Quantifying deterrence—in operationally meaningful terms—is notoriously difficult. Although there are many mathematical deterrence models in use, their inputs are usually treated as free-ranging parameters, or as subjects for informed guesstimate. As a result, policymakers (for example, officials in the U.S. Customs and Border Protection’s (CBP’s) Non-Intrusive Inspection (NII) program, credited with over 1,500 contraband seizures per year) must make decisions about scanning technologies, screening rates, and other deployment issues with less than a full understanding of the deterrence impacts. This project aims to help CBP measure the deterrence value of scanning technology to prevent the smuggling of illegal goods or instruments of terror. It takes a novel approach: namely, arriving at deterrence measures indirectly, through systematic face validation of extended deterrence models. Extended models are those that incorporate assumptions outside the framework of deterrence per se, such as the different motivations, perceptions, and behaviors exhibited by different types of smuggling enterprises or smuggling populations. The project will test a minimum of two mathematical constructs by identifying parameters directly and indirectly related to deterrence, establishing a-priori bounds for these parameters, and comparing model predictions to documented cases of smuggling activity. Mismatches—defined as instances in which models predicted no smuggling activity for a particular type of smuggling population but activity was observed—will signal the need for refinements in the estimated parameter values. The results of the study will include the models, parameters, and resulting (refined) range of parameter values, along with the non-deterrence related assumptions on which the extended models are based. The study will be performed using only publicly-available information; however, the resulting framework will be transitioned to CBP, enabling them to further extend the results by incorporating other sources, including those containing law-enforcement-sensitive information.