Simone Reale
abstract
This project investigates the transition from static optimization frameworks for urban greening
toward a dynamic, learning-based decision-making process supported by agent-based modeling. The
overarching goal is to design adaptive strategies that guide the spatial and temporal distribution of
green infrastructure to enhance environmental equity and urban climate resilience over medium to
long time horizons.
By reframing urban greening as a sequential decision-making problem, the proposed framework
enables the simulation of evolving intervention policies and their cumulative environmental and
social impacts. The integration of autonomous agents allows both environmental processes (e.g.,
vegetation growth and heat dynamics) and human behaviors (e.g., demographic evolution, mobility,
and socio-economic adaptation) to be represented as interacting components within a complex urban
ecosystem. This approach supports the development of adaptive, data-driven policies capable of
learning from feedback and optimizing long-term outcomes under uncertainty.
outcomes