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Studying cities and ecosystems as complex systems

Read time: 5 mins
8 Apr 2022
Studying cities and ecosystems as complex systems

Have you ever wondered why birds, when flying in a group, flock? Surprisingly, in such flocking behaviour exhibited by birds, there is no central coordination. And all individual birds follow simple rules that result in an emergent behaviour called flocking. Researchers are applying a new and emerging field of complexity theory, by building models 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. This was also in response to the limitations of conventional scientific approaches that studied many behaviours and characteristics in a mechanistic framework and reductionist assumptions. The systems theory allowed deviating from this convention.

Jay Forrester and Donella Meadows pioneered and applied this extensively to social systems that also gave way to what is called System Dynamics.

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

The ideas of complex systems have emerged from systems theory. However, there doesn’t exist a single definition of complex systems. Essentially, any system that self-organises, produces adaptive, dynamic, and emergent behaviour, they are characterised as complex systems. The terms complexity theory, complex systems, and complex adaptive systems are often used interchangeably. This holds good for both cities and ecological systems, also resulting in a sub-domain called socio-ecological systems.

In addition, complexity theory has also resulted in the development of tools and methods to study these systems. Notable among them is through stocks and flows (as in System Dynamics) or building agent-based models. The origins of agent-based models are in distributed artificial intelligence. 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 that while typing in English, it would not jam. Thus resulting in what is popularly known as the QWERTY keyboard. However, over the last three decades, we have had electronic keyboards that do not have any such mechanical constraints, and yet we continue to use the QWERTY keyboard.

The prevalence of a historical path (outcome) often resulting in a point of no return is called path dependence. Another such example for path dependency is why in some countries we drive on the left hand side while in some countries they drive on the right. One can find many such examples all around. In studying evolution, we find many examples of such path dependence, notably evolution of species is path dependent.

Social exclusion of communities notably based on caste among many others is also unfortunately an artefact of path dependence. However, as we design systems, by way of embracing a constitution and formulating laws over time, affirmative action has emerged to ensure social inclusion. Despite the legal frameworks of affirmative action, the societal norms entrenched in path dependence require course corrections. Many of these are also practised with incomplete information or due to information asymmetry. It is thus crucial to break these barriers with more awareness, orientation, and appropriate education.

Scaling in complex systems

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

Human social organisation has evolved from hunter-gather to initially settling along river valleys and to settling in villages, towns, cities and large urban agglomerations. Interestingly, the hierarchical organisation of societies (towns and cities), conform to the scaling laws.

Researchers have observed that city-size distributions fit 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 reveals it is a straight line. Accordingly, it has been found that the systems of cities in the US, France, or even Karnataka 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 following a power law. Luis Bettencourt and Anand Sahasranaman have also attempted a detailed analysis of Indian cities as complex systems applying scaling laws for crime and technological innovation. 

The same scaling law also holds good in biological systems. As in any system of cities, the rank-order of the number of species across different orders or families also fits a power law.  Jayanth Banavar and colleagues have applied to a host of biological systems. In particular, they have reported unique scaling behavior that leads to links between ecological measures such as relative species abundance and the species area relationship for plant communities in tropical forests.

Thus, scaling behaviour in systems has 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 identify 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 through this lens, we realise that many of them are evolving, often exhibiting 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’.

Editor's Note: This article was first published in Deccan Herald.