Computational Complexity Characterization of Protecting Elections from Bribery

The bribery problem in election has received considerable attention in the literature, upon which various algorithmic and complexity results have been obtained. It is thus natural to ask whether we can protect an election from potential bribery. We assume that the protector can protect a voter with some cost (e.g., by isolating the voter from potential bribers). A protected voter cannot be bribed. Under this setting, we consider the following bi-level decision problem: Is it possible for the protector to protect a proper subset of voters such that no briber with a fixed budget on bribery can alter the election result? The goal of this paper is to give a full picture on the complexity of protection problems. We give an extensive study on the protection problem and provide algorithmic and complexity results. Comparing our results with that on the bribery problems, we observe that the protection problem is in general significantly harder. Indeed, it becomes \(\varSigma _2^p\)-complete even for very restricted special cases, while most bribery problems lie in NP. However, it is not necessarily the case that the protection problem is always harder. Some of the protection problems can still be solved in polynomial time, while some of them remain as hard as the bribery problem under the same setting.

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

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

[3]  Piotr Faliszewski,et al.  Elections with Few Voters: Candidate Control Can Be Easy , 2014, AAAI.

[4]  A. McLoughlin,et al.  The complexity of computing the covering radius of a code , 1984, IEEE Trans. Inf. Theory.

[5]  Bo An,et al.  Optimally Protecting Elections , 2016, IJCAI.

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

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

[8]  Robert Bredereck,et al.  NP-hardness of two edge cover generalizations with applications to control and bribery for approval voting , 2016, Inf. Process. Lett..

[9]  Lin Chen,et al.  Approximation Algorithms for a Bi-level Knapsack Problem , 2011, COCOA.

[10]  Gábor Erdélyi,et al.  The complexity of bribery and control in group identification , 2017, Autonomous Agents and Multi-Agent Systems.

[11]  Walter Kern,et al.  Improved approximation algorithms for a bilevel knapsack problem , 2015, Theor. Comput. Sci..

[12]  Y. Narahari,et al.  Frugal bribery in voting , 2017, Theor. Comput. Sci..

[13]  Shu-Cherng Fang,et al.  Two-group knapsack game , 2010, Theor. Comput. Sci..

[14]  Celia Wrathall,et al.  Complete Sets and the Polynomial-Time Hierarchy , 1976, Theor. Comput. Sci..

[15]  Edith Hemaspaandra,et al.  A Control Dichotomy for Pure Scoring Rules , 2014, AAAI.

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

[17]  Piotr Faliszewski,et al.  Weighted electoral control , 2013, AAMAS.

[18]  Gerhard J. Woeginger,et al.  A Study on the Computational Complexity of the Bilevel Knapsack Problem , 2014, SIAM J. Optim..

[19]  Viggo Kann,et al.  Maximum Bounded 3-Dimensional Matching is MAX SNP-Complete , 1991, Inf. Process. Lett..

[20]  Andrew Lin,et al.  The Complexity of Manipulating k-Approval Elections , 2010, ICAART.

[21]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .