Hybrid Threats under Simulation: From Empirical Data to Strategic Modeling - Esteban Ceballos, Arturo (Journal of the Spanish Institute for Strategic Studies, 2026)
Hybrid strategies have become a central concern in contemporary strategic analysis, yet their systematic modeling remains limited. This article proposes a replicable methodology that integrates empirical analysis, formal classification, statistical modeling, and adaptive simulation. Drawing on the reclassification of events from the GDELT database into the DIMEFL scheme (Diplomatic, Informational, Military, Economic, Financial, and Legal), a quarterly panel of Turkish activity in Libya, Somalia, Mali, and Niger was constructed. This panel was analyzed through first-order Markov chains, negative binomial regression, and SARIMAX models, enabling the identification of sequential patterns and long-term trends. Complementing this approach, an agent-based model with reinforcement learning (ABM-RL) was developed, simulating Turkey as a strategic actor seeking to maximize influence while minimizing reputational costs. The simulation reproduced observed tactical sequences and uncovered adaptive logics that are less evident in empirical data alone. Although cognitive processes were not incorporated, cyber-physical-social systems are identified as a promising extension for future research. The proposed architecture constitutes a scalable framework for the study of hybrid threats and provides a robust foundation for the development of early-warning systems in multidomain environments.
Keywords: hybrid strategies; DIMEFL; ABM-RL; GDELT; Turkey; Russia, Turkey, Nagorno-Karabakh, Influence, Armenia, Azerbaijan, Syria