This simple experiment shows how easy it is for society to become segregated
OPINION: It seems obvious: if we are tolerant of people who are different from us, then our friends should come from all sections of society, our neighbourhoods should include people from all different races and our workplaces should have a good balance of men and women.
It seems obvious: if we are tolerant of people who are different from us, then our friends should come from all sections of society, our neighbourhoods should include people from all different races and our workplaces should have a good balance of men and women.
But that’s not how society works. In reality, segregation is widespread: in residential neighbourhoods, at the workplace, in schools, even online. And segregation is not a good thing: people who are physically separated are unlikely to exchange ideas, share resources, or resolve problems. Segregation worsens inequality and conflict.
In the workplace, segregation by gender is one of the culprits for the persistent wage gap between men and women. Likewise, residential segregation by race and social class likely intensified the urban unrest we witnessed in the past decade, including the 2005 French riots, the 2011 riots in England and the recent turmoil in American cities.
Modelling segregation
So does this mean that we are intolerant? Is segregation persistent because people are racist, sexist or bigoted? Not exactly. As the visualisation of New York below shows, segregation looms large even in the most tolerant countries, such as the USA, the UK and Sweden.
Racial segregation in New York City. Image Copyright, 2013, Weldon Cooper Center for Public Service, Rector and Visitors of the University of Virginia (Dustin A. Cable, creator)
To explain why segregation might occur in otherwise tolerant societies, the Nobel Prize-winning economist Thomas Schelling proposed a model. Schelling imagined a world where two types of individuals (we’ll make them blue and yellow) are randomly located on a flat square world. In Schelling’s model, individuals prefer to have some similar neighbours, but they do not discriminate against different neighbours – in short, they are tolerant. If individuals are unhappy with their neighbourhood, they can freely move to a neighbourhood with a more preferable composition.
In the example below, the yellow individual is unhappy about her assigned location because she does not have enough yellow neighbours, so she decides to move to a new neighbourhood. But when she moves, the composition of both her old and new neighbourhoods change. As a result, an old yellow neighbour and a new blue neighbour also decide to move.
This causes a domino effect that leads neighbourhoods to separate into yellow and blue ghettos. In the end, although no single individual prefers it, everyone ends up in segregated neighbourhoods.
Domino effects in the Schelling model of segregation.
Schelling’s model suggests that people inevitably end up living in a segregated world, even if they are tolerant. Other economists and sociologists have taken this theory one step further, and shown that segregation is likely even if people actively seek diversity. You can test how different models lead to different patterns of segregation using our online simulator, or by playing Parable of the Polygons.
A futile exercise?
These models have a dangerous implication: namely, that public policies which promote openness and tolerance will never improve integration. Some economists went so far as to suggest that “the welfare effect of educating people to have preferences for integration might be adverse” because the “segregated outcome will be unsatisfying for the majority of people”.
Models are one thing, but real people are different. We decided to test different versions of the Schelling model using an interactive game. We went to 20 different high-school classrooms and let the students play a game, which involved moving yellow and blue circles.
We didn’t tell them that they were playing a “segregation” game, we just asked them to follow the rules. Some students were given incentives to find similar neighbours, while others were given incentives to look for mixed neighbourhoods.
In our experiment, students controlled a blue or yellow numbered avatar on a shared screen. They followed rules we provided for their ‘preferences’ for the colour of their neighbours.
Our results confirm the prediction of Schelling’s original model; that when people are simply tolerant, they still become segregated. But we also found that when people strive for diversity, they are able to achieve integration.
Models and mingles
To understand our results, think about how we behave at a social mingle. People often attempt to optimise the composition of the group they are talking to, trying to get a good mix of different, interesting people around them. But as everyone moves to achieve their own optimised mix, the group composition continually changes.
As a result, the group never settles down and the composition of groups is more or less random. And randomly composed groups are integrated, rather than segregated. This is exactly what happened in our experiment. Students were unable to identify when no better locations existed, and continued moving in the pursuit of perfect happiness.
Our experiments reveal why we should be cautious when offering policy advice on the basis of theoretical models. The models fail because they assume that we are always perfectly informed about the best available options – and perfectly able to pursue them. In reality, we often face constraints when we gather information and make decisions. This can be the case, not only at mingles, but even for serious decisions with lifetime consequences such as moving house, choosing schools and changing jobs.
Mathematical models are important, but we need to test them empirically before applying them in practice. Segregation is not unavoidable, but there is a need to continue educating people in the benefits of diversity and to continue devising polices and incentives that prevent or ease segregation.
Milena Tsvetkova, Postdoctoral Researcher in Computational Social Science, University of Oxford and David Sumpter, Professor of Applied Mathematics, Uppsala University
This article was originally published on The Conversation. Read the original article.
Milena Tsvetkova receives funding from the National Science Foundation, USA.
David Sumpter receives funding from Vetenskapsrådet, Sweden.
