8,280 research outputs found

    Class dealignment and the neighbourhood effect: Miller revisited

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    The concept of a neighbourhood effect within British voting patterns has largely been discarded, because no data have been available for testing it at the appropriate spatial scales. To undertake such tests, bespoke neighbourhoods have been created around the home of each respondent to the 1997 British Election Study survey in England and Wales, and small-area census data have been assembled for these to depict the socio-economic characteristics of voters' local contexts. Analyses of voting in these small areas, divided into five equal-sized status areas, provides very strong evidence that members of each social class were much more likely to vote Labour than Conservative in the low-status than in the high-status areas. This is entirely consistent with the concept of the neighbourhood effect, but alternative explanations are feasible. The data provide very strong evidence of micro-geographical variations in voting patterns, for which further research is necessary to identify the processes involved

    Feng-Rao decoding of primary codes

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    We show that the Feng-Rao bound for dual codes and a similar bound by Andersen and Geil [H.E. Andersen and O. Geil, Evaluation codes from order domain theory, Finite Fields Appl., 14 (2008), pp. 92-123] for primary codes are consequences of each other. This implies that the Feng-Rao decoding algorithm can be applied to decode primary codes up to half their designed minimum distance. The technique applies to any linear code for which information on well-behaving pairs is available. Consequently we are able to decode efficiently a large class of codes for which no non-trivial decoding algorithm was previously known. Among those are important families of multivariate polynomial codes. Matsumoto and Miura in [R. Matsumoto and S. Miura, On the Feng-Rao bound for the L-construction of algebraic geometry codes, IEICE Trans. Fundamentals, E83-A (2000), pp. 926-930] (See also [P. Beelen and T. H{\o}holdt, The decoding of algebraic geometry codes, in Advances in algebraic geometry codes, pp. 49-98]) derived from the Feng-Rao bound a bound for primary one-point algebraic geometric codes and showed how to decode up to what is guaranteed by their bound. The exposition by Matsumoto and Miura requires the use of differentials which was not needed in [Andersen and Geil 2008]. Nevertheless we demonstrate a very strong connection between Matsumoto and Miura's bound and Andersen and Geil's bound when applied to primary one-point algebraic geometric codes.Comment: elsarticle.cls, 23 pages, no figure. Version 3 added citations to the works by I.M. Duursma and R. Pellikaa

    Clearing Contamination in Large Networks

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    In this work, we study the problem of clearing contamination spreading through a large network where we model the problem as a graph searching game. The problem can be summarized as constructing a search strategy that will leave the graph clear of any contamination at the end of the searching process in as few steps as possible. We show that this problem is NP-hard even on directed acyclic graphs and provide an efficient approximation algorithm. We experimentally observe the performance of our approximation algorithm in relation to the lower bound on several large online networks including Slashdot, Epinions and Twitter. The experiments reveal that in most cases our algorithm performs near optimally

    Learning with many experts: model selection and sparsity

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    Experts classifying data are often imprecise. Recently, several models have been proposed to train classifiers using the noisy labels generated by these experts. How to choose between these models? In such situations, the true labels are unavailable. Thus, one cannot perform model selection using the standard versions of methods such as empirical risk minimization and cross validation. In order to allow model selection, we present a surrogate loss and provide theoretical guarantees that assure its consistency. Next, we discuss how this loss can be used to tune a penalization which introduces sparsity in the parameters of a traditional class of models. Sparsity provides more parsimonious models and can avoid overfitting. Nevertheless, it has seldom been discussed in the context of noisy labels due to the difficulty in model selection and, therefore, in choosing tuning parameters. We apply these techniques to several sets of simulated and real data.Comment: This is the pre-peer reviewed versio

    On the Hardness of Bribery Variants in Voting with CP-Nets

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    We continue previous work by Mattei et al. (Mattei, N., Pini, M., Rossi, F., Venable, K.: Bribery in voting with CP-nets. Ann. of Math. and Artif. Intell. pp. 1--26 (2013)) in which they study the computational complexity of bribery schemes when voters have conditional preferences that are modeled by CP-nets. For most of the cases they considered, they could show that the bribery problem is solvable in polynomial time. Some cases remained open---we solve two of them and extend the previous results to the case that voters are weighted. Moreover, we consider negative (weighted) bribery in CP-nets, when the briber is not allowed to pay voters to vote for his preferred candidate.Comment: improved readability; identified Cheapest Subsets to be the enumeration variant of K.th Largest Subset, so we renamed it to K-Smallest Subsets and point to the literatur; some more typos fixe

    How Hard Is It to Control an Election by Breaking Ties?

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    We study the computational complexity of controlling the result of an election by breaking ties strategically. This problem is equivalent to the problem of deciding the winner of an election under parallel universes tie-breaking. When the chair of the election is only asked to break ties to choose between one of the co-winners, the problem is trivially easy. However, in multi-round elections, we prove that it can be NP-hard for the chair to compute how to break ties to ensure a given result. Additionally, we show that the form of the tie-breaking function can increase the opportunities for control. Indeed, we prove that it can be NP-hard to control an election by breaking ties even with a two-stage voting rule.Comment: Revised and expanded version including longer proofs and additional result

    On the Computational Complexity of Non-dictatorial Aggregation

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    We investigate when non-dictatorial aggregation is possible from an algorithmic perspective, where non-dictatorial aggregation means that the votes cast by the members of a society can be aggregated in such a way that the collective outcome is not simply the choices made by a single member of the society. We consider the setting in which the members of a society take a position on a fixed collection of issues, where for each issue several different alternatives are possible, but the combination of choices must belong to a given set XX of allowable voting patterns. Such a set XX is called a possibility domain if there is an aggregator that is non-dictatorial, operates separately on each issue, and returns values among those cast by the society on each issue. We design a polynomial-time algorithm that decides, given a set XX of voting patterns, whether or not XX is a possibility domain. Furthermore, if XX is a possibility domain, then the algorithm constructs in polynomial time such a non-dictatorial aggregator for XX. We then show that the question of whether a Boolean domain XX is a possibility domain is in NLOGSPACE. We also design a polynomial-time algorithm that decides whether XX is a uniform possibility domain, that is, whether XX admits an aggregator that is non-dictatorial even when restricted to any two positions for each issue. As in the case of possibility domains, the algorithm also constructs in polynomial time a uniform non-dictatorial aggregator, if one exists. Then, we turn our attention to the case where XX is given implicitly, either as the set of assignments satisfying a propositional formula, or as a set of consistent evaluations of an sequence of propositional formulas. In both cases, we provide bounds to the complexity of deciding if XX is a (uniform) possibility domain.Comment: 21 page
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