In facing extremely large and interconnected systems, decision-makers must often combine evidence obtained from multiple expert domains, each informed by a distinct panel of experts. To guide this combination so it takes place in a coherent manner, we need an integrating decision support system (IDSS). This enables the user to calculate the subjective expected utility scores of candidate policies as well as providing a framework for incorporating measures of uncertainty into the system. Throughout this chapter we justify and describe the use of IDSS models and how this procedure is being implemented to inform decision-making for policies impacting food poverty within the UK. In particular, we provide specific details of this elicitation process when the overarching framework of the IDSS is a dynamic Bayesian Network (DBN).

Citation information

Barons, M. & K. Wright, S. & Smith, J. (2018). Eliciting Probabilistic Judgements for Integrating Decision Support Systems. International Series in Operations Research and Management Science. 445-478. 10.1007/978-3-319-65052-4_17.

Additional information

Martine J. Barons, Sophia K. Wright and Jim Q. Smith

Turing affiliated authors