Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)

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Neural Network and Evolutionary Game Theory

Authors
Linshu Xu1, *, Shuchen Zhang2, Kai Cheng3, Hai Ci4, Ruotong Shen5
1No. 2 High School of East China Normal University, Shanghai, 201203, China
2University of Science and Technology of China, Hefei, 210026, China
3Suzhou Industrial Park Xinghai High School, Suzhou, 251000, China
4Dalian American International School, Dalian, 116650, China
5Hangzhou High School Qianjiang Campus, Hangzhou, 310000, China
*Corresponding author. Email: xls2818291092@163.com
Corresponding Author
Linshu Xu
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-198-2_156How to use a DOI?
Keywords
neural network; evolutionary game theory; optimization
Abstract

This thesis proposes the feasibility to advance evolutionary game theory by parameterizing evolutionary game problems with a finite payoff matrix. It eliminates subjectivity while determining the parameters in evolutionary game problems. A neural network trained through learning a large database of the existing examples of similar models outputs the parameters of the model with a few data of the model provided as the inputs. Unlike directly putting the whole adjacency matrix into a training set, in which the complexity to train the algorithm increases quadratically as the amount of parameters increases, the scale of the network is most acceptable and plausible. Therefore, it produces the parameters with both precision and efficiency. With the acquisition of a precise payoff matrix, the evolutionary process modeled by differential equations would be more closely fitted to real datasets than that with a subjectively assumed payoff matrix.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
10 August 2023
ISBN
978-94-6463-198-2
ISSN
2589-4900
DOI
10.2991/978-94-6463-198-2_156How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Linshu Xu
AU  - Shuchen Zhang
AU  - Kai Cheng
AU  - Hai Ci
AU  - Ruotong Shen
PY  - 2023
DA  - 2023/08/10
TI  - Neural Network and Evolutionary Game Theory
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 1504
EP  - 1512
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_156
DO  - 10.2991/978-94-6463-198-2_156
ID  - Xu2023
ER  -