The SP algorithm requires the voters to predict other voters' report in the form of a full probability distribution over all rankings of alternatives.

We introduce a framework for inference in general state-space hidden Markov models (HMMs) under likelihood misspecification.

A simple and robust implementation algorithm is also provided, which can be used for any pre-trained neural network classifiers.