Comparing Voting Systems

Nurmi, Hannu

doi:10.1007/978-94-009-3985-1

Cited by 316 publications

(117 citation statements)

References 0 publications

Supporting

Mentioning

112

Contrasting

Unclassified

Order By: Relevance

“…Unfortunately, the unanimous winner usually does not exist. In case more than half of the voters agree on the candidate that should be selected as the winner, one talks about the majority 1 winner [107]. Obviously, the majority winner might not exist either.…”

Section: Winning Candidatesmentioning

confidence: 99%

See 1 more Smart Citation

Monotonicity-based consensus states for the monometric rationalisation of ranking rules with application in decision making

Pérez‐Fernández¹

2017

4OR-Q J Oper Res

View full text Add to dashboard Cite

Section: Winning Candidatesmentioning

confidence: 99%

“…Instead, a weaker version is often considered requiring that more than half of the voters agree on the candidate that should be selected as the winner [107].…”

Section: Some Existing Notions Of Best Candidatementioning

confidence: 99%

Monotonicity-based consensus states for the monometric rationalisation of ranking rules with application in decision making

Pérez‐Fernández¹

2017

4OR-Q J Oper Res

View full text Add to dashboard Cite

“…It is extensively studied in social choice. Normally, it is presented under two different perspectives: as a way to aggregate different opinions, where different voting rules are analyzed to verify if they satisfy a set of axioms that are considered to be important to achieve fairness [63]; or as a way to discover an optimal choice, where different voting rules are analyzed to verify if they converge to always picking the action that has the highest probability of being correct [14,19,49].…”

Section: Related Workmentioning

confidence: 99%

Every team deserves a second chance: an extended study on predicting team performance

Marcolino

Lakshminarayanan

Nagarajan

et al. 2016

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance in finding the correct answer to complex problems. We present a novel benefit of voting, that has not been observed before: we can use the voting patterns to assess the performance of a team and predict their final outcome. This prediction can be executed at any moment during problem-solving and it is completely domain independent. Hence, it can be used to identify when a team is failing, allowing an operator to take remedial procedures (such as changing team members, the voting rule, or increasing the allocation of resources). We present three main theoretical results: (1) we show a theoretical explanation of why our prediction method works; (2) contrary to what would be expected based on a simpler explanation using classical voting models, we show that we can make accurate predictions irrespective of the strength (i.e., performance) of the teams, and that in fact, the prediction can work better for diverse teams composed of different agents than uniform teams made of copies of the best agent; (3) we show that the quality of our prediction increases with the size of the action space. We perform extensive experimentation in two different domains: Computer Go and Ensemble Learning. In Computer Go, we obtain high Multi-Agent Syst (2017) 31:1003-1054 quality predictions about the final outcome of games. We analyze the prediction accuracy for three different teams with different levels of diversity and strength, and show that the prediction works significantly better for a diverse team. Additionally, we show that our method still works well when trained with games against one adversary, but tested with games against another, showing the generality of the learned functions. Moreover, we evaluate four different board sizes, and experimentally confirm better predictions in larger board sizes. We analyze in detail the learned prediction functions, and how they change according to each team and action space size. In order to show that our method is domain independent, we also present results in Ensemble Learning, where we make online predictions about the performance of a team of classifiers, while they are voting to classify sets of items. We study a set of classical classification algorithms from machine learning, in a data-set of hand-written digits, and we are able to make high-quality predictions about the final performance of two different teams. Since our approach is domain independent, it can be easily applied to a variety of other domains.

show abstract

“…They appear in game theory [10], voting theory [17,33], psychological studies on preference and discrimination in decision-making methods [9]. Next, in group decision making, preference relations represent collective preferences and are built from individual preferences, either by aggregation methods [16], or by consensus-reaching processes [27].…”

Section: Motivation; Historical Backgroundmentioning

confidence: 99%