A Unifying Framework for Manipulation Problems

被引:0
|
作者
Knop, Dusan [1 ,2 ]
Koutecky, Martin [3 ]
Mnich, Matthias [4 ,5 ]
机构
[1] Univ Bergen, Dept Informat, Bergen, Norway
[2] Charles Univ Prague, Prague, Czech Republic
[3] Technion Israel Inst Technol, IL-32000 Haifa, Israel
[4] Univ Bonn, Bonn, Germany
[5] Maastricht Univ, Maastricht, Netherlands
关键词
Swap bribery; Dodgson's rule; Young's rule; fixed-parameter algorithms;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Manipulation models for electoral systems are a core research theme in social choice theory; they include bribery (unweighted, weighted, swap, shift,...), control (by adding or deleting voters or candidates), lobbying in referenda and others. We develop a unifying framework for manipulation models with few types of people, one of the most commonly studied scenarios. A critical insight of our framework is to separate the descriptive complexity of the voting rule R from the number of types of people. This allows us to finally settle the computational complexity of R-Swap Bribery, one of the most fundamental manipulation problems. In particular, we prove that R-Swap Bribery is fixed-parameter tractable when R is Dodgson's rule and Young's rule, when parameterized by the number of candidates. This way, we resolve a long-standing open question from 2007 which was explicitly asked by Faliszewski et al. [JAIR 40, 2011]. Our algorithms reveal that the true hardness of bribery problems often stems from the complexity of the voting rules. On one hand, we give a fixed-parameter algorithm parameterized by number of types of people for complex voting rules. Thus, we reveal that R-Swap Bribery with Dodgson's rule is much harder than with Condorcet's rule, which can be expressed by a conjunction of linear inequalities, while Dodson's rule requires quantifier alternation and a bounded number of disjunctions of linear systems. On the other hand, we give an algorithm for quantifier-free voting rules which is parameterized only by the number of conjunctions of the voting rule and runs in time polynomial in the number of types of people. This way, our framework explains why Shift Bribery is polynomial-time solvable for the plurality voting rule, making explicit that the rule is simple in that it can be expressed with a single linear inequality, and that the number of voter types is polynomial.
引用
收藏
页码:256 / 264
页数:9
相关论文
共 50 条
  • [21] Computing as a profession: A unifying framework
    Coulter, N
    28TH ANNUAL FRONTIERS IN EDUCATION CONFERENCE - CONFERENCE PROCEEDINGS, VOLS 1-3, 1998, : 147 - 148
  • [22] A unifying framework for iterative approximate best-response algorithms for distributed constraint optimization problems
    Chapman, Archie C.
    Rogers, Alex
    Jennings, Nicholas R.
    Leslie, David S.
    KNOWLEDGE ENGINEERING REVIEW, 2011, 26 (04): : 411 - 444
  • [23] Unifying Color and Texture Transfer for Predictive Appearance Manipulation
    Okura, Fumio
    Vanhoey, Kenneth
    Bousseau, Adrien
    Efros, Alexei A.
    Drettakis, George
    COMPUTER GRAPHICS FORUM, 2015, 34 (04) : 53 - 63
  • [24] A UNIFYING ANALYTICAL FRAMEWORK FOR LOYALTY REBATES
    Morton, Fiona M. Scott
    Abrahamson, Zachary
    ANTITRUST LAW JOURNAL, 2017, 81 (03) : 777 - 836
  • [25] Representational structures as a unifying framework for attention
    Chapman, Angus F.
    Stormer, Viola S.
    TRENDS IN COGNITIVE SCIENCES, 2024, 28 (05) : 416 - 427
  • [26] A Unifying Framework for Agency in Hypermedia Environments
    Charpenay, Victor
    Kaefer, Tobias
    Harth, Andreas
    ENGINEERING MULTI-AGENT SYSTEMS, 2022, 13190 : 42 - 61
  • [27] Requirements quality control: a unifying framework
    Artem Katasonov
    Markku Sakkinen
    Requirements Engineering, 2006, 11 : 42 - 57
  • [28] A unifying theoretical framework for clinical psychology
    Leedom, Liane
    CURRENT ISSUES IN PERSONALITY PSYCHOLOGY, 2018, 6 (04) : 343 - 348
  • [29] A Framework for Unifying Formal and Empirical Analysis
    Granato, Jim
    Lo, Melody
    Wong, M. C. Sunny
    AMERICAN JOURNAL OF POLITICAL SCIENCE, 2010, 54 (03) : 783 - 797
  • [30] A Unifying Framework for GPR Image Reconstruction
    Busche, Andre
    Janning, Ruth
    Horvath, Tomas
    Schmidt-Thieme, Lars
    DATA ANALYSIS, MACHINE LEARNING AND KNOWLEDGE DISCOVERY, 2014, : 325 - 332