| Abstract: | An efficient graph-theoretical decomposition technique is introduced that treats inconsistencies in behavioral data as systematic adaptations rather than random errors. This technique, which is known as ear decomposition, reduces inconsistencies in any binary data set to a basis of directed cycles. Such a basis characterizes the data set in terms of inconsistencies and its size offers an improved measure of internal consistency. In two examples it is illustrated how different implementations of the ear decomposition technique can help to identify choices that are critical for violations of transitivity. |
