This research focuses on a modeling approach and set of mathematical tools that were derived from research on intelligence systems, namely fuzzy system modeling. This study systematically evaluates these tools as an approach for modeling human decision making, contrasting the approach with more traditional methods based on regression. The research was conducted using experts and a simulated task environment related to allocating rewards in the form of merit pay. The results indicate that fuzzy system models generally perform as well as or better than both linear and nonlinear regression methods in terms of model fit. These results are discussed in terms of issues regarding modeling precision versus parsimony, the value of adaptive modeling techniques, empirical versus subjective approaches to model building, and individual differences in judgment strategies. Potential applications of this research include using the modeling approach studied to build higher-fidelity models that yield new insights and a better understanding of decision-making strategies and environments.