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Open Access

Parallel-Data-Based Social Evolution Modeling

China University of Petroleum, Qingdao 266580, China
Qingdao Academy of Intelligent Industry, Qingdao 266111, China
China University of Petroleum, Qingdao 266580, China
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100049, China
Qingdao Academy of Intelligent Industry, Qingdao 266111, China
Institute of National Security, National Defense University, Beijing 100081, China
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Abstract

Abnormal or drastic changes in the natural environment may lead to unexpected events, such as tsunamis and earthquakes, which are becoming a major threat to national economy. Currently, no effective assessment approach can deduce a situation and determine the optimal response strategy when a natural disaster occurs. In this study, we propose a social evolution modeling approach and construct a deduction model for self-playing, self-learning, and self-upgrading on the basis of the idea of parallel data and reinforcement learning. The proposed approach can evaluate the impact of an event, deduce the situation, and provide optimal strategies for decision-making. Taking the breakage of a submarine cable caused by earthquake as an example, we find that the proposed modeling approach can obtain a higher reward compared with other existing methods.

Keywords

parallel datareinforcement learningdecision-making

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Tsinghua Science and Technology
Volume 26 Issue 6,
December 2021
Pages 878-885
DOI: 10.26599/TST.2020.9010052
Cite this article:
Zhang W, Hou Z, Wang X, et al. Parallel-Data-Based Social Evolution Modeling. Tsinghua Science and Technology, 2021, 26(6): 878-885. https://doi.org/10.26599/TST.2020.9010052

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Received: 20 September 2020
Accepted: 09 October 2020
Published: 09 June 2021
© The author(s) 2021.

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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