The current Risk Reporting Matrix used by the Department of Defense is a useful tool for determining the risk associated with an action, but has limited value due to the discrete nature of the metric. The Risk Reporting Matrix is built using two Likert Scales, one for the likelihood of the event, and one for its consequence. This gives the user 25 possibilities for a likelihood-consequence combination. While this gives a concise and easy to understand answer, it does not fully inform the user of the true risk level for the decision.

At I/ITSEC 2023, Engel presented the Continuous Asymmetric Risk Assessment (CARA) which aids in alleviating these shortcomings by transforming the discrete risk matrix to a continuous gradient field. It is designed to provide the user with infinite combinations of likelihood and consequence which more accurately describe the risk associated with the decision in question. Furthermore, by leveraging the use of asymmetric Gaussian distributions, CARA creates confidence intervals around nominal risk, displaying likely outcomes and its variation.

In this paper, we will present an extension of CARA by pairing the existing risk analysis tool with optimization. By leveraging optimization algorithms, we will demonstrate how optimal risk-adjusted decisions can be made using CARA. Through this process, we will demonstrate how a user of CARA can minimize risk based on factors such as cost, time, variability, and resources available. This reduces the human subjectivity in a decision-making process by analyzing risk decisions analytically through optimizing the risk reduction strategy. 

Keywords
ANALYTICS;DECISION;EMERGING TECHNOLOGIES;RISK ASSESSMENT

Additional Keywords