Segregation Model

Segregation models explore the spatial distribution of different entities, whether rock fragments in a quarry, individuals in a city, or even neural pathways in the brain. Current research focuses on developing and applying sophisticated computational models, including deep learning architectures like U-Net and agent-based models incorporating reinforcement learning, to analyze and predict segregation patterns across diverse systems. These studies aim to quantify segregation levels, understand the underlying mechanisms driving it (e.g., density, preferences, moving costs), and ultimately inform better resource management (e.g., quarry optimization) or policy decisions (e.g., urban planning) by revealing the complex interplay of factors contributing to segregation.