The authors propose a general modeling framework called the general monotone model (GeMM), which allows one to model psychological phenomena that manifest as nonlinear relations in behavior data without the need for making (overly) precise assumptions about functional form. Using both simulated and real data, the authors illustrate that GeMM performs as well as or better than standard statistical approaches (including ordinary least squares, robust, and Bayesian regression) in terms of power and predictive accuracy when the functional relations are strictly linear but outperforms these approaches under conditions in which the functional relations are monotone but nonlinear. Finally, the authors recast their framework within the context of contemporary models of behavioral decision making, including the lens model and the take-the-best heuristic, and use GeMM to highlight several important issues within the judgment and decision-making literature.