We consider distributed elections, where there is a center and k sites. In such distributed elections, each voter has preferences over some set of candidates, and each voter is assigned to exactly one site such that each site is aware only of the voters assigned to it. The center is able to directly communicate with each of the sites. We are interested in designing communication-efficient protocols, allowing the center to maintain (i.e., declare) a candidate which, with arbitrary high probability, is guaranteed to be a winner, or at least close to being a winner. We consider various single-winner voting rules, such as variants of Approval voting and scoring rules, tournament-based voting rules, and several round-based voting rules. For these voting rules, we show that, using communication which is logarithmic in the number of voters, it is possible for the center to maintain such approximate winners. We complement our protocols with lower bounds. Our results have implications in various scenarios, such as aggregating customer preferences in on-line shopping websites or supermarket chains and collecting votes from different polling stations of political elections.
In a recent paper, @cite_19 study winner determination for several multiwinner voting rules aiming at proportional representation. Dey and Narahari @cite_21 study sampling algorithms for estimating the margin of victory. These works deal with centralized elections, while the current paper considers distributed elections. Another paper worth mentioning in this context is the paper of @cite_7 which argues for the importance of developing fast communication-efficient protocols for computing winner in (centralized) streams; they also provide a simple sampling-based algorithm for approximating Borda winners.
@cite_19 @cite_21 @cite_7
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Preference elicitation is a central problem in AI, and has received significant attention in single-agent settings. It is also a key problem in multiagent systems, but has received little attention here so far. In this setting, the agents may have different preferences that often must be aggregated using voting. This leads to interesting issues because what, if any, information should be elicited from an agent depends on what other agents have revealed about their preferences so far. In this paper we study effective elicitation, and its impediments, for the most common voting protocols. It turns out that in the Single Transferable Vote protocol, even knowing when to terminate elicitation is mathcal NP-complete, while this is easy for all the other protocols under study. Even for these protocols, determining how to elicit effectively is NP-complete, even with perfect suspicions about how the agents will vote. The exception is the Plurality protocol where such effective elicitation is easy. We also show that elicitation introduces additional opportunities for strategic manipulation by the voters. We demonstrate how to curtail the space of elicitation schemes so that no such additional strategic issues arise. The margin of victory of an election is a useful measure to capture the robustness of an election outcome. It also plays a crucial role in determining the sample size of various algorithms in post election audit, polling etc. In this work, we present efficient sampling based algorithms for estimating the margin of victory of elections. More formally, we introduce the problem, where given an election @math on @math voters, the goal is to estimate the margin of victory @math of @math within an additive factor of @math . We study the problem for many commonly used voting rules including scoring rules, approval, Bucklin, maximin, and Copeland @math We observe that even for the voting rules for which computing the margin of victory is NP-Hard, there may exist efficient sampling based algorithms, as observed in the cases of maximin and Copeland @math voting rules. We present theoretical and empirical results demonstrating the usefulness of social choice functions in crowdsourcing for participatory democracies. First, we demonstrate the scalability of social choice functions by defining a natural notion of epsilon-approximation, and giving algorithms which efficiently elicit such approximations for two prominent social choice functions: the Borda rule and the Condorcet winner. This result circumvents previous prohibitive lower bounds and is surprisingly strong: even if the number of ideas is as large as the number of participants, each participant will only have to make a logarithmic number of comparisons, an exponential improvement over the linear number of comparisons previously needed. Second, we apply these ideas to Finland's recent off-road traffic law reform, an experiment on participatory democracy in real life. This allows us to verify the scaling predicted in our theory and show that the constant involved is also not large. In addition, by collecting data on the time that users take to complete rankings of varying sizes, we observe that eliciting partial rankings can further decrease elicitation time as compared to the common method of eliciting pairwise comparisons.