Evolutionary game theory to predict the population growth in Few districts of Tamil Nadu and Kerala

The conventional models in Game Theory focuses entirely on the relative fitness of the population agents leaving the dynamic behaviour of the size of population left uncovered. Therefore, a robust model is required to combine both the growth dynamics and internal evolution of agents. Evolutionary Ga...

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Vydáno v:Measurement. Sensors Ročník 27; s. 100736
Hlavní autoři: N, Kalaivani, Visalakshidevi, E. Mona
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.06.2023
Elsevier
Témata:
Deterministic game theory
Evolutionary game theory
Game theory
Mathematics subject classification
Tamil Nadu and Kerala
Deterministic game theory
Mathematics subject classification
Game theory
Tamil Nadu and Kerala
Evolutionary game theory
ISSN:2665-9174, 2665-9174
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Shrnutí:The conventional models in Game Theory focuses entirely on the relative fitness of the population agents leaving the dynamic behaviour of the size of population left uncovered. Therefore, a robust model is required to combine both the growth dynamics and internal evolution of agents. Evolutionary Game Theory to Predict the Population Growth in Few Districts of Tamil Nadu and Kerala. In this paper, we develop an Evolutionary Game Theory (EGT) model which is integrated with the Residual deterministic learning (RDL) model to increase the rate of prediction of the population growth. Various deterministic models including Arithmetic Increase, Geometric Increase, Incremental Increase and Simple graphical models are used to improve the prediction ability of EGT. The EGT combined with RDL forms REGT model that is found scalable to predict the long-term population growth in near proximal districts of Tamil Nadu and Kerala. Various inputs affecting the population growth are considered as the inputs to the REGT model to the Matlab simulator. The experimentation shows that the proposed REGT model is better in predicting the population growth than the existing game theory models.
ISSN:2665-9174
2665-9174
DOI:10.1016/j.measen.2023.100736