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

OPTIMAL TESTING STRATEGIES FOR GENETICALLY MODIFIED WHEAT

A stochastic optimization model was developed to determine optimal testing strategies, costs, and risks of a dual marketing system. The model chooses the testing strategy (application, intensity, and tolerance) that maximizes utility (minimizes disutility) of additional system costs due to testing and quality loss and allows simulation of the risk premium required to induce grain handlers to undertake a dual marketing system versus a Non-GM system. Cost elements including those related to testing, quality loss, and a risk premium were estimated for a model representing a grain export chain. Uncertainties were incorporated and include test accuracy, risk of adventitious commingling throughout, and variety declaration. Sensitivities were performed for effects of variety risks, penalty differentials, re-elevation discounts, import tolerances, variety declaration, risk aversion, GM adoption, and domestic end-user.Segregation, Testing, Tolerance, Genetically Modified, Wheat, Risk Premium, Crop Production/Industries, Research and Development/Tech Change/Emerging Technologies,