Approximation and hardness of Shift-Bribery

Abstract In the Shift-Bribery problem we are given an election, a preferred candidate, and the costs of shifting this preferred candidate up the voters' preference orders. The goal is to find such a set of shifts that ensures that the preferred candidate wins the election. We give the first polynomial-time approximation scheme for the Shift-Bribery problem for the case of positional scoring rules, and for the Copeland rule we show strong inapproximability results.

[1]  Irit Dinur,et al.  Mildly exponential reduction from gap 3SAT to polynomial-gap label-cover , 2016, Electron. Colloquium Comput. Complex..

[2]  Piotr Faliszewski,et al.  Approximation and Hardness of Shift-Bribery , 2019, AAAI.

[3]  Edith Elkind,et al.  On elections with robust winners , 2013, AAMAS.

[4]  Piotr Faliszewski,et al.  Algorithms for destructive shift bribery , 2019, Autonomous Agents and Multi-Agent Systems.

[5]  Jörg Rothe,et al.  Taking the final step to a full dichotomy of the possible winner problem in pure scoring rules , 2010, Inf. Process. Lett..

[6]  Luca Trevisan,et al.  On the Efficiency of Polynomial Time Approximation Schemes , 1997, Inf. Process. Lett..

[7]  P. Faliszewski,et al.  Control and Bribery in Voting , 2016, Handbook of Computational Social Choice.

[8]  Prasad Raghavendra,et al.  A Birthday Repetition Theorem and Complexity of Approximating Dense CSPs , 2016, ICALP.

[9]  Piotr Faliszewski,et al.  Bribery as a Measure of Candidate Success: Complexity Results for Approval-Based Multiwinner Rules , 2017, AAMAS.

[10]  Aditya Bhaskara,et al.  Polynomial integrality gaps for strong SDP relaxations of Densest k-subgraph , 2011, SODA.

[11]  Ronald L. Rivest,et al.  Computing the Margin of Victory in IRV Elections , 2011, EVT/WOTE.

[12]  Vincent Conitzer,et al.  Determining Possible and Necessary Winners under Common Voting Rules Given Partial Orders , 2008, AAAI.

[13]  Ildikó Schlotter,et al.  Multivariate Complexity Analysis of Swap Bribery , 2010, Algorithmica.

[14]  Vincent Conitzer,et al.  Barriers to Manipulation in Voting , 2016, Handbook of Computational Social Choice.

[15]  J. Mirrlees An Exploration in the Theory of Optimum Income Taxation an Exploration in the Theory of Optimum Income Taxation L Y 2 , 2022 .

[16]  Kevin Roberts,et al.  Voting over income tax schedules , 1977 .

[17]  Piotr Faliszewski,et al.  Robustness among multiwinner voting rules , 2021, Artif. Intell..

[18]  Russell Impagliazzo,et al.  Which Problems Have Strongly Exponential Complexity? , 2001, J. Comput. Syst. Sci..

[19]  David Cary,et al.  Estimating the Margin of Victory for Instant-Runoff Voting , 2011, EVT/WOTE.

[20]  Jörg Rothe,et al.  The complexity of probabilistic lobbying , 2014, Discret. Optim..

[21]  Piotr Faliszewski,et al.  Swap Bribery , 2009, SAGT.

[22]  Pasin Manurangsi,et al.  Almost-polynomial ratio ETH-hardness of approximating densest k-subgraph , 2016, STOC.

[23]  Piotr Faliszewski,et al.  On the Robustness of Winners: Counting Briberies in Elections , 2020, ArXiv.

[24]  Piotr Faliszewski,et al.  How Hard Is Bribery in Elections? , 2006, J. Artif. Intell. Res..

[25]  Paolo Turrini,et al.  The Complexity of Bribery in Network-Based Rating Systems , 2018, AAAI.

[26]  Jörg Rothe,et al.  Complexity of Shift Bribery in Iterative Elections , 2018, AAMAS.

[27]  Piotr Faliszewski,et al.  Multiwinner Voting: A New Challenge for Social Choice Theory , 2017 .

[28]  Lirong Xia,et al.  Computing the margin of victory for various voting rules , 2012, EC '12.

[29]  Jérôme Lang,et al.  Voting procedures with incomplete preferences , 2005 .

[30]  Piotr Faliszewski,et al.  Campaign Management Under Approval-Driven Voting Rules , 2011, Algorithmica.

[31]  Martin Koutecký,et al.  Voting and Bribing in Single-Exponential Time , 2018, STACS.

[32]  Piotr Faliszewski,et al.  Large-Scale Election Campaigns: Combinatorial Shift Bribery , 2015, AAMAS.

[33]  Yijia Chen,et al.  The Constant Inapproximability of the Parameterized Dominating Set Problem , 2015, 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS).

[34]  Michal Pilipczuk,et al.  Parameterized Algorithms , 2015, Springer International Publishing.

[35]  Russell Impagliazzo,et al.  On the Complexity of k-SAT , 2001, J. Comput. Syst. Sci..

[36]  Guy Kortsarz,et al.  From Gap-Exponential Time Hypothesis to Fixed Parameter Tractable Inapproximability: Clique, Dominating Set, and More , 2020, SIAM J. Comput..

[37]  Michael R. Fellows,et al.  On complexity of lobbying in multiple referenda , 2006 .

[38]  Piotr Faliszewski,et al.  Algorithms for Swap and Shift Bribery in Structured Elections , 2020, AAMAS.

[39]  D. Black The theory of committees and elections , 1959 .

[40]  Edith Elkind,et al.  Preference Restrictions in Computational Social Choice: Recent Progress , 2016, IJCAI.

[41]  Piotr Faliszewski,et al.  Complexity of Shift Bribery in Committee Elections , 2016, AAAI.

[42]  Piotr Faliszewski,et al.  Approximation Algorithms for Campaign Management , 2010, WINE.

[43]  Piotr Faliszewski,et al.  Prices matter for the parameterized complexity of shift bribery , 2014, Inf. Comput..

[44]  Jörg Rothe,et al.  Complexity of manipulation and bribery in judgment aggregation for uniform premise-based quota rules , 2015, Math. Soc. Sci..

[45]  David S. Johnson,et al.  Some Simplified NP-Complete Graph Problems , 1976, Theor. Comput. Sci..

[46]  Dorothea Baumeister,et al.  Complexity of Election Evaluation and Probabilistic Robustness: Extended Abstract , 2020, AAMAS.

[47]  Stephen T. Hedetniemi,et al.  Independence and Irredundance in k-Regular Graphs , 1998, Ars Comb..

[48]  Pasin Manurangsi,et al.  On the Parameterized Complexity of Approximating Dominating Set , 2019, J. ACM.

[49]  Orgad Keller,et al.  Approximating Weighted and Priced Bribery in Scoring Rules , 2019, J. Artif. Intell. Res..

[50]  Jiong Guo,et al.  Parameterized Complexity of Shift Bribery in Iterative Elections , 2020, AAMAS.

[51]  Luca Trevisan,et al.  From Gap-ETH to FPT-Inapproximability: Clique, Dominating Set, and More , 2017, 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS).