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Agent based simulation on the process of human flesh search—From perspective of knowledge and emotion

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  • Zhu, Hou
  • Hu, Bin

Abstract

Human flesh search as a new net crowed behavior, on the one hand can help us to find some special information, on the other hand may lead to privacy leaking and offending human right. In order to study the mechanism of human flesh search, this paper proposes a simulation model based on agent-based model and complex networks. The computational experiments show some useful results. Discovered information quantity and involved personal ratio are highly correlated, and most of net citizens will take part in the human flesh search or will not take part in the human flesh search. Knowledge quantity does not influence involved personal ratio, but influences whether HFS can find out the target human. When the knowledge concentrates on hub nodes, the discovered information quantity is either perfect or almost zero. Emotion of net citizens influences both discovered information quantity and involved personal ratio. Concretely, when net citizens are calm to face the search topic, it will be hardly to find out the target; But when net citizens are agitated, the target will be found out easily.

Suggested Citation

  • Zhu, Hou & Hu, Bin, 2017. "Agent based simulation on the process of human flesh search—From perspective of knowledge and emotion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 71-80.
  • Handle: RePEc:eee:phsmap:v:469:y:2017:i:c:p:71-80
    DOI: 10.1016/j.physa.2016.11.085
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    1. Guillaume Deffuant & Frederic Amblard & Gérard Weisbuch, 2002. "How Can Extremism Prevail? a Study Based on the Relative Agreement Interaction Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(4), pages 1-1.
    2. Qingpeng Zhang & Fei-Yue Wang & Daniel Zeng & Tao Wang, 2012. "Understanding Crowd-Powered Search Groups: A Social Network Perspective," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-16, June.
    3. Richard M. Burton & Børge Obel, 2011. "Computational Modeling for What-Is, What-Might-Be, and What-Should-Be Studies---And Triangulation," Organization Science, INFORMS, vol. 22(5), pages 1195-1202, October.
    4. Crokidakis, Nuno, 2012. "Effects of mass media on opinion spreading in the Sznajd sociophysics model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1729-1734.
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