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"The average number of prediction available in the 20 data sets was 7.7. Frugality indicates the mean number of cues actually used by each strategy. Accuracy indicates the percentage of correct answers achieved by the heuristics and strategies when fitting data (i.e., fit a strategy to a given set of data ) and when generalizing to new data (i.e., use a strategy to predict new data).

"The average number of prediction available in the 20 data sets was 7.7. Frugality indicates the mean number of cues actually used by each strategy. Accuracy indicates the percentage of correct answers achieved by the heuristics and strategies when fitting data (i.e., fit a strategy to a given set of data ) and when generalizing to new data (i.e., use a strategy to predict new data).

all the information Take The Best uses and more). The reason is that by being simple, the heuristics can avoid being too matched to any particular environment— that is, they can escape the curse of overfitting.

Overfitting refers to the problem of a model that is closely matched to one situation (set of data) failing to predict accurately in another similar situation (another set of data). This phenomenon can arise from assuming that every detail in a given environment is of great relevance. Consider forecasting of the U.S. presidential elections as an example. Beyond traditional variables such as incumbency and the state of the election-year economy, a plethora of additional variables have been suggested as predictors of recent U.S. presidential elections, including the voting behavior in Okanogan County (a rural stretch of north-central Washington), the rise or fall of women's hemlines, and the height of the candidates. General strategies such as multiple regression can in fact incorporate each of these and many more variables into the unlimited collection of free variables in their forecast models. As accurate as such parameter-laden forecast models may be for describing particular recent presidential elections, their accuracy in predicting other situations (e.g., earlier U.S. presidential elections or elections in other locations) may well be minimal. That is, these models can easily overfit the particular (training) data set and thereby fail to generalize to the new (testing) data set. In contrast, if a forecast model uses many fewer parameters, for instance, just incumbency and height of the candidates (which predicted the winner of every election since World War II, except in 1976 and 2000), it is likely to avoid overfitting and thereby generalize better to new situations.

Fast and frugal heuristics (like lexicographic strategies) are noncompensatory, meaning that once they have used a single cue to make a decision no further cues in any combination can undo or compensate for that one cue's effect. When the information in the decision environment is structured in a matching noncompensatory fashion (i.e., the importance or validity of cues decreases rapidly such that each weight of a cue is larger than the sum of all weights to come, e.g., one-half, one-fourth, one-eighth, and so on), the Take The Best heuristic can exploit that structure to make correct decisions as often as compensatory rules. Take The Best also performs comparatively well when information is scarce; that is, when there are many more objects than cues to distinguish them. Further research is needed to explore what environment structures can be exploited by different fast and frugal heuristics.

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