Multi-Agent Influence Diagrams to Hybrid Threat Modeling

2026-03-03

AI Summary

This study addresses the fragmentation of existing hybrid threat modeling approaches, which struggle to quantitatively assess the effectiveness of countermeasures against adversarial actions falling below traditional military thresholds. To bridge this gap, the paper introduces multi-agent influence diagrams into the domain for the first time, establishing a unified framework that systematically integrates cross-domain, ambiguous, and strategically interactive hybrid threat scenarios while jointly evaluating the cost, deterrent value, and mitigation efficacy of response measures. Drawing on 1,000 semi-synthetic simulation experiments, the research identifies the performance characteristics and parameter sensitivities of five categories of countermeasures—including resilience enhancement, capability denial, and punitive deterrence—thereby offering policymakers a quantifiable and generalizable basis for strategic assessment and decision-making.

Technology Category

• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning
• Search and Optimization: Adversarial Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Western governments have adopted an assortment of counter-hybrid threat measures to defend against hostile actions below the conventional military threshold. The impact of these measures is unclear because of the ambiguity of hybrid threats, their cross-domain nature, and uncertainty about how countermeasures shape adversarial behavior. This paper offers a novel approach to clarifying this impact by unifying previously bifurcating hybrid threat modeling methods through a (multi-agent) influence diagram framework. The model balances the costs of countermeasures, their ability to dissuade the adversary from executing hybrid threats, and their potential to mitigate the impact of hybrid threats. We run 1000 semi-synthetic variants of a real-world-inspired scenario simulating the strategic interaction between attacking agent A and defending agent B over a cyber attack on critical infrastructure to explore the effectiveness of a set of five different counter-hybrid threat measures. Counter-hybrid measures range from strengthening resilience and denial of the adversary's ability to execute a hybrid threat to dissuasion through the threat of punishment. Our analysis primarily evaluates the overarching characteristics of counter-hybrid threat measures. This approach allows us to generalize the effectiveness of these measures and examine parameter impact sensitivity. In addition, we discuss policy relevance and outline future research avenues.

Problem

• hybrid threats
• countermeasures
• adversarial behavior
• strategic interaction
• impact assessment

Innovation

• Multi-Agent Influence Diagrams
• Hybrid Threat Modeling
• Counter-Hybrid Measures
• Strategic Interaction
• Semi-Synthetic Simulation