Free-Riding in Multi-Issue Decisions

Fuente: arXiv
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Main Authors: Lackner, Martin, Maly, Jan, Nardi, Oliviero
Format: Preprint
Published: 2023
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author Lackner, Martin
Maly, Jan
Nardi, Oliviero
author_facet Lackner, Martin
Maly, Jan
Nardi, Oliviero
contents Voting in multi-issue domains allows for compromise outcomes that satisfy all voters to some extent, but such fairness considerations open the possibility of a special form of manipulation: free-riding, where voters untruthfully oppose a popular opinion in one issue to receive increased consideration in other issues; we study under which conditions this is possible and show that even weak fairness considerations enable free-riding, and through computational and experimental analysis, we find that while free-riding in multi-issue domains is often possible, it comes at a non-negligible individual risk for voters, making its allure smaller than one could intuitively assume.
format Preprint
id arxiv_https___arxiv_org_abs_2310_08194
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Free-Riding in Multi-Issue Decisions
Lackner, Martin
Maly, Jan
Nardi, Oliviero
Computer Science and Game Theory
Voting in multi-issue domains allows for compromise outcomes that satisfy all voters to some extent, but such fairness considerations open the possibility of a special form of manipulation: free-riding, where voters untruthfully oppose a popular opinion in one issue to receive increased consideration in other issues; we study under which conditions this is possible and show that even weak fairness considerations enable free-riding, and through computational and experimental analysis, we find that while free-riding in multi-issue domains is often possible, it comes at a non-negligible individual risk for voters, making its allure smaller than one could intuitively assume.
title Free-Riding in Multi-Issue Decisions
topic Computer Science and Game Theory
url https://arxiv.org/abs/2310.08194