Towards a dichotomy for the Possible Winner problem in elections based on scoring rules

To make a joint decision, agents (or voters) are often required to provide their preferences as linear orders. To determine a winner, the given linear orders can be aggregated according to a voting protocol. However, in realistic settings, the voters may often only provide partial orders. This directly leads to the Possible Winner problem that asks, given a set of partial votes, whether a distinguished candidate can still become a winner. In this work, we consider the computational complexity of Possible Winner for the broad class of voting protocols defined by scoring rules. A scoring rule provides a score value for every position which a candidate can have in a linear order. Prominent examples include plurality, k-approval, and Borda. Generalizing previous NP-hardness results for some special cases, we settle the computational complexity for all but one scoring rule. More precisely, for an unbounded number of candidates and unweighted voters, we show that Possible Winner is NP-complete for all pure scoring rules except plurality, veto, and the scoring rule defined by the scoring vector (2,1,...,1,0), while it is solvable in polynomial time for plurality and veto.

[1]  Piotr Faliszewski,et al.  Copeland voting: ties matter , 2008, AAMAS.

[2]  Dimitrios M. Thilikos,et al.  Invitation to fixed-parameter algorithms , 2007, Comput. Sci. Rev..

[3]  Piotr Faliszewski,et al.  Approximability of Manipulating Elections , 2008, AAAI.

[4]  Piotr Faliszewski Nonuniform Bribery (Short Paper) , 2008 .

[5]  Vincent Conitzer,et al.  Vote elicitation: complexity and strategy-proofness , 2002, AAAI/IAAI.

[6]  Vincent Conitzer,et al.  When are elections with few candidates hard to manipulate? , 2007, J. ACM.

[7]  Rolf Niedermeier,et al.  A logic for causal reasoning , 2003, IJCAI 2003.

[8]  Piotr Faliszewski,et al.  The shield that never was: Societies with single-peaked preferences are more open to manipulation and control , 2011, Inf. Comput..

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

[10]  Vincent Conitzer,et al.  A scheduling approach to coalitional manipulation , 2010, EC '10.

[11]  Michael R. Fellows,et al.  Parameterized Complexity , 1998 .

[12]  Toby Walsh,et al.  Winner Determination in Sequential Majority Voting , 2007, IJCAI.

[13]  Jörg Flum,et al.  Parameterized Complexity Theory (Texts in Theoretical Computer Science. An EATCS Series) , 2006 .

[14]  Clifford Stein,et al.  Introduction to Algorithms, 2nd edition. , 2001 .

[15]  Toby Walsh,et al.  Uncertainty in Preference Elicitation and Aggregation , 2007, AAAI.

[16]  Toby Walsh,et al.  Incompleteness and Incomparability in Preference Aggregation , 2007, IJCAI.

[17]  Martin Grohe The complexity of homomorphism and constraint satisfaction problems seen from the other side , 2007, JACM.

[18]  Michael R. Fellows,et al.  On the parameterized complexity of multiple-interval graph problems , 2009, Theor. Comput. Sci..

[19]  Sandeep K. Shukla,et al.  Fundamental Problems in Computing, Essays in Honor of Professor Daniel J. Rosenkrantz , 2009, Fundamental Problems in Computing.

[20]  Ariel D. Procaccia,et al.  Complexity of unweighted coalitional manipulation under some common voting rules , 2009, IJCAI 2009.

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

[22]  Salil P. Vadhan,et al.  Computational Complexity , 2005, Encyclopedia of Cryptography and Security.

[23]  Rolf Niedermeier,et al.  Reflections on Multivariate Algorithmics and Problem Parameterization , 2010, STACS.

[24]  Nadja Betzler On Problem Kernels for Possible Winner Determination under the k-Approval Protocol , 2010, MFCS.

[25]  Christian Komusiewicz,et al.  Average parameterization and partial kernelization for computing medians , 2010, J. Comput. Syst. Sci..

[26]  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..

[27]  Rolf Niedermeier,et al.  Parameterized computational complexity of Dodgson and Young elections , 2010, Inf. Comput..

[28]  Edith Hemaspaandra,et al.  Dichotomy for voting systems , 2005, J. Comput. Syst. Sci..

[29]  Narges Simjour Improved Parameterized Algorithms for the Kemeny Aggregation Problem , 2009, IWPEC.

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

[31]  Rolf Niedermeier,et al.  Fixed-parameter algorithms for Kemeny rankings , 2009, Theor. Comput. Sci..

[32]  Piotr Faliszewski,et al.  Nonuniform Bribery , 2007, AAMAS.

[33]  Piotr Faliszewski,et al.  Probabilistic Possible Winner Determination , 2010, AAAI.

[34]  Jörg Flum,et al.  Parameterized Complexity Theory , 2006, Texts in Theoretical Computer Science. An EATCS Series.

[35]  Piotr Faliszewski,et al.  Llull and Copeland Voting Computationally Resist Bribery and Constructive Control , 2009, J. Artif. Intell. Res..

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

[37]  Yann Chevaleyre,et al.  A Short Introduction to Computational Social Choice , 2007, SOFSEM.

[38]  Yann Chevaleyre,et al.  Possible Winners when New Candidates Are Added: The Case of Scoring Rules , 2010, AAAI.

[39]  Ariel D. Procaccia,et al.  Algorithms for the coalitional manipulation problem , 2008, SODA '08.

[40]  Rolf Niedermeier,et al.  Parameterized computational complexity of Dodgson and Young elections , 2008, Inf. Comput..

[41]  Vincent Conitzer,et al.  Communication complexity of common voting rules , 2005, EC '05.