A Probabilistic Approach to Group Decision Making

Large-scale judgment analysis from multiple opinions is a challenging job in terms of time and cost involved. Over the last few years, with the popularity of crowd-powered models, the process of decision making is efficiently getting done by using the knowledge of crowd. In management science, a closely related class of problems, popularly known as group decision making, is often addressed. Unfortunately, majority of the algorithms developed for this purpose work for binary or multiple opinions without taking care of the semantic meaning of the options. Moreover, group decision considers a feedback set comprising range of continuous values unlike the judgment analysis problem. In this paper, we address this problem, hereafter termed as multi-opinion group decision making, with a probabilistic approach taking into account the annotator accuracy, annotator bias and question difficulty. The effectiveness of the approach is demonstrated by applying this on a benchmark group decision making dataset.

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