In:
Kybernetes, Emerald, Vol. 44, No. 6/7 ( 2015-6-1), p. 1122-1133
Abstract:
– The purpose of this paper is to focus on the underpinning dynamics that explain collective intelligence. Design/methodology/approach – Collective intelligence can be understood as the capacity of a collective system to evolve toward higher order complexity through networks of individual capacities. The authors observed two collective systems as examples of the dynamic processes of complex networks – the wiki course PeSO at the Universidad de Los Andes, Bogotá, Colombia, and an agent-based model inspired by wiki systems. Findings – The results of the wiki course PeSO and the model are contrasted with a random network baseline model. Both the wiki course and the model show dynamics of accumulation, in which statistical properties of non-equilibrium networks appear. Research limitations/implications – The work is based on network science. The authors analyzed data from two kinds of networks: the wiki course PeSO and an agent-based model. Limitations due to the number of computations and complexity appeared when there was a high order of magnitude of agents. Practical implications – Better understanding can allow for the measurement and design of systems based on collective intelligence. Originality/value – The results show how collective intelligence emerges from cumulative dynamics.
Type of Medium:
Online Resource
ISSN:
0368-492X
DOI:
10.1108/K-11-2014-0245
Language:
English
Publisher:
Emerald
Publication Date:
2015
detail.hit.zdb_id:
1479781-1
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