Journal Home > Volume 7 , Issue 2
International Journal of Crowd Science 2023, 7(2): 55-62 https://doi.org/10.26599/IJCS.2022.9100035
Open Access | Issue | Published: 22 June 2023

Turning the Cacophony of the Internet’s Tower of Babel into a Coherent General Collective Intelligence

Show Author's Information Andy E. Williams1( )
Nobeah Foundation, Nairobi 00100, Kenya
Keywords:
general collective intelligence, human-centric functional modeling, functional state space, collective social brain hypothesis
Cite this article:
Williams AE. Turning the Cacophony of the Internet’s Tower of Babel into a Coherent General Collective Intelligence. International Journal of Crowd Science, 2023, 7(2): 55-62. https://doi.org/10.26599/IJCS.2022.9100035
Download citation
EndNote(RIS)
BibTeX

731

Views

32

Downloads

Citations

0

Crossref

N/A

WoS

0

Scopus

N/A

CSCD

Abstract Full text About this article

Increasing the number, diversity, or uniformity of opinions in a group does not necessarily imply that those opinions will converge into a single more “intelligent” one, if an objective definition of the term intelligent exists as it applies to opinions. However, a recently developed approach called human-centric functional modeling provides what might be the first general model for individual or collective intelligence. In the case of the collective intelligence of groups, this model suggests how a cacophony of incoherent opinions in a large group might be combined into coherent collective reasoning by a hypothetical platform called “general collective intelligence” (GCI). When applied to solving group problems, a GCI might be considered a system that leverages collective reasoning to increase the beneficial insights that might be derived from the information available to any group. This GCI model also suggests how the collective reasoning ability (intelligence) might be exponentially increased compared to the intelligence of any individual in a group, potentially resulting in what is predicted to be a collective superintelligence.


menu
Abstract
Full text
Outline
About this article

Turning the Cacophony of the Internet’s Tower of Babel into a Coherent General Collective Intelligence

Show Author's information Andy E. Williams1( )
Nobeah Foundation, Nairobi 00100, Kenya

Abstract

Increasing the number, diversity, or uniformity of opinions in a group does not necessarily imply that those opinions will converge into a single more “intelligent” one, if an objective definition of the term intelligent exists as it applies to opinions. However, a recently developed approach called human-centric functional modeling provides what might be the first general model for individual or collective intelligence. In the case of the collective intelligence of groups, this model suggests how a cacophony of incoherent opinions in a large group might be combined into coherent collective reasoning by a hypothetical platform called “general collective intelligence” (GCI). When applied to solving group problems, a GCI might be considered a system that leverages collective reasoning to increase the beneficial insights that might be derived from the information available to any group. This GCI model also suggests how the collective reasoning ability (intelligence) might be exponentially increased compared to the intelligence of any individual in a group, potentially resulting in what is predicted to be a collective superintelligence.

Keywords: general collective intelligence, human-centric functional modeling, functional state space, collective social brain hypothesis

References(20)

[1]
W. Zhang, State-Space Search: Algorithms, Complexity, Extensions, and Applications. New York, NY, USA: Springer, 1999.
[2]
M. E. J. Newman, Networks: An Introduction. Oxford, UK: Oxford University Press, 2010.
[3]

P. Gardenfors, Conceptual spaces as a framework for knowledge representation, Mind and Matter, vol. 2, no. 2, pp. 9–27, 2004.
Google Scholar
[4]

A. W. Woolley, C. F. Chabris, A. Pentland, N. Hashmi, and T. W. Malone, Evidence for a collective intelligence factor in the performance of human groups, Science, vol. 330, no. 6004, pp. 686–688, 2010.
DOI Google Scholar
[5]

T. W. Malone and M. Klein, Harnessing collective intelligence to address global climate change, Innovations: Technology, Governance, Globalization, vol. 2, no. 3, pp. 15–26, 2007.
DOI Google Scholar
[6]
A. E. Williams, Human-centric functional modeling and the unification of systems thinking approaches: A short communication, Journal of Systems Thinking, doi: 10.54120/jost.v1i1.1369.
DOI
[7]

A. E. Williams, Defining a continuum from individual, to swarm, to collective intelligence, and to general collective intelligence, International Journal of Collaborative Intelligence, vol. 2, no. 3, pp. 205–209, 2021.
DOI Google Scholar
[8]
A. E. Williams, A generalized model for distributed, autonomic and self-organizing computing, in Proc. 2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), Hangzhou, China, 2022, pp. 27–33.
DOI
[9]
A. E. Williams, Innate collective intelligence and the collective social brain hypothesis, https://doi.org/10.31234/osf.io/upgfw, 2022.
DOI
[10]
G. -J. Qi, C. C. Aggarwal, J. Han, and T. Huang, Mining collective intelligence in diverse groups, in Proc. 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil, 2013, pp. 1041–1052.
[11]

R. P. Mann and D. Helbing, Optimal incentives for collective intelligence, Proceedings of the National Academy of Sciences, vol. 114, no. 20, pp. 5077–5082, 2017.
DOI Google Scholar
[12]

C. Yu, Y. Chai, and Y. Liu, Literature review on collective intelligence: A crowd science perspective, International Journal of Crowd Science, vol. 2, no. 1, pp. 64–73, 2018.
DOI Google Scholar
[13]

A. W. Woolley, I. Aggarwal, and T. W. Malone, Collective intelligence and group performance, Current Directions in Psychological Science, vol. 24, no. 6, pp. 420–424, 2015.
DOI Google Scholar
[14]
T. Buecheler, J. H. Sieg, R. M. Füchslin, and R. Pfeifer, Crowdsourcing, open innovation and collective intelligence in the scientific method: A research agenda and operational framework, in Proc. 12th International Conference on the Synthesis and Simulation of Living Systems, Odense, Denmark, 2010, pp. 679–686.
[15]
D. C. Brabham, Crowdsourcing as a model for problem solving: Leveraging the collective intelligence of online communities for public good, PhD dissertation, the Department of Communication, University of Utah, Salt Lake City, UT, USA, 2010.
[16]
D. C. Brabham, Crowdsourcing. Cambridge, MA, USA: MIT Press, 2013.
DOI
[17]
T. Buecheler, R. Lonigro, R. M. Füchslin, and R. Pfeifer, Modeling and simulating crowdsourcing as a complex biological system: Human crowds manifesting collective intelligence on the Internet, in Proc. 11th European Conference on the Synthesis and Simulation of Living Systems (ECAL 2011), Paris, France, 2011, pp. 109–116.
[18]

A. E. Williams, Are wicked problems a lack of general collective intelligence, AI & Soc., vol. 38, pp. 343–348, 2021.
DOI Google Scholar
[19]

A. E. Williams, Approximating an artificial general intelligence or a general collective intelligence, International Journal of Collaborative Intelligence, vol. 2, no. 3, pp. 210–223, 2022.
DOI Google Scholar
[20]
A. C. Clarke, Profiles of the Future: An Inquiry into the Limits of the Possible. London, UK: Harper & Row, 1973.
Publication history
Copyright
Rights and permissions

Publication history

Received: 23 September 2022
Revised: 19 October 2022
Accepted: 24 October 2022
Published: 22 June 2023
Issue date: June 2023

Copyright

© The author(s) 2023.

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return