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produced by a process. Since emergence is considered as a ratio, it is a unitless measure. If there is a random input, then I$_{in}$ = 1 can be assumed, so emergence is simplified to: 

<img src='content_image/313437.jpg'>

Concerning self-organization, we have already seen that this can be measured as a change of organization in time (Gershenson and Heylighen, 2003). Organization can be seen as the opposite of information, since high organization (order) is characterized by a low information and low organization (chaos) is characterized by a high information. In the context of information transformation, self-organization S can be defined as: 

<img src='content_image/313438.jpg'>

This implies that self-organization occurs ( S > 0) if the process reduces information, i.e. I$_{in}$ > I$_{out}$ . If the process generates more information, i.e. emergence occurs, then S will be negative. S measures information increase or reduction, so its units are also bits. As with emergence, if a random input I$_{in}$ = 1 is assumed, then self-organization can be simplified to: 

<img src='content_image/313439.jpg'>

It should be noted that S represents how much order there is in a system, while E represents how much variety there is. Following L´ opez-Ruiz et al. (1995), we can define complexity C as their multiplication: 

<img src='content_image/313440.jpg'>

Since a high E implies a low S and vice versa, a high C can occur only when E and S are balanced. This is consistent with several notions of complexity which see it as a balance between order (high S ) and chaos (high E ). Since E is a unitless measure and S is 

