Exploring Conflict Escalation: Power Imbalance, Alliances, Diplomacy, Media, and Big Data in a Multipolar World

Abstract

The analysis in this study covers how power imbalance, alliance cohesion, diplomatic and media framing, and big data analytics affect scaling up in the conflict in a multipolar world. This research applies the Constructivist International Relations Theory to examine survey data of 250 international relations experts, policymakers, and analysts using Survey Structured Equation Modeling (SEM) via SMART-PLS. Power imbalance and the way the media frames the situation are found to lead to an escalation of conflicts, but strong alliance cohesion, diplomatic effort, and big data analytics can mitigate the risk of the escalation. Strategic diplomacy, media regulation, and real-time data monitoring have thus shown their capacity to prevent conflict. These contribute to conflict studies by incorporating political IR models, data science knowledge, and policy advice on global security governance. This means they can support the prediction and prevention of conflicts by means of diplomatic transparency, ethical media practice, and AI early warning systems. This study is limited by the use of self-reported data; however, the results of this study indicate that this topic is under-explored in cultural and geopolitical terms. The results help inform policymakers and security entities on ways to address conflict resolution as a matter of discretion and from a multidimensional perspective. Survey Structured Equation Modeling (SEM) via SMART-PLS is a technique used for analyzing structural relationships between measured variables and latent constructs, providing valuable insights into complex models. Survey Structured Equation Modeling (SEM) via SMART-PLS is a technique used for analyzing structural relationships between measured variables and latent constructs, providing valuable insights into complex models.