Artificial Swarming Shown to Amplify Accuracy of Group Decisions in Subjective Judgment Tasks

Abstract

New technologies enable distributed human teams to form real-time systems modeled after natural swarms. Often referred to as Artificial Swarm Intelligence (ASI) or simply “human swarming”, these real-time systems have been shown to amplify group intelligence across a wide range of tasks, from handicapping sports to forecasting financial markets. While most prior research has studied human swarms with 20–100 members, the present study explores the ability of ASI to amplify accuracy in small teams of 3–6 members. The present study also explores if conducting multiple swarms and aggregating by taking a “vote of swarms” can further amplify the accuracy. A large set of 66 small teams were engaged in this study. Each team was given a standard subjective judgement test. Participants took the test both as individuals and real-time swarms. The average individual scored 69% correct, while the average swarm scored 84% correct (p < 0.001). In addition, aggregation of multiple swarms revealed additional amplifications of accuracy. For example, by randomly selecting sets of 3 swarms and aggregating by plurality vote, average accuracy increased to 91% (p < 0.001). These results suggest that when small teams make subjective judgements as real-time swarms, they can be significantly more accurate than individual members, and that their accuracy can be further amplified by aggregating the output across small sets of swarms.