TY - JOUR
T1 - Representative committees of peers
AU - Meir, Reshef
AU - Sandomirskiy, Fedor
AU - Tennenholtz, Moshe
N1 - Publisher Copyright: © 2021 AI Access Foundation. All rights reserved.
PY - 2021/7/1
Y1 - 2021/7/1
N2 - A population of voters elects representatives among themselves to decide on a sequence of possibly unforeseen binary issues. Voters care only about the final decision, not the elected representatives. The disutility of a voter is proportional to the fraction of issues, where her preferences disagree with the decision. While an issue-by-issue majority vote by all voters would maximize the social welfare, we are interested in how well the preferences of the population can be approximated by a small committee. We show that a k-sortition (a random committee of k voters with the majority vote within the committee) leads to an outcome within the factor 1 + O(1/√k) of the optimal social cost for any number of voters n, any number of issues m, and any preference profile. For a small number of issues m, the social cost can be made even closer to optimal by delegation procedures that weigh committee members according to their number of followers. However, for large m, we demonstrate that the k-sortition is the worst-case optimal rule within a broad family of committee-based rules that take into account metric information about the preference profile of the whole population.
AB - A population of voters elects representatives among themselves to decide on a sequence of possibly unforeseen binary issues. Voters care only about the final decision, not the elected representatives. The disutility of a voter is proportional to the fraction of issues, where her preferences disagree with the decision. While an issue-by-issue majority vote by all voters would maximize the social welfare, we are interested in how well the preferences of the population can be approximated by a small committee. We show that a k-sortition (a random committee of k voters with the majority vote within the committee) leads to an outcome within the factor 1 + O(1/√k) of the optimal social cost for any number of voters n, any number of issues m, and any preference profile. For a small number of issues m, the social cost can be made even closer to optimal by delegation procedures that weigh committee members according to their number of followers. However, for large m, we demonstrate that the k-sortition is the worst-case optimal rule within a broad family of committee-based rules that take into account metric information about the preference profile of the whole population.
UR - http://www.scopus.com/inward/record.url?scp=85111667969&partnerID=8YFLogxK
U2 - https://doi.org/10.1613/JAIR.1.12521
DO - https://doi.org/10.1613/JAIR.1.12521
M3 - مقالة
SN - 1076-9757
VL - 71
SP - 401
EP - 429
JO - Journal Of Artificial Intelligence Research
JF - Journal Of Artificial Intelligence Research
ER -