6 resultados para Shapley Supercluster

em Deakin Research Online - Australia


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We evaluate the accuracy of power indices by experimentally measuring the political power embodied in blocks of votes per se.  The experiment incorporates several players interacting in online chat rooms under supervision.  The chat rooms and the selection process reduce or eliminate extraneous political forces leaving logrolling as the primary political force.  Results show that two standard power indices reflect voting power while other power indices and proportionality do not.

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We evaluate power indices by experimentally measuring the political power embodied in blocks of votes per se. Subjects with differing votes divide a fixed purse by majority rule in online chat rooms under supermision. Chat rooms and processes for selecting subjects reduce or eliminate extraeous political forces leaving logrolling as the primary political force. Results show that various power indices bracket voting power which is more than proportionally concentrated in large players.

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The explosion of the Web 2:0 platforms, with massive volume of user generated data, has presented many new opportunities as well as challenges for organizations in understanding consumer's behavior to support for business planning process. Feature based sentiment mining has been an emerging area in providing tools for automated opinion discovery and summarization to help business managers with achieving such goals. However, the current feature based sentiment mining systems were only able to provide some forms of sentiments summary with respect to product features, but impossible to provide insight into the decision making process of consumers. In this paper, we will present a relatively new decision support method based on Choquet Integral aggregation function, Shapley value and Interaction Index which is able to address such requirements of business managers. Using a study case of Hotel industry, we will demonstrate how this technique can be applied to effectively model the user's preference of (hotel) features. The presented method has potential to extend the practical capability of sentiment mining area, while, research findings and analysis are useful in helping business managers to define new target customers and to plan more effective marketing strategies.

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Variations between journal rankings may cause confusion. As such, prior attempts were made to compare and evaluate journal ranking criteria for obtaining insightful knowledge on how different research communities have ranked journals. However, existing approaches are unable to model the journal ranking process closely enough as they are incapable of considering the relationship between multiple criteria simultaneously. In this paper, we address the challenges by introducing the Choquet Integral (CI) for evaluating journal ranking criteria. The new approach is able to account for interactions between criteria in relation to overall ranking score, using a fuzzy measure in its computation. Its properties, the Shapley value and the Interaction index, allow for good representations of importance and interactions between criteria. We demonstrate the efficiency of the CI through a case study of journal ranking lists in tourism and service journals.

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This paper proposes using the Shapley values in allocating the total tail conditional expectation (TCE) to each business line (X j, j = 1, ... , n) when there are n correlated business lines. The joint distributions of X j and S (S = X1 + X2 + ⋯ + X n) are needed in the existing methods, but they are not required in the proposed method.