is given by v = C/(C+W), where C is the number of correct inferences when a cue discriminates, and W is the number of wrong inferences, all estimated from samples. Take-the-best for the comparison task Consider the task to infer which object, A or B, has a higher value on a numerical criterion. As an example imagine someone having to judge whether the German city of Cologne has a larger population than the other German city of Stuttgart. This judgment or inference has to be based on information provided by binary cues, like "Is the city a state capital?". From a formal point of view, the task is a categorization: A pair (A, B) is to be categorized as XA > XB or XB > XA (where X denotes the criterion), based on cue information. Cues are binary; this means they assume two values and can be modeled, for instance, as having the values 0 and 1 (for "yes" and "no"). They are ranked according to their cue validity, defined as the proportion of correct comparisons among the pairs A and B, for which it has different values, i.e., for which it discriminates between A and B. Take-the-best analyses each cue, one after the other, according to the ranking by validity and stopping the first time a cue discriminates between the items and concluding that the item with the larger value has also a larger value on the criterion. The matrix of all objects of the reference class, from which A and B have been taken, and of the cue values which describe these objects constitutes a so-called environment. Gigerenzer and Goldstein, who introduced Take-The-Best (see Gerd Gigerenzer & Daniel Goldstein, D. G. (1996) ) considered, as a walk-through example, precisely pairs of German cities. yet only those with more than 100.000 inhabitants. The comparison task for a given pair (A,B) of German cities in the reference class, consisted in establishing which one has a larger population, based on nine cues. Cues were binary-valued, such as whether the city is a state capital or whether it has a soccer team in the national league. The cue values could modeled by 1's (for "yes") and 0's (for "no") so that each city could be identified with its "cue profile", i.e., e vector of 1' and 0's, ordered according to the ranking of cues. The question was: How can one infer which