JOURNAL ARTICLE
Analysis of Parameter Space, Bifurcation and Symbolic Dynamics of Evolutionary Strategies on a One-Dimensional Regular Lattice.
Published In: International Journal of Bifurcation & Chaos in Applied Sciences & Engineering, 2025, v. 35, n. 7. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Jin, Weifeng; ADAMATZKY, ANDREW 3 of 3
Abstract
The payoff matrix parameters, bifurcation, topological conjugacy and symbolic dynamics of the evolutionary game dynamical systems on a one-dimensional regular lattice are thoroughly investigated in this paper. Based on the properties of addition and scalar multiplication invariance, an effective dimensionality reduction method is proposed to classify the payoff matrix parameters, resulting in 27 evolutionary game dynamical systems. Furthermore, their qualitative dynamics is presented and 14 topological conjugacy classes are obtained through two homeomorphisms, revealing the parametric bifurcation in the payoff matrix. Subsequently, their symbolic dynamics is examined in the context of the snowdrift game, and chaotic characteristics such as nonzero topological entropy and mixing property are derived. These analytical results indicate that even simple interactive elements have diverse dynamical expressiveness to capture the complexity or simplicity of spatial evolutionary games. Therefore, it is recommended that numerical simulations with different parameters should focus on parameter selection, dynamical properties and their combined effects to understand the factors affecting the evolutionary behaviors. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Bifurcation & Chaos in Applied Sciences & Engineering. 2025/06, Vol. 35, Issue 7, p1
- Document Type:Article
- Subject Area:Mathematics
- Publication Date:2025
- ISSN:0218-1274
- DOI:10.1142/S0218127425500865
- Accession Number:185394153
- Copyright Statement:Copyright of International Journal of Bifurcation & Chaos in Applied Sciences & Engineering is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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