From Complexity to Life: On the Emergence of Life and Meaning edited by Niels Henrik Gregersen (Oxford University Press) brings together an impressive group of leading scholars in the sciences of complexity, and a few workers on the interface of science and religion, to explore the wider implications of complexity studies. It includes an introduction to complexity studies and explores the concept of information in physics and biology and various philosophical and religious perspectives. Chapter authors include Paul Davies, Greg Chaitin, Charles Bennett, Werner Loewenstein, Paul Dembski, Ian Stewart, Stuart Kauffman, Harold Morowitz, Arthur Peacocke, and Niels H. Gregersen.

A common idea of complexity is that complex things have a long complicated history, and that complexity must be understood in the context of processes in Nature generating systems with more parts, different parts, and special relations between various kinds of parts, forming a structure which must be described on several distinct levels of organization and as involving entities with emergent properties. These terms -- complexity, system, part, relation, structure, levels, emergent -- are all vague to some degree and should in principle be defined first, but for the present purpose it is sufficient to let the context fix their meaning. In this note I will address some questions about evolution of complexity and the attempts to measure complexity from the perspective of the philosophy of biology and the cross-disciplinary field of Complex Systems research (including the study of non-linear dynamical systems, chaos theory, Artificial Life, cellular automata, etc.).

General concepts about life, organization and complexity have a peculiar status within philosophy of science. In a sense, they reveal that one cannot draw clear demarcating lines between natural science and metaphysics taken as general ontological assumptions about our world. Indeed, this can be done analytically, but in practice the everyday life world of people in a modern society is perfused with the products of science and technology and moreover with ideologies and world views that are at least historically dependent upon the development of science. Still we can distinguish between phenomenological areas of experience (that are common to all and non-scientific) and specific scientific ways of exploring and explaining the everyday world. There is little doubt that we can talk about a more or less shared everyday notion of `complexity', vague, ill-defined, and fuzzy as it may be, which belongs to this pheno-world, and which scientists bring with them in their mental baggage into their respective disciplines, and which, however, for most of the time have been considered uninteresting and irrelevant for study in the exact sciences. One of the interested tellings reported to the public from the physical sciences during the past 15 years is that in contrast to the traditional scientific interests in the microscopically minute world of elementary particles and the cosmological very large aspects of the universe, which are both felt very remote and open to rather idealized but exact mathematical treatments, the physical sciences have taken new interest in medium sized everyday world of complex, living, irregular (but not totally random) phenomena that we encounter in our everyday lives. Complexity, not simplicity, is purported to have become the focus of research, and we are all apparently supposed to know, at least intuitively, what it means.

As science for all its history has studied the complex phenomenal world to reveal the secrets of is appearances, it should not surprise us that `complexity' itself could be its subject matter. However, from a certain `local' perspective, it might seem a bit bizarre to imagine a truly general scientific concept of complexity. In specific fields such as evolutionary biology, molecular genetics, or the computational study of `life-like' automata within Artificial Life, one finds precise and even operational concepts of complexity for specific scientific purposes, but the point of departure for these concepts is often rooted in everyday notions of complexity, and the concluding insights drawn from such studies may also interfere with pre-scientific everyday ideas about the subject. From a scientific point of view, doubts can be raised about the use of any general notion of complexity. Natural science is partitioned in a set of very specialized methods and approaches -- why then, should any particular concept of complexity not have a very restricted scope, relevance and validity? Scepticism about any new general all-encompassing theory of complexity is certainly warranted, as one is reminded about previous unsuccessful attempts to construct grand syntheses about everything, such as general systems theory (compare Lilienfeld 1978). Nevertheless, as science contributes to a common world picture (or a loose mosaic of such pictures), it is tempting to draw general lessons from a large set of particular investigations from various areas of inquiry. The search for common structures across individual theories and local fields of knowledge is a truly legitimate aim of science. Complexity studies should thus be seen not as aiming at a new "synthetic theory" of complexity of any kind, but as a cross-disciplinary field of research and meeting place for dialogue between specialized groups of people such as biologists, physicists, philosophers, mathematicians, computer scientists, and, last but not least, science writers (with a background in science or journalism or both) who have contributed to popularise the field for a wider public and perhaps facilitated the meeting of experts from the specialised areas.¹ Let us briefly and preliminary characterize a few general meanings of the term complexity when used in connection with science (Ravn et al. 1995).

First, we have descriptive complexity. This applies to a situation when several different methods are needed to describe a phenomenon in a reasonably complete way. An organism, a photon, an individual consciousness are all in their own way descriptively complex: An organism may be described on different levels, each with a specific descriptive apparatus (biochemical, cell biologic, anatomic, ecological) if one endeavours a comprehensive picture. In quantum mechanics, even simple entities like a photon (a light quantum) require the use of two complementary descriptions which are both necessary and mutually exclusive (the wave particle duality). The consciousness of a person can, on the one hand, be described qualitatively from within as the content of what is subjectively experienced, i.e., from the "first person point of view", and, on the other hand, by the neurophysiological processes we can observe (and to some extent observe as correlated with a given person's reported conscious experiences), that is, from without, from the "third person point of view".

Second, we have what may be called ontological complexity. Something is complex in the ontological sense (disregarding whether we can know it completely or not), when it is organized as a system of many non-identical components who themselves have systems-like properties (such as being further decomposable), and whose mutual interactions bring forth a kind of collective behaviour which is different from the behaviour of the parts. (A degenerate versions of complexity in this sense is a system whose components are simple but have complicated relations that give rise to a special higher-order behaviour). A phenomenon is complex if it has a specific sort of order which is `interesting', i.e., which objectively is located equally far from the totally ordered and predictable on the one hand, and the completely random and disordered on the other hand. A living cell, the brain, the growing body as a morphogenetic system, a society, clusters of galaxies, are examples. To say that X is complex doesn't in itself say much about X. (As with all ontologic properties, we often need to specify how and from what perspective we know about this property, so to consider something as complex in the ontological sense often invokes the need to identify its descriptive complexity -- or an alternative epistemic concept of complexity).

Third, we have the name of the above mentioned field, complex dynamic systems (sometimes called complex adaptive systems, Gell-Mann 1994) where a lot of research from the perspective of natural science (but also with a growing interest from economics and social science) endeavours to investigate self-organizing systems, co-operative behaviour of agents, and non-linear dynamical systems creating emergent properties during their time evolution. Here we find efforts to define quantitative measures of a system's degree of complexity, for instance based on such notions as logical depth (Bennett 1988), hierarchical structure (Simon 1962, Huberman and Hogg 1986); algorithmic complexity (Chaitin 1974); or measures related to Shannon's information entropy concept (see review by Grassberger 1986). Some work in this field is related to interesting puzzles in chaos theory, artificial life and neural networks. We will return to this research below.

Fourth, the appearance of this field has stimulated some work in philosophy of science, for instance about the role of causality, interlevel relations and prediction in science (Newman 1996; Andersen et al., in prep.); about the implication of complexity for the `disunity of science' and instrumentalism in biology (the debate between Dupr 1993 and Rosenberg 1994); and there have been attempts to describe the wider implications of what is seen by some philosophers to be a major transition from a classic, simplifying paradigm to a new `complexity paradigm' of science (e.g., Morin 1977-91). Speculations have been made that complexity and the new focus on self-synthesising wholes is becoming a central part of a new scientific mode of thinking, substituting the former mode which is purported to be entirely reductionist and analytic. Some of these thoughts may derive more from story telling mediated by science writers than from concrete studies of science at the working bench. It hardly plausible that one can talk in general about a shift in methodology on the high-level of science. The specific kinds of methodologies (with a small m) close to science, that continuously develop and change, are really important, but, as Ronald N. Giere once remarked, "appeals to grand things like simplicity, fruitfulness, and all this stuff, that is part of the rhetoric of science".² Complexity, or at least `the complexity paradigm' may well be part of all this stuff too. In any event, one needs closer analyses of the whole paradigmatic structure (in Kuhn's original sense) of these areas before one can evaluate claims of a truly shift in scientific paradigm, whatever that exactly means.

Fifth, there is a quite separate set of notions of complexity in the social sciences, dealing with complicated social systems, their differentiation and segmentation, and with the various decision making processes in these systems that constantly rely on incomplete information. In the theory of Niklas Luhmann, complexity reduction is the phenomenon that social systems are exposed to a much greater `information pressure' than what they can handle in real time by rational methods. This is why they must reduce this complexity, and this is in part done arbitrarily: A chosen action is simply just one out of a large set of probably just as reasonable actions, but the very decision to chose a particular one reduces the complexity. The particular possibility, qua being realized as an action, is subsequently ascribed a higher value. Reduction of complexity is also a property of the system's own self-observation, because no system can possess total self-insight. Luhmann's approach to social systems may also be applied to science. Accordingly, complexity reduction in scientific research is not necessarily so much a question of abstracting the right properties out of a physical system, or of choosing a crucial experiment, or of making an inference to the best explanation, or of choosing between alternative theories all underdetermined by data -- as the epistemological concerns of traditional philosophy of science might suggest. Looking at `science in action' in a Luhmannian optics, complexity reduction is more like social system's attempt to handle the ever increasing production of attention-demanding communication that goes on in every social system, including the scientific one, and including the micro-social level, that is, during usual practical work in the science labs, where interaction and communication between scientists and students, post-docs, laboratory assistants, science policy makers, fund raisers etc., are just as important as the interaction between an isolated inquirer and an isolated piece of nature. We should not forget these micro-social aspects of science, even when dealing with its most abstract and `theoretical' ideas, such as the idea of studying complexity. I leave it to others to speculate on the possibility that the emergence of the "sciences of complexity" is a reflection of the changing social situation for the scientific subsystem in a postmodern and hyper-differentiated world.

DAVID F. NOBLE is Professor of History at York University in Toronto. Currently the Hixon/Riggs Visiting Professor at Harvey Mudd College in Claremont, California, he has also taught at the Massachusetts Institute of Technology and Drexel University, and was a curator of modern technology at the Smithsonian Institution. His previous books include America by Design: Science, Technology, and the Rise of Corporate Capitalism and Forces of Production: A Social History of Industrial Automation.

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