The introduction of the partial information decomposition generated a flurry of proposals for defining an intersection information that quantifies how much of “the same information” two or more random variables specify about a target random variable. As of yet, none is wholly satisfactory. A palatable measure of intersection information would provide a principled way to quantify slippery concepts, such as synergy. Here, we introduce an intersection information measure based on the Gacs-Korner common random variable that is the first to satisfy the coveted target monotonicity property. Our measure is imperfect, too, and we suggest directions for improvement.
['Virgil Griffith', 'Edwin K. P. Chong', 'Ryan G. James', 'Christopher J. Ellison', 'James P. Crutchfield']
Intersection Information Based on Common Randomness