Implicit utilitarian voting

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Implicit utilitarian voting (IUV) is a voting system in which the agents express their preferences by ranking the alternatives (like in ranked voting), and the system tries to select an alternative which maximizes the sum of utilities, as in the utilitarian social choice rule and utilitarian voting.[1]

The main challenge in IUV is that the rankings do not contain sufficient information to calculate the utilities. For example, if Alice ranks option 1 above option 2, we do not know whether Alice's utility from option 1 is much higher than from option 2, or only slightly higher. So if Bob ranks option 2 above option 1, we cannot know which of the two options maximizes the sum of utilities.

Since a voting-rule that can only access the rankings cannot find the max-sum alternative in all cases, IUV aims to find a voting-rule that approximates the max-sum alternative. The quality of an approximation can be measured in several ways.

  1. The distortion of a voting-rule is the worst-case (over utility functions consistent with the reported profile of rankings) ratio between the maximum utility-sum and the utility-sum of the alternative selected by the rule.[2]
  2. The regret of a voting-rule is the worst-case (over utility functions consistent with the reported profile of rankings) difference between the maximum utility-sum and the utility-sum of the alternative selected by the rule.[1]

Some achievements in the theory of IUV are:

  • Analyzing the distortion of various existing voting rules;[2]
  • Designing voting rules that minimize the distortion in single-winner elections[3] and in multi-winner elections;[1]
  • Analyzing the distortion of various input formats for Preference elicitation in participatory budgeting.[4]

Implementation[]

Implicit utilitarian voting rules are used in the RoboVote website.

See also[]

References[]

  1. ^ a b c Caragiannis, Ioannis; Nath, Swaprava; Procaccia, Ariel D.; Shah, Nisarg (16 January 2017). "Subset Selection Via Implicit Utilitarian Voting". Journal of Artificial Intelligence Research. 58: 123–152. doi:10.1613/jair.5282.
  2. ^ a b Procaccia, Ariel D.; Rosenschein, Jeffrey S. (2006). "The Distortion of Cardinal Preferences in Voting". Cooperative Information Agents X. Lecture Notes in Computer Science. Vol. 4149. pp. 317–331. CiteSeerX 10.1.1.113.2486. doi:10.1007/11839354_23. ISBN 978-3-540-38569-1.
  3. ^ Boutilier, Craig; Caragiannis, Ioannis; Haber, Simi; Lu, Tyler; Procaccia, Ariel D.; Sheffet, Or (October 2015). "Optimal social choice functions: A utilitarian view" (PDF). Artificial Intelligence. 227: 190–213. doi:10.1016/j.artint.2015.06.003.
  4. ^ Benadè, Gerdus; Nath, Swaprava; Procaccia, Ariel D.; Shah, Nisarg (May 2021). "Preference Elicitation for Participatory Budgeting". Management Science. 67 (5): 2813–2827. doi:10.1287/mnsc.2020.3666.
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