For the Technion Prediction Tournament, we developed a model of making repeated binary choices between a safe option and a risky option. The model is based on the ACT-R declarative memory system, with the use of the Blending mechanism and sequential dependencies. By using established cognitive theory, rather than specialized machine learning techniques, our model was the most predictive when generalizing to new conditions. However, we did not tweak parameters to minimize prediction error; instead we maximized the number of different conditions producing statistically equivalent behavior. If we had not done this the model would not have won the tournament. This leads to the paradoxical result that by emphasizing cognitive explanation over prediction, we achieve more accurate predictions.