The authors test methods, based on cognitively-simple decision rules, that predict which products consumers select for their consideration sets. Drawing on qualitative research the authors propose dis-junctions-of-conjunctions (DOC) decision rules that generalize well-studied decision models such as disjunctive, conjunctive, lexicographic, and subset conjunctive rules.The authors propose two machine-learning methods to estimate cognitively-simple DOC rules. The authors observe consumers’ consideration sets for global positioning systems for both calibration and validation data. We compare the proposed methods to both machine-learning and hierarchical-Bayes methods each based on five extant compensatory and non-compensatory rules. On validation data the cognitively-simple DOC-based methods predict better than the ten benchmark methods on an informa-tion theoretic measure and on hit rates; significantly so in all but one test. An additive machine-learning model comes close on hit rate.These results are robust with respect to format by which consideration is measured (four formats tested), sample (German representative vs. US student), and presentation of profiles (pictures vs. text). We close by illustrating how DOC-based rules can affect managerial decisions.