Internal Code: MAS1119
Journal Article Critique Assignment:
Task: Journal Article Critique (500 Words)
Generating Effective Patrol Strategies to Enhance U.S. Border Security
Introduction
The United States Customs and Border Protection agency and the United States Border Patrol are tasked with securing the U.S. borders against illegal entry of people and goods. Due to limited resources, it is not possible to have a constant security presence at every point along the border, and intelligent criminals are constantly seeking ways to circumvent security measures. Given this context, an important question facing analysts and decision makers for the Border Patrol is how to effectively allocate the available resources at both the strategic and tactical levels to maximize border security. We use a game-theoretic framework coupled with simulation and genetic algorithms to generate, analyze, and visualize patrolling and resource allocation strategies. The goal of the analysis is to find effective strategies to improve interdiction rates. Game theory is used to account for the adversarial aspect of the problem, in which criminals will adapt to new security policies. Simulation and genetic algorithms provide scalability to analyze
more complex patrolling problems with realistic problem features including varying terrain and movement costs.
Border security is an important element in the national security focus of the United States in the last decade. The United States Customs and Border Protection agency (CBP) and the United States Border Patrol (USBP) under the Department of Homeland Security (DHS) both have the primary responsibility to defend and secure both land and sea borders. Technology and highly trained personnel have been incorporated into a national strategy that is aimed at securing the U.S. borders from illegal intrusion.1 This includes the detection and apprehension of illegal trafficking of humans, drugs, weapons, contraband, and the prevention of terrorist activity.
Since the 1990s, the USBP has adopted an operational strategy that focuses on preventing illegal entry into the U.S. through deterrence.2 This has included deploying a combination of infrastructure, manpower, and technology to detect and apprehend individuals involved in illegal activity, as well as executing legal strategies to deter illegal activity based on the risk of being caught and prosecuted. While many illegal entry attempts take place at official ports of entry (POEs), others take place in the long and sometimes remote stretches of border in between POEs.3 In these areas, the Border Patrol uses patrols and sensor technology to detect illegal entry attempts.4 Due to limitations in resources and the difficulty in making precise estimates about the total flow of illegal activity that is undetected, it is not always possible to assess how successful the patrolling strategies are, since apprehension data alone does not provide acomplete picture. The objective of our work is to provide improved analysis capabilities to better understand and optimize the effectiveness of patrolling and resource allocation strategies.
Related Work
Several studies have introduced methods to enhance patrolling strategies. For example, experiments have been conducted to produce effective patrolling strategies using multiple autonomous robots. This experiment used a swarm approach, where the defenders could communicate.7 A similar experiment using police patrolling was also conducted. This program used weights to symbolize importance of patrol areas.8 9 A RAND study focused on using pattern/trend analysis and systematic randomness to better distribute resources to border zones to increase interdiction rates. This study addressed the problem that intruders will change their tactics based on border patrol actions. Their solution was to implement systematic randomnes into the decision-making process of allocating resources to border zones, which would make border patrol actions less predictable.10 A program has also been designed that uses a genetic algorithm that is similar to the one used by GAMMASys to observe adversarial interaction in war games. This genetic algorithm was used to increase attacker strategies, while GAMMASys
uses a genetic algorithm to generate stronger patrolling strategies for the defender.11
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