D4RPoMM:About

The Policy-Making Module (PoMM) is the DSS component of D4Runoff AI-assisted platform for improving the policymaking related to hybrid NBS by explicitly including the science-policy relationships in decision-making.

The PoMM does NOT pursue any

• Decision-making capability / automation / prescription: it is upon the human actor to assess which potential action to simulate, assess and eventually put in practice.

Predictive capabilities: the technology used is intrinsically exploratory and to a certain extent explanatory, not predictive.

• Control capabilities: there’s no closed loop. Though if the box may be fed by real-world data (mediated by other systems like the IIOT and federated-AI systems), no feed-back will be given to such components (output will be given to human actors).
• Concurrent simulation. Simulation occurs only with one experiment (configuration) and only one user at a time. If multiple user will be allowed, each one will take the user role in turn (resulting in step by step change of settings and rerun from a breakpoint).
• Modeling of transformation of operations, logistics and other industrial or urban processes. The PoMM assumes that each process considered is a black-box whose function is known (it can even be stochastic, but ranges and patterns must be given).
• Generalization capability. While several configuration capabilities are granted, these are confined to the science-policy relationships and to the hNBS for CECs configuration adopted.