Robust Sequential Learning in Random Order Networks

Researchers have discovered how to make networks where people learn from each other more robust. They studied systems where individuals make predictions based on private info and their neighbors' past choices. The team identified network structures that reliably converge on the correct answer even when the order of decisions is random. They also created an algorithm to modify any existing network to be resilient against a limited number of bad actors or changes.