الفهرس 
IntroductionOnly 14 pages are availabe for public view
Abstract
Decision making is about selecting the best choice from a range of possible options. Based
on the number of the participants in the decision making process, it is classified into individual
and group decision making. Group decision making presents a number of challenges
that need to be handled by a tool that facilitates communication and deliberation. A number
of models and frameworks have been proposed to handle specific types of problems
due to their limited features. Therefore, there are problems that require the use of more
than one model due to the limitations that an existing single model would impose, such as
shortage in communications between participants, disallowing participants from adding
new alternatives or criteria, or providing no statistics about the problem. The main contribution
of the thesis is to build a robust model that can be used over a wide range of
problem scenarios by integrates the key features from the models and frameworks that addressed
the issues that related to group decision making. The proposed model makes use
of a trust and delegation feature to manage the communications among the participants
and handle the differences in their levels of expertise. It applies the analytic hierarchical
processes as a decision analysis tool for gathering participants decisions. It also introduces
the sentiment analysis feature as a way for exploring and explaining the group decisions
during and after the process. The model has a generic and flexible architecture which
allows it to adapt to the requirements of a given problem. A simple embodiment is implemented
and utilized in a small experiment. The experiment evaluation showed that 65%
of participants agreed on the final decision and the process fairness; 76% of participants
recommended to use the proposed model in the real political processes in which representatives
are elected to make decision on behalf of a group. However, we couldn’t compare
these results due to the lack of experimental results for the models and frameworks that
we build the proposed model upon them.