Dennis Egan – Missed Detections: From Data to Actionable Estimates

Rutgers University will extend formal tools of data science, adapting techniques from ecology and operations research. The resulting insights into missed detections will help CBP and other agencies assess and document performance; get early warning of change; assess trends in a timely fashion; and understand the effect of specific resource allocations on deterrence and detection.

The research will use synthetic data on interdictions, recidivism, and on effort, by station. The two novel methods proposed to exploit these data are: Extended/multi-type models of the Capture-Recapture concept (ECR), both passive and active, and the optimization techniques of Data Envelopment Analysis (DEA).

Simple capture-recapture models have already been applied in this context, and underlie some Border Patrol metrics. The currently employed “naïve” Capture-Recapture models assume that all apprehended persons try again, and are as likely to be caught as any other person. This project will develop more sophisticated ECR models of this complex process, define the data needed to apply it, and validate it with both simulated and actual data.

The DEA method arose in governmental and non-profit settings where multiple “Decision Making Units” (DMUs) deal with similar problems. DEA recognizes that each DMU, such as a border station or sector, differs from other DMUs. Using mathematical techniques including Linear Programming, DEA provides a principled way to assess resources and their relations to impacts. The management benefit of DEA is that each unit can be considered in its own context and with its own specific mix of resources and impacts. The engineering and scientific challenges lie in adapting models to the peculiarities of the border security problem, and in dealing with practical operational limits on the data that are, or can be, available to decision-makers.