“How Can We Compare Different Voting Methods?” A Voting Theory Project

McCune, David; McCune, Lori

doi:10.1080/10511970.2018.1489924

Cited by 2 publications

(3 citation statements)

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“…The total number of votes cast ranking the given candidate in the last place. This feature is obtained from the criterion called "Absolute Loser Criterion", which states that if a candidate has the highest last place votes, the candidate should not win [21].…”

Section: Last Place Votesmentioning

confidence: 99%

“…From the figures we observe that for instances 3 and 4a, where the winning candidate has the highest first place votes (i.e., the plurality criterion holds), the presence of explanations does not improve a participant's satisfaction and only slightly improves acceptance. Indeed, the Plurality voting rule enjoys higher familiarity than the others among people [21] and was found to be dominant in experiments studying people's vote manipulation and bias towards certain candidates [27,28,29]. When the winning candidate does not have the highest first place votes, the choice needs to be explicitly justified for people to be satisfied and accepting.…”

Section: Experimental Designmentioning

confidence: 99%

“…Finally, in the context of ranked voting that is used in our experiments as the application domain, there is much we can learn from insights obtained in prior work related to human behavior. This includes a higher rate of manipulation for the Plurality voting rule [27,28], bias towards the candidate with the highest first place votes [29] and a higher familiarity of the Plurality voting rule [21]. Surveys to analyze the understanding of ranked choice voting have reported that the respondents do not find it as difficult to follow the rules of ranked choice voting as it was perceived by the researchers [55,56].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Justifying Social-Choice Mechanism Outcome for Improving Participant Satisfaction

Suryanarayana¹,

Sarne²,

Kraus³

2022

Preprint

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In many social-choice mechanisms the resulting choice is not the most preferred one for some of the participants, thus the need for methods to justify the choice made in a way that improves the acceptance and satisfaction of said participants. One natural method for providing such explanations is to ask people to provide them, e.g., through crowdsourcing, and choosing the most convincing arguments among those received. In this paper we propose the use of an alternative approach, one that automatically generates explanations based on desirable mechanism features found in theoretical mechanism design literature. We test the effectiveness of both of the methods through a series of extensive experiments conducted with over 600 participants in ranked voting, a classic social choice mechanism. The analysis of the results reveals that explanations indeed affect both average satisfaction from and acceptance of the outcome in such settings. In particular, explanations are shown to have a positive effect on satisfaction and acceptance when the outcome (the winning candidate in our case) is the least desirable choice for the participant. A comparative analysis reveals that the automatically generated explanations result in similar levels of satisfaction from and acceptance of an outcome as with the more costly alternative of crowdsourced explanations, hence eliminating the need to keep humans in the loop. Furthermore, the automatically generated explanations significantly reduce participants' belief that a different winner should have been elected compared to crowdsourced explanations.

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Section: Last Place Votesmentioning

confidence: 99%