By Vishwanath Varma
Post-doctoral Fellow, Max Planck Institute for Animal Behaviour
Years after Martin Luther King Jr’s riveting recountal of his dream for the future of America, the
overturning of “separate but equal” Jim Crow laws, and much hand-wringing by liberal Americans,
racial segregation in the cities was continuing unabated. Perplexed by this pernicious problem, future
Nobel laureate Thomas Schelling began doodling pluses and zeros on a piece of paper to create one of
the earliest models of a complex social system  .
Later, he proceeded to chalk out the movements of coins on his son’s checkerboard to mimic racial segregation, by assuming that zinc coins preferred zinc neighbours and copper ones gravitated towards their copper compatriots. Remarkably, he found that even a small preference for similar neighbours or just a reluctance to be a minority in one’s neighbourhood (a micromotive) would lead to large scale segregation of communities (macrobehaviour) [2,3] . This was a poignant indication that human societies can often be accurately characterized as complex systems composed of biases and incentives that produce emergent patterns.
In my first apartment as a bachelor, unsurprisingly, I had a pest problem. While I had chanced upon
the odd cockroach during my visits to the kitchen, nothing prepared me for the spectacle I witnessed
as I turned on the light late one night, looking for a midnight snack. Dozens of roaches dashed around
while others were frozen to their spots in cold blood at the unexpected illumination. I knew
cockroaches were creatures of the night but I had stumbled into the kitchen on several previous nights
without event. How then had this horde avoided my sight? Curious, I started looking out for them on
subsequent nights. Early every night, a single pair of antennae would dangle out through a crevice.
Prolonged periods of inactivity in the kitchen would prompt a scout cockroach or two to wander out,
and if the coast was clear and the outlook was gloomy, all hell would break loose. Had I not
unwittingly interrupted their festivities at an unseemly hour, I would have continued being oblivious
to their menace; this bunch of holed up cretins were, as a group, incredibly adept at picking just the
right time to collectively surface from their hideouts.
Clearly, animal societies can transcend individual capabilities. Having been mobbed by crows and
attacked by bees, I have experienced first-hand the power of animal collectives. Sharp rises in
numbers upon crossing a ‘Threshold’, such as the scourge of winged bugs after the season’s first rains
and the sudden outpourings of the creepy crawlies in my kitchen, are common features of animal
groups. Feedback loops play prominent roles in mediating such non-linear responses to small changes
in the system. Magnificently intricate termite mounds and spectacular displays of coordinated motion
in schools of fish and flocks of birds represent emergent properties, as they are known in complex
Flightless solitary locusts aggregate together, and upon reaching a critical density or
a ‘tipping point’, form the large locust swarms that are currently sweeping across parts of Africa and
Asia. Collective behaviour models inspired by complex systems are increasingly being used to
describe such phenomena  . But are these overarching principles compatible with highly
individualistic human societies?
From synchronization of applause to vehicular traffic, models of collective behaviour are surprisingly
useful to explain concerted human activities  . Inexplicable fashion fads, mass hysteria in America
over the perceived threat from Communists, and historical social movements such as the Indian
Freedom movement and Russian Revolution may all be instances of the collective force eclipsing
rational self-interest. Gustave Le Bon, a pioneer of Crowd Psychology, offered these choice words to
describe the subsuming of the individual mind to the fervour of the crowd– “An individual in a crowd
is a grain of sand …which the wind stirs up at will” and “by the mere fact that he forms part of an
organised crowd, a man descends several rungs in the ladder of civilisation”  . Having repeatedly
witnessed my sensitive, considerate male friends turn into brash, boorish brutes in a group, I can attest
to that. Xenophobia, racial and communal riots, class hostility, stock market crashes and echo
chambers in the media are all manifestations of irrational fears and sentiments amplified and reinforced by the feedback loops that govern human societies .
Combating such social problems requires a substantive theoretical framework appropriate for dealing with complex systems. Viewing social issues through the prism of collective behaviour has proven effective across various domains. Extensions of Thomas Schelling’s segregation model recommend affirmative action or taxation on segregating moves by individuals to reverse existing residential segregation  . Studies on collective intelligence endorse cognitive diversity and the inclusion of women (since they generally rank higher in social perceptiveness) as factors that drive team performance [8,9] . Policy directives to catalyze behavioural changes for improving health and environmental sustainability are being
informed by complex systems theory [10,11].
However, these may be just the tip of the iceberg. An integrative approach promises to transform our understanding of the world. Enlightenment ideals of empiricism and reductionism have served us well, but failed to address several systemic problems such as crime, poverty and inequality. While acknowledging the giant strides that such an analysis of nature had made possible, Friedrich Engels criticised this method of investigation for leaving a legacy of “observing natural objects and natural processes in their isolation, detached from the whole vast interconnection of things.. not in their life, but in their death”  . Like the mythical Tower of Babel, scientists today have come to speak different tongues, incomprehensible to colleagues from other disciplines. Perhaps at a time when we approach the realization of the global village, we should be exhorted to emphasize the relationships between entities, and not their properties in isolation. And while we may encounter hazards of making simplistic assumptions in our quest for a grand theory of human societies, complex systems may be our best bet in an increasingly integrated world. For,
otherwise, we may be in danger of missing the forest for the trees.
 Hartford, T. (2016, December 17). Thomas Schelling, economist, 1921-2016. Financial Times,
Retrieved from https://www.ft.com/content/aa04e73a-c3a6-11e6-9bca-2b93a6856354
 Schelling, T. C. (1971). Dynamic models of segregation. Journal of mathematical sociology, 1(2),
 Schelling, T. C. (2006). Micromotives and macrobehavior. WW Norton & Company.
 Sumpter, D. J. (2006). The principles of collective animal behaviour. Philosophical transactions
of the royal society B: Biological Sciences, 361(1465), 5-22.
 Le Bon, G. (2009). Psychology of Crowds. Sparkling Books edition. Sparkling Books.
 Van Ness, J., & Summers-Effler, E. (2016). Reimagining Collective Behavior. In Handbook of
Contemporary Sociological Theory (pp. 527-546). Springer, Cham.
 Grauwin, S., Goffette-Nagot, F., & Jensen, P. (2012). Dynamic models of residential segregation:
An analytical solution. Journal of Public Economics, 96(1-2), 124-141.
 Aggarwal, I., Woolley, A. W., Chabris, C. F., & Malone, T. W. (2019). The impact of cognitive
style diversity on implicit learning in teams. Frontiers in psychology, 10, 112.
 Woolley, A. W., Aggarwal, I., & Malone, T. W. (2015). Collective intelligence and group
performance. Current Directions in Psychological Science, 24(6), 420-424.
 Mercure, J. F., Pollitt, H., Bassi, A. M., Viñuales, J. E., & Edwards, N. R. (2016). Modelling
complex systems of heterogeneous agents to better design sustainability transitions policy. Global
environmental change, 37, 102-115.
 van Wietmarschen, H. A., Wortelboer, H. M., & van der Greef, J. (2018). Grip on health: A
complex systems approach to transform health care. Journal of evaluation in clinical practice, 24(1),
 Engels, F. (1934). Herr Eugen Duhring’s Revolution in Science. Martin Lawrence.