Application of probabilistic simulation and Bayesian decision theory in the selection of mold remediation actions

This paper utilizes a probabilistic mold risk assessment method, introducing a novel mold risk indicator (MRI). The MRI captures the risk of mold occurrence at identified “trouble spots” under uncertainty. It will show how the MRI can enhance decision-making in a mold remediation case. When used in decision making under uncertainty, the MRI enables the best selection of remediation actions in the light of given preferences of the decision maker. In particular, decision makers are empowered to make a more rational decision based on a mold risk assessment that exceeds the usual deterministic performance evaluations. We will apply the Bayesian decision theory to the decision-making problem that involves the selection of two possible remediation actions in an existing building case. This approach demonstrates how to use additional information from mold simulation and uncertainty analysis in practical decision making problems and increasing the confidence of the decision maker.