  Voting rules based on evaluation inputs rather than preference orders have
been recently proposed, like majority judgement, range voting or approval
voting. Traditionally, probabilistic analysis of voting rules supposes the use
of simulation models to generate preferences data, like the Impartial Culture
(IC) or Impartial and Anonymous Culture (IAC) models. But these simulation
models are not suitable for the analysis of evaluation-based voting rules as
they generate preference orders instead of the needed evaluations. We propose
in this paper several simulation models for generating evaluation-based voting
inputs. These models, inspired by classical ones, are defined, tested and
compared for recommendation purpose.

Artificial Intelligence