TY - GEN
T1 - Multi-Party Campaigning
AU - Koutecký, Martin
AU - Talmon, Nimrod
N1 - Funding Information: Koutecky´ was partially supported by Charles University project UNCE/SCI/004 and by the project 19-27871X of GA Cˇ R. Talmon was supported by the Israel Science Foundation (ISF; Grant No. 630/19). Funding Information: Koutecký was partially supported by Charles University project UNCE/SCI/004 and by the project 19-27871X of GA ČR. Talmon was supported by the Israel Science Foundation (ISF; Grant No. 630/19). Publisher Copyright: Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - We study a social choice setting of manipulation in elections and extend the usual model in two major ways: first, instead of considering a single manipulating agent, in our setting there are several, possibly competing ones; second, instead of evaluating an election after the first manipulative action, we allow several back-and-forth rounds to take place. We show that in certain situations, such as in elections with only a few candidates, optimal strategies for each of the manipulating agents can be computed efficiently. Our algorithmic results rely on formulating the problem of finding an optimal strategy as sentences of Presburger arithmetic that are short and only involve small coefficients, which we show is fixed-parameter tractable – indeed, one of our contributions is a general result regarding fixed-parameter tractability of Presburger arithmetic that might be useful in other settings. Following our general theorem, we design quite general algorithms; in particular, we describe how to design efficient algorithms for various settings, including settings in which we model diffusion of opinions in a social network, complex budgeting schemes available to the manipulating agents, and various realistic restrictions on adversary actions.
AB - We study a social choice setting of manipulation in elections and extend the usual model in two major ways: first, instead of considering a single manipulating agent, in our setting there are several, possibly competing ones; second, instead of evaluating an election after the first manipulative action, we allow several back-and-forth rounds to take place. We show that in certain situations, such as in elections with only a few candidates, optimal strategies for each of the manipulating agents can be computed efficiently. Our algorithmic results rely on formulating the problem of finding an optimal strategy as sentences of Presburger arithmetic that are short and only involve small coefficients, which we show is fixed-parameter tractable – indeed, one of our contributions is a general result regarding fixed-parameter tractability of Presburger arithmetic that might be useful in other settings. Following our general theorem, we design quite general algorithms; in particular, we describe how to design efficient algorithms for various settings, including settings in which we model diffusion of opinions in a social network, complex budgeting schemes available to the manipulating agents, and various realistic restrictions on adversary actions.
KW - cs.DS
KW - cs.MA
KW - cs.SI
UR - http://www.scopus.com/inward/record.url?scp=85129959398&partnerID=8YFLogxK
U2 - https://doi.org/10.1609/aaai.v35i6.16693
DO - https://doi.org/10.1609/aaai.v35i6.16693
M3 - Conference contribution
T3 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
SP - 5506
EP - 5513
BT - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
T2 - 35th AAAI Conference on Artificial Intelligence, AAAI 2021
Y2 - 2 February 2021 through 9 February 2021
ER -