Open Access
Issue
Published:
01 March 2021
A measurement framework of crowd intelligence
),
Download citation
595
Views
33
Downloads
1
Crossref
N/A
WoS
1
Scopus
N/A
CSCD
The originality of the crowd cyber system lies in the fact that it possesses the intelligence of multiple groups including intelligence of people, intelligence of objects and intelligence of machines. However, quantitative analysis of the level of intelligence is not sufficient, due to many limitations, such as the unclear definition of intelligence and the inconformity of human intelligence quotient (IQ) test and artificial intelligence assessment methods. This paper aims to propose a new crowd intelligence measurement framework from the harmony of adaption and practice to measure intelligence in crowd network.
The authors draw on the ideas of traditional Confucianism, which sees intelligence from the dimensions of IQ and effectiveness. First, they clarify the related concepts of intelligence and give a new definition of crowd intelligence in the form of a set. Second, they propose four stages of the evolution of intelligence from low to high, and sort out the dilemma of intelligence measurement at the present stage. Third, they propose a framework for measuring crowd intelligence based on two dimensions.
The generalized IQ operator model is optimized, and a new IQ algorithm is proposed. Individuals with different IQs can have different relationships, such as cooperative, competitive, antagonistic and so on. The authors point out four representative forms of intelligence as well as its evolution stages.
The authors, will use more rigorous mathematical symbols to represent the logical relationships between different individuals, and consider applying the measurement framework to a real-life situation to enrich the research on crowd intelligence in the further study.
Intelligence measurement is one of foundations of crowd science. This research lays the foundation for studying the interaction among human, machine and things from the perspective of crowd intelligence, which owns significant scientific value.
menu
A measurement framework of crowd intelligence
),
Abstract
The originality of the crowd cyber system lies in the fact that it possesses the intelligence of multiple groups including intelligence of people, intelligence of objects and intelligence of machines. However, quantitative analysis of the level of intelligence is not sufficient, due to many limitations, such as the unclear definition of intelligence and the inconformity of human intelligence quotient (IQ) test and artificial intelligence assessment methods. This paper aims to propose a new crowd intelligence measurement framework from the harmony of adaption and practice to measure intelligence in crowd network.
The authors draw on the ideas of traditional Confucianism, which sees intelligence from the dimensions of IQ and effectiveness. First, they clarify the related concepts of intelligence and give a new definition of crowd intelligence in the form of a set. Second, they propose four stages of the evolution of intelligence from low to high, and sort out the dilemma of intelligence measurement at the present stage. Third, they propose a framework for measuring crowd intelligence based on two dimensions.
The generalized IQ operator model is optimized, and a new IQ algorithm is proposed. Individuals with different IQs can have different relationships, such as cooperative, competitive, antagonistic and so on. The authors point out four representative forms of intelligence as well as its evolution stages.
The authors, will use more rigorous mathematical symbols to represent the logical relationships between different individuals, and consider applying the measurement framework to a real-life situation to enrich the research on crowd intelligence in the further study.
Intelligence measurement is one of foundations of crowd science. This research lays the foundation for studying the interaction among human, machine and things from the perspective of crowd intelligence, which owns significant scientific value.
References(20)
Abreu, F.B. and Carapuca, R. (1994), “Candidate metrics for object-oriented software within a taxonomy framework”, Journal of Systems and Software, Vol. 26 No. 1, pp. 87-96.
Almaatouq, A., Noriega-Campero, A., Alotaibi, A., Krafft, P.M., Moussaid, M. and Pentland, A. (2020), “Adaptive social networks promote the wisdom of crowds”, Proceedings of the National Academy of Sciences, Vol. 117 No. 21, pp. 11379-11386.
Bansiya, J. and Davis, C. (2015), “A hierarchical model for object-oriented design quality assessment”, IEEE Transactions on Software Engineering, Vol. 28 No. 1, pp. 4-17.
Cameron, K. (2015), “Organizational effectiveness”, Wiley Encyclopedia of Management, pp. 1-4.
Chai, Y., Miao, C., Sun, B., Zheng, Y. and Li, Q. (2017), “Crowd science and engineering: concept and research framework”, International Journal of Crowd Science, Vol. 1 No. 1, pp. 2-8.
Gottfredson, S.L. (1997), “Mainstream science on intelligence: an editorial with 52 signatories, history, and bibliography”, Intelligence, Vol. 24 No. 1, pp. 13-23.
Greenstein, S. and Zhu, F. (2018), “Do experts or Crowd-Based models produce more bias? Evidence from encyclopedia britannica and wikipedia”, Mis Quarterly, Vol. 42 No. 3, pp. 945-959.
Kaplan, A. and Haenlein, M. (2019), “Siri, Siri, in my hand: who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence”, Business Horizons, Vol. 62 No. 1, pp. 15-25.
Lee, T., Nam, J., Han, D., Kim, S. and In, H.P. (2016), “Developer micro interaction metrics for software defect prediction”, IEEE Transactions on Software Engineering, Vol. 42 No. 11, pp. 1015-1035.
Li, W., Wu, W.J., Wang, H.M., Cheng, X.Q., Chen, H.J., Zhou, Z.H. and Ding, R. (2017), “Crowd intelligence in AI 2.0 era”, Frontiers of Information Technology and Electronic Engineering, Vol. 18 No. 1, pp. 15-43.
Woolley, A.W., Chabris, C.F., Pentland, A., Hashmi, N. and Malone, T.W. (2010), “Evidence for a collective intelligence factor in the performance of human groups”, Science, Vol. 330 No. 6004, pp. 686-688.
Publication history
Copyright
Acknowledgements
The authors thank the researchers in China Center for Internet Economy Research for their useful comments and our two anonymous referees for their helpful advice. All remaining errors are ours. They are grateful for the financial support from the National Key Research and Development Program of China (2017YFB1400100), the National Natural Science Foundation of China (NSFC71804206), the Beijing Social Science Foundation Research Base Project (17JDYJB018), the Young Teachers Development Fund of Central University of Finance and Economics (QJJ1826), the Program for First-class Discipline Construction in Central University of Finance and Economics, and the Beijing Program for “Double First-class” Construction Project of Central Universities.
Rights and permissions
Yiqiang Feng, Leiju Qiu and Baowen Sun. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
FEEDBACK 
TOP