Many domains of the natural and designed world ― cosmology, biology, economics, the environment, even society―exhibit behavioural patterns, which appear to have emerged by chance. Events in such complex systems may occur in relatively long time scales as in history, creating the impression that historical tools and the narrative method may be the only way to describe them. If, however, one could replay the "tape" in these systems, with just the minutest of changes in the starting state, events might actually unfold differently. Indeed, the remarkable similarity between these behavioural patterns with those at the critical state of phase transitions in equilibrium thermodynamics, has led to the suggestion that they may be fruitfully explored using analogous paradigms as those evolved in the sciences to study equilibrium systems at the critical state. This explains the implied association with criticality, and the definition of the tern self-organised criticality (SOC).

All such systems belong to a class of slowly driven, nonlinear, non-equilibrium dynamical systems with extended degrees of freedom. These conditions apparently vest them with a tendency to self-drive themselves through a period of stasis to a state poised for activity when minor perturbations lead to events, called avalanches, of various sizes. These, whilst not predictable for specific events, follow systematic probability distributions, which have been found to represent many real world systems: mountain landscapes, galaxy formation, drainage patterns, earthquakes, ecologies, financial markets, vortices, solar flares, evolutionary biology, neural systems and their many analogues. However, unlike equilibrium systems which are fairly stable, except, at the transition point, insofar as any local disturbance remains confined to its neighbourhood, SOC is distinguished by the response of the system as a whole to material and/or energy inputs, by system-wide communication, evolving through transition states of varying degrees of stability, requiring no external control such as that mediating phase transitions in equilibrium states, but driven intrinsically by their internal dynamics.

In particular, SOC recognises the concepts of supramolecular chemistry mediating in the formation of crystals and extended molecular assemblies, in protein folding and related phenomena. All these events proceed through discontinuities and reversals whilst still obeying the laws of kinetics and thermodynamics. Inherent in these ideas are ways of describing processes that characterize living systems, essentially chemical compounds, and a search for the distinctive characteristics of life forms that sustain certain chemical reactions in conditions far from equilibrium.

The idea that biological organisms are complex self-organizing entities was first articulated by Kant and has been a subject of scientific research and philosophical debate ever since. However it was only in the late 20th century that the study of complex biological systems reached a certain level of maturity. Systems biology is, in a way, the expression of the ideas of supramolecular chemistry in a biological context. One attempts to obtain a knowledge of living systems at the molecular level but at the same time becomes aware of the shortcomings of a purely reductionist approach. A good deal of biological complexity is due to degeneracy, or the ability of different structural elements to perform the same function as well as multiple structural elements acting in concert to produce a desired function― the whole is greater than the sum of its parts. These issues are particularly appropriate in the areas of molecular, developmental and neurobiology.

Because SOC exhibited by a wide range of phenomena governed by different dynamical environments exhibit qualitatively similar behavioural trends, there has been a long cherished hope that one may be able to develop a counterpart formalism to the renormalization group theory for the equilibrium statistical mechanics, which could be used to determine, for example, the occurrence probabilities of a given system configuration during its evolution. This has not yet happened, withholding the possibility to precisely calculate the statistical properties of a dynamical system, such as correlation functions.

Notwithstanding this outstanding question, some helpful aspects of the statistical properties of such systems have been modelled, notably the power law distribution in some observable of the system. These have been shown to characterize the behaviour of a wide range of phenomena such as earthquakes, turbulence and the evolving forms of forest fires, epidemics and a host of structures in astrophysical, planetary, ecological and biological systems. Meanwhile, some recent developments open up interesting avenues for analytically exploring the implied possibilities, for instance a demonstration that the critical attractor of the iconized sandpile model is characterized in terms of an Abelian group. This opens up the possibility of calculating the number of states belonging to the critical attractor and their respective convergence rates, using the structure of Abelian algebra.

The proposed meeting will be designed to create an interactive space by forging mutually reinforcing interfaces between young researchers and more advanced workers in diverse fields of SOC research. This would be accomplished through advance planning and careful identification of speakers and discussant groups. The meeting would be structured through sessions spread over 4 days, to provide ample opportunities for extended and serious discussion sessions that would, at one level, enable advances in disciplinary fields to be dealt with rigorously, and at another, purposively encourage trans-disciplinary treatment of ideas that whilst originating in a specific context, would prove fruitful in addressing wider analogous issues. Special efforts would also be made to draw in the meeting, perceptive individuals from the fields of sociology, economics and linguistics where structure formations are apparently mediated by the same self-similar processes.