term we use for something we cannot yet model. I have much sympathy for this argument. If one maintains that there is nothing metaphysical about a complex system, and that the notion of causality has to be retained, then perhaps a complex system is ultimately nothing more than extremely complicated. It should therefore be possible to model complex systems in principle, even though it may not be practical.
Would the advent of adequate models of complex systems relieve us from our ethical responsibility? My contention is that it would not. Here is why: We cannot make simple models of complex systems. Their nonlinear nature, or, in other words, their incompressibility, demands that the model of a system be as complex as the system itself. If it is in the nature of the system to behave, at least sometimes, in novel and unpredictable ways, the model must also do so. In any case, how would we be able to determine if the model were indeed an adequate model of the system if we were already in trouble when trying to decide what constitutes the system itself? It would be as difficult to interpret the model as to interpret the system itself.7 Good models of complex systems can be extremely useful; I just do not believe that they will allow us to escape the moment of interpretation and decision.
Whatever we take the notion of ethics to mean, our analysis of what we can and cannot learn from a theory of complexity has shown that a proper reflection on complex organizations will have to involve the humanities. Perhaps we can describe the humanities as those disciplines that realize that their subject matter cannot be studied only by formal means.
There are, of course, a number of disciplines that immediately come to mind: political science, sociology, psychology, and, of course, philosophy. Allow me the opinion that philosophy, the mother of all the sciences—but in an instrumental- and outcomes-based world often seen as redundant—may yet prove to be one of our greatest resources. The need to reflect critically on the nature and the limits of our knowledge and understanding is indispensable to a study of complexity.
I do not, however, want to end with that cheer for the home team. I also want to stress the importance of the arts. Artists through the ages have attempted to find new ways of portraying and understanding the complexities of our world. Under certain conditions, a good novel may teach us more about human nature than mathematical models of the brain, or the theories of cognitive psychology. An engagement with the arts should not be a luxury in which we indulge after “work,” it should be intertwined with our work. Faced with the complexities of life, we all have to be artists in some sense of the word. It is to be hoped that this will not only help us to a better understanding of our organizations, it will also make us better human beings.
This article is based on a paper delivered at Managing the Complex, the Third Annual Symposium of the New England Complex Systems Institute, held in Boston, March 1999.
Bak, P. (1997) How Nature Works: The Science of Self-organized Criticality, Oxford: Oxford University Press.
Caputo, J. D. (1997) Deconstruction in a Nutshell, New York: Fordham University Press.
Cilliers, P. (1998) Complexity and Postmodernism: Understanding Complex Systems, London: Routledge.