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| 008 | 240724s2017 xx o ||||0 eng d | ||
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_a9781443892308 _q(electronic bk.) |
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| 020 | _z9781443898799 | ||
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| 100 | 1 | _aGohar, Neelam. | |
| 245 | 1 | 0 | _aManipulative Voting Dynamics. |
| 250 | _a1st ed. | ||
| 264 | 1 |
_aNewcastle-upon-Tyne : _bCambridge Scholars Publishing, _c2017. |
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| 264 | 4 | _c©2017. | |
| 300 | _a1 online resource (153 pages) | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 505 | 0 | _aIntro -- Contents -- Abstract -- Acknowledgments -- List of Figures -- Chapter One -- 1.1 Background -- 1.1.1 Manipulative Dynamics -- 1.1.2 Tactical Voting Dynamics -- 1.2 Related Work -- 1.3 Problem Statement -- 1.3.1 Contribution and Comparison with Previous Work -- 1.3.2 Significance and Importance of the Problem -- 1.3.3 Specific Research Questions -- 1.4 Structure of Book -- Chapter Two -- 2.1 Notation and Assumptions -- 2.2 Definitions -- 2.2.1 Manipulations -- 2.2.1.1 Types of Moves -- 2.2.1.2 Types of Manipulations -- 2.2.1.3 Weights Settings -- 2.2.2 Existence of Potential Functions and Pure Nash Equilibria -- 2.3 Summary -- Chapter Three -- 3.1 Tactical Voting -- 3.1.1 Process Termination for Plurality Rule -- 3.1.2 Process Termination for other Positional Scoring Rules -- 3.1.2.1 Borda -- 3.1.2.2 Veto and K-approval Voting Rule -- 3.2 Weighted Votes -- 3.2.1 The Plurality Rule -- 3.2.2 Borda -- 3.3 Conclusions -- Chapter Four -- 4.1 Increased Support Manipulative Dynamics with Weighted Votes -- 4.1.1 A Few Examples of Manipulative Dynamics with Increased Support for the Winning Candidate at Each State -- 4.1.2 Upper Bound for General Weight Setting -- 4.1.3 Bound for a Small Number of Voters -- 4.1.3.1 Upper Bound for Bounded Real Weight Setting -- 4.1.4 Upper Bound when the Smallest Weight is < -- 1 -- 4.1.5 An Upper Bound under Bounded Integer Weight Setting -- 4.1.6 Efficient Process -- 4.2 Other Voting Rules like Copeland -- 4.2.1 Process Termination -- 4.2.2 A Few Examples of Manipulative Dynamics with Copeland Voting Scheme -- 4.3 Decreased Support Manipulative Dynamics -- 4.3.1 How Long is the Sequence of Moves? -- 4.4 Conclusions -- Chapter Five -- 5.1 Mixture of Different Moves -- 5.2 Bounds in Terms of the Number of Distinct Weights -- 5.2.1 Manipulation dynamics with un-weighted voters -- 5.3 Conclusions -- Chapter Six. | |
| 505 | 8 | _a6.1 Termination with a Tie-breaking Rule -- 6.1.1 Veto Rule -- 6.1.2 Borda Rule -- 6.1.3 k-Ma jority Rule or k-Approval Voting Rule -- 6.1.4 Copeland's Rule -- 6.1.5 Bucklin Scheme -- 6.1.6 Plurality with Run-off -- 6.2 Process Termination when in Initial Settings, True and Declared Preferences of Voters are the same -- 6.2.1 Borda Rule -- 6.2.2 k-Approval Voting Rule -- 6.2.3 Copeland's Rule -- 6.2.4 Bucklin Scheme -- 6.2.5 Veto Rule -- 6.3 Conclusions -- Chapter Seven -- 7.1 Summary of Major Findings -- 7.2 Implications of the Findings -- 7.3 Suggestions for Further Research -- Endnotes -- Bibliography. | |
| 520 | _aOne of the most actively growing subareas in multi-agent systems is computational social choice theory, which provides a theoretical foundation for preference aggregation and collective decision-making in multi-agent domains. It is concerned with the application of techniques developed in computer science, including complexity analysis and algorithm design, in the study of social choice mechanisms, such as voting. It seeks to import concepts from social choice theory into Artificial Intelligence and computing. People often have to reach a joint decision despite conflicting preferences over the alternatives. This joint decision can be reached by an informal negotiating process or by a carefully specified protocol. Over the course of the past decade or so, computer scientists have also become deeply involved in this study. Within computer science, there is a number of settings where a decision must be made based on the conflicting preferences of multiple parties. The paradigms of computer science give a different and useful perspective on some of the classic problems in economics and related disciplines. A natural and very general approach for deciding among multiple alternatives is to vote on them. Voting is one of the most popular ways of reaching common decisions. As such, the study of elections is an area where fields such as computer science, economics, business, operations research, and political science can be brought together. Social choice theory deals with voting scenarios, in which a set of individuals must select an outcome from a set of alternatives. This book focuses on convergence to pure strategy Nash equilibria in plurality voting games and a number of other positional and non-positional scoring rules. In such games, the voters strategically choose a candidate to vote for, and the winner is determined by the plurality (or other) | ||
| 520 | 8 | _avoting rules. Voters take turns modifying their votes; these manipulations are classified according to the way in which they affect the outcome of the election. The focus is on achieving a stable outcome, taking strategic behaviour into account. A voting profile is in equilibrium, when no voter can change his vote so that his more preferable candidate gets elected. The book investigates restrictions on the number of iterations that can be made for different voting rules, considering both weighted and equi-weighted voting settings. | |
| 588 | _aDescription based on publisher supplied metadata and other sources. | ||
| 590 | _aElectronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. | ||
| 650 | 0 | _aVoting. | |
| 655 | 4 | _aElectronic books. | |
| 776 | 0 | 8 |
_iPrint version: _aGohar, Neelam _tManipulative Voting Dynamics _dNewcastle-upon-Tyne : Cambridge Scholars Publishing,c2017 _z9781443898799 |
| 797 | 2 | _aProQuest (Firm) | |
| 856 | 4 | 0 |
_uhttps://ebookcentral.proquest.com/lib/orpp/detail.action?docID=4857985 _zClick to View |
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_c126200 _d126200 |
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