Abstract

In the natural world, Swarm Intelligence (SI) is a commonly occurring process in which biological groups amplify their collective intelligence by forming closed-loop systems. It is well known in schools of fish, flocks of bird, and swarms of bees. In recent years, new AI technologies have enabled networked human groups to form systems modeled after natural swarms. Known as Artificial Swarm Intelligence (ASI), the technique has been shown to amplify the effective intelligence of human groups. This study compares the predictive ability of ASI systems against large betting markets when forecasting sporting events. Groups of average sports fans were tasked with predicting the outcome of 200 hockey games (10 games per week for 20 weeks) in the NHL. The expected win rate for Vegas favorites was 62% across the 200 games based on the published odds. The ASI system achieved a win rate of 85%. The probability that the system outperformed Vegas by chance was extremely low (p = 0.0057), indicating a significant result. In addition, researchers compared the winnings from two betting models – one that wagered weekly on the Vegas favorite, and one that wagered weekly on the ASI favorite. At the end of 20 weeks, the Vegas model generated a 41% financial loss, while the ASI model generated a 170% financial gain.

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