  A subjective expected utility policy making centre, managing complex, dynamic
systems, needs to draw on the expertise of a variety of disparate panels of
experts and integrate this information coherently. To achieve this, diverse
supporting probabilistic models need to be networked together, the output of
one model providing the input to the next. In this paper we provide a
technology for designing an integrating decision support system and to enable
the centre to explore and compare the efficiency of different candidate
policies. We develop a formal statistical methodology to underpin this tool. In
particular, we derive sufficient conditions that ensure inference remains
coherent before and after relevant evidence is accommodated into the system.
The methodology is illustrated throughout using examples drawn from two
decision support systems: one designed for nuclear emergency crisis management
and the other to support policy makers in addressing the complex challenges of
food poverty in the UK.
