A logistic regression-based pairwise comparison method to aggregate preferences
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In a group decision making process, several individuals or a committee have the responsibility to choose the best alternative from a set. The problem addressed in this paper is how to aggregate personal preferences to arrive at an optimal group decision. New technologies allow individuals that may seldom or never meet to make group decisions. This paper proposes a methodology to obtain the group preference ordering in two steps. Firstly, each individual studies the problem isolated, and then, in a possibly virtual meeting, the group must agree on the preferences on some pairs of alternatives. Then, the group criterion is achieved by using a logistic regression model within the pairwise comparison framework proposed here. Properties of the procedure are studied and two illustrative examples are presented.
Keywordse-democracy Group decision making Group preference aggregation Logistic regression Pairwise comparison
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The authors would like to thank the reviewers for comments and suggestions which have highly improved not only the readability of the paper but also its content. This research was partially supported by Ministerio de Educación y Ciencia, Spain (Project TSI2004-06801-C04-03) and the European Science Foundation (Towards Electronic Democracy program).
- Agresti A (1996) An introduction to categorical data analysis. WileyGoogle Scholar
- Arrow KJ (1951) Social choice and individual values. WileyGoogle Scholar
- Freeman DH (1987) Applied categorical data analysis. Marcel DekkerGoogle Scholar
- French S (2006) Web-enabled strategic GDSS, e-democracy, and Arrow’s theorem: a Bayesian perspective. Decision support systems (to appear)Google Scholar
- Keeney RL, Raiffa H (1976) Decisions with multiple objectives: preferences and value tradeoffs. WileyGoogle Scholar
- Kelly FS (1978) Arrow impossibility theorems. Academic Press, New YorkGoogle Scholar
- McCullagh P, Nelder JA (1989) Generalized lineal models, monographs on statistics and applied probability. Chapman & HallGoogle Scholar
- Ryan TP (1997) Modern regression methods. WileyGoogle Scholar
- Saaty TL (1980) The analytic hierarchy process. McGraw-HillGoogle Scholar