7 Conclusions We proposed abstract measures of emergence, self-organization, complexity, and homeostasis based on information theory with the intention of clarifying their meaning with formal defi- nitions. We illustrated the measures with computational experiments of discrete dynamical systems (RBNs and ECA). Emergence can be defined as a measure of the information that a system produces, relative to the information that the system receives. Self-organization can be defined as the difference between the information the system receives (input) and the information the system produces (output). These measures can be simplified assuming random inputs, in which case emergence becomes the opposite of self-organization. Complexity represents the balance between self-organization and emergence, related to the balance of order and chaos. There are different methods to guide dynamical systems to this balance (Gershenson, In Pressa). This balance can be achieved by natural selection, or also by designers aiming to exploit the benefits of complexity. Homeostasis measures the stability of information in time. Our measures can be applied at different scales, yielding different results in particular cases. There are several research topics in which the ideas presented here could be extended. Complexity, emergence, self-organization, and homeostasis are present all around us (ev- erything can be described in terms of information (Gershenson, 2007b), so we can measure the C , E , S , and H of everything). Given their pervasiveness, we should all make an effort to further understand and study these properties of all phenomena. Acknowledgments We should like to thank Michele Amoretti, Mario Cosenza, Carlos Maldonado, Daniel Polani, Mikhail Prokopenko, H´ ector Zenil, and two anonymous referees for fruitful exchanges and useful suggestions. This work was supported SNI membership 47907 of CONACyT, Mexico, UNAM-DGAPA-IACOD project T2100111, by Intel R $^{○}$, and by IIMAS, UNAM with a visiting 29