2002; Newman, 2003; Schweitzer, 2003; Chaitin, 2004; Batty, 2005; Morin, 2007; Prokopenko et al., 2009). The diversity—and sometimes ambiguity—of notions, definitions and measures of com- plexity (Edmonds, 1999) and related concepts has not only hampered the path to an elusive unified framework, but also induced the abuse of the terminology in non-scientific discourses, leading in many cases to the confusion of the general public and the contempt of established disciplines. Joining the effort of the community to bring clarity and agreement to these essential questions, we propose general measures of emergence, self-organization, homeostasis, and complexity based on information theory. The measures are general enough so that several previously proposed definitions can be seen as particular cases of them. While being formal and precise, the proposed measures are simple enough so as to clarify the studied concepts to people without an extensive mathematical background. In the next section, we present a brief review of information theory and how it has been applied to complexity, including the concepts of emergence, self-organization, and homeosta- sis. In Section 3, we present our proposal of abstract measures and extend them to multiple scales. Section 4 describes our experiments and results with random Boolean networks and elementary cellular automata, which illustrate the proposed measures. This is followed by a discussion in Section 5. The paper closes with proposals for future work and conclusions. 2 Information Theory and Complexity Information can be seen as a quantifiable pattern. Shannon (1948) studied it formally in the context of telecommunication. He was interested on how reliable communication could take place with unreliable components (Gleick, 2011). Given a string X , composed by a sequence of values x which follow a probability distribution P ( x ), information (according to Shannon) is defined as: 3