Study cities and ecosystems as complex systems

Have you ever wondered why birds, when flying in groups, flock together? Surprisingly, in such herding behavior exhibited by birds, there is no central coordination. And all individual birds follow simple rules that result in an emergent behavior called flocking. Researchers are applying a new and emerging area of ​​complexity theory, building models to mimic such behavior.

The origins of the formal treatment of cities and ecosystems as systems emerged when Ludwig von Bertalanffy proposed general systems theory. This gave impetus to an emerging interdisciplinary field that showed promise in a variety of disciplines. It was also in response to the limitations of conventional scientific approaches that studied many behaviors and characteristics within a mechanistic framework and reductionist assumptions. Systems theory made it possible to deviate from this convention.

Jay Forrester and Donella Meadows pioneered and applied it extensively to social systems which also gave way to so-called system dynamics.

In particular, in systems theory, it consists of entities or parts, whether cells, molecules, species or people, which interact with each other in space and in the weather. He also notes that the dynamic interactions between these interconnected parts lead to the emergence of patterns of behavior over time. It is important to note that these interactions produce effects where the whole is greater than the sum of the parts.

The ideas of complex systems come from systems theory. However, there is no single definition of complex systems. Essentially any self-organizing system produces adaptive, dynamic and emergent behavior, they are characterized as complex systems. The terms complexity theory, complex systems, and complex adaptive systems are often used interchangeably. This applies to both cities and ecological systems, which also gives rise to a subfield called socio-ecological systems.

Moreover, complexity theory has also resulted in the development of tools and methods to study these systems. Among them are stocks and flows (as in System Dynamics) or the construction of models based on agents. The origins of agent-based models lie in distributed artificial intelligence. These allow researchers to build models that mimic real systems. A key deviation from this paradigm is that we build models to ‘understand’ and ‘explain’ the behavior of systems and not necessarily ‘predict’ or ‘predict’. At best, we use these models to generate scenarios.

path dependency

A classic example of path dependency in social systems is that of a typewriter. When the “mechanical” typewriter was invented, the letters were mixed up so that when typing in English, it didn’t get stuck. Thus resulting in what is popularly known as the QWERTY keyboard. However, for the past three decades we have had electronic keyboards that have no such mechanical constraints, and yet we continue to use the QWERTY keyboard.

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

The social exclusion of communities notably based on caste among many others is also unfortunately an artefact of path addiction. However, as we design systems, adopt a constitution and formulate laws over time, affirmative action has emerged to ensure social inclusion. Despite affirmative action legal frameworks, societal norms entrenched in trail addiction require course corrections. Many of them are also practiced with incomplete information or due to information asymmetry. It is therefore crucial to break down these barriers with more awareness, guidance and proper education.

Scaling in Complex Systems

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

Human social organization evolved from hunting-gathering to initial settlement along river valleys and settlement in villages, towns, cities, and large urban agglomerations. Interestingly, the hierarchical organization of societies (towns and cities) conforms to the laws of scale.

The researchers observed that the city size distributions correspond to a power law, also known as Zipf’s law. In simpler terms, if you rank cities by population, a log-log plot reveals that it is a straight line. As a result, city systems in the United States, France or even Karnataka have been found to correspond to a power law. It is rather surprising that despite the differences in geography, economy and the nature of political organization, it seems that human and social organization is self-organizing according to a law of power. Luis Bettencourt and Anand Sahasranaman have also attempted a detailed analysis of Indian cities as complex systems enforcing laws of scale for crime and technological innovation.

The same scaling law is also valid in biological systems. As in any system of cities, the ranking of the number of species in different orders or families also corresponds to a power law. Jayanth Banavar and his colleagues have applied to a multitude of biological systems. In particular, they 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 behavior in systems has become a characteristic artifact even when systems appear to be out of order, because there appears to be an underlying order. For researchers, it is intriguing to understand them and determine what might happen if there was a deviation or under what circumstances they would deviate.

Application of complexity theory

Applying this theory in practice has involved developing collaborative environmental governance mechanisms to achieve collective action to identify principles for building resilience to maintain ecosystem services in socio-ecological systems. Brian Arthur also tried to apply this theory to understand the mechanisms of self-reinforcement in economics.

In the context of global climate change and the achievement of the Sustainable Development Goals, it becomes paramount that we embrace this theory to improve our understanding of how systems work. As we begin to observe the systems around us through this lens, we realize that many of them are evolving, often exhibiting collective behavior.

Michael Batty, a pioneer in applying complexity theory to cities, sums it up well: “the more we understand, the less we would want to intervene, but in a more meaningful way.”

Editor’s Note: This article first appeared in the Deccan Herald.