Analyzing credibility of arguments in a web‐based intelligent argumentation system for collective decision support based on K‐means clustering algorithm

We developed an intelligent argumentation and collaborative decision support system which allows stakeholders to exchange arguments and captures their rationale. Arguments with lack of credibility in an argumentation tree may negatively affect decisions in a collaborative decision making process if they are not identified collectively by the group. To address this issue, we perform clustering analysis on an argumentation tree using K‐means clustering algorithm on credibility factors of arguments such as degree of an argument, and collective determination of an argument. Arguments are classified into multiple groups: from highly credible to lack of credibility. It helps capture rationale of selection of the most favorable solution alternative by the system. It helps decision makers identify arguments with high credibility based on collective determination. We perform an empirical study of the method and its results indicate that it is effective in supporting collective decision making using the system.