Earlier this year, KCA published a 30 page technical paper representing the culmination of 18 months of applied research to assist the U.S. Border Patrol develop its Risk-Based Approach (RBA) for implementing its Strategic Plan to secure our borders. In developing a probabilistic risk analysis model for assessing risks to border security, the near-term objectives of the RBA included the ability of Border Patrol to capture and model expert data in ways that:
• Minimize the use of point estimates of uncertain events and reduce the impact of using simple mathematical averages for these estimates
• Enable decision makers to accurately appreciate both the uncertainty and risks associated with data and decisions when trying to interdict future threats
• Enable agents across the enterprise to better appreciate the value of information when determining whether or not risk is at acceptable levels
• Allow commanders, at all levels, to apply their own risk preference/tolerance to RBA outputs—i.e., RBA outputs are not prescriptive, but rather provide valuable context for quality risk-based decisions
• Encourage value-based thinking across the organization that centers on measurably reducing risk across our nation’s borders