I__. - International Military Testing Association

The Use of Artificial Neural Networks
in Military Manpower Modeling
Jack R. Dempsey, D.A. Harris, and Brian K. Waters
Human Resources Research Organization
A new ides is delicate. It can be killed by a sneer or a yawn: it can be stabbed to death by a
quip, and worried to death by a frown on the right man’s brow.
-Charlie Brower--
The military has been a trailblazer in the realm of manpower modeling and personnel measurement. According
to an old saying, “Necessity is the mother of invention.” Well, due to the formidable recruiting and selection tasks
facing the Services, pioneering efforts have been made and continue to push the military to or past the state of the
art. There arc again innovative techniques which the Services are (or should be) considering to aid military selection
strategies.
Military selection policies are a topic of high level interest and scrutiny. Each of the Services sets standards
for selection on the basis of citizenship, age, moral character, physical fitness. aptitude, and education credential.
The latter two entry criteria are the ‘most visible screening mechanisms and the ones which the Department of
Defense (DOD) uses to define and report recruit quality levels to Congress and other interested parties. Aptitude, as
measured by composite scores from the Armed Services Vocational Aptitude Battery (ASVAB), is used to predict
military technical school performance. Education credentials are used for adaptability screening. That is, they assess
the likelihood of attrition, or positively, that a recruit will complete an obligated term of service. Both aptitude and
education crcdcntial standards have been called into question of late by Congressional watchdogs. Actually, the
flurry of interest in aptitude standards dates back to 1980 when Congress learned that between 1976 and 1980 the
ASVAB norms were incorrect. This resulted in accepting hundreds of thousands of recruits who did not meet the
intended minimum aptitude standards. Furthermore, Congress learned, much to its dismay, that enlistment standards
were validated against training performance not actual job performance. Congress continues to inquire: What is the
relationship between aptitude and job performance? And, how much quality is needed to ensure adequate job
performance? A Herculean, on-going, multi-year job performance measurement (JPM) project has provided answers
to the fist question while the answer to the second is in progress.
More recently, education standards have come under attack by Congress and educational lobbying groups.
Currently the plethora of credentials are categorized into one of three tiers based upon attrition rates. Each tier has
differential aptitude standards and recruiting preferences. While education credential is the single best predictor of
attrition, objections to this policy revolve around the fact that many individual members of the non-preferred tiers
arc successful in service and are therefore wrongfully denied enlistment on the basis of group membership.
The dual problems of linking quality requirements to job performance and implementing more cquiuble
adaptability screening methods requires innovation. Classical statistical techniques may not provide the answer.
These military selection questions require more sophisticated and less familiar modeling techniques. Just how do
techniques such as neural networks complement the more common modeling procedures? The performance prediction
and attrition screening applications described below provide at least a little food for thought and may suggest that
a more in-depth look is required.
Linking Standards to Job Performance
This project’s purpose is to bring the Joint-Service Job Performance Measurement/Enlistment Standards (JPM)
Project to fruition. This will be accomplished through four lines of endeavor. First, the military’s recruit selection
measures (e.g., ASVAB) must be related to job performance in virtually all occupations. Second, a methodology
must be developed so that empirical data can inform the setting of enlistment standards. That is, me expecled job
performance of recruits, over their first term of enlistment, should match total job performance requirements. Third,
improved. trade-off model(s) must be developed so that force quality requirements--based on empirically grounded
job performance requirements--are considered along with related costs in the determination of enlistment standards.
Finally, the Services’ personnel allocation systems must be made responsive to empirical information about the
pcrformancc requirements of particular jobs.
The data used for the Linkage Project consisted of 8,464 individual scrvicc mcmbcrs in 24 different occupations
who had been administered hands-on performance tests as a part of the IPM Project. Each record contained ASVAB
25
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