1,721,033 research outputs found
Strategyproof mechanisms for friends and enemies games
No full textWe investigate strategyproof mechanisms for Friends and Enemies Games, a subclass of Hedonic Games in which every agent classifies any other one as a friend or as an enemy. In this setting, we consider the two classical scenarios proposed in the literature, called Friends Appreciation (FA) and Enemies Aversion (EA). Roughly speaking, in the former each agent gives priority to the number of friends in her coalition, while in the latter to the number of enemies.
We provide strategyproof mechanisms for both settings. More precisely, for FA we first present a deterministic n-approximation mechanism, and then show that a much better result can be accomplished by resorting to randomization. Namely, we provide a randomized mechanism whose expected approximation ratio is 4, and arbitrarily close to 4 with high probability. For EA, we give a simple (1+√2)n-approximation mechanism, and show that its performance is asymptotically tight by proving that it is NP-hard to approximate the optimal solution within O(n^{1−ɛ}) for any fixed ɛ > 0.
Finally, we show how to extend our results in the presence of neutrals, i.e., when agents can also be indifferent about other agents, and we discuss anonymity
Strategyproof mechanisms for Friends and Enemies Games
No full textWe investigate strategyproof mechanisms for Friends and Enemies Games, a subclass of Hedonic Games in which every agent classifies any other one as a friend or as an enemy. In this setting, we consider the two classical scenarios proposed in the literature, called Friends Appreciation (FA) and Enemies Aversion (EA). Roughly speaking, in the former each agent gives priority to the number of friends in her coalition, while in the latter to the number of enemies. We focus on the objective of maximizing the sum of the utilities of the agents and provide strategyproof mechanisms for both settings. More precisely, for FA we first present a deterministic n-approximation mechanism, n being the number of agents, and then show that a much better approximation can be achieved by resorting to randomization. Namely, we provide a randomized mechanism whose expected approximation ratio is 4, and arbitrarily close to 4 with high probability. For EA, we give a simple (1+2)n-approximation mechanism, and show that its performance is asymptotically tight by proving that it is NP-hard to approximate the optimal solution within O(n1−ε) for any fixed ε>0. We also show that, if computational efficiency is not a concern, it is possible to achieve a (1+2)-approximation by means of a deterministic strategyproof mechanism with exponential runtime. Finally, we show how to extend our results in the presence of neutrals, i.e., when agents can also be indifferent about other agents
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Distance Hedonic Games
No full textIn this paper we consider Distance Hedonic Games (DHGs), a class of non-transferable utility coalition formation games that properly generalizes previously existing models, like Social Distance Games (SDGs) and unweighted Fractional Hedonic Games (FHGs). In particular, in DHGs we assume the existence of a scoring vector α, in which the i-th coefficient αi expresses the extent to which an agent x contributes to the utility of an agent y if they are at distance i. We focus on Nash stable outcomes in the arising games, i.e., on coalition structures in which no agent can unilaterally improve her gain by deviating. We consider two different natural scenarios for the scoring vector, with monotonically increasing and monotonically decreasing coefficients. In both cases we give NP-hardness and inapproximability results on the problems of finding a social optimum and a best Nash stable outcome. Moreover, we characterize the topologies of coalitions that provide high social welfare and consequently give suitable bounds on the Price of Anarchy and on the Price of Stability
Distance hedonic games
No full textIn this paper we consider Distance Hedonic Games, a class of nontransferable utility coalition formation games that properly generalizes previously existing models, like Social Distance Games and Fractional Hedonic Games. In particular, in Distance Hedonic Games we assume the existence of a scoring vector α, in which the i-th coefficient αi expresses the extent to which x contributes to the utility of y if they are at distance i. We focus on Nash stable outcomes and consider two natural scenarios for the scoring vector: monotonically decreasing and monotonically increasing coefficients. In both cases we give NP-hardness and inapproximability results for the problems of finding a social optimum and a best Nash stable outcome. Moreover, we characterize the topologies of coalitions with high social welfare and give bounds on the Price of Anarchy and on the Price of Stability
PAC Learning and Stabilizing Hedonic Games: Towards a Unifying Approach
No full textWe study PAC learnability and PAC stabilizability of Hedonic Games (HGs), i.e., efficiently inferring preferences or core-stable partitions from samples. We first expand the known learnability/stabilizability landscape for some of the most prominent HGs classes, providing results for Friends and Enemies Games, Bottom Responsive, and Anonymous HGs. Then, having a broader view in mind, we attempt to shed light on the structural properties leading to learnability/stabilizability, or lack thereof, for specific HGs classes. Along this path, we focus on the fully expressive Hedonic Coalition Nets representation of HGs. We identify two sets of conditions that lead to efficient learnability, and which encompass all of the known positive learnability results. On the side of stability, we reveal that, while the freedom of choosing an ad hoc adversarial distribution is the most obvious hurdle to achieving PAC stability, it is not the only one. First, we show a distribution independent necessary condition for PAC stability. Then, we focus on W-games, where players have individual preferences over other players and evaluate coalitions based on the least preferred member. We prove that these games are PAC stabilizable under the class of bounded distributions, which assign positive probability mass to all coalitions. Finally, we discuss why such a result is not easily extendable to other HGs classes even in this promising scenario. Namely, we establish a purely computational property necessary for achieving PAC stability
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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