  While justifying that independence is a canonic coupling, the authors show
the existence of a second equilibrium to reduce the information conveyed from
the margins to the joined distribution: the so-called indetermination. They use
this null information property to apply indetermination to graph clustering.
Furthermore, they break down a drawing under indetermination to emphasis it is
the best construction to reduce couple matchings, meaning, the expected number
of equal couples drawn in a row. Using this property, they notice that
indetermination appears in two problems (Guessing and Task Partitioning) where
couple matchings reduction is a key objective.
