There has been a bit of discussion about how to define synergy and redundancy in the information carried by set of random variables X1, X2, … Xn about a random variable Y. Now V. Griffith and C. Koch have entered the debate with another measure which has several attractive features. The surprising result is that a set of variables can be both synergistic and redundant. This seems counterintuitive, but it makes sense as part of the information about Y could be carried synergistically, and another part redundantly in the set {Xi}. The definition is fairly intuitive: Define a variable Y* which has minimal entropy among all variables Z dependent on Y, such that I(Xi : Y) = I(Xi : Z) for all i. Synergy is now the difference between the mutual information I(X1, X2, … Xn : Y) and the variable I(X1, X2, … Xn : Y*). Redundancy can also be defined in a related way. Unfortunately, the fact that we need to minimize entropy means that analytical expressions for the synergy may be difficult to obtain.

# Synergy and redundancy

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