Managing the computational cost in a Monte Carlo simulation by considering the value of information

Monte Carlo simulation is a popular tool for reliability assessment because of its robustness and ease of implementation. However, the computational cost is a major concern. The standard deviation of the estimated failure probability of a system from N replications is proportional to 2 / 1  N . Efforts to reduce computational cost focus on development of intelligent sampling procedures and efficient calculation of the performance function of a system. This paper proposes a new way to manage the computational cost that views design as a decision among alternatives with uncertain reliabilities. Information from a simulation has value only if it enables the designer to make a more informed choice among the alternative designs. Consequently, the value of information from the simulation is equal to the gain from using this information to select the best design.