Abstract
We study group identification problems, the objective of which is to classify agents into groups based on individual opinions. Our point of departure from the literature is to allow membership to be fractional, to qualify the extent of belonging. Examining implications of independence of irrelevant opinions, we identify and characterize four nested families of rules. The four families include the weighted-average rules, which are obtained by taking a weighted average of all entries of a problem, and the fractional consent rules, which adapt the consent rules from the binary model to our multinary setup, balancing two principles in group identification, namely liberalism and social consent. Existing characterizations of the one-vote rules, the consent rules, and the liberal rule follow from ours.
| Original language | English |
|---|---|
| Pages (from-to) | 66-75 |
| Number of pages | 10 |
| Journal | Journal of Mathematical Economics |
| Volume | 77 |
| DOIs | |
| Publication status | Published - 2018 Aug 1 |
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Keywords
- Fractional consent rules
- Fractional membership
- Independence of irrelevant opinions
- Liberal rule
- Weighted-average rules
ASJC Scopus subject areas
- Economics and Econometrics
- Applied Mathematics
Cite this
Fractional group identification. / Cho, Wonki; Park, Chang Woo.
In: Journal of Mathematical Economics, Vol. 77, 01.08.2018, p. 66-75.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Fractional group identification
AU - Cho, Wonki
AU - Park, Chang Woo
PY - 2018/8/1
Y1 - 2018/8/1
N2 - We study group identification problems, the objective of which is to classify agents into groups based on individual opinions. Our point of departure from the literature is to allow membership to be fractional, to qualify the extent of belonging. Examining implications of independence of irrelevant opinions, we identify and characterize four nested families of rules. The four families include the weighted-average rules, which are obtained by taking a weighted average of all entries of a problem, and the fractional consent rules, which adapt the consent rules from the binary model to our multinary setup, balancing two principles in group identification, namely liberalism and social consent. Existing characterizations of the one-vote rules, the consent rules, and the liberal rule follow from ours.
AB - We study group identification problems, the objective of which is to classify agents into groups based on individual opinions. Our point of departure from the literature is to allow membership to be fractional, to qualify the extent of belonging. Examining implications of independence of irrelevant opinions, we identify and characterize four nested families of rules. The four families include the weighted-average rules, which are obtained by taking a weighted average of all entries of a problem, and the fractional consent rules, which adapt the consent rules from the binary model to our multinary setup, balancing two principles in group identification, namely liberalism and social consent. Existing characterizations of the one-vote rules, the consent rules, and the liberal rule follow from ours.
KW - Fractional consent rules
KW - Fractional membership
KW - Independence of irrelevant opinions
KW - Liberal rule
KW - Weighted-average rules
UR - http://www.scopus.com/inward/record.url?scp=85049431540&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049431540&partnerID=8YFLogxK
U2 - 10.1016/j.jmateco.2018.06.004
DO - 10.1016/j.jmateco.2018.06.004
M3 - Article
AN - SCOPUS:85049431540
VL - 77
SP - 66
EP - 75
JO - Journal of Mathematical Economics
JF - Journal of Mathematical Economics
SN - 0304-4068
ER -