International Journal of Science and Technology (IJST)

A Simple Mathematical Model for Analysing Vote Shares in the Assembly Election in West Bengal in 2026 Following Special Intensive Revision (SIR)

This paper presents a simple mathematical model to analyse the impact of the Special Intensive Revision (SIR) of electoral rolls on the vote shares of AITC, BJP & Others in the 2026 West Bengal Assembly Election. Nearly 0.91 crore ineligible voters were deleted from the electoral rolls by the Election Commission of India (ECI). Using the results of 2024 Lok Sabha Election in West Bengal, this model examines how the removal of voters and the anti-incumbency effects may have influenced the results of the 2026 Assembly Election. The model defines key parameters representing the turnout percentage of ineligible voters, the percentage of their votes in favour of AITC (with the remaining in favour of BJP), and the percentage of votes that shifted from AITC and Others to BJP due to anti-incumbency. By solving a system of equations, the study identifies combinations of these parameters that reproduce the observed results of the 2026 West Bengal Assembly Election. The analysis shows that the observed vote shares are consistent with the model under various plausible assumptions regarding voter behaviour. This model suggests that, if SIR had not been conducted, AITC would have received a higher vote share than BJP in 2026 if the anti-incumbency swing was less than about 6.61%. The model further indicates that, for the observed 2026 election results to be reproduced under the present assumptions, the anti-incumbency swing from AITC and Others to BJP must be at least about 6.8%. An analysis involving different combinations of parameter values highlights how electoral roll revisions may meaningfully influence results in close contests. For simplicity, the present study uses only state-wide aggregate data. A limitation is that the constituency-wise information has not been used. The findings demonstrate the usefulness of simple mathematical modelling in understanding the effects of administrative decisions on election outcomes.