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

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

Bayesian decision support for complex systems with many distributed experts

Complex decision support systems often consist of component modules which, encoding the judgements of panels of domain experts, describe a particular sub-domain of the overall system. Ideally these modules need to be pasted together to provide a comprehensive picture of the whole process. The challenge of building such an integrated system is that, whilst the overall qualitative features are common knowledge to all, the explicit forecasts and their associated uncertainties are only expressed individually by each panel, resulting from its own analysis. The structure of the integrated system therefore needs to facilitate the coherent piecing together of these separate evaluations. If such a system is not available there is a serious danger that this might drive decision makers to incoherent and so indefensible policy choices. In this paper we develop a graphically based framework which embeds a set of conditions, consisting of the agreement usually made in practice of certain probability and utility models, that, if satisfied in a given context, are sufficient to ensure the composite system is truly coherent. Furthermore, we develop new message passing algorithms entailing the transmission of expected utility scores between the panels, that enable the uncertainties within each module to be fully accounted for in the evaluation of the available alternatives in these composite systems

Decision-analytic frameworks for multi-hazard mitigation and adaptation

Developing effective decision-support for multiple hazards needs to build on a foundation of existing research into best practices for the management of single hazards analysis. This comes from the hazards literature, recent and ongoing EU research projects, and from the climate vulnerability literature, in which the theoretical focus on multiple drivers of vulnerability is already well established. The first part of this task will rely on a desk study of established management practices and decision-analytic methods. The latter include several standard methods for conducting sound formal decision-analysis, including cost-benefit analysis, risk- benefit analysis, and multi-criteria analysis. Each of these has its strengths, weaknesses, and set to best practices in particular contexts. The second part of this task will identify these in the case of multiple hazards, and appraise how they may differ in their application and appropriateness from the single-hazard case. It will rely on an application of these modeling methods to the simulated city case study

Decision Maps for Distributed Scenario-Based Multi-Criteria Decision Support

This thesis presents the Decision Map approach to support decision-makers facing complex uncertain problems that defy standardised solutions. First, scenarios are generated in a distributed manner: the reasoning processes can be adapted to the problem at hand whilst respecting constraints in time and availability of experts. Second, by integrating scenarios and MCDA, this approach facilitates robust decision-making respecting multiple criteria in a transparent well-structured manner