D4RPoMM:About

The 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.