Many democracies in the world use district-based elections, where the region is divided into districts and there is a seat corresponding to each district in the governing body. In each district, several candidates representing different parties contest the election and residents of the district cast their votes. A candidate is declared the winner of the corresponding seat using a scoring rule on the votes, and the election result is understood in terms of the number of seats won by the candidates from different parties. This system is known to have a number of flaws, for example there is no guarantee that the election result truly reflect the popular support for different parties. The system may be gamed by different parties and authorities in different ways to produce a result that they prefer, and some 11 people’s opinions may get fully neglected and they find themselves unrepresented 12 and outside the power structure. It is important to identify the appropriate rules 13 for drawing districts, voting and scoring, so that the results satisfy various criteria 14 related to ethics and fairness. However, this requires exploring the huge range of 15 possible public opinions, which is difficult to do analytically. Here we propose 16 a framework for stochastic simulation of election results, which will allow all 17 stakeholders to explore the possible effects of different rules and enable them to 18 choose a more robust and ethical system. We also discuss how the simulation 19 models can be calibrated with available information using Machine Learning, so 20 that it can produce feasible results.