Descripció del projecte
This doctoral research project focuses on the development and refinement of data-driven algorithms to model and predict behavioral dynamics within organizational environments. The study aims to explore how individual and collective patterns emerge and evolve, leveraging algorithmic approaches that integrate both internal and external data sources.
A key aspect of the research is to adapt, improve and combine various algorithmic techniques to improve the sensitivity and robustness of behavior prediction. External data will be incorporated to enrich the models and capture latent influences that may not be evident through internal records alone.
While the technical implementation remains flexible, the project is based on the search for scalable and interpretable models capable of anticipating social behavior in complex operational environments. This work also lays the foundation for future applications in workforce planning, strategic decision-making, and organizational resilience.
