Heath Mccormick’s scientific contributions

A risk management process is most effective when the users are properly educated on the process and the process itself promotes a uniform perception of risk. Every soldier in the US Army is expected to be capable of managing risk— eliminating it when possible or mitigating it to an acceptable level through the principles and application a formal, multi-step, iterative process known as the US Army Risk Management program. This paper describes a study in which the researchers developed and used a fuzzy rule based expert system to evaluate a respondent population's ability to assess risk using the US Army's Risk Management program, and to determine if there were any common characteristics amongst those respondents with similar output. The results showed that while some factors such as active duty versus reserve status yielded negligible differences, there existed a significant difference between the way the commissioned and non-commissioned officer participants perceived risk. This information is one key to understanding that the differences in the way commissioned and non-commissioned officers are taught the Risk Management process and how it can affect their perceptions of risk and suggests that a better, more uniform method of risk training could be developed for the training audiences.

This research considers whether a person's demographic and experiential attributes play a significant role in how they perceive the presence or absence of hazards in a given situation. The goal of the research is to show that participants with enlisted military experience, prior to being commissioned as a junior officer, would be more successful at identifying the hazards presented in military scenarios than those who had only been trained on the process via their pre-commissioning and initial entry courses of instruction. The research study involves the use of two surveys with realistic military scenarios including both Foot March and Maintenance scenarios. The data collected from the surveys was analyzed using data mining techniques, in particular Nearest Neighbor (NN) algorithm and Logistic Regression Model (LRM). NN determines how similar a participant's case is to an expert case and LRM analyzes the outputs in a way that allows us to see if any of the seven experiential and demographic attributes considered had a significant impact on a participant's ability to perform well on the assessment. While the results did not conclusively prove that experience or other demographic attributes had a statistically significant impact on a participant's overall performance, the results did suggest that the idea that those same attributes do not have an impact cannot be rejected. This research could provide useful feedback to the U.S. Army on the way they train and educate junior officers on their Risk Management process.