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Original Articles

Axiomatic aggregation of incomplete rankings

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Pages 475-488
Received 10 Dec 2014
Accepted 04 Oct 2015
Accepted author version posted online: 02 Dec 2015
Published online: 21 Feb 2016
 

ABSTRACT

In many different applications of group decision-making, individual ranking agents or judges are able to rank only a small subset of all available candidates. However, as we argue in this article, the aggregation of these incomplete ordinal rankings into a group consensus has not been adequately addressed. We propose an axiomatic method to aggregate a set of incomplete rankings into a consensus ranking; the method is a generalization of an existing approach to aggregate complete rankings. More specifically, we introduce a set of natural axioms that must be satisfied by a distance between two incomplete rankings; prove the uniqueness and existence of a distance satisfying such axioms; formulate the aggregation of incomplete rankings as an optimization problem; propose and test a specific algorithm to solve a variation of this problem where the consensus ranking does not contain ties; and show that the consensus ranking obtained by our axiomatic approach is more intuitive than the consensus ranking obtained by other approaches.

Additional information

Notes on contributors

Erick Moreno-Centeno

Erick Moreno-Centeno is an Assistant Professor at Texas A&M University’s Department of Industrial and Systems Engineering. He received an M.S. and a Ph.D. in Industrial Engineering & Operations Research (2006 and 2010, resp.) and a M.S. in Computer Science (2010), all from the University of California at Berkeley. He received a B.S. in Industrial Physics Engineering from the Instituto Tecnológico y de Estudios Superiores de Monterrey (ITESM), Mexico (2002). His research interests are in the general area of mathematical programming, particularly in the subareas of decision-making, design and analysis of combinatorial optimization models and algorithms, and roundoff-error-free optimization methods.

Adolfo R. Escobedo

Adolfo R. Escobedo is a Ph.D. candidate at the Texas A&M University’s Department of Industrial and Systems Engineering (expected graduation date of May 2016). He received a B.A. degree in Mathematics from California State University, Los Angeles, in 2009. His research interests lie in the areas of mathematical programming and computing and their application to decision-making, power systems, and optimization solvers.

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