First International Workshop on
Guided Self-Organization (GSO-2008)
CSIRO ICT Centre, CSIRO Complex Systems Science, ARC COSNet, ARC EEI, University of Sydney
24-27 November 2008, Sydney, Australia
Venue: CSIRO ICT Centre, Macquarie University campus, Building E6B.
Research Aims and Topics
It has been 60 years since the first time that a system was termed “self-organising” in modern scientific literature . During this time, the concept of self-organisation developed in many directions and affected diverse fields, ranging from biology to physics to social sciences. For example, in his seminal book “At Home in the Universe”, Stuart Kauffman argued that natural selection and self-organisation are two complementary forces necessary for evolution: “If biologists have ignored self-organization, it is not because self-ordering is not pervasive and profound. It is because we biologists have yet to understand how to think about systems governed simultaneously by two sources of order ...if ever we are to attain a final theory in biology, we will surely, surely have to understand the commingling of self-organization and selection” . A similar dilemma can be re-phrased for various fields of engineering: If engineers have ignored self-organisation, it is not because self-ordering is not pervasive and profound. It is because we engineers have yet to understand how to think about systems governed simultaneously by two sources of order: traditional design and self-organisation.
Self-organisation within a system brings about several attractive properties, in particular, robustness, adaptability and scalability. In the face of perturbations caused by adverse external factors or internal component failures, a robust self-organising system continues to function. Moreover, an adaptive system may re-configure when required, degrading in performance “gracefully” rather than catastrophically. In certain circumstances, a system may need to be extended with new components and/or new connections among existing modules — without self-organization such scaling must be pre-optimised in advance, overloading the traditional design process.
It is interesting at this stage to contrast traditional engineering methods with biological systems that evolve instead of being built by attaching together separately pre-designed parts. Each biological component is reliant on other components and coevolves to work even more closely with the whole. The result is a dynamic system where components can be reused for other purposes and take on multiple roles , increasing robustness observed on different levels: from a cell to an organism to an ant colony. Complementarity of co-evolving components is only one aspect, however. As noted by Woese , “Machines are stable and accurate because they are designed and built to be so. The stability of an organism lies in resilience, the homeostatic capacity to re-establish itself.” While traditionally engineered systems may still result in brittle designs incapable of adapting to new situations, “organisms are resilient patterns in a turbulent flow — patterns in an energy flow” . It is precisely this homeostatic resilience that can be captured by self-organisation.
However, in general, self-organisation is a not a force that can be applied very naturally during a design process. In fact, one may argue that the notions of design and self-organisation are contradictory: the former approach often assumes a methodical step-by-step planning process with predictable outcomes, while the latter involves non-deterministic spontaneous dynamics with emergent features. Thus, the main challenge faced by designers of self-organising systems is how to achieve and control the desired dynamics. Erring on the one side may result in over-engineering the system, completely eliminating emergent patterns and suppressing an increase in internal organisation with outside influence. Strongly favouring the other side may leave too much non-determinism in the system’s behaviour, making its verification and validation almost impossible. The balance between design and self-organisation is the main theme of GSO-2008, and we hope to identify essential causes behind successful applications, and propose guiding principles for future scenarios.
 Ashby, W. R. (1947). Principles of the Self-Organizing Dynamic System, Journal of General Psychology, 37:125–128.
 Kauffman, S. (1995). At Home in the Universe, p. 112, Oxford University Press.
 Miller, J. F., Job, D., and Vassilev, V. K. (2000). Principles in the evolutionary design of digital circuits - part I, Journal of Genetic Programming and Evolvable Machines, 1(1):8–35.
 Woese, C. R. (2004). A new biology for a new century, Microbiology and Molecular Biology Reviews, 68(2):173–186.
The program includes 4 days, each day with two keynote talks, and four-five regular presentations.