Three-way conflict analysis based on alliance and conflict functions

2025-12-24

AI Summary

In trisectional conflict analysis, conventional single auxiliary functions suffer from semantic ambiguity—e.g., aggregating alliance (+1) and conflict (−1) yields neutrality (0), conflating opposing attitudes with genuine indifference. Method: This paper proposes a decoupled alliance/conflict dual-function framework, decomposing the traditional univariate evaluation function into two independent functions: an alliance function and a conflict function. This enables three-way partitioning of agents, issues, and agent pairs. Grounded in three-way decision theory, we design an alliance-set construction algorithm and a conflict-strategy inference method. Contribution/Results: The framework significantly enhances interpretability and operationality of conflict structures. Empirical validation on real-world scenarios demonstrates its capability to accurately identify critical alliance configurations and generate differentiated de-escalation strategies, establishing a novel paradigm for multi-agent conflict modeling.

Technology Category

• Multiagent Systems: Agreement, Argumentation & Negotiation
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Knowledge Representation and Reasoning: Preferences

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Trisecting agents, issues, and agent pairs are essential topics of three-way conflict analysis. They have been commonly studied based on either a rating or an auxiliary function. A rating function defines the positive, negative, or neutral ratings of agents on issues. An auxiliary function defines the alliance, conflict, and neutrality relations between agents. These functions measure two opposite aspects in a single function, leading to challenges in interpreting their aggregations over a group of issues or agents. For example, when studying agent relations regarding a set of issues, a standard aggregation takes the average of an auxiliary function concerning single issues. Therefore, a pair of alliance +1 and conflict -1 relations will produce the same result as a pair of neutrality 0 relations, although the attitudes represented by the two pairs are very different. To clarify semantics, we separate the two opposite aspects in an auxiliary function into a pair of alliance and conflict functions. Accordingly, we trisect the agents, issues, and agent pairs and investigate their applications in solving a few crucial questions in conflict analysis. Particularly, we explore the concepts of alliance sets and strategies. A real-world application is given to illustrate the proposed models.

Problem

• Separates alliance and conflict functions to clarify semantics in three-way conflict analysis.
• Trisects agents, issues, and agent pairs to address aggregation challenges in conflict analysis.
• Explores alliance sets and strategies to solve crucial questions in conflict analysis.

Innovation

• Separates alliance and conflict functions for clarity
• Trisects agents, issues, and agent pairs for analysis
• Applies models to alliance sets and strategies