Decision models are essential theoretical tools in the study of choice behavior, but there is little consensus about the best model for describing choice, with different fields and different research programs favoring their own idiosyncratic sets of models. Even within a given field, decision models are seldom studied alongside each other, and insights obtained using 1 model are not typically generalized to others. We present the results of a large-scale computational analysis that uses landscaping techniques to generate a representational structure for describing decision models. Our analysis includes 89 prominent models of risky and intertemporal choice, and results in an ontology of decision models, interpretable in terms of model spaces, clusters, hierarchies, and graphs. We use this ontology to measure the properties of individual models and quantify the relationships between different models. Our results show how decades of quantitative research on human choice behavior can be synthesized within a single representational framework. (PsycInfo Database Record (c) 2022 APA, all rights reserved).