{'fraction_non_alphanumeric': 0.021156558533145273, 'fraction_numerical': 0.0, 'mean_word_length': 5.116379310344827, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
New consensus measure for group decision-making based on Spearman’s correlation coefficient for reciprocal fuzzy preference relations

ABSTRACT In group decision-making, reaching to a level of agreement about the decision between the group members is important. Indeed, it is considered as the main challenge to be solved in any group decision-making problem. Typically, similarity and distance measures are used to measure the degree of similarities of preference intensities in group decision-making. In this paper, we propose a new consensus measure based on Spearman’s correlation coefficient for group decision-makers with reciprocal preference relations. We rely on ranked preference intensities of a decision-maker and find its correlation with respect to other decision-makers in the group. The novelty of this work relies on considering the coherence of decision-maker preference degree ranks as a whole rather than in a pairwise manner. This work does not rely directly on similarity/distance functions, as do most of the consensus models in the literature, but rather adopts the idea of measuring the monotonic degree among them. In addition, based on this model, we propose a feedback mechanism to act as a mediator to guide the group into the consensus solution. We illustrate the new model by presenting a numerical example. Moreover, the model results show robustness when validated on several problems.