JQ Smith, MJ Barons and M Leonelli
Coherent frameworks for statistical inference serving integrating decision support systems
Abstract: 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 integrat-
ing decision support system and to enable the centre to explore and compare the eciency of dierent candidate policies. We develop a formal statistical methodology to underpin this tool. In particular, we derive sucient 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.
Keywords: Bayesian multi-agent models, causality, coherence, decision support, graphical models, likelihood separation.