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Research paper | Open Access

Meta measurement of intelligence with crowd network

Zheming Yang1, Wen Ji2( )
Institute of Computing Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China
Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
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Abstract

Purpose

The multiple factors of intelligence measurement are critical in intelligent science. The intelligence measurement is typically built as a model based on multiple factors. The different agent is generally difficult to measure because of the uncertainty between multiple factors. The purpose of this paper is to solve the problem of uncertainty between multiple factors and propose an effective method for universal intelligence measurement for the different agents.

Design/methodology/approach

In this paper, the authors propose a universal intelligence measurement method based on meta-analysis for crowd network. First, the authors get study data through keywords in the database and delete the low-quality data. Second, they compute the effect value by odds ratio, relative risk and risk difference. Then, they test the homogeneity by Q-test and analyze the bias by funnel plots. Third, they select the fixed effect and random effect as a statistical model. Finally, through the meta-analysis of time, complexity and reward, the weight of each factor in the intelligence measurement is obtained and then the meta measurement model is constructed.

Findings

This paper studies the relationship among time, complexity and reward through meta-analysis and effectively combines the measurement of heterogeneous agents such as human, machine, enterprise, government and institution.

Originality/value

This paper provides a universal intelligence measurement model for crowd network. And it can provide a theoretical basis for the research of crowd science.

Keywords

Citizen scienceModels and methods for crowd science and engineeringCrowd networkMeta-analysisIntelligenceMeasurement

References

 

Azucar, D., Marengo, D. and Settanni, M. (2018), “Predicting the big 5 personality traits from digital footprints on social media: a meta-analysis”, Personality and Individual Differences, Vol. 124, pp. 150-159.
Crossref Google Scholar
 

Braga, T., Gonçalves, L.C., Basto-Pereira, M. and Maia, A. (2017), “Unraveling the link between maltreatment and juvenile antisocial behavior: a meta-analysis of prospective longitudinal studies”, Aggression and Violent Behavior, Vol. 33, pp. 37-50.
Crossref Google Scholar
 

Bien, Z., Bang, W.C., Kim, D.Y. and Han, J.S. (2002), “Machine intelligence quotient: its measurements and applications”, Fuzzy Sets and Systems, Vol. 127 No. 1, pp. 3-16.
Crossref Google Scholar
 

Cochrane, P. (2010), “A measure of machine intelligence (point of view)”, Proceedings of the IEEE, Vol. 98 No. 9, pp. 1543-1545.
Crossref Google Scholar
 

Gavane, V. (2013), “A measure of real-time intelligence”, Journal of Artificial General Intelligence, Vol. 4 No. 1, pp. 31-48.
Crossref Google Scholar
 

Gignac, G.E. and Bates, T.C. (2017), “Brain volume and intelligence: the moderating role of intelligence measurement quality”, Intelligence, Vol. 64, pp. 18-29.
Crossref Google Scholar
 

Gavin, A., Pim, C. and Craske, M.G. (2018), “Computer therapy for the anxiety and depressive disorders is effective, acceptable and practical health care: a meta-analysis”, Plos One, Vol. 5 No. 10, p. e13196.
Crossref Google Scholar
 
Insacabrera, J. Dowe, D.L. and Sergio, E. (2011), “Comparing humans and AI agents”,Artificial General Intelligence, pp. 122-132.https://doi.org/10.1007/978-3-642-22887-2_13
Crossref
 

Hernandez-Orallo, J. (2000), “Beyond the Turing test”, Journal of Logic, Language and Information, Vol. 9 No. 4, pp. 447-466.
Crossref Google Scholar
 

Hernandez-Orallo, J. and Dowe, D.L. (2013), “Measuring universal intelligence: towards an anytime intelligence test”, Artificial Intelligence, Vol. 174 No. 18, pp. 1508-1539.
Crossref Google Scholar
 
Kahraman, C. Onar, S.Ç. and Oztaysi, B. (2018), “Fuzzy collective intelligence for performance measurement in energy systems”, Energy Management Collective and Computational Intelligence with Theory and Applications, pp. 497-517.https://doi.org/10.1007/978-3-319-75690-5_22
Crossref
 

Ke, Q. and Cheng, Y. (2015), “Applications of meta-analysis to library and information science research: content analysis”, Library and Information Science Research, Vol. 37 No. 4, pp. 370-382.
Crossref Google Scholar
 
Legg, S. and Hutter, M. (2006), “A formal measure of machine intelligence”, arXiv preprint cs/0605024.
 

Legg, S. and Hutter, M. (2007), “Universal intelligence: a definition of machine intelligence”, Minds and Machines, Vol. 17 No. 4, pp. 391-444.
Crossref Google Scholar
 

Lundh, A., Lexchin, J., Mintzes, B., Schroll, J.B. and Bero, L. (2018), “Industry sponsorship and research outcome: systematic review with meta-analysis”, Intensive Care Medicine, Vol. 44 No. 10, pp. 1603-1612.
Crossref Google Scholar
 
McGrath, M.C. (2011), “Deviation IQ”.https://doi.org/10.1007/978-0-387-79061-9_838
Crossref
 

Myszkowski, N., Celik, P. and Storme, M. (2018), “A meta-analysis of the relationship between intelligence and visual ‘taste’ measures”, Psychology of Aesthetics Creativity and the Arts, Vol. 12 No. 1, p. 24.
Crossref Google Scholar
 
Mesiar, R., Mesiarová, A. and Valášková, L.U. (2006), “Generated universal fuzzy measures”, International Conference on Modeling Decisions for Artificial Intelligence, Springer, Berlin, Heidelberg, pp. 191-202.https://doi.org/10.1007/11681960_20
Crossref
 

Saxton, M.L. (2006), “Meta-analysis in library and information science: method, history, and recommendations for reporting research”, Library Trends, Vol. 55 No. 1, pp. 158-170.
Crossref Google Scholar
 

Turing, A.M. (1950), “Computing machinery and intelligence”, Mind (New Series), Vol. 59 No. 236, pp. 433-460.
Crossref Google Scholar
 

Vamsi, V. and Bose, I. (2018), “Business intelligence for performance measurement: a case based analysis”, Decision Support Systems, Vol. 111, pp. 72-85.
Crossref Google Scholar
International Journal of Crowd Science
Volume 4 Issue 3,
September 2020
Pages 295-307
DOI: 10.1108/IJCS-03-2020-0008
Cite this article:
Yang Z, Ji W. Meta measurement of intelligence with crowd network. International Journal of Crowd Science, 2020, 4(3): 295-307. https://doi.org/10.1108/IJCS-03-2020-0008

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Received: 07 March 2020
Revised: 13 May 2020
Accepted: 13 May 2020
Published: 02 July 2020
© The author(s)

Zheming Yang and Wen Ji. 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
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