TY - GEN
T1 - Targeted Negative Campaigning
T2 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
AU - Zagoury, Avishai
AU - Keller, Orgad
AU - Hassidim, Avinatan
AU - Hazon, Noam
N1 - Publisher Copyright:
Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2021
Y1 - 2021
N2 - Given the ubiquity of negative campaigning in recent political elections, we find it important to study its properties from a computational perspective. To this end, we present a model where elections can be manipulated by convincing voters to demote specific non-favored candidates, and study its properties in the classic setting of scoring rules. When the goal is constructive (making a preferred candidate win), we prove that finding such a demotion strategy is easy for Plurality and Veto, while generally hard for t-approval and Borda. We also provide a t-factor approximation for tapproval for every fixed t, and a 3-factor approximation algorithm for Borda. Interestingly enough—following recent trends in political science that show that the effectiveness of negative campaigning depends on the type of candidate and demographic—when assigning varying prices to different possible demotion operations, we are able to provide inapproximability results. When the goal is destructive (making the leading opponent lose), we show that the problem is easy for a broad class of scoring rules.
AB - Given the ubiquity of negative campaigning in recent political elections, we find it important to study its properties from a computational perspective. To this end, we present a model where elections can be manipulated by convincing voters to demote specific non-favored candidates, and study its properties in the classic setting of scoring rules. When the goal is constructive (making a preferred candidate win), we prove that finding such a demotion strategy is easy for Plurality and Veto, while generally hard for t-approval and Borda. We also provide a t-factor approximation for tapproval for every fixed t, and a 3-factor approximation algorithm for Borda. Interestingly enough—following recent trends in political science that show that the effectiveness of negative campaigning depends on the type of candidate and demographic—when assigning varying prices to different possible demotion operations, we are able to provide inapproximability results. When the goal is destructive (making the leading opponent lose), we show that the problem is easy for a broad class of scoring rules.
UR - http://www.scopus.com/inward/record.url?scp=85129944140&partnerID=8YFLogxK
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AN - SCOPUS:85129944140
T3 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
SP - 5768
EP - 5778
BT - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
PB - Association for the Advancement of Artificial Intelligence
Y2 - 2 February 2021 through 9 February 2021
ER -