Entropic Spatial Auto-correlation of Voter Uncertainty and Voter Transitions in Parliamentary Elections

TL;DR

This paper introduces a novel spatial auto-correlation model using entropy measures to analyze voter uncertainty and transitions across districts, revealing strong sectarian influences in Lebanese elections.

Contribution

It develops a new spatial auto-correlation model based on entropy measures and applies it to Lebanese elections, highlighting sectarian influence and introducing a method for estimating voter transition matrices.

Findings

Strong geographic and adjacency correlations in voter uncertainty.

High correlation of sectarian adjacency in election results.

Introduces a maximized general entropy estimation method.

Abstract

This paper studies a novel spatial auto-correlation model of voter uncertainty across districts. We use the Moran $I$ index to measure the auto-correlation of Shannon, relative Shannon, Tsallis and relative Tsallis entropies of regional electoral outcomes with respect to geographic adjacency, proximity and sectarian adjacency. Using data from the Lebanese parliamentary elections, we find strong geographic and gravitational adjacency correlations. More importantly, there is a notably strong correlation in sectarian adjacency in both $2018$ and $2022$ elections, with a very high level of confidence. This result asserts the dominance of the sectarian factor in Lebanese politics. We also introduce the method of maximized general entropy estimation that allows us to determine the Markov transition matrix of voters between consecutive elections.