Introduces four synergy-based measures of spacetime integration from partial information decomposition and finds them more suitable than current IIT practice for simple deterministic networks.
Quantifying synergistic mutual information
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abstract
Quantifying cooperation or synergy among random variables in predicting a single target random variable is an important problem in many complex systems. We review three prior information-theoretic measures of synergy and introduce a novel synergy measure defined as the difference between the whole and the union of its parts. We apply all four measures against a suite of binary circuits to demonstrate that our measure alone quantifies the intuitive concept of synergy across all examples. We show that for our measure of synergy that independent predictors can have positive redundant information.
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Quantifying Spacetime Integration across a Partition with Synergy
Introduces four synergy-based measures of spacetime integration from partial information decomposition and finds them more suitable than current IIT practice for simple deterministic networks.