TY - GEN
T1 - Amplifying prediction accuracy using Swarm A.I.
AU - Rosenberg, Louis
AU - Pescetelli, Niccolo
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - In the natural world, many species amplify the accuracy of their decision-making abilities by working together real-time closed-loop systems that converge on optimal solutions in synchrony. Known as Swarm Intelligence (SI), the process has been deeply studied in schools of fish, flocks of birds, and swarms of bees. The present study looks at the ability of human groups to make decisions as an Artificial Swarm Intelligence (ASI) by forming similar real-time closed-loop systems online. More specifically, the present study tasked groups of typical sports fans with predicting English Premier League matches over a period of five consecutive weeks by working together in real-time as swarm-based systems. Results showed that individuals, who averaged 55% accuracy when predicting games alone, were able to amplify their accuracy to 72% when predicting together as real-time swarms. This corresponds to 131% amplification in predictive accuracy across five consecutive weeks (50 games).
AB - In the natural world, many species amplify the accuracy of their decision-making abilities by working together real-time closed-loop systems that converge on optimal solutions in synchrony. Known as Swarm Intelligence (SI), the process has been deeply studied in schools of fish, flocks of birds, and swarms of bees. The present study looks at the ability of human groups to make decisions as an Artificial Swarm Intelligence (ASI) by forming similar real-time closed-loop systems online. More specifically, the present study tasked groups of typical sports fans with predicting English Premier League matches over a period of five consecutive weeks by working together in real-time as swarm-based systems. Results showed that individuals, who averaged 55% accuracy when predicting games alone, were able to amplify their accuracy to 72% when predicting together as real-time swarms. This corresponds to 131% amplification in predictive accuracy across five consecutive weeks (50 games).
KW - Artificial Swarm Intelligence
KW - Swarm Intelligence
KW - artificial intelligence
KW - collective intelligence
KW - human swarming
UR - http://www.scopus.com/inward/record.url?scp=85019245676&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019245676&partnerID=8YFLogxK
U2 - 10.1109/IntelliSys.2017.8324329
DO - 10.1109/IntelliSys.2017.8324329
M3 - Conference contribution
AN - SCOPUS:85019245676
T3 - 2017 Intelligent Systems Conference, IntelliSys 2017
SP - 61
EP - 65
BT - 2017 Intelligent Systems Conference, IntelliSys 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 Intelligent Systems Conference, IntelliSys 2017
Y2 - 7 September 2017 through 8 September 2017
ER -