An attitudinal trust recommendation mechanism to balance consensus and harmony in group decision making
This article puts forward a trust based framework for building a recommendation mechanism for consensus in group decision making with interval-valued intuitionistic fuzzy information. To do that, it first presents an attitudinal trust model where experts assign trust weights to others considering the concept of attitude of the group. This approach allows for the implementation of the group attitude in a continuous scale ranging from a pessimistic attitude to an indifferent attitude. Thus, it can express the continuous trust status, and consequently it generalizes the traditional simplified trust model: ‘trusting’ and ‘distrusting’. In particular, three typical policies are defined as: ‘extreme trust policy’, ‘bounded trust policy’ and ‘indifferent trust policy’. Secondly, the attitudinal trust induced recommendation mechanism is established by a reasonable rule: the closer the experts, the higher their trust degree. This can guarantee that the consensus level of the inconsistent expert is increased after adopting the recommended advices. In addition to group consensus, experts envisage to keep their original opinions as much as possible. A harmony degree (HD) is defined to determine the extent of the difference between an original opinion and the corresponding revised opinion after adopting the recommended advices. Combining the HD index and the consensus index, a sensitivity analysis with attitudinal parameter is proposed to verify the rationality of the proposed attitudinal trust recommendation mechanism. In practice this will facilitate the inconsistent experts to achieve a balance between consensus degree and harmony degree by selecting an appropriate attitudinal parameter.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
Citation : Wu, J., Li, X., Chiclana, F., Yager, R.R. (2019) An attitudinal trust recommendation mechanism to balance consensus and harmony in group decision making. IEEE Transactions on Fuzzy Systems, 27 (11), pp. 2163-2175
ISSN : 1063-6706
Research Group : Institute of Artificial Intelligence (IAI)
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes