In order to meet its commitment to stay aligned with and representative of the nation’s demographics, MILO Range Training Systems has engaged in an extensive study of the demographics of all interactions and scenarios offered within its database of scenarios. MILO Range takes its responsibility to provide robust and demographically representative training for law enforcement agencies very seriously and pledges to continually measure and verify the representation of populations in its entire product portfolio with each library upgrade.
The study, designed by MILO Range and carried out by a 3rd party, found that the demographics in MILO Range simulations as pertains to race, gender, and age are closely aligned with the representation of these factors nationally. The US Census Bureau (2019) reports that people of white origin make up 76.3% of the US population, with Hispanic (18.5%), Black (13.4%), and Asian (5.9%) populations making up the next biggest race-based categories. The MILO Range database catalogs white representation in its training systems at 75%, with Hispanic (5%), Black (10%) and Asian (6%) representation also represented.
It is important to note, however, that communities throughout the nation vary vastly from one another, which is where MILO Range simulations can be used so effectively. Detroit, for instance, is a population made up of 14.6% white-origin and 78.6% Black-origin residents. In contrast, 64% of San Antonio residents are of Hispanic origin.
“We travel around the country training officers in communities that don’t look exactly like ours at home,” explains Jesse Trevino, San Antonio police instructor and President of Solution Point +. “One size doesn’t fit all when it comes to training, so it’s critical that we have access to scenarios that are representative of the populations we visit.”
Controlling for demographic factors can allow officers to work within simulations that are representative of their communities. And, as demographics within communities are constantly changing as areas grow and evolve, MILO Range simulations can be tailored in each iteration to provide the most comprehensive and representative interactions possible.
In addition, implicit bias training and de-escalation training must be representative not only of the demographics of the majority of residents within each community, but also provide opportunities to successfully interact with outliers in each community, and with populations that officers might not frequently encounter. “The MILO training space is where we challenge what the officer expects to see, which helps develop deeper cognitive skills,” explains Dr. Joy VerPlanck of MILO Range. “The interactions of their job aren’t well-defined like a math problem or a recipe for cake– it’s an ill-defined set of problems every day, so we have to include outliers in training to ensure officers don’t anticipate results before they happen.” By placing officers in scenarios outside of their comfort zone, law enforcement agencies can continually hone the creative critical thinking of their officers and mitigate consequences that might otherwise occur when officers are faced with the unknown.
Each scenario in the MILO Range catalog is tagged with data that catalogs age, race, gender, dress, socioeconomic status, language, location, time of day, weather, and even whether or not the actor in the scenario has visible tattoos. The end result is a product portfolio that is reflective of the community at large and yet can be tailored to meet the specific needs of local agencies. MILO Range Training Systems is committed to providing and sustaining the most comprehensive research and metric-based law enforcement training program possible for the feet on the ground.
To receive a copy of the most current MILO Range Content Analytics document, contact firstname.lastname@example.org.