 This general goal of the Fast and Frugal program leads to a second difference between the two schools. Because the Fast and Frugal program aims to specify the conditions under which a heuristic will lead to better outcomes than competing models, heuristics are treated as algorithmic models of decision-making rather than descriptions of errant effects; heuristics are themselves objects of study. To that end, all heuristics in the fast and frugal tradition are conceived to have three components: (i) a search rule, (ii) a stopping rule, and (iii) a decision rule. For example, Take-the-Best (Gigerenzer &amp; Goldstein 1996), is a heuristic applied to binary, forced-choice problems. Specifically, the task is to pick the correct option according to an external criterion, such as correctly picking which of a pair of cities has a larger population, based on cue information that is available to the decision-maker, such as whether she has heard of one city but not the other, whether one city is known to have a football franchise in the professional league, et cetera. Based on data sets, one can compute the predictive validity of different cues, and thus derive their weights. Take-the-Best then has the following structure: Search rule: Look up the cue with the highest cue-validity; Stopping rule: If the pair of objects have different cue values, that is, one is positive and the other negative, stop the search. If the cue values are the same, continue searching down the cue-order; Decision rule: Predict that the alternative with the positive cue value has the higher target-criterion value. If all cues fail to discriminate, that is, if all cue values are the same, then predict the alternative randomly by a coin flip. The bias of Take-the-Best is that it ignores relevant cues. Another example is tallying, which is a type of improper linear model  (section 2.3).  Tallying has the following structure for a binary, forced-choice task: Search rule: Look up cues in a random order; Stopping rule: After some exogenously determined m \((1 &lt; m \leq N)\) of the N available cues are evaluated, stop the search; Decision rule: Predict that the alternative with the higher number of positive cue values has the higher target-criterion value. The bias in tallying is that it ignores cue weights. One can see then how models are compared to one another by how they process cues and their performance is evaluated with respect to a specified criterion for success, such as the number of correct answers to the city population task.