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International Journal of Crowd Science 2018, 2(1): 64-73 https://doi.org/10.1108/IJCS-08-2017-0013
Literature review | Open Access | Issue | Published: 11 April 2018

Literature review on collective intelligence: a crowd science perspective

Show Author's Information Chao Yu,  Yueting Chai( ),  Yi Liu
National Engineering Laboratory for E-commerce Technologies, Tsinghua University, Beijing, China
Keywords:
Collective intelligence, Crowd science, Open innovation, Group wisdom
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
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Abstract Full text About this article
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.


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Abstract
Full text
Outline
About this article

Literature review on collective intelligence: a crowd science perspective

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

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: Collective intelligence, Crowd science, Open innovation, Group wisdom

References(50)

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.
DOI 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.
DOI 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
DOI

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

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

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

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

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.
DOI Google Scholar

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

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

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.
DOI 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.
DOI 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.
DOI 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.
DOI 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
DOI

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

Sunstein, C.R. (2002), “The law of group polarization”, Journal of Political Philosophy, Vol. 10 No. 2, pp. 175-195.
DOI 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.
DOI Google Scholar

Thomas, E.J. and Fink, C.F. (1963), “Effects of group size”, Psychological Bulletin, Vol. 60 No. 4, pp. 371-384.
DOI 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.
DOI 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.
DOI 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
Publication history
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Publication history

Received: 13 August 2017
Revised: 25 August 2017
Accepted: 26 August 2017
Published: 11 April 2018
Issue date: July 2018

Copyright

©2018 International Journal of Crowd Science

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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

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