  In district-based multi-party elections, electors cast votes in their
respective districts. In each district, the party with maximum votes wins the
corresponding seat in the governing body. Election Surveys try to predict the
election outcome (vote shares and seat shares of parties) by querying a random
sample of electors. However, the survey results are often inconsistent with the
actual results, which could be due to multiple reasons. The aim of this work is
to estimate a posterior distribution over the possible outcomes of the
election, given one or more survey results. This is achieved using a prior
distribution over vote shares, election models to simulate the complete
election from the vote share, and survey models to simulate survey results from
a complete election. The desired posterior distribution over the space of
possible outcomes is constructed using Synthetic Dirichlet Likelihoods, whose
parameters are estimated from Monte Carlo sampling of elections using the
election models. We further show the same approach can also use be used to
evaluate the surveys - whether they were biased or not, based on the true
outcome once it is known. Our work offers the first-ever probabilistic model to
analyze district-based election surveys. We illustrate our approach with
extensive experiments on real and simulated data of district-based political
elections in India.
