[ University | Math Dept | CBD at BU | Homepage | Research | Links ]


The Demystification of Emergence and Complexity
-- An Introduction


Robert Clewley


1. Overview

Being able to describe and explain processes of natural self-organisation in physics, biology or psychology is a major goal of modern theoretical science. If only from a pragmatic view, such an understanding could allow us to design our own "naturally" self-organising systems, for a more integrated engineering approach to real world problems. This would include the development of more "naturally"-inspired intelligent systems, that solve the problems of robustness, flexibility, and computation in a similar way to the brain.

My work currently falls into three areas:

(1) The development of a scientific methodology and conceptual framework that can facilitate the understanding of complex systems without mystical, holistic assumptions, or vulgar oversimplifications of ontology or mechanism. Various ideas in the modern philosophy of science are required to give the methodology a sound footing, but much inspiration has come from related approaches (be they old or new, insightful or naïve) in the mathematical and cybernetics literature. This work is partially developed in Ref. [Cle].

(2) My PhD work was aimed at applying some of these concepts to neural modelling. The principle goal of the PhD was to gain insight into how to bridge the gap between small, detailed models of neuron dynamics, and large-scale, statistically-based models of neural systems. There still only appear to be a few biologically realistic neural network simulations which use more than a couple of distinct types of neural sub-system. Mathematical analysis of these networks is currently even more sparse (e.g. see Refs [Bre, Coo, Ger, Maa, Ter]). My belief is that the tools have not yet been developed with which to adequately represent these networks in a simple and appropriate fashion with which to manipulate the salient high-level computational and systemic features of the models in terms of their low-level structure. The issue is analogous to problems in physics, where for instance, Haken [Hak], Nicolis and Prigogine [Nic] have contributed theories of complexity and self-organisation to bridge the gap between detailed particle models and the large-scale models of statistical physics.

(3) Most recently, I have become interested in applying the same sorts of concepts to the emerging discipline of memetics. My personal focus on memetics is to see it as a path towards the closing of an age-old gap in the understanding of the mind. The gap is between, on one hand, individual models of cognitive psychology, and on the other, sociological models of the behaviour of social groups. The gap seems to have remained so long in the discipline of social psychology due to the historical weight of a form of "homuncularism". The many philosophical shortcomings of traditional social science theories in this vein have been collectively dubbed the Standard Social Sciences Model by Tooby and Cosmides [Too]. The solution seems to require a multi-level approach, and the concept of a level at which there can be talk of ideas as social contagions (i.e. using some definition of "meme") seems appropriate. References [Cl2, Cl3] discuss memetics in this role in greater depth.

The following introductory discussion is adapted from Section 2 of Ref.[Cle].

 

2.1 Theoretical reasons for the persistence of the dilemma

A particularly well-known issue that is crucial to the idea of ontological unity is reductionism. For instance, in the examples at the beginning of the section one has to assume that physical processes can ultimately underlie the causation of mental events and the origin of organic life (respectively). These assumptions allow scientific investigation to proceed, since they avoid the untestable possibilities of strict Cartesian duality of mind and vitalism (respectively). The philosophical debate surrounding reductionism is technical, often unintuitive, and full of subtleties that are easily missed. It is no surprise that progress in such a debate is slow, relative to the rate of advance within disciplines having clear boundaries and foundations.

There are two other major reasons why the issue of ontological unity remains a dilemma. Firstly, there is the fact that many natural processes simultaneously operate at very different scales of time, or space, or both. Secondly, an observer is able to appreciate the variety of form and behaviour exhibited by that process at the different scales taken individually, but has difficulty relating them precisely in an appreciation of the process as a whole.

Thus there is a need for an analysis of the philosophical inconsistencies between traditional single-scale models that ultimately represent aspects of the same process. Consequently, it might be possible to synthesise theories of an intrinsically multi-scale flavour. This is one way in which one might base a programme of ontological unification.

2.2 Emotive reasons for the persistence of the dilemma

The issue of reductionism in science is the subject of heated debate. The intricacies inherent in it demonstrate an emotive aspect to the persistence of the dilemma. If one is not very careful, talk of an attempt to "ontologically unify" science can give the misleading impression that the goal is to demonstrate that, for instance, "biology is just a form of physics, and can be studied using the empirical techniques and ontology typical of (respectively) the experiments and theories of physics". This has been dubbed "greedy reductionism" by Dan Dennett, and is still a common (although often tacit) belief to be found in scientists' theories.

However, there are modern forms of reductionism -- which were developed with the study of "complexity" and "emergence" in mind -- that do not deny the utility and efficiency of studying phenomena in language that is well-suited to the preferred level of analysis. These forms of reductionism argue that there can be a complete logical relationship between theories at all levels of analysis (although only in principle). Thus in some sense there can be a feeling of security in the knowledge that there is no "mystical" factor involved in a phenomenon when there is a clear connection between all appropriate levels of its analysis and some currently accepted level of "physical reality".

When trying to solve the reductionism problem with a multi-level view, another difficult issue gets drawn in. That is the issue of subjective relativism. There have been several recent attacks on the general tenets of the so-called post-modern analysis of science (e.g. [Sok]) that debunk misuses of scientific terminology and vague pseudo-analyses of modern scientific method. However, there are nevertheless many respected modern philosophers, including big guns like Wittgenstein and Whitehead, whose work shows insight beyond the "traditional physicist's naive realism", and gives coherent motivation for using a precise and restrained style of relativism in a mature scientific methodology.

These issues are developed further in Ref. [Cle].

2.3 Historical reasons for the persistence of the dilemma

Understanding some of the history behind the fragmented nature of modern science is also instructive. Over the last century the study of the world has split up according to domains of an increasingly fine-grained scheme of classification. Scientists often treat this historically recent situation as if it was second nature. Such deep familiarity may make it hard to imagine the purpose, the value, or the feasibility of re-unifying some or all of the scientific disciplines.

Traditionally, science has focused on sub-domains of natural processes, using simplifying assumptions that narrow the scope of a theory sufficiently to afford an ease in its formulation and application. This is to be expected when, until this century, there had still been a need to develop mutually consistent theories of easily-demonstrated natural phenomena that can now be taught to schoolchildren (e.g. electricity, magnetism, classical mechanics, molecular bonding, etc.). This has been done in a context of a mostly unified methodological and epistemological scientific practice, throughout the natural sciences [Kan]. Modern science has progressed largely by the continued sharpening of its tools for use in these appropriately narrowed domains, and has thereby capitalised on the well-defined nature of problems that arise within them.

It is often still useful to construct a model that is based on empirical observations at a single scale, without needing to relate it to observations at any other scale. This is the very successful style of science that many critics have solely in mind when they dismiss the purpose and value of ontological unification. Increasingly, however, the separated pursuits of contemporary science are reaching impasses in their progress. It is precisely because these modern disciplines were conceived from a focus on easily-demonstrated (and hence relatively philosophically uncontentious) phenomena that science as a whole is left with such fundamental problems as were exemplified at the beginning of this section. It is clear to see why the dilemma of ontological unity, which underlies these problems, involves all the hard philosophical issues that were avoided when there were easier problems to formulate and examine. Critics of the feasibility of ontological unity often rely on the difficulties of these philosophical issues to argue the a priori impossibility of unity. On the other hand, simplistic and naive attempts at ontological unification tend to gloss over the difficulties with vague concepts and weak reasoning. The philosophical problems should inspire a challenge -- in critics and proponents alike -- to understand and overcome them using a well-founded methodology.

2.4 The benefits of "ontological unity"

The general advantages of ontological unity are essentially twofold: obtaining logical consistency between theories having non-identical domains of applicability, and obtaining a coherence which can "transcend the narrow borderlines of each [traditional] discipline" [Kan, p.262]. Together, these goals motivate the idealised scientific methodology developed in [Cle]. A full account of the argument advocating a search for ontological unity in the social sciences can be found in the work of Tooby and Cosmides [Too], although it is not difficult to see how these advantages could benefit any scientific discipline.

References

[Bre] Bressloff, P. C. & Coombes, S., 1997, Physics of the Extended Neuron, International Journal of Modern Physics B, Vol. 11, No. 20, pp. 2343-2392.

[Cle] Clewley, R. H., in preparation, The Role of Scientific Methodology in the Demystification of Emergence and Complexity.

[Cl2] Clewley, R. H., 1998, Emergence Without Magic: The Role of Memetics in Multi-Scale Models of Evolution and Behaviour, Proceedings of the 15th International Congress on Cybernetics.

[Cl3] Clewley, R. H., 1998, Reinterpreting Memetics in a Multi-Level View of Evolution and Behaviour - Part I: Conceptual Analysis.

and Reinterpreting Memetics in a Multi-Level View of Evolution and Behaviour - Part II: Towards an Integration.

[Coo] Coombes, S., Doole, S.H., & Campbell, C., 1995, Central Pattern Generation in a Model Neuronal Network with Post Inhibitory Rebound and Reciprocal Inhibition, Preprint, Dept of Engineering Maths, University of Bristol.

[Hak] Haken, H., 1983, Synergetics, 3rd ed., Springer-Verlag.

[Ger] Gerstner, W., 1998, Spiking Neurons, Ch. 1 of W. Maass and C.M. Bishop (eds), Pulsed Neural Networks, MIT press, pp. 3-54

[Maa] Maas, W., 1997, Networks of Spiking Neurons: The Third Generation of Neural Network Models, Neural Networks. vol 10(9), pp. 1659-1671.

[Nic] Nicolis, G. and Prigogine, I., 1989, Exploring Complexity: An Introduction, W.H. Freeman.

[Sok] Sokal, A. & Bricmont, J., 1998, Intellectual Impostures, Profile.

[Ter] Terman, D. & Lee, E., 1997, Partial Synchronization in a Network of Neural Oscillators, SIAM J. Appl. Math., Vol. 57, No. 1, pp. 252-293.


[ University | Homepage | Research | Links ]

Back to top

This page was written and maintained by Rob Clewley