The paper entitled “Automated Preference Determination in Graph Model for Conflict Resolution based on Text Extraction”, co-authored by Shawei He and two Master students under his supervision, has been recently published in the journal of European Journal of Operational Research. This research is the first of its kind to elicit preferences “100% automatically” from online text within the paradigm of Graph Model for Conflict Resolution (GMCR).

As human judgment is usually required to determine preferences in the existing approaches, this research could achieve the automatic conversion from online text to the preference statements without human judgement, given that the decision makers and their options are known. By employing the Natural Language Processing (NLP) tools, algorithms are constructed to realize the automatic preference elicitation via the retrieval of URLs, identification of signs for sentences from text, identification of signs for options, and the construction of preference statements defined in three types. This novel methodology is applied to air carbon negotiation, suggesting guidance of actions for DMs regarding how to be involved into the international carbon mitigation in aviation industry. This study could be utilized in the future to analyze strategic conflicts, especially the geopolitical ones, in which the preferences for decision makers can be determined automatically from either websites or accessible archives.

This paper can be cited as:

He, S., Song, H., Ji, B. (2026). Automated Preference Determination in Graph Model for Conflict Resolution based on Text Extraction. European Journal of Operational Research, https://doi.org/10.1016/j.ejor.2026.05.020

A complete version of this paper can be accessed via:

https://www.sciencedirect.com/science/article/pii/S0377221726004650