TY - GEN
T1 - Complexity of shift bribery in committee elections
AU - Bredereck, Robert
AU - Faliszewski, Piotr
AU - Niedermeier, Rolf
AU - Talmon, Nimrod
N1 - Publisher Copyright:
© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - We study the (parameterized) complexity of SHIFT BRIBERY for multiwinner voting rules. We focus on the SNTV, Bloc, k-Borda, and Chamberlin-Courant rules, as well as on approximate variants of the Chamberlin-Courant rule, since the original rule is NP-hard to compute. We show that SHIFT BRIBERY tends to be significantly harder in the multiwinner setting than in the single-winner one by showing settings where SHIFT BRIBERY is easy in the single-winner cases, but is hard (and hard to approximate) in the multiwinner ones. We show that the non-monotonicity of those rules which are based on approximation algorithms for the Chamberlin-Courant rule sometimes affects the complexity of SHIFT BRIBERY.
AB - We study the (parameterized) complexity of SHIFT BRIBERY for multiwinner voting rules. We focus on the SNTV, Bloc, k-Borda, and Chamberlin-Courant rules, as well as on approximate variants of the Chamberlin-Courant rule, since the original rule is NP-hard to compute. We show that SHIFT BRIBERY tends to be significantly harder in the multiwinner setting than in the single-winner one by showing settings where SHIFT BRIBERY is easy in the single-winner cases, but is hard (and hard to approximate) in the multiwinner ones. We show that the non-monotonicity of those rules which are based on approximation algorithms for the Chamberlin-Courant rule sometimes affects the complexity of SHIFT BRIBERY.
UR - http://www.scopus.com/inward/record.url?scp=84977557956&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84977557956
T3 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
SP - 2452
EP - 2458
BT - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
PB - AAAI press
T2 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
Y2 - 12 February 2016 through 17 February 2016
ER -