Conflict Dynamic

Conflict dynamics research aims to understand and model the complex processes driving conflicts, from interpersonal disputes to large-scale international wars. Current research focuses on developing improved methods for analyzing conflict data, including the use of machine learning techniques like active learning and neural networks (e.g., neural forward-intensity Poisson processes) to extract insights from textual and spatio-temporal data, and employing novel logical frameworks to enhance conflict modeling. These advancements are improving our ability to predict conflict escalation, support peace negotiations, and ultimately contribute to more effective conflict resolution strategies.