In elections, irrelevant alternatives provide relevant data

TL;DR

This paper challenges the independence of irrelevant alternatives criterion in voting systems, showing through simulations that dropout candidate data can be valuable, and finds that majority judgment often underperforms compared to other methods.

Contribution

It demonstrates that dropout data is useful for candidate selection and argues against the IIA criterion, providing empirical evidence from simulation studies.

Findings

Dropout data can improve candidate selection accuracy.

Majority judgment voting system often performs worse than alternatives.

Abandoning IIA can lead to better electoral outcomes.

Abstract

The electoral criterion of independence of irrelevant alternatives, or IIA, states that a voting system is unacceptable if it would choose a different winner if votes were recounted after one of the losers had dropped out. But IIA confuses the candidate who withdrew with the data which was generated by that candidate. This paper reports a wide variety of simulation studies which consistently show that data from dropout candidates can be very useful in choosing the best of the remaining candidates. These studies use well-validated spatial models in which the most centrist candidates are considered to be the best candidates. Thus IIA should be abandoned. The majority judgment or MJ voting system was created specifically to satisfy IIA. Some of these studies also show the substantial inferiority of MJ to other voting systems. Discussions of IIA have usually treated dropouts as strictly hypothetical, but our conclusions about the usefulness of dropout data may apply even to real dropouts.