Using graphical models and multi-attribute utility theory for probabilistic uncertainty handling in large systems, with application to the nuclear emergency management

Abstract

Abstract-Although many decision-making problems involve uncertainty, uncertainty handling within large decision support systems (DSSs) is challenging. One domain where uncertainty handling is critical is emergency response management, in particular nuclear emergency response, where decision making takes place in an uncertain, dynamically changing environment. Assimilation and analysis of data can help to reduce these uncertainties, but it is critical to do this in an efficient and defensible way. After briefly introducing the structure of a typical DSS for nuclear emergencies, the paper sets up a theoretical structure that enables a formal Bayesian decision analysis to be performed for environments like this within a DSS architecture. In such probabilistic DSSs many input conditional probability distributions are provided by different sets of experts overseeing different aspects of the emergency. These probabilities are then used by the decision maker (DM) to find her optimal decision. We demonstrate in this paper that unless due care is taken in such a composite framework, coherence and rationality may be compromised in a sense made explicit below. The technology we describe here builds a framework around which Bayesian data updating can be performed in a modular way, ensuring both coherence and efficiency, and provides sufficient unambiguous information to enable the DM to discover her expected utility maximizing policy.

Coherent combination of probabilistic outputs for group decision making: an algebraic approach

“…the decision centre responsible for the implementation of any policy needs to consist of individuals who act collaboratively and strive to behave as a single coherent unit would. In the food poverty (Barons et al, 2016) and nuclear emergency management (Leonelli and Smith, 2013) applications this condition was broadly met. We suppose the centre consists of m panels of experts denoted by G 1 , .…”

Bayesian decision support for complex systems with many distributed experts

“…A DSS that provides expected utility scores from equation (20) could thus lead decision centres to behave as non expected utility maximizer and put them in danger of adopting indefensible countermeasures (see Leonelli and Smith, 2013b, for another example).…”