Agent-based Simulation of District-based Elections

TL;DR

This paper develops an agent-based simulation model for district-based elections, incorporating social and geographical voter attributes, to analyze election outcomes and explore counterfactual scenarios.

Contribution

It introduces a probabilistic agent-based model calibrated with ABC to simulate and analyze district-based elections, capturing spatial and social influences on voting behavior.

Findings

Model can reproduce Indian and US election results.

Simulation explores full outcome space of electoral scenarios.

Counterfactual election scenarios can be generated.

Abstract

In district-based elections, electors cast votes in their respective districts. In each district, the party with maximum votes wins the corresponding seat in the governing body. The election result is based on the number of seats won by different parties. In this system, locations of electors across the districts may severely affect the election result even if the total number of votes obtained by different parties remains unchanged. A less popular party may end up winning more seats if their supporters are suitably distributed spatially. This happens due to various regional and social influences on individual voters which modulate their voting choice. In this paper, we explore agent-based models for district-based elections, where we consider each elector as an agent, and try to represent their social and geographical attributes and political inclinations using probability distributions. This model can be used to simulate election results by Monte Carlo sampling. The models allow us to explore the full space of possible outcomes of an electoral setting, though they can also be calibrated to actual election results for suitable values of parameters. We use Approximate Bayesian Computation (ABC) framework to estimate model parameters. We show that our model can reproduce the results of elections held in India and USA, and can also produce counterfactual scenarios.