Studying complex systems

Have you ever wondered why birds flock together when flying in a group? Surprisingly, there is no central coordination in such flocking behaviour by birds. All individual birds follow simple rules that result in an emergent behaviour called flocking.

Researchers are using the new and emerging field of complexity theory to emulate such behaviour.

The origins for formally treating cities and ecosystems as systems emerged when Ludwig von Bertalanffy came up with ‘General Systems Theory’. This gave impetus to an emerging interdisciplinary domain that had promise in a variety of disciplines.

The systems theory also allowed a deviation from conventional scientific approaches that studied behaviours and characteristics in a mechanistic framework and had reductionist frameworks.

Jay Forrester and Donella Meadows pioneered and applied this extensively to social systems that also gave way to ‘System Dynamics’.

Notably, systems theory consists of entities or parts — be it cells, molecules, species, or people — that are interacting with each other over space and time. It posits that the dynamic interactions between these interconnected parts result in emerging patterns of behaviour over time. In turn, these interactions produce effects where the whole is larger than the sum of the parts.

Though the ideas of complex systems have emerged from systems theory, it doesn’t have one definite explanation.

And the study of complex systems has resulted in a sub-domain called socio-ecological systems.

Alongside, complexity theory has also resulted in development of tools and methods to study these systems. These are allowing researchers to build models emulating the actual systems.

A key deviation in this paradigm is that we build models to ‘understand’ and ‘explain’ the behaviour of systems and not necessarily, ‘predict’ or ‘forecast’. At best, we use these models to generate scenarios.

Path dependence

A classic example of path dependence in social systems is that of a typewriter. When the mechanical typewriter was invented, the letters were jumbled so it would not jam while typing in English resulting in the popular 'QWERTY' keyboard.

However, despite the ubiquity of electronic keyboards that do not have any such mechanical constraints, the QWERTY keyboard continues to be used.

The prevalence of a historical path (outcomes) often resulting in the point of no return is called path dependence. Another example for path dependency is why people drive on the left hand side in some countries, while they drive on the right in others.

In studying evolution, we find many examples of such path dependence. Even the evolution of species is path dependent.

The social exclusion of communities, especially based on caste, is also an artefact of path dependence.

And despite the legal framework of affirmative action, the societal norms entrenched in path dependence need course correction. It is crucial to break these with more awareness, orientation, and appropriate education.

Scaling complex systems

Another unique characteristic of complex systems is that most of these exhibit scaling behaviour across social and ecological systems. Physcists have found particular interest in applying and exploring these systems.

The human social organisation has evolved from hunter-gather society to those settling along river valleys and then settling in villages, towns, cities and large urban agglomerations. Interestingly, the hierarchical organisation of societies (towns and cities), conforms to the scaling laws.

Researchers have observed that city-size distribution fits a power law, also popularly known as Zipf’s law. In simpler words, if you rank-order cities by their population, a log-log plot (a plot that uses logarithmic scales across both vertical and horizontal axes) reveals it is a straight line. Accordingly, the systems of cities in the US, France, or even Karnataka, also fits a power law.

It is rather puzzling that despite differences in geography, economy, and nature of political organisation, it appears human-social organisation self-organises according to a power law.

The same scaling law also holds good in biological systems. As in any system of cities, the rank-order of number of species across different order or families also fits a power law. Jayanth Banavar and colleagues have applied the law to a host of biological systems. In particular, the researchers have observed scaling behaviour for species abundance amongst others.

For instance, when we observe birds for species abundance, the number of individuals (abundance) are counted along with different species. And when the number of individual birds observed are rank-ordered across different species, they show scaling behaviour.

Thus, scaling behaviour in systems have become a characteristic artefact even when systems appear to be in disorder, as there seems to be an underlying order. For researchers, understanding these and figuring out what could happen if there is a deviation or under what circumstances would they deviate is intriguing.

Applying complexity theory

The application of this theory in practice has been to evolve mechanisms for collaborative environmental governance for achieving collective action to identifying principles for building resilience towards sustaining ecosystem services in social-ecological systems.

Brian Arthur has also attempted to apply this theory to understand self-reinforcing mechanisms in economics.

In the context of global climate change and achieving sustainable development goals, it becomes paramount that we embrace this theory for enhancing our understanding of how systems work. As we start observing systems around us in this lens, we realise that many of them are evolving to exhibit collective behaviour.

Michael Batty, a pioneer in applying complexity theory for cities, sums it well, ‘the more we understand, the less we would want to intervene but in more meaningful ways’.

(The author is with Research Matters)