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Open Access

An Evolutionary Adaptive System for Prediction of Strategy Influence: A Case Study of Government Regulation Guided Brand Innovation

Jiali Lin1, Qiaomei Li1, Guangsheng Lin2, Zhihui He2, Dazhi Jiang2( ), Hao Liu2( )
Business School, Shantou University, Shantou 515063, China
Department of Computer Science, Shantou University, Shantou 515063, China
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Abstract

Decision making is one of the common human activities. But in complex, interactive, and dynamic systems, it is extremely important to make decisions scientifically because the influence of the behavior after decision making is generally irreversible. The predictability of behavior influence is an effective way to improve the scientific decision making. As a new branch of computing, computational experiment is an emerging management method for research on complex systems. In this paper, based on particle swarm intelligence, an evolutionary adaptive system model of brand innovation in the toy industry cluster is constructed. By imitating the evolution process of the complex adaptive system, this method is helpful to analyze the impact of the management behavior brought to simulation system, predict the management behavior in real world, and finally choose the best management strategy. This simulation tried to figure out the affection of government regulation strategies and provide corresponding assessments and recommendations, which gives a new solution to assist the government to effectively judge the influence of the macro policy, as well as provides a new way of thinking of the related intelligent decision making.

Keywords

decision makingbehavior influencecomputational experimentevolution adaptive systembrand innovation

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Complex System Modeling and Simulation
Volume 2 Issue 3,
September 2022
Pages 197-212
DOI: 10.23919/CSMS.2022.0011
Cite this article:
Lin J, Li Q, Lin G, et al. An Evolutionary Adaptive System for Prediction of Strategy Influence: A Case Study of Government Regulation Guided Brand Innovation. Complex System Modeling and Simulation, 2022, 2(3): 197-212. https://doi.org/10.23919/CSMS.2022.0011

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Received: 16 April 2022
Revised: 02 June 2022
Accepted: 04 July 2022
Published: 30 September 2022
© The author(s) 2022

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/).
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