Skip to content
naulon
Not checkedNot checked for notices yet.

Hierarchical Modeling and Analysis of an International Conflict Based on Hesitant Fuzzy Linguistic Term Sets

Junji Hao, Bingfeng Ge, Yuming Huang and 3 more

Systems · 2025

doi:10.3390/systems13070557 (opens in a new tab)

We email you when a new notice or a question about this paper appears.

Abstract

In this article, to address the uncertainty of preference information in interrelated conflicts in the real world, a hierarchical conflict modeling and analysis approach based on hesitant fuzzy linguistic term sets (HFLTSs) is proposed. First, considering the hesitancy and fuzziness of decision makers (DMs) when expressing preferences in hierarchical conflicts, a preference representation approach based on HFLTSs is introduced. Building upon hesitant fuzzy linguistic preference, four distinct types of hesitant fuzzy stability definitions of the two-level hierarchical graph model for conflict resolution (HGMCR) are extended, and a corresponding algorithm is developed to solve the global conflict hesitant fuzzy equilibrium states. Finally, this study is applied to investigate the outbreak and development of a specific international conflict, verifying the feasibility and effectiveness of the proposed approach. The hesitant fuzzy equilibrium states of an international conflict indicate that the attitudes of domestic forces reflect a nation’s performance in the warand that the conflict may endure for an extended duration. The hierarchical conflict modeling and analysis approach based on HFLTSs allows DMs to express the hesitation and fuzziness of preferences under uncertainty, facilitates the comprehension of the intrinsic logic behind interactions among DMs at various levels, and enhances the analysis to achieve more foresighted equilibria.

Abstract licence: CC BY

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.