Artificial Swarm Intelligence vs human experts

Artificial Swarm Intelligence (ASI) strives to facilitate the emergence of a super-human intellect by connecting groups of human users in closed-loop systems modeled after biological swarms. Prior studies have shown that “human swarms” can make more accurate predictions than traditional methods for tapping the wisdom of groups, such as votes and polls. To further test the predictive ability of swarms, 75 random sports fans were assembled in the UNU platform for human swarming and tasked with predicting College Bowl football games against the spread. Expert predictions from ESPN were compared. The results are as follows: (i) Individuals - when working alone, test subjects achieved on average, 5 correct predictions out of 10 games (50% accuracy); (ii) Group Poll - aggregating data across all 75 subjects, the group achieved 6 correct predictions out of 10 games (60% accuracy); (iii) Experts - as published by ESPN, the college football experts averaged 5 correct predictions out of 10 games (50% accuracy); and (iv) Swarm - when the 75 subjects worked together as a real-time swarm, they achieved 7 correct predictions out of 10 games (70% accuracy). Thus by forming a real-time swarm intelligence, the group of random sports fans boosted their collective performance and out-performed experts.