How to Put Through Your Agenda in Collective Binary Decisions

We consider the following decision-making scenario: a society of voters has to find an agreement on a set of proposals, and every single proposal is to be accepted or rejected. Each voter supports a certain subset of the proposals—the favorite ballot of this voter—and opposes the remaining ones. He accepts a ballot if he supports more than half of the proposals in this ballot. The task is to decide whether there exists a ballot approving a specified number of selected proposals (agenda) such that all voters (or a strict majority of them) accept this ballot. We show that, on the negative side, both problems are NP-complete, and on the positive side, they are fixed-parameter tractable with respect to the total number of proposals or with respect to the total number of voters. We look into further natural parameters and study their influence on the computational complexity of both problems, thereby providing both tractability and intractability results. Furthermore, we provide tight combinatorial bounds on the worst-case size of an accepted ballot in terms of the number of voters.

[1]  Michael R. Fellows,et al.  Towards fully multivariate algorithmics: Parameter ecology and the deconstruction of computational complexity , 2013, Eur. J. Comb..

[2]  P. Fishburn,et al.  Approval Voting for Committees : Threshold Approaches , 2004 .

[3]  Rolf Niedermeier,et al.  Parameterized Complexity of Vertex Cover Variants , 2007, Theory of Computing Systems.

[4]  D. Marc Kilgour,et al.  Approval Balloting for Fixed-Size Committees , 2012 .

[5]  Jean Lainé,et al.  Pareto efficiency in multiple referendum , 2012 .

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

[7]  Steven J. Brams,et al.  Proportional Representation , 1998 .

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

[9]  Saket Saurabh,et al.  Incompressibility through Colors and IDs , 2009, ICALP.

[10]  Piotr Faliszewski,et al.  Fully Proportional Representation as Resource Allocation: Approximability Results , 2012, IJCAI.

[11]  John R. Chamberlin,et al.  Representative Deliberations and Representative Decisions: Proportional Representation and the Borda Rule , 1983, American Political Science Review.

[12]  Jörg Rothe,et al.  Computational Aspects of Manipulation and Control in Judgment Aggregation , 2013, ADT.

[13]  Hans L. Bodlaender,et al.  Kernelization: New Upper and Lower Bound Techniques , 2009, IWPEC.

[14]  Jean Lainé,et al.  Searching for a Compromise in Multiple Referendum , 2012 .

[15]  Xi Wu,et al.  A Completeness Theory for Polynomial (Turing) Kernelization , 2013, Algorithmica.

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

[17]  András Frank,et al.  An application of simultaneous diophantine approximation in combinatorial optimization , 1987, Comb..

[18]  Ariel D. Procaccia,et al.  On the complexity of achieving proportional representation , 2008, Soc. Choice Welf..

[19]  Noga Alon,et al.  Bundling Attacks in Judgment Aggregation , 2013, AAAI.

[20]  Rolf Niedermeier,et al.  Invitation to data reduction and problem kernelization , 2007, SIGA.

[21]  Hendrik W. Lenstra,et al.  Integer Programming with a Fixed Number of Variables , 1983, Math. Oper. Res..

[22]  Rolf Niedermeier,et al.  A Multivariate Complexity Analysis of Lobbying in Multiple Referenda , 2012, AAAI.

[23]  Rolf Niedermeier,et al.  Studies in Computational Aspects of Voting - A Parameterized Complexity Perspective , 2012, The Multivariate Algorithmic Revolution and Beyond.

[24]  Meena Mahajan,et al.  Parametrizing Above Guaranteed Values: MaxSat and MaxCut , 1997, Electron. Colloquium Comput. Complex..

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

[26]  Saket Saurabh,et al.  Kernelization Lower Bounds Through Colors and IDs , 2014, ACM Trans. Algorithms.

[27]  Vincent Conitzer,et al.  How hard is it to control sequential elections via the agenda , 2009, IJCAI 2009.

[28]  Jörg Rothe,et al.  On Approximating Optimal Weighted Lobbying, and Frequency of Correctness Versus Average-Case Polynomial Time , 2007, FCT.

[29]  D. Marc Kilgour,et al.  Approval Balloting for Multi-winner Elections , 2010 .

[30]  Rolf Niedermeier,et al.  Invitation to Fixed-Parameter Algorithms , 2006 .

[31]  Nadja Betzler,et al.  On the Computation of Fully Proportional Representation , 2011, J. Artif. Intell. Res..

[32]  Craig Boutilier,et al.  Social Choice : From Consensus to Personalized Decision Making , 2011 .

[33]  D. West Introduction to Graph Theory , 1995 .

[34]  Jörg Flum,et al.  W-Hierarchies Defined by Symmetric Gates , 2008, Theory of Computing Systems.

[35]  Piotr Faliszewski,et al.  Parameterized Algorithmics for Computational Social Choice: Nine Research Challenges , 2014, ArXiv.

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

[37]  Edith Elkind,et al.  Choosing Collectively Optimal Sets of Alternatives Based on the Condorcet Criterion , 2011, IJCAI.

[38]  ConitzerVincent,et al.  The ACM transactions on economics and computation , 2013 .

[39]  Jörg Rothe,et al.  How Hard Is it to Bribe the Judges? A Study of the Complexity of Bribery in Judgment Aggregation , 2011, ADT.

[40]  Ravi Kannan,et al.  Minkowski's Convex Body Theorem and Integer Programming , 1987, Math. Oper. Res..

[41]  Piotr Faliszewski,et al.  Finding a collective set of items: From proportional multirepresentation to group recommendation , 2014, Artif. Intell..

[42]  室 章治郎 Michael R.Garey/David S.Johnson 著, "COMPUTERS AND INTRACTABILITY A guide to the Theory of NP-Completeness", FREEMAN, A5判変形判, 338+xii, \5,217, 1979 , 1980 .

[43]  Stefan Kratsch,et al.  Recent developments in kernelization: A survey , 2014, Bull. EATCS.

[44]  Noga Alon,et al.  Regular hypergraphs, Gordon's lemma, Steinitz' lemma and invariant theory , 1986, J. Comb. Theory, Ser. A.

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

[46]  Noga Alon,et al.  How to Put through Your Agenda in Collective Binary Decisions , 2013, ADT.

[47]  Ulrich Endriss,et al.  Complexity of Judgment Aggregation , 2012, J. Artif. Intell. Res..

[48]  Michael R. Fellows,et al.  Fundamentals of Parameterized Complexity , 2013 .

[49]  Noga Alon,et al.  Anti-Hadamard Matrices, Coin Weighing, Threshold Gates, and Indecomposable Hypergraphs , 1997, J. Comb. Theory, Ser. A.

[50]  Jean Lainé,et al.  Condorcet choice and the Ostrogorski paradox , 2009, Soc. Choice Welf..

[51]  J. Spencer Six standard deviations suffice , 1985 .

[52]  Joachim Gudmundsson,et al.  Computational Aspects of Multi-Winner Approval Voting , 2014, MPREF@AAAI.

[53]  Jörg Rothe,et al.  The complexity of probabilistic lobbying , 2009, Discret. Optim..

[54]  Piotr Faliszewski,et al.  Achieving fully proportional representation is easy in practice , 2013, AAMAS.

[55]  Xi Wu,et al.  Hierarchies of Inefficient Kernelizability , 2011, ArXiv.

[56]  Burt L. Monroe,et al.  Fully Proportional Representation , 1995, American Political Science Review.

[57]  Kanesan Muthusamy Approximate solutions on scheduling problems , 2002 .