Predicting adult crimes from observing toddlers


Big data makes it possible to predict which children will grow up to be the greatest economic burden on the community, according to an article in a new journal, Nature Human Behaviour. Researchers who analysed the lives of nearly a thousand people from birth to age 38 in the New Zealand city of Dunedin found that 20% of the population accounts for 80% of social costs such as crime, welfare dependence and health-care needs when they are adults.

Just one-fifth of the study population accounted for 81% of criminal convictions and 77% of deadbeat dads, consumed three-quarters of drug prescriptions, two-thirds of welfare benefits and more than half of the hospital nights and cigarettes smoked.

The researchers found they could have predicted which adults were likely to incur these costs as early as age 3 based on assessments of “brain health,” giving them hope that early interventions could avoid some of these social costs.

Big Data is essential to identify candidates. “We know every location they’ve lived, every name they’ve used. We’re able to match them with pretty much 100% accuracy back for many years,” said Terrie Moffitt, a Duke researcher.

“The digitization of people’s lives allows us to quantify precisely how much a person costs society and which people are using multiple different costly health and social services. Apparently, the same few clients use the courts, welfare benefits, disability services, children’s services, and the health-care system. These systems could be more joined up.”

The Duke researchers stress that this ability to identify and predict a person’s life course from their childhood status is an invitation to intervene, not to discriminate.  “This study really gives a pretty clear picture of what happens if you don’t intervene,” says Dr Moffitt.

The research group was also testing the Pareto principle: or the “80-20 rule.” Italian engineer and social scientist Vilfredo Pareto observed a century ago that 80% of wealth is controlled by 20% of the population. This is also a rule of thumb in computer science, biology, physics, economics and many other fields.

“Most expenses from social problems are concentrated in a small segment of the population,” said Avshalom Caspi, of Duke University, the lead author. “So whatever segment of the health, social or judicial system that you look at, we find a concentration. And that concentration approximates what Pareto anticipated over 100 years ago. We called the group 'high-needs/high-costs’.”




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