Open AccessMeasuring Group Personality with Swarm AI
Author(s) -
Gregg Willcox
Publication year - 2020
Publication title -
international journal of transdisciplinary artificial intelligence
Language(s) - English
Resource type - Journals
ISSN - 2641-7618
DOI - 10.35708/tai1869-126249
Subject(s) - personality , artificial intelligence , personality assessment inventory , psychology , computer science , swarm intelligence , team composition , machine learning , applied psychology , social psychology , particle swarm optimization
The aggregation of individual personality assessments to predict team performance is widely accepted in management theory buthas significant limitations: the isolated nature of individual personality surveys fails to capture much of the team dynamics that drive realworld team performance. Artificial Swarm Intelligence (ASI)—a technology that enables networked teams to think together in real-time andanswer questions as a unified system—promises a solution to these limitations by enabling teams to collectively complete a personality assessment, whereby the team uses ASI to converge upon answers that bestrepresent the group’s disposition. In the present study, the group personality of 94 small teams was assessed by having teams take a standardBig Five Inventory (BFI) assessment both as individuals, and as a realtime system enabled by an ASI technology known as Swarm AI. Thepredictive accuracy of each personality assessment method was assessedby correlating the BFI personality traits to a range of real-world performance metrics. The results showed that assessments of personalitygenerated using Swarm AI were far more predictive of team performancethan the traditional aggregation methods, showing at least a 91.8% increase in average correlation with the measured outcome variables, andin no case showing a significant decrease in predictive performance. Thissuggests that Swarm AI technology may be used as a highly effectiveteam personality assessment tool that more accurately predicts futureteam performance than traditional survey approaches.