{"tema_id":"376","string":"Policy Making Module","created":"2024-11-07 19:28:47","code":"","modified":"0000-00-00 00:00:00","notes":[{"@type":"Scope note","@lang":"en-EN","@value":"This definition applies to the D4Runoff project scope. "},{"@type":"Definition note","@lang":"en-EN","@value":"The PoMM is the DSS component of D4Runoff AI-assisted platform for improving\u00a0 the policymaking related to hybrid NBS by explicitly including the science-policy relationships in decision-making.\nThe PoMM does NOT pursue any\n\nDecision-making capability \/ automation \/ prescription: it is upon the human actor to assess which potential action to simulate, assess and eventually put in practice.\nPredictive capabilities: the technology used is intrinsically exploratory and to a certain extent explanatory, not predictive.\nControl capabilities: there\u2019s 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).\n\u00a0Concurrent 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).\nModeling 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).\nGeneralization capability. While several configuration capabilities are granted, these are confined to the science-policy relationships and to the hNBS for CECs configuration adopted.\n "}]}