How Robots Acting Randomly Can Help Speed Human Problem-Solving


Robots that occasionally act randomly can help groups of humans solve collective-action problems faster, new research has shown.

Playing a game with someone unpredictable can be annoying, particularly when you’re on the same team. But in an online game designed to test group decision-making, adding computer-controlled players that sometimes behave randomly more than halved the time it took to solve the problem, according to the new study.

That shouldn’t come as too much of a surprise, said study leader Nicholas Christakis, director of the Human Nature Lab at Yale University. Random mutations make evolution possible; random movements by animals in flocks and schools enhances group survival; and computer scientists often introduce noise — a statistical term for random or meaningless information — to improve search algorithms, he said.

But the discovery that these effects are mirrored in combined groups of humans and machines could have wide-ranging implications, Christakis told Live Science. To start, self-driving cars will soon share roads with human drivers, and more people may soon find themselves working alongside robots or with “smart” software.

In the study, published online today (May 17) in the journal Nature, the researchers describe how they recruited 4,000 human workers from Amazon’s Mechanical Turk online crowdsourcing platform to play an online game.

Each participant was assigned at random to one of 20 locations, or “nodes,” in an interconnected network. Players can select from three colors and the goal is for every node to have a different color from the neighbors they are connected to.

Players can see only their neighbors’ colors, which means that while the problem may seem to have been solved from their perspective, the entire game may still be unsolved.

While highly simplified, this game mimics a number of real-world problems, such as climate change or coordinating between different departments of a company, Christakis said, where from a local perspective, a solution has been reached but globally it has not.

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