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Joel Predd and Henry Willis February 26, 2009

Toward Adaptive, Risk-Informed Allocation of Border Security Assets. Joel Predd and Henry Willis February 26, 2009. RAND Research on Counter-IED Operations in Iraq Illustrates Benefits of Tools. Problem: Ground forces in Iraq had limited resources for counter-IED operations

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Joel Predd and Henry Willis February 26, 2009

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  1. Toward Adaptive, Risk-Informed Allocation of Border Security Assets Joel Predd and Henry Willis February 26, 2009

  2. RAND Research on Counter-IED Operations in Iraq Illustrates Benefits of Tools • Problem: Ground forces in Iraq had limited resources for counter-IED operations • Method: RAND developed methods and tools to predict location and time of future IED threats based on database of recent attacks • Application: Threat predictions helped brigades decide where to direct surveillance W. Perry and J. Gordon, “Analytic Support to Intelligence in Counterinsurgencies”, RAND MG-682-OSD, 2008.

  3. The Problem Concerns Operational Resource Allocation U.S. law enforcement agencies need to direct limited border resources to detect and identify risks along the border

  4. This Problem Statement Includes Four Key Terms That Need to be Further Defined U.S. law enforcement agencies need to direct limited border resources to detect and identify risks along the border • Resources include both technology and people • Focus on resources that detect and identify, enable engagement and resolution • Potential risks include both smuggling and border crossing • Southwestern land border is the near-term focus, plan for extensions to North

  5. Study Objective To develop and evaluate machine learning-based methods and tools to facilitate adaptive, data-driven, risk-based allocation of border security resources

  6. Four Principles Guide The Study Objective To develop and evaluate machine learning-based methods and tools to facilitate adaptive, data-driven, risk-based allocation of border security resources • Machine learning refers to a set of statistical and computational methods • Method should • be adaptive, because border crossers are • be informed by data • incorporate border threats, vulnerabilities and consequences (i.e., risk)

  7. Example 1: Allocating Counter-IED Surveillance Assets • Problem: Ground forces in Iraq had limited resources for counter-IED operations • Method: RAND developed methods and tools to predict location and time of future IED threats based on database of recent attacks • Application: Threat predictions helped brigades decide where to direct surveillance W. Perry and J. Gordon, “Analytic Support to Intelligence in Counterinsurgencies”, RAND MG-682-OSD, 2008.

  8. Example 2: A Meta-Allocation of Problem of Choosing Predictive Tools • (Meta-)Problem: Ground forces in Iraq had to choose one of multiple predictive tools • Each tool was itself designed to facilitate surveillance resource allocation, and better in different circumstances • Method: RAND developed online learning methods to adaptively aggregate suite of tools based on historical performance • Application: Aggregate tools could support original surveillance asset allocation problems W. Perry and J. Gordon, “Analytic Support to Intelligence in Counterinsurgencies”, RAND MG-682-OSD, 2008.

  9. Example 3: Research at USC CREATE Provides Another Illustration • Problem: Airport security has limited resources to allocate to checkpoints and canine patrols • Method: Researchers at USC CREATE developed methods and tools to systematically schedule checkpoints and canine patrols based on theory of Bayesian Stackelberg games • Application: Software tool called ARMOR is used to schedule canine patrols Pita, J., Jain, M., Western, C., Paruchuri, P., Marecki, J., Tambe, M., Ordonez, F., Kraus, S., Deployed ARMOR, "Protection: The Application of a Game Theoretic Model for Security at the Los Angeles International Airport," in Proceedings of the Seventh International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS) (Industry Track), 2008

  10. We Are Working to Leverage This Research to Benefit CBP Operations • Limited resources require tactical decisions about how to allocate • Ground sensors • Patrols • UAVs • Detection • … • How to do so in way the adaptively integrates tactical data about threats, vulnerabilities and consequences?

  11. The Product: A Tool To Help Sector Chiefs Deploy Sensors and Patrols According to Risk • The tool will identify future risks by making predictions from historical data • Threat data • E.g., data may include a record of the location and time of past detections or interdictions • Vulnerability data • E.g., GIS data about cross-border roads or paths, sector boundaries • E.g., GIS data about topography and weather • E.g., Location and time records of previous border security operations, sensor deployments, and patrols • Consequence data • E.g, information on mission-types

  12. Methodology and Work Plan Year 1: Understand border operations, environment, and available intelligence data and collection assets Year 2: Evaluate machine learning-based methods in a simulated environment Year 3: Explore with CBP interest in conducting field evaluation of prototype tools • Plans to visit San Diego Sector • Operation Red Zone • Border Intelligence Center • Air and Marine Operations Center • Plans to visit Rio Grande Valley Sector

  13. Summary • A project funded through the National Center for Border Security and Immigration • The objective is to develop and evaluate predictive methods and tools to facilitate adaptive, data-driven and risk-based allocation of CBP assets • The outcome will be that Office of Border Patrol and the Secure Border Initiative program office will have methods and tool to dynamically allocate assets in the tactical environment

  14. The Tool Automatically Identified Actionable Hot Spots of Enemy Activity • Hot spot – an area consistently and recently targeted by enemy forces • Actionable hot spot – a hotspot where limited surveillance resources can be focused Past IED event Road

  15. The Tool Automatically Identified Actionable Hot Spots of Enemy Activity • Hot spot – an area consistently and recently targeted by enemy forces • Actionable hot spot – a hotspot where limited surveillance resources can be focused 5 miles Hot spots

  16. The Tool Automatically Identified Actionable Hot Spots of Enemy Activity • Hot spot – an area consistently and recently targeted by enemy forces • Actionable hot spot – a hotspot where limited surveillance resources can be focused 500 meters Actionable Hot spots

  17. The Tool Automatically Identified Actionable Hot Spots of Enemy Activity • Hot spot – an area consistently and recently targeted by enemy forces • Actionable hot spot – a hotspot where limited surveillance resources can be focused 500 meters Highest ranking actionable hotspots were candidates for surveillance

  18. NC-BSI: Adaptive, Risk-Informed Resource Allocation

  19. NC-BSI: Adaptive, Risk-Informed Resource Allocation

  20. Field (Live) M&S Virtual M&S Iterative process Constructive M&S Computational Models RAND Analysis Uses Models and Simulations To Support Operational Integration

  21. We Are Seeking Guidance on Three Topics • What operational constraints must we take into account • Visit border sites • Operation REDZONE, JTF-North Campaign Planning Workshop, El Paso Information Center, Air and Marine Operations Center • Discuss CBP operations at sectors • Recommendations related to scope of focus • Which sector(s) or station(s) to visit? • Which tactical operations might benefit most? • Who to meet? Where to visit? • What sample data is available? • Location and time of past detections, interdictions • Location and time of past operations, sensor deployments, and patrols • GIS data about border roads, paths, topography, weather, etc. • After Action Reviews (AARs)

  22. Study Plan is to Build Tools That Integrate With Current Practices • We have learned that sectors may use different methods, and possibly share data and lessons learned • Southwest sectors have employed some predictive methods for resource allocation • Data about the location and time of some border activities are archived, shared Source: Operation Gulf Watch Provided By: PAIC Mark Butler, Fort Brown Station, RGV Sector Provided To: MAJ Eloy Cuevas, JTF-N Intelligence Planner Date: February 2006

  23. RAND Research on Counter-IED Operations in Iraq Illustrates Benefits of Tools (Example 2) • Problem: Intelligence had developed many predictive tools, but had difficult choosing which heuristic to use for resource allocation • Method: RAND developed methods to adaptively aggregate large suites of predictive tools using online learning • Application: The aggregate tool provided a way to make a useful tool out of many W. Perry and J. Gordon, “Analytic Support to Intelligence in Counterinsurgencies”, RAND MG-682-OSD, 2008.

  24. Example 1: Allocating Counter-IED Surveillance Assets (2/3) • Hot spot – an area consistently and recently targeted by enemy forces 5 miles Hot spots

  25. Example 1: Allocating Counter-IED Surveillance Assets (3/3) • Hot spot – an area consistently and recently targeted by enemy forces • Actionable hot spot – a hotspot where limited surveillance resources can be focused 500 meters Actionable Hot spots

  26. Example 1: Allocating Counter-IED Surveillance Assets (3/3) • Hot spot – an area consistently and recently targeted by enemy forces • Actionable hot spot – a hotspot where limited surveillance resources can be focused 500 meters Highest ranking actionable hotspots were candidates for surveillance

  27. Example 1: Allocating Counter-IED Surveillance Assets (3/3) • Hot spot – an area consistently and recently targeted by enemy forces • Actionable hot spot – a hotspot where limited surveillance resources can be focused 500 meters The main success of this research was the integration of predictive methods with operational constraints Highest ranking actionable hotspots were candidates for surveillance

  28. Example 2: A Meta-Allocation of Problem of Choosing Predictive Tools (2/3) • Predictive heuristics admitted essentially no theoretical analysis of effectiveness. • Existing empirical analyses are optimistic; the results generalize only if the methods are not actually used in the field. • in practice, enemy reacts to allocation methods use of a method; existing data does not reflect adaptation • Long-term trends and normal reactive behaviors can go undetected. location … time

  29. Example 2: A Meta-Allocation of Problem of Choosing Predictive Tools (3/3) • RAND developed online learning algorithms to adaptively aggregate a suite predictive tools • Algorithms have provable performance guarantees • Laboratory experiments suggest competitive to rival methods Cumulative loss Day

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