zone of a system. A dynamical system has a high homeostatic capacity if it is able to maintain its dynamics close to a certain state or states (attractors). When perturbations or environmental changes occur, the system adapts to face the changes within the viability zone, i.e. without the system “breaking” (Ashby, 1947a). Homeostasis is also strongly related to robustness (Wagner, 2005; Jen, 2005). 3 An abstract proposal Our proposal builds on previous work (Fern´ andez et al., 2010, 2012). The main purpose of the measures presented below is to clarify the meaning of the concepts they represent, while being formal and precise. Information can be seen as a defined structure (Cohen, 2000, 2006), in other words, a pattern . Any information has a pattern, let it be ordered, recursive, complex, or chaotic. One way of understanding emergence is as information at a higher scale that is not present at a lower scale. This can be generalized in terms of computation (information transformation (Gershenson, 2007b)). If we describe the dynamics of a system as a process, then we can define emergence as the novel information produced by that process which was not present beforehand. Emergence as a product of a change of scale is a particular case, since the change of scale can be seen as a process that transforms information. If we are interested on how much information was produced by a process, we have to consider how much information was introduced. Thus, emergence E can be formalized as: E = I$_{out}$ I$_{in}$ , (2) where I$_{in}$ is the “input information” (which can also be seen as an initial state or conditions) and I$_{out}$ is the “output information”, which can be seen as I$_{in}$ transformed by a computational process or function f , i.e. I$_{out}$ = f ( I$_{in}$ ). Emergence is proportional to the information ratio 8