Fractional group identification

Wonki Jo Cho; Chang Woo Park

doi:10.1016/j.jmateco.2018.06.004

Fractional group identification

Wonki Jo Cho, Chang Woo Park

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

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	https://doi.org/10.1016/j.jmateco.2018.06.004
Publication status	Published - 2018 Aug

Bibliographical note

Funding Information:
We thank Biung-Ghi Ju, Sam-Ho Lee, Alan D. Miller, and William Thomson for helpful comments. This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A5A8022100) and by a Korea University grant (K1706341).

Publisher Copyright:
© 2018 Elsevier B.V.

Keywords

Fractional consent rules
Fractional membership
Independence of irrelevant opinions
Liberal rule
Weighted-average rules

ASJC Scopus subject areas

Economics and Econometrics
Applied Mathematics

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10.1016/j.jmateco.2018.06.004

Cite this

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title = "Fractional group identification",

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.",

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AU - Park, Chang Woo

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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.

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KW - Independence of irrelevant opinions

KW - Liberal rule

KW - Weighted-average rules

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ER -

Fractional group identification

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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