78 research outputs found
Detecting Possible Manipulators in Elections
Manipulation is a problem of fundamental importance in the context of voting
in which the voters exercise their votes strategically instead of voting
honestly to prevent selection of an alternative that is less preferred. The
Gibbard-Satterthwaite theorem shows that there is no strategy-proof voting rule
that simultaneously satisfies certain combinations of desirable properties.
Researchers have attempted to get around the impossibility results in several
ways such as domain restriction and computational hardness of manipulation.
However these approaches have been shown to have limitations. Since prevention
of manipulation seems to be elusive, an interesting research direction
therefore is detection of manipulation. Motivated by this, we initiate the
study of detection of possible manipulators in an election.
We formulate two pertinent computational problems - Coalitional Possible
Manipulators (CPM) and Coalitional Possible Manipulators given Winner (CPMW),
where a suspect group of voters is provided as input to compute whether they
can be a potential coalition of possible manipulators. In the absence of any
suspect group, we formulate two more computational problems namely Coalitional
Possible Manipulators Search (CPMS), and Coalitional Possible Manipulators
Search given Winner (CPMSW). We provide polynomial time algorithms for these
problems, for several popular voting rules. For a few other voting rules, we
show that these problems are in NP-complete. We observe that detecting
manipulation maybe easy even when manipulation is hard, as seen for example, in
the case of the Borda voting rule.Comment: Accepted in AAMAS 201
Dominating Manipulations in Voting with Partial Information
We consider manipulation problems when the manipulator only has partial
information about the votes of the nonmanipulators. Such partial information is
described by an information set, which is the set of profiles of the
nonmanipulators that are indistinguishable to the manipulator. Given such an
information set, a dominating manipulation is a non-truthful vote that the
manipulator can cast which makes the winner at least as preferable (and
sometimes more preferable) as the winner when the manipulator votes truthfully.
When the manipulator has full information, computing whether or not there
exists a dominating manipulation is in P for many common voting rules (by known
results). We show that when the manipulator has no information, there is no
dominating manipulation for many common voting rules. When the manipulator's
information is represented by partial orders and only a small portion of the
preferences are unknown, computing a dominating manipulation is NP-hard for
many common voting rules. Our results thus throw light on whether we can
prevent strategic behavior by limiting information about the votes of other
voters.Comment: 7 pages by arxiv pdflatex, 1 figure. The 6-page version has the same
content and will be published in Proceedings of the Twenty-Fifth AAAI
Conference on Artificial Intelligence (AAAI-11
Resolving the Complexity of Some Fundamental Problems in Computational Social Choice
This thesis is in the area called computational social choice which is an
intersection area of algorithms and social choice theory.Comment: Ph.D. Thesi
How many candidates are needed to make elections hard to manipulate?
In multiagent settings where the agents have different preferences,
preference aggregation is a central issue. Voting is a general method for
preference aggregation, but seminal results have shown that all general voting
protocols are manipulable. One could try to avoid manipulation by using voting
protocols where determining a beneficial manipulation is hard computationally.
The complexity of manipulating realistic elections where the number of
candidates is a small constant was recently studied (Conitzer 2002), but the
emphasis was on the question of whether or not a protocol becomes hard to
manipulate for some constant number of candidates. That work, in many cases,
left open the question: How many candidates are needed to make elections hard
to manipulate? This is a crucial question when comparing the relative
manipulability of different voting protocols. In this paper we answer that
question for the voting protocols of the earlier study: plurality, Borda, STV,
Copeland, maximin, regular cup, and randomized cup. We also answer that
question for two voting protocols for which no results on the complexity of
manipulation have been derived before: veto and plurality with runoff. It turns
out that the voting protocols under study become hard to manipulate at 3
candidates, 4 candidates, 7 candidates, or never
Swap Bribery
In voting theory, bribery is a form of manipulative behavior in which an
external actor (the briber) offers to pay the voters to change their votes in
order to get her preferred candidate elected. We investigate a model of bribery
where the price of each vote depends on the amount of change that the voter is
asked to implement. Specifically, in our model the briber can change a voter's
preference list by paying for a sequence of swaps of consecutive candidates.
Each swap may have a different price; the price of a bribery is the sum of the
prices of all swaps that it involves. We prove complexity results for this
model, which we call swap bribery, for a broad class of election systems,
including variants of approval and k-approval, Borda, Copeland, and maximin.Comment: 17 page
Combining Voting Rules Together
We propose a simple method for combining together voting rules that performs
a run-off between the different winners of each voting rule. We prove that this
combinator has several good properties. For instance, even if just one of the
base voting rules has a desirable property like Condorcet consistency, the
combination inherits this property. In addition, we prove that combining voting
rules together in this way can make finding a manipulation more computationally
difficult. Finally, we study the impact of this combinator on approximation
methods that find close to optimal manipulations
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