This was part of Verification, Validation, and Uncertainty Quantification Across Disciplines

Simulation-Informed Decision Making

William Oberkampf

Tuesday, May 11, 2021

Abstract: Computer simulation is becoming a critical tool in predicting the behavior of an exceedingly wide range of physical and social phenomena. Although the foundation of computational simulation was built in physics, chemistry, and engineering, applications are now common in areas such as environmental modeling, biology, economics, and societal planning. Results from computational simulations can be used for improving scientific knowledge or technological knowledge. Scientific knowledge is focused on improving understanding the workings of the system of interest, whether it be an inanimate physical system or a living/social system. Technological knowledge is generally used for designing new systems, as well as optimizing or influencing existing systems. As a result, decision making is a crucial element in the application of technological knowledge. The credibility of the simulation information can be assessed by the techniques developed in the fields of verification of the computational procedures and validation of simulation results. I contend that the impact of uncertainty quantification on the credibility of simulation results is different because it deals with likelihoods and possibilities of potential outcomes. Decision makers, whether in business or government, can have mixed reactions to comprehensive uncertainty quantification of simulation results. Many of these decision makers understand that some uncertainties are well characterized, whereas some are very poorly understood; potentially not even included in the simulation. To capture a wide range of uncertainty sources and characterizations, the term predictive capability or total predictive uncertainty has been used in certain communities. In contrast to traditional uncertainty estimation which concentrates on random variables, predictive capability attempts to capture all potential sources of uncertainty. These include numerical solution error, model form uncertainty, and uncertainty in the environments and scenarios to which the system could be exposed, either intentionally or unintentionally. This talk will discuss a wide range of uncertainties and factors, both technical and value-oriented, that influence decision makers when simulation is a critical ingredient.