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    ABSTRACT: We consider the problem of helping a decision maker (DM) choose from a set of multiattributed objects when her preferences are "concavifiable," i.e. representable by a concave value function. We establish conditions under which preferences or preference intensities are concavifiable. We also derive a characterization for the family of concave value functions compatible with a set of such preference statements expressed by the DM. This can be used to validate dominance relations over discrete sets of alternatives and forms the basis of an interactive procedure. We report on the practical use of this procedure with several DMs for a flat-choice problem and its computational performance on a set of project-portfolio selection problem instances. The use of preference intensities is found to provide significant improvements to the performance of the procedure.
    Preview · Article · Jun 2014 · Operations Research
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    ABSTRACT: Is political agreement in social networks the product of selection or influence? We investigate this question using the first large, general population sample survey to track changes in the political discussion partners named by respondents over the course of an election campaign. We identify two social processes at work during the nine months prior to the election: “selection”, or the likelihood that people choose discussion partners based on their political views, and “influence”, or the likelihood that respondents exposed to political disagreement change their intended vote choice. We find evidence of both positive and negative selection for political agreement, as well as evidence that people are influenced by their friends and family.
    No preview · Article · Jan 2014 · Social Networks
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    ABSTRACT: We propose a simple algorithm for linear programming feasibility, that can be considered as a polynomial-time implementation of the relaxation method. It can be seen as an alternative variant of Chubanov's recent algorithm. The key idea of both algorithms is unfolding the recursion in the "Divide-and-Conquer algorithm" in Chubanov's earlier paper, although the details are somewhat different.
    Preview · Article · Jul 2013 · Operations Research Letters
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Journal of Risk Research 03/2005; 8(2). DOI:10.1080/1366987032000123856
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Handbook of industrial and organizational psychology (Vol. 3) Edited by S. Zedeck, H. Aguinis, W. Cascio, M. Gelfand, K. Leung, S. Parker, J. Zhou, 01/2011: pages 223-248; American...
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