Analyzing election trends incorporating memory effect through a fractional-order mathematical modeling

Abstract

This study proposes a new mathematical model to analyze and predict the results of a political election. In general, we predict or analyze the results using statistical methods; however, to minimize the effort of the study, we propose a fractional-order modeling approach. This study proposes a model to analyze and predict general election result trends in India, focusing on the state of West Bengal. To incorporate memory into the model, we consider the Caputo fractional derivative. The model solution's positivity, boundedness, existence, and uniqueness were tested analytically. Numerical simulations were carried out to investigate the impact of the parameters and evaluate the model's performance by incorporating the implications of the previous election for realistic situations. Following this, a qualitative analysis of the performance of political parties is discussed, and a prediction of the electoral victory is obtained.