Table of Contents
Entropy, Volume 20, Issue 3 (March 2018)
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Cover Story (view full-size image) The Integrated Information Theory (IIT) proposes that in order to quantify information integration [...] Read more. The Integrated Information Theory (IIT) proposes that in order to quantify information integration in a system as a whole, we should find the optimal part of the system where information loss by partitioning is minimized, which is called the Minimum Information Partition (MIP). Information loss is measured by integrated information (Φ), which is defined as the Kullback–Leibler divergence between the probability distribution of the system, P, and that of the partitioned system, Q. In general, searching the MIP requires an exponentially large amount of computational time. We propose an efficient algorithm that reduces the computational time to a polynomial order.