We consider the following problem: There is a set of items (e.g., movies) and a group of agents (e.g., passengers on a plane); each agent has some intrinsic utility for each of the items. Our goal is to pick a set of @math items that maximize the total derived utility of all the agents (i.e., in our example we are to pick @math movies that we put on the plane's entertainment system). However, the actual utility that an agent derives from a given item is only a fraction of its intrinsic one, and this fraction depends on how the agent ranks the item among the chosen, available, ones. We provide a formal specification of the model and provide concrete examples and settings where it is applicable. We show that the problem is hard in general, but we show a number of tractability results for its natural special cases.
@cite_5 define a family of committee election rules (which can also be used for multiple referenda) based on the following principle. Each voter specifies his or her preferred committee and each voter's disutility for a committee is given by the Hamming distance between the committee and the voter's preferred one. Then the disutilities of the voters are aggregated using an OWA operator. The committee with the lowest aggregated disutility wins. (In the particular case of the sum operator, the obtained rule is the Bloc committee election rule, while in the case of the minimum, the obtained rule is the Minimax Approval Voting rule; see the work of @cite_43 for the definition and other works for computational discussions @cite_48 @cite_33 @cite_41 .) They obtain a number of hardness and approximability results, which cannot be compared to ours because in their work, again, OWAs are used for aggregating scores coming from different agents .
@cite_33 @cite_41 @cite_48 @cite_43 @cite_5
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We study multiple referenda and committee elections, when the ballot of each voter is simply a set of approved binary issues (or candidates). Two well-known rules under this model are the commonly used candidate-wise majority, also called the minisum rule, as well as the minimax rule. In the former, the elected committee consists of the candidates approved by a majority of voters, whereas the latter picks a committee minimizing the maximum Hamming distance to all ballots. As these rules are in some ways extreme points in the whole spectrum of solutions, we consider a general family of rules, using the Ordered Weighted Averaging (OWA) operators. Each rule is parameterized by a weight vector, showing the importance of the i-th highest Hamming distance of the outcome to the voters. The objective then is to minimize the weighted sum of the (ordered) distances. We study mostly computational, but also manipulability properties for this family. We first exhibit that for many rules, it is NP-hard to find a winning committee. We then proceed to identify cases where the problem is either efficiently solvable, or approximable with a small approximation factor. Finally, we investigate the issue of manipulating such rules and provide conditions that make this possible. We consider approval voting elections in which each voter votes for a (possibly empty) set of candidates and the outcome consists of a set of k candidates for some parameter k, e.g., committee elections. We are interested in the min-imax approval voting rule in which the outcome represents a compromise among the voters, in the sense that the maximum distance between the preference of any voter and the outcome is as small as possible. This voting rule has two main drawbacks. First, computing an outcome that minimizes the maximum distance is computationally hard. Furthermore, any algorithm that always returns such an outcome provides incentives to voters to misreport their true preferences. In order to circumvent these drawbacks, we consider approximation algorithms, i.e., algorithms that produce an outcome that approximates the minimax distance for any given instance. Such algorithms can be considered as alternative voting rules. We present a polynomial-time 2-approximation algorithm that uses a natural linear programming relaxation for the underlying optimization problem and deterministically rounds the fractional solution in order to compute the outcome; this result improves upon the previously best known algorithm that has an approximation ratio of 3. We are furthermore interested in approximation algorithms that are resistant to manipulation by (coalitions of) voters, i.e., algorithms that do not motivate voters to misreport their true preferences in order to improve their distance from the outcome. We complement previous results in the literature with new upper and lower bounds on strategyproof and group-strategyproof algorithms. A new voting procedure for electing committees, called the minimax procedure, is described. Based on approval balloting, it chooses the committee that minimizes the maximum Hamming distance to voters’ ballots, where these ballots are weighted by their proximity to other voters’ ballots. This minimax outcome may be diametrically opposed to the outcome obtained by aggregating approval votes in the usual manner, which minimizes the sum of the Hamming distances and is called the minisum outcome. The manipulability of these procedures, and their applicability when election outcomes are restricted in various ways, are also investigated. 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 protocols where determining a beneficial manipulation is hard. Especially among computational agents, it is reasonable to measure this hardness by computational complexity. Some earlier work has been done in this area, but it was assumed that the number of voters and candidates is unbounded. Such hardness results lose relevance when the number of candidates is small, because manipulation algorithms that are exponential only in the number of candidates (and only slightly so) might be available. We give such an algorithm for an individual agent to manipulate the Single Transferable Vote (STV) protocol, which has been shown hard to manipulate in the above sense. This motivates the core of this article, which derives hardness results for realistic elections where the number of candidates is a small constant (but the number of voters can be large). The main manipulation question we study is that of coalitional manipulation by weighted voters. (We show that for simpler manipulation problems, manipulation cannot be hard with few candidates.) We study both constructive manipulation (making a given candidate win) and destructive manipulation (making a given candidate not win). We characterize the exact number of candidates for which manipulation becomes hard for the plurality, Borda, STV, Copeland, maximin, veto, plurality with runoff, regular cup, and randomized cup protocols. We also show that hardness of manipulation in this setting implies hardness of manipulation by an individual in unweighted settings when there is uncertainty about the others' votes (but not vice-versa). To our knowledge, these are the first results on the hardness of manipulation when there is uncertainty about the others' votes.