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Conformity 'doubly hard' to beat

Conformity rules Even when people try to be different from each other, they may end up conforming with the majority around them, a new model suggests.

Breaking the stranglehold of conformity only depends on a few extreme outliers, say the authors of a new paper published today in the Royal Society journal Open Science.

"Conformity is 'doubly hard' to beat -- it can obviously happen when people imitate one another, but it can also set in even when folks are trying to be distinct," says mathematical social scientist Professor Joshua Epstein of Johns Hopkins University.

"You have to be creative if you want to resist it, reverse it or interrupt it."

Epstein says previous attempts to model conformity in social systems have focused on how it results from some kind of imitation.

In their model, he and colleague Dr Paul Smaldino of the University of California, Davis did something different. They allowed for people wanting to be a bit different from the average.

Each individual in the model had a starting position -- it could be a political view or a fashion statement -- that was measured in standard deviations from the mean.

And they had a preferred position, which might be different from this starting position.

As the model ran, an individual would move in the direction of their preferred position (towards or away from the mean).

But as each individual moved, this changed the mean and also changed the movement of all other individuals.

"There's this constant feedback between people's positions and their objectives."

The amazing thing was that the end point of this process was that all positions collapsed in to the average position.

"If all they [people] care about is moving in the direction of the goal, they all end up in the same darn position," says Epstein.

"It is a very counter-intuitive result ... Nobody had really thought that you could get conformity out of people trying to be distinct."

Epstein says this could help explain phenomenon such as fashion cycles.

Breaking the mould

Epstein and Smaldino played around a bit more with their model to explore what it would take to break this stranglehold of conformity.

They introduced into their model a force of repulsion from the mean to counter the powerful underlying force to conform.

And they found that just a small minority of people who refused to be average could shake up the conformist trend.

Even 8 per cent of non-conformists could have a large influence on the mean and force everyone else to recalculate their positions and increasing diversity, says Epstein.

"A small minority of extreme nonconformists can have a large influence on a population," he says, adding that extremity is a relative thing: for example it doesn't take much to be extremely different when everyone is identical, but when there is a lot of diversity, it takes a lot more effort to be extremely different.

Of course, like conformity, non-conformity can have good and bad impacts, he says, but it is important in democratic societies where sustained diversity is essential.

Epstein says the findings suggest that breaking up the power of large "monolithically conformist organisations like ISIS" depends on outliers within the group.

"The best way to combat monolithic conformist organisations may not be to attack them from without -- because the conformist tendency/stability is so deep -- but, rather, to foster diversity from within," he says.

Smaldino says the model shows the more extreme minority views, the fewer of them are required to shift the average view and create more diversity.

He says the findings could also have applications in generating more diversity in political debate.

Psychologists and sociologists have been interested in how individual level behaviours relate to conformity and this is the first mathematical model to look at this.

But, says Smaldino, there are other factors from peer pressure to law enforcement that influence our attitude to conformity.

"I view this model as a building block towards a more complete model of human behaviour."

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