Maximizing Consensus in Portfolio Selection in Multicriteria Group Decision Making | Zendy

Michael Emmerich | Zendy; André Deutz | Zendy; Longmei Li | Zendy; Asep Maulana | Zendy; Iryna Yevseyeva | Zendy

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Maximizing Consensus in Portfolio Selection in Multicriteria Group Decision Making

Author(s) -

Michael Emmerich,

André Deutz,

Longmei Li,

Asep Maulana,

Iryna Yevseyeva

Publication year - 2016

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2016.09.234

Subject(s) - computer science , portfolio , selection (genetic algorithm) , aka , set (abstract data type) , decision problem , weighted sum model , moderation , decision analysis , group decision making , mathematical optimization , optimal decision , operations research , mathematical economics , data mining , machine learning , mathematics , decision tree , algorithm , economics , finance , library science , political science , law , programming language

Open Access articleThis paper deals with a scenario of decision making where a moderator selects a (sub)set (aka portfolio) of decision alternatives from a larger set. The larger the number of decision makers who agree on a solution in the portfolio the more successful the moderator is. We assume that decision makers decide independently from each other but indicate their preferences with respect to different objectives in terms of desirability functions, which can be interpreted as cumulative (probability) density functions. A procedure to select a solution with maximal expected number of decision makers that accept it is provided. Moreover, this is generalized to sets of solutions. An algorithm for computing and maximizing the expected number of decision makers that can agree on at least one solution in a subset of decision alternatives is developed. Computational aspects, as well as practical examples for using this for item selection from a database will be discussed

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