Complexity & Postmodernism

Complexity & Postmodernism

Understanding Complex Systems

by Paul Cilliers

London: Routledge, 1998

ISBN 0-415-15287-9

Review Copyright © 2000 Garret Wilson

January 31, 2000, 2:00 p.m.

When discussing the new science of complexity, there's often a tendency to address the issues that one more readily thinks of as "science," with equations; pictures of chaotic attractors and fractals; and discussions of the Feigenbaum number. Applications of complexity theory to something other than charting shorelines or tracking blood flow are often only alluded to. Paul Cilliers' Complexity & Postmodernism goes beyond the numbers and pictures to discuss how complexity provides paradigms for discussing learning, philosophy, and even ethics. In a relatively short, readable work Cilliers manages to fit not only an introduction to complexity theory in general but also an explanation of neural networks, approaches to linguistic representation theory, and branches of philosophy — and it all fits together very nicely.

Cilliers first makes clear that his discussion centers around complexity itself, which is in his mind quite separate from the older science of chaos. "My claim is rather that chaos theory, and especially the notions of determinisitic chaos and universality, does not really help us to understand the dynamics of complex systems," he says, and thus embarks on a road of holism, of coherence, and of sums-greater-than-parts.

The fundamental model of a complex system presented here is, appropriately enough, is the neural network; Cilliers provides an excellent explanation which even the non-computer-literate can easily comprehend. From this groundwork he immediately addresses differences in models of language structure and understanding. Most of the thoughts here are based upon the work of Saussure, later embellished by Derrida. Here, as in other areas of the book, Cilliers excellently illustrates how complexity theory re-frames, supports, and furthers independent lines of thought across several disciplines.

Saussure claims that words in themselves carry no meanings, but that meaning is communicated by how words (as signs) differ from others in the language (or system) (39). Derrida furthers this by saying that meaning is only held in this différance between signs, and that meaning is only found in a work through traces. As soon as a word is used in the system in a particular context, that use slightly modifies any meaning found in the system, both for the word being used and for other signs in the system. "As soon as a certain meaning is generated for a sign, it reverberates through the system" (44).

These thoughts by Saussure and Derrida are very much part of a specific area of thought pertaining to the philosophy of meaning and representation in language. After Cilliers is finished with his explanation, it seems quite obvious how complex systems apply: words in a language (as signs in a system) are no more than nodes in a neural network. There is a very real analogy to how connectionist systems (i.e. neural networks) encode meaning not in any particular node, but by a holistic arrangement of all the nodes. Signs then do not take on any particular meaning (82), but rather represent meanings through their use in the system, at the same time changing whatever meaning is being generated by the very usage of these signs.

The signs in a system therefore encode no actual meaning — there is no true global representation. Similarly, history is important but elusive in complex systems. "No complex system, whether biological or social, can be understood without considering its history. Two similar systems placed in identical conditions may respond in vastly different ways if they have different histories. To be more precise, the history of a system is not merely important in the understanding of the system, it co-determines the structure of the system" (107-108). However, "Global behaviour of the system is the result of 'patterns of traces'... The same arguments hold for memory in the context of the brain. Memories are not stored in the brain as discrete units that can be recalled as if from a filing cabinet" (108). This entire discussion brings up the fascinating aspect that "there are no 'memories'" (108). While there may be actual events that happened in the past, they can never be accurately reconstructed from the contents of the network, because what are left are only traces. The formation of the system interacts with the actual history reflexively, each altering the other.

Throughout these discussions, Cilliers advances deep into philosophical territory, yet explains all the concepts as if one were completely unfamiliar with the subject. Derrida's deconstruction, for example, is explained as the pointing out of contradictions that occur when one uses traditional ideas of meaning and representation and in doing so claims a "privileged position" for a particular view or expression (81).

If complexity invades thought, memories, history, and meaning, it is not a large stretch to assume that it might affect ideas of morality as well. Cilliers, following logically from his analysis of neural networks and philosophy of representation, makes some startling conclusions about ethics, following ideas of Cornell:

Cornell's suggestion (following Derrida, and reformulated in my terminology) is to take present ethical (and legal) principles seriously — to resist change — but to be keenly aware of when they should not be applied, or have to be discarded. We therefore do follow principles as if they were universal rules (Cornell and Derrida use the term "quasi-transcendental"), but we have to remotivate the legitimacy of the rule each time we use it. To behave ethically means not to follow rules blindly — to merely calculate — but to follow them responsibly, which may imply that the rules must be broken (139).

Of course, complexity and postmodernism having similarities in the areas stated above, they have similar problems of explanation: by denying overall algorithms (or, as Lyotard says, "meta-narratives), an effort to encapsulate such a system would therefore in itself seem to be invalid. Cilliers is aware of this, and does not attempt to meet every criticism that might be offered to his approach; instead, he tries to share "the spirit in which this book is offered: one of openness, provisionality and adventure" (142).

There are many introductory works on complexity theory, but few that strike a balance between algorithmic explanations and postmodern philosophy as does Complexity and Postmodernism. Cilliers does a fine job of bringing together two areas of thought, mathematics and philosophy, into one coherent holistic (to use a term important to complexity theory itself) pictures. It would be a loss for someone from either field to miss this well-discussed work.

Note: This work was read over a period of many months as part of research on my MA dissertation. General notes on the book are included below.