Researchers Propose Trisected Framework to Clarify Three-Way Conflict Analysis

Global: Researchers Propose Trisected Framework to Clarify Three-Way Conflict Analysis

A team of scholars released a new preprint on arXiv in December 2025 that introduces a trisected approach to three-way conflict analysis, aiming to separate alliance and conflict dimensions for clearer interpretation of agent interactions.

Background

Traditional models in conflict analysis have relied on either rating functions, which assign positive, negative, or neutral values to agents on issues, or auxiliary functions that blend alliance, conflict, and neutrality into a single metric. This blending often obscures the distinct meanings of opposing relationships when aggregated across multiple issues or agents.

Proposed Framework

The authors suggest dividing the auxiliary function into two separate components: an alliance function and a conflict function. By trisecting agents, issues, and agent pairs, the framework treats these opposing aspects independently, allowing analysts to capture nuanced relational dynamics.

Methodological Advantages

Under the new scheme, aggregating relationships no longer yields identical averages for fundamentally different configurations. For instance, a pair consisting of an alliance (+1) and a conflict (‑1) will produce a net zero average, distinct from a pair of neutral (0) relations, preserving the semantic differences that standard averaging would mask.

Practical Applications

The paper explores how the trisected model can define alliance sets and inform strategic decision‑making. A real‑world case study is presented to demonstrate how the refined metrics guide the identification of cooperative clusters and potential points of contention among agents.

Implications and Future Work

By clarifying the semantics of agent interactions, the framework may enhance the precision of conflict resolution tools and support more targeted policy or organizational interventions. The authors indicate plans to test the approach across larger datasets and to integrate it with existing decision‑support systems.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.