Abstract

A technical system exhibits emergence when it has certain properties or qualities that can be termed to be irreducible in the sense that they are not traceable down to the constituent parts of the system. The article summarises three techniques for emergence detection and emergence measurement that were proposed by members of the Organic Computing community. These techniques are based on information-theoretic and probabilistic viewpoints: the discrete entropy difference discussed in detail in the previous article, the Hellinger distance which is a divergence measure for probability densities, and an iterative approach motivated by divergence measures. Advantages and drawbacks of these measures are demonstrated by means of some simulation experiments using artificial data sets. It is shown that these techniques are able to deal with different kinds of emergent phenomena such as transitions from chaos to order, concept drift, or novelty. That is, with these techniques it is possible to cover a wide range of possible applications.

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