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Research paper | Open Access

Projecting real world into CrowdIntell network: a methodology

Shipeng Wang1Lizhen Cui2Lei Liu2Xudong Lu2Qingzhong Li1( )
Computer Science Department, Shandong University, Jinan, China
Shandong University, Jinan, China
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Abstract

Purpose

The purpose of this paper is to build cyber-physical-psychological ternary fusion crowd intelligence network and realize comprehensive, real, correct and synchronous projection in cyber–physical–psychological ternary fusion system. Since the network of crowd intelligence is the future interconnected network system that takes on the features of large scale, openness and self-organization. The Digital-selfs in the network of crowd intelligence interact and cooperate with each other to finish transactions and achieve co-evolution eventually.

Design/methodology/approach

To realize comprehensive, real, correct and synchronous projection between cyber–physical–psychological ternary fusion system, the authors propose the rules and methods of projection from real world to the CrowdIntell Network. They build the mental model of the Digital-self including structure model and behavior model in four aspects: identity, provision, demand and connection, thus forming a theoretical mental model framework of Digital-self.

Findings

The mental model is excepted to lay a foundation for the theory of modeling and simulation in the research of crowd science and engineering.

Originality/value

This paper is the first one to propose the mental model framework and projection rules and methods of Digital-selfs in network of crowd intelligence, which lays a solid foundation for the theory of modeling, simulation, intelligent transactions, evolution and stability of CrowdIntell Network system, thus promoting the development of crowd science and engineering.

References

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International Journal of Crowd Science
Pages 138-154

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Cite this article:
Wang S, Cui L, Liu L, et al. Projecting real world into CrowdIntell network: a methodology. International Journal of Crowd Science, 2019, 3(2): 138-154. https://doi.org/10.1108/IJCS-01-2019-0006

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Received: 30 January 2019
Revised: 19 May 2019
Accepted: 20 May 2019
Published: 06 August 2019
© The author(s)

Shipeng Wang, Lizhen Cui, Lei Liu, Xudong Lu and Qingzhong Li. 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