AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home International Journal of Crowd Science Article
PDF (128.7 KB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Literature review | Open Access

Literature review on collective intelligence: a crowd science perspective

Chao Yu, Yueting Chai( ), Yi Liu
National Engineering Laboratory for E-commerce Technologies, Tsinghua University, Beijing, China
Show Author Information

Abstract

Purpose

Collective intelligence has drawn many scientists’ attention in many centuries. This paper shows the collective intelligence study process in a perspective of crowd science.

Design/methodology/approach

After summarizing the time-order process of related researches, different points of views on collective intelligence’s measurement and their modeling methods were outlined.

Findings

The authors show the recent research focusing on collective intelligence optimization. The studies on application of collective intelligence and its future potential are also discussed.

Originality/value

This paper will help researchers in crowd science have a better picture of this highly related frontier interdiscipline.

Keywords

Open innovationCollective intelligenceCrowd scienceGroup wisdom

References

 

Aaltonen, A. and Seiler, S. (2016), “Cumulative growth in user-generated content production:: Evidence from Wikipedia”, Management Science, Vol. 62 No. 7, pp. 2054-2069.
Crossref Google Scholar
 

Agur, Z. (1991), “Fixed points of majority rule cellular automata with application to plasticity and precision of the immune system”, Complex Systems, Vol. 5 No. 3.
Google Scholar
 

Arrow, K.J., Forsythe, R., Gorham, M., Hahn, R., Hanson, R., Ledyard, J.O., Levmore, S., Litan, R., Milgrom, P., Nelson, F.D., Neumann, G.R., Ottaviani, M., Schelling, T.C., Shiller, R.J., Smith, V.L., Snowberg, E., Sunstein, C.R., Tetlock, P.C., Tetlock, P.E., Varian, H.R., Wolfers, J. and Zitzewitz, E. (2008), “The promise of prediction markets”, Science, Vol. 320 No. 5878, pp. 877-878.
Crossref Google Scholar
 

Asch, S.E. (1951), “Effects of group pressure upon the modification and distortion of judgments”, Groups, Leadership, and Men, pp. 222-236.
Google Scholar
 
Bon, G.L. (2011), The Crowd: A Study of the Popular Mind, Dover Publications, New York, NY.
 
Bonabeau, E., Dorigo, M. and Theraulaz, G. (1999), Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, Oxford.https://doi.org/10.1093/oso/9780195131581.001.0001
Crossref
 

Brabham, D.C. (2009), “Crowdsourcing the public participation process for planning projects”, Planning Theory, Vol. 8 No. 3, pp. 242-262.
Crossref Google Scholar
 

Chiu, C.M., Liang, T.P. and Turban, E. (2014), “What can crowdsourcing do for decision support?”, Decision Support Systems, Vol. 65 No. 3, pp. 40-49.
Crossref Google Scholar
 

Coe, A., Paquet, G. and Roy, J. (2000), “E-governance and smart communities: a social learning challenge”, Social Science Computer Review, Vol. 19 No. 1, pp. 80-93.
Crossref Google Scholar
 
Collective intelligence (2018), available at: https://en.wikipedia.org/wiki/Collective_intelligence
 

Conradt, L. and Roper, T.J. (2003), “Group decision-making in animals”, Nature, Vol. 421 No. 6919, pp. 155-158.
Crossref Google Scholar
 

Couzin, I.D. (2009), “Collective cognition in animal groups”, Trends in Cognitive Sciences, Vol. 13 No. 1, p. 36
Crossref Google Scholar
 

Fadul, J.A. (2009), “Collective learning: applying distributed cognition for collective intelligence”, International Journal of Learning, Vol. 16 No. 4, pp. 211-220.
Crossref Google Scholar
 

Gallupe, R.B., Dennis, A.R., Cooper, W.H., Valacich, J.S., Bastianutti, L.M. and Nunamaker, J.F. (1992), “Electronic brainstorming and group size”, Academy of Management Journal, Vol. 35 No. 2, pp. 350-369.
Crossref Google Scholar
 

Galton, F. (1907), “Vox Populi”, Nature, Vol. 75 No. 1949, pp. 450-451.
Crossref Google Scholar
 

Gregg, D.G. (2010), “Designing for collective intelligence”, Communications of the ACM, Vol. 53 No. 4, pp. 134-138.
Crossref Google Scholar
 

Heylighen, F. (1999), “Collective intelligence and its implementation on the web: algorithms to develop a collective mental map”, Computational & Mathematical Organization Theory, Vol. 5 No. 3, pp. 253-280.
Crossref Google Scholar
 

Holley, R.A. and Liggett, T.M. (1975), “Ergodic theorems for weakly interacting infinite systems and the voter model”, The Annals of Probability, Vol. 3 No. 4, pp. 643-663.
Crossref Google Scholar
 
Hosio, S., Goncalves, J., Anagnostopoulos, T., et al. (2016), “Leveraging wisdom of the crowd for decision support”, British HCI 2016 Conference, Poole.https://doi.org/10.14236/ewic/HCI2016.38
Crossref
 

Huang, S.L. and Shiu, J.H. (2012), “A user-centric adaptive learning system for E-learning 2.0”, Journal of Educational Technology & Society, Vol. 15 No. 3, pp. 214-225.
Google Scholar
 
Kaplan, C.A. (2001), “Collective intelligence: a new approach to stock price forecasting”, IEEE International Conference on Systems, Man, and Cybernetics,IEEE, Tucson, AZ, Vol. 5, 2893-2898.
 

Katsikopoulos, K.V. and King, A.J. (2010), “Swarm intelligence in animal groups: when can a collective out-perform an expert?”, Plos One, Vol. 5 No. 11, pp. e15505-e15505.
Crossref Google Scholar
 

Koriat, A. (2012), “When are two heads better than one and why?”, Science, Vol. 336 No. 6079, pp. 360-362.
Crossref Google Scholar
 

Krause, J., Ruxton, G.D. and Krause, S. (2010), “Swarm intelligence in animals and humans”,Trends in Ecology & Evolution, Vol. 25 No. 1, pp. 28-34.
Crossref Google Scholar
 

Krause, S., James, R., Faria, J.J., Ruxton, G.D. and Krause, J. (2011), “Swarm intelligence in humans: diversity can trump ability”, Animal Behaviour, Vol. 81 No. 5, pp. 941-948.
Crossref Google Scholar
 

Leimeister, J.M. (2010), “Collective intelligence”, Business & Information Systems Engineering, Vol. 2 No. 4, pp. 245-248.
Crossref Google Scholar
 

Lorenz, J., Rauhut, H., Schweitzer, F. and Helbing, D. (2011), “How social influence can undermine the wisdom of crowd effect”, Proceedings of the National Academy of Sciences of the United States of America, Vol. 108 No. 22, pp. 9020-9025.
Crossref Google Scholar
 

Lorenz, J., Rauhut, H., Schweitzer, F. and Helbing, D. (2011), “How social influence can undermine the wisdom of crowd effect”, Proceedings of the National Academy of Sciences, Vol. 108 No. 22, pp. 9020-9025.
Crossref Google Scholar
 
Mackay, C. (1841), Extraordinary Popular Delusions and the Madness of Crowds, Farrar, Straus, New York, NY.
 
Makowiec, D. (2004), Cellular Automata with Majority Rule on Evolving Network, Springer Berlin Heidelberg, Heidelberg, pp. 141-150.https://doi.org/10.1007/978-3-540-30479-1_15
Crossref
 

Malone, T.W., Laubacher, R. and Dellarocas, C. (2009), “Harnessing crowds: mapping the genome of collective intelligence”, Social Science Electronic Publishing.
Google Scholar
 

Muller, P. (1970), “Collective intelligence tests and educational selection Neuchatel experiences 12 years later”, Psychologie, Vol. 29 Nos 1/2, pp. 194-202.
Google Scholar
 

Prelec, D. (2017), “A solution to the single-question crowd wisdom problem”, Nature, Vol. 541 No. 7638, pp. 532-535.
Crossref Google Scholar
 

Sachs, J.D. and Mcarthur, J.W. (2005), “The millennium project: a plan for meeting the millennium development goals”, Lancet, Vol. 365 No. 9456, pp. 347-353.
Crossref Google Scholar
 

Safferstone, M.J. (1998), “The distributed mind: achieving high performance through the collective intelligence of knowledge work teams”, Academy of Management Executive, Vol. 12 No. 2, pp.107-108.
Crossref Google Scholar
 
Seely, T.D. (1995), The Wisdom of the Hive, Harvard University Press, MA, pp. 20-35
 
Smith, J.B. (1994), Collective Intelligence in Computer-Based Collaboration, CRC Press, Boca Raton.
 

Smith, V.L., Arrow, K., Forsythe, R., et al. (2008), “The promise of prediction markets”, Science, Vol. 320 No. 5878, pp. 877-878.
Crossref Google Scholar
 

Steiner, I.D. (1972), “Group process and productivity”, Computing Science & Statistics.
Google Scholar
 
Stiles, E. and Cui, X. (2010), “Workings of collective intelligence within open source communities”, International Conference on Social Computing, Behavioral Modeling, and Prediction, Springer Berlin Heidelberg, Heidelberg, pp. 282-289.https://doi.org/10.1007/978-3-642-12079-4_35
Crossref
 

Strogatz, S.H. (2001), “Exploring complex networks”, Nature, Vol. 410 No. 6825, pp. 268-276.
Crossref Google Scholar
 

Sunstein, C.R. (2002), “The law of group polarization”, Journal of Political Philosophy, Vol. 10 No. 2, pp. 175-195.
Crossref Google Scholar
 
Surowiecki, J. (2005), The Wisdom of Crowds, Anchor.
 

Szuba, T. (2001), “A formal definition of the phenomenon of collective intelligence and its IQ measure”, Future Generation Computer Systems, Vol. 17 No. 4, pp. 489-500.
Crossref Google Scholar
 

Thomas, E.J. and Fink, C.F. (1963), “Effects of group size”, Psychological Bulletin, Vol. 60 No. 4, pp. 371-384.
Crossref Google Scholar
 
Wechsler, D. (1964), Die Messungder Intelligenz Erwachsener, Bern-Stuttgart, Huber, pp. 20-50.
 

West, J. and Gallagher, S. (2006), “Challenges of open innovation: the paradox of firm investment in open-source software”, R&D Management, Vol. 36 No. 3, pp. 319-331.
Crossref Google Scholar
 

Wheeler, W.M. (1924), “Social life among the insects”, Isis, 15, Vol. 6, No. 4, pp. 119-131.
Google Scholar
 

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.
Crossref Google Scholar
 

Xi'an Jiaotong (2016), “Effects of centralized, decentralized and reintegration group structures on group performance”, Operation Research and Management, Vol. 25 No. 5, pp. 6-14.
Google Scholar
International Journal of Crowd Science
Volume 2 Issue 1,
July 2018
Pages 64-73
DOI: 10.1108/IJCS-08-2017-0013
Cite this article:
Yu C, Chai Y, Liu Y. Literature review on collective intelligence: a crowd science perspective. International Journal of Crowd Science, 2018, 2(1): 64-73. https://doi.org/10.1108/IJCS-08-2017-0013

1355

Views

31

Downloads

25

Crossref

26

Scopus

Altmetrics
Received: 13 August 2017
Revised: 25 August 2017
Accepted: 26 August 2017
Published: 11 April 2018
©2018 International Journal of Crowd Science

Chao Yu, Yueting Chai and Yi Liu. 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
Return