Tjurunga tools

Our tools encompass new ideas and ways of thinking about your problems as well as advanced mathematical and computer software tools for their analysis, modelling and simulation. We constantly refine our ideas and thinking and enhance our toolkit through our own research at the frontier of the science of complexity. We have an entirely pragmatic approach, judging a tool’s utility only on its performance on real problems. Tjurunga is thus able to bring the best tools to bear on your problems, free from the fads and fashions of the moment.

Thinking tools

Thinking about the problems faced by the enterprise as complex systems problems requires us all to shift mental gears. Most approaches to business organisation and design stem from an earlier scientific revolution, that of René Descartes and Isaac Newton in the 17^th-century. But there are increasingly obvious limits to what this 'mechanical' approach can do, because it was conceived to explain systems consisting of stable parts operated on by fixed physical rules. Such systems ignore the effects of learning, evolution, or changes in the rules. But biological and social systems, unlike mechanical systems, are organic, they metabolise, they learn, they adapt, they evolve. They are not precisely predictable even in the short term, and they are wholly unpredictable over the long term. The new scientific revolution that is the science of complexity has had great success in explicitly addressing these messy systems.

The most powerful formulation of this new thinking accessible to the business world has been created by our partners, Santa Fe Associates International of Santa Fe, New Mexico. SFAI’s principals have been involved for many years in the delivery of complexity research to corporate clients. They have evolved their thinking into a well-tested theory of the enterprise as a complex adaptive system, as published in the book Open Boundaries, and have developed a unique process — diagnostics for emergent strategies. These diagnostics are a process of structured, increasingly penetrating questions and revealing discussions about the ideas underlying an enterprise. The process helps people in an organisation discover new opportunities, resolve persistent problems, and understand what the organisation can become.

Tjurunga is proud to offer these diagnostic tools in Australia as the centrepiece of our thinking tools.

Mathematical tools

Recent advances in game theory, the powerful mathematical tool used in economics, ecology and other sciences of complex living systems, have made it much more useful for the problems of real organisations. Research at the Economic Learning and Social Evolution Centre (ELSE) Centre at University College London and elsewhere has extended our ability to deal with complex problems where learning and adaptation occur, where the rules of the game change and where knowledge is imperfectly shared across the system — in short, where the problems have all the character of the messy problems of the real world

These new adaptive game theory tools have been applied with great success to difficult problems of market design, resource auctions and electronic commerce. They can be fully integrated in a broader analytical framework with modelling and simulation tools to handle large problems.

Our partners, Haight & Seymour pl of London, are the pre-eminent exponents of the use of these tools on practical problems. Tjurunga is proud to include these tools in its toolbox.

Modelling and simulation tools

We distinguish between two sorts of complexity, and hence two sorts of tools for their analysis. The complexity of things like the weather is different to the complexity of things like organisms. The weather is complex because it is big and heterogeneous - vast numbers of physical and chemical processes, each individually well understood, together create the weather. Living things are complex because they have vast numbers of interacting physical and chemical processes from which emerge the recognisable entities we call organisms. We call such things ‘complex adaptive systems' to distinguish them from complex systems like the weather.

Tools to analyse the merely complex are usually tools that try to tackle the sheer computation problems implicit in size and heterogeneity. Traditional approaches such as coupled systems of differential equations vie with newer approaches from complexity research such as simulated annealing, genetic algorithms and neural nets. None offers a perfect solution to such messy problems as predicting the weather, organising a logistics network, or optimising a production mix. But when used skilfully, each can help resolve previously intractable problems in particular areas.

Tools to analyse complex adaptive systems are different. Because such systems are inherently unpredictable, we cannot use the common modelling and simulation tools of science such as statistical regression, Fourier analysis or linear programming. Instead, we build model complex adaptive systems to simulate real ones. As in any simulation, we try to capture the essence of the real system in the model system. But since the model is also a complex adaptive system, it will behave like one, exhibiting novelty and surprise, unpredictability and emergence. Clearly we do not read such models as we would simpler ones - we must create a new relationship between system, model and observer. Instead of saying the model is a simplification of the world whose dynamics predict the dynamics of the world, we say that the model is sufficiently like the world that its behaviour will be sufficiently interesting. We allow the model to live its life, to learn and explore its model world, and from that we learn about the behaviour of the real system. Understanding emerges from the interactions among three complex adaptive systems - the real system being modelled, the model and ourselves, the observers.

The distinction between merely complex systems and complex adaptive systems is never clear cut. Ecosystems contribute to the weather, a social system includes its complex physical environment. Nor is the distinction between the tools clear cut. The delineation of the system under study and the choice of modelling and simulation tools are therefore as much an art as a science. Experience counts in making such choices. Tjurunga’s principals, Dr Roger Bradbury and Bohdan Durnota, with many years at the leading edge of complexity research, have the skills and experience to choose the right modelling and simulation tools for any problem.