of two objects, for example, city A with cue profile (100101010) and city B with cue profile (100010101), scores higher on the established criterion, i.e., population size? The take-the-best heuristic simply compares the profiles lexicographically, just as numbers written in base two are compared: the first cue value is 1 for both, which means that the first cue does not discriminate between A and B. The second cue value is 0 for both, again with no discrimination. The same happens for the third cue value, while the fourth cue value is 1 for A and 0 for B, implying that A is judged as having a higher value on the criterion. In other words, XA > XB if and only if (100101010) > (100010101) . Mathematically this means that the cues found for the comparison allow a quasi-order isomorphism between the objects compared on the criterion, in this case cities with their populations, and their corresponding binary vectors. Here "quasi" means that the isomorphism is, in general, not perfect, because the set of cues is not perfect. What is surprising is that this simple heuristic has a great performance compared with other strategies. One obvious measure for establishing the performance of an inference mechanism is determined by the percentage of correct judgements. Furthermore, what matters most is not just the performance of the heuristic when fitting known data, but when generalizing from a known training set to new items. Czerlinski, Goldstein and Gigerenzer compared several strategies with Take-the-best: a simple Tallying, or unit weight model (also called "Dawes' Rule" in that literature), a weighted linear model on the cues weighted by their validties (also called "Franklin's Rule" in that literature), Linear Regression, and Minimalist. Their results show the robustness of Take-the-best in generalization. For example, consider the task of selecting the bigger city of two cities when Models are fit to a data set of 83 German cities Models select the bigger of a pair of cities for all 83*82/2 pairs of cities. The percent correct was roughly 74% for regression, Take-the-best, unit weight linear,. More specifically, the scores were 74.3%, 74.2%, and 74.1%, so regression won by a small margin. However, the paper also considered generalization (also known as out-of-sample prediction). Models are fit to a data set of a randomly-selected half of 83 German cities Models select the bigger of a pair of cities drawn from the *other*