How to Predict the Frequency of Voting Events in Actual Elections

Abstract

Two commonly-used criteria for evaluating voting rules are how infrequently the rules provide opportunities for strategic voting and how infrequently they encounter voting paradoxes. The lack of ranking data from enough actual elections to determine these frequencies with reasonable accuracy makes it attractive to investigate ranking data simulated with Monte Carlo methods. But such simulations permit inferences about actual frequencies only if they are conducted through statistical models that generate ranking data with the same statistical properties as ranking data from actual elections. We offer statistical evidence that ranking data simulated with a spatial model of vote-casting are extremely similar to ranking data from actual elections.

Keywords: spatial model of voting, ordinal ranking data, urn models, Kullback-Leibler

JEL Classification: C4, C15, D72