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Policy making is required in cases in which a public good needs to be either maintained or created, and private or civil initiatives cannot deal alone with this. Policy making thus starts with a phase of problem identification and determining whether there is a problem that needs to be dealt with. Rapidly evolving contexts exert influence on policy makers who have to take decisions much faster and more accurately than in the past, also facing greater complexity. There is a need for a method that lowers the lead time of the exploratory phase of the policy cycle. At the same time the method should create a joint understanding of the most important interactions. This paper proposes QUICKScan, a method, process and spatially explicit tool, to jointly scope policy problems in a participatory setting, investigate the most important interactions and feedbacks and assesses the state of knowledge and data of relevance to the problem. QUICKScan uses strongly moderated participatory workshops bringing together a wide range of stakeholders relevant to the policy issue. These moderated workshops jointly build an expert system in a spatially explicit tool using functionality of bayesian belief networks, python programming, simple map algebra and knowledge matrices, with a strong focus on visualization of results. QUICKScan has been applied in 70 different applications in a range of different policy contexts, stakeholders and physical locations. Through these applications participants were able to internalize the knowledge that was usually handed to them in briefs and reports, to develop a joint understanding of the main interactions and their link to impacts and to develop a problem statement and solution space in a reduced lead time. Ultimately, QUICKScan demonstrates another role of science, not solely as a knowledge production, but also facilitating the knowledge consumption. [ABSTRACT FROM AUTHOR]