Advanced distributed simulations (ADS) along with computer generated forces (CGFs) are used to provide troops with tactical combat training and to perform research. Current CGFs behave as perfectly trained troops, their ability to perform missions to do not vary. This is an inaccurate portrayal of human performance. If the military cannot model human factors, such as training and physiological stressors in ADS, they cannot perform trade-off analyses. For the military to be able to use ADS and CGFs to answer resource allocation and system design questions, the CGFs have to be affected by a human performance model. Micro Analysis & Design, Inc. (MA&D) was awarded a Phase II SBIR entitled "Improving Soldier Factors in Prediction Models" by the Army Research Institute (ARI). The goal of this SBIR was to develop a model that uses training and other performance shaping factors (PSFs) to affect the abilities of CGFs. This performance effects model incorporates the benefits of different types of training, the effects of skill decay, physiological stressors and aptitude. The final model will allow users to affect a wide range of tasks. It is generalizable to both military and non-military applications. The military will be able to use it to affect the performance of CGFs in ADS. Once the model is implemented, the military will be able to conduct trade-off analyses. They will also be able to better prepare troops for combat by having them train against opponents of different skill levels.