Under what circumstances are algorithms better predictors of human behaviour than humans? This piece of work from MIT shows that under certain circumstances computers can much more reliably predict human behaviour than people experienced in those systems. Presumably that holds true in stable patterns of behaviour, but I’m curious as to what happens when we face rapidly adapting circumstances where rapid judgements need to be made as to what might be occurring. I’m also curious about what happens when humans have to make snap judgements rather than considered ones, are we more or less accurate in such situations? The MIT work points us at some answers, but certainly not all. (Introduced by John Atkinson)

By Olivia Goldhill.  Available at Quartz

You might presume, or at least hope, that humans are better at understanding fellow humans than machines are. But a new MIT study suggests an algorithm can predict someone’s behavior faster and more reliably than humans can.

Max Kanter, a master’s student in computer science at MIT, and his advisor, Kalyan Veeramachaneni, a research scientist at MIT’s computer science and artificial intelligence laboratory, created the Data Science Machine to search for patterns and choose which variables are the most relevant. Their paper on the project results (pdf) will be presented at the IEEE Data Science and Advanced Analytics conference in Paris this week.

It’s fairly common for machines to analyze data, but humans are typically required to choose which data points are relevant for analysis. In three competitions with human teams, a machine made more accurate predictions than 615 of 906 human teams. And while humans worked on their predictive algorithms for months, the machine took two to 12 hours to produce each of its competition entries.

For example, when one competition asked teams to predict whether a student would drop out during the next ten days, based on student interactions with resources on an online course, there were many possible factors to consider. Teams might have looked at how late students turned in their problem sets, or whether they spent any time looking at lecture notes. But instead, MIT News reports, the two most important indicators turned out to be how far ahead of a deadline the student began working on their problem set, and how much time the student spent on the course website. These statistics weren’t directly collected by MIT’s online learning platform, but they could be inferred from data available.

The Data Science Machine performed well in this competition. It was also successful in two other competitions, one in which participants had to predict whether a crowd-funded project would be considered “exciting” and another if a customer would become a repeat buyer.

Kanter told MIT News that there are many possible uses for his Data Science Machine. “There’s so much data out there to be analyzed,” he said. “And right now it’s just sitting there not doing anything.”