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Many democratic societies use district-based elections, where the regionunder consideration is geographically divided into districts and arepresentative is chosen for each district based on the preferences of theelectors who reside there. These representatives belong to political parties,and the executive powers are acquired by that party which has a majority of theelected district representatives. In most systems, each elector can expresspreference for one candidate, though they may have a complete or partialranking of the candidates/parties. We show that this can lead to situationswhere many electors are dissatisfied with the election results, which is notdesirable in a democracy. The results may be biased towards the supporters of aparticular party, and against others. Inspired by current literature onfairness of Machine Learning algorithms, we define measures of fairness toquantify the satisfaction of electors, irrespective of their political choices.We also consider alternative election policies using concepts of voting rulesand rank aggregation, to enable voters to express their detailed preferenceswithout making the electoral process cumbersome or opaque. We then evaluatethese policies using the aforementioned fairness measures with the help ofMonte Carlo simulations. Such simulations are obtained using a proposedstochastic model for election simulation, that takes into account communityidentities of electors and its role in influencing their residence andpolitical preferences. We show that this model can simulate actual multi-partyelections in India. Through extensive simulations, we find that allowing votersto provide 2 preferences reduces the disparity between supporters of differentparties in terms of the election result.

Exploring Fairness in District-based Multi-party Elections under different Voting Rules using Stochastic Simulations