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A review on collective behavior modeling and simulation: building a link between cognitive psychology and physical action.

Authors :
Zhang, Junqiao
Qu, Qiang
Chen, Xue-Bo
Source :
Applied Intelligence; Nov2023, Vol. 53 Issue 21, p25954-25983, 30p
Publication Year :
2023

Abstract

Collective behavior is a widespread and important phenomenon among human society, biological populations, swarm robots and multi-agents. The mathematical model shows that the collective behavior originates from the simple local interaction rules among the individuals. However, the nature of the interaction is rarely understood, and most models and theories only rely on physical assumptions. Thus, some researchers look for evidences and concepts to understand collective behavior from social science. To enhance the authenticity of simulation, more dimensions and psychological details of the individual are needed. This paper reviews the traditional physical model and the concept of group psychology. Classical physics models are systematically reviewed, discussed, and analyzed, and simulation methods commonly used in this field are introduced. Physical models can simulate the general collective behavior and have the characteristics of microscopic and macroscopic models. The results show that the traditional physical model can simulate most groups, but it lacks the expression of individual psychological mechanism. Psychological concepts can reveal the internal evolutionary mechanism of collective behavior. In the real world, the cognitive psychology of an individual is dynamic, diverse, and complex. As a result, the individual psychology in the group will change at any time, then the collective behavior will be transformed into the dispersion behavior. Finally, we emphasize some interdisciplinary issues in this field, which can produce a more comprehensive understanding of collective behavior. In future work, the simulation of collective behavior will be developed to be more natural, efficient and realistic, and to produce a multidisciplinary understanding of social aggregation. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0924669X
Volume :
53
Issue :
21
Database :
Complementary Index
Journal :
Applied Intelligence
Publication Type :
Academic Journal
Accession number :
173152578
Full Text :
https://doi.org/10.1007/s10489-023-04924-7