Harvard scientists help develop algorithm that predicts social cooperation

Large social networks foster connections by erasing national, geographic, and even linguistic barriers. But when it comes to fostering cooperation, global connectivity leaves something to be desired, new research says.

Working with colleagues at Emmanuel College, Harvard scientists have developed an algorithm that predicts whether a social structure is likely to favor cooperation, and the findings suggest that strong pairwise relationships — not loose networks scattered across the globe — are the most conducive to cooperation. The study is described in a March 29 paper in Nature.

“What we are able to do is calculate the critical benefit-to-cost ratio for cooperation to thrive on any fixed population structure,” said senior author Martin Nowak, a professor of mathematics and of biology and director of the Program for Evolutionary Dynamics. “And what we find is truly interesting. We can take any graph or social network, and if it has strong pairwise ties, that is what is most conducive for cooperation. This is a mathematical argument for stable families or for stable friendships.”

“I think one of the messages here is that while global interconnectedness has increased rapidly over the past few decades, there are downsides to that,” said first author Benjamin Allen, an assistant professor of mathematics at Emmanuel College and a researcher at the Program for Evolutionary Dynamics. “More connectivity won’t necessarily promote people being good to each other. It’s not that global connections are bad, but they are no substitute for a small number of strong local connections.”

Scientists for decades have sought to understand the interplay between social structure and evolution, beginning with “well-mixed” populations. Mathematical models have demonstrated that in such populations — where every individual interacts with every other individual — evolution selects against cooperation, causing it to eventually die out.

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