This chapter reviews measures of emergence, self-organization, complexity, homeostasis, and autopoiesis based on information theory. These measures are derived from proposed axioms and tested in two case studies: random Boolean networks and an Arctic lake ecosystem. Emergence is defined as the information produced by a system or process. Self-organization is defined as the opposite of emergence, while complexity is defined as the balance between emergence and self-organization. Homeostasis reflects the stability of a system. Autopoiesis is defined as the ratio between the complexity of a system and the complexity of its environment. The proposed measures can be applied at different scales, which can be studied with multi-scale profiles.

1 aFernández, Nelson1 aMaldonado, Carlos1 aGershenson, Carlos1 aProkopenko, Mikhail uhttp://arxiv.org/abs/1304.184201084nas a2200109 4500008004100000245006400041210006000105490001500165520073600180100002300916856003500939 2013 eng d00aThe Implications of Interactions for Science and Philosophy0 aImplications of Interactions for Science and Philosophy0 vEarly View3 aReductionism has dominated science and philosophy for centuries. Complexity has recently shown that interactions–-which reductionism neglects–-are relevant for understanding phenomena. When interactions are considered, reductionism becomes limited in several aspects. In this paper, I argue that interactions imply non-reductionism, non-materialism, non-predictability, non-Platonism, and non-nihilism. As alternatives to each of these, holism, informism, adaptation, contextuality, and meaningfulness are put forward, respectively. A worldview that includes interactions not only describes better our world, but can help to solve many open scientific, philosophical, and social problems caused by implications of reductionism.1 aGershenson, Carlos uhttp://arxiv.org/abs/1105.282701234nas a2200181 4500008004100000245004600041210004400087260001500131300001400146520075300160100002300913700001400936700001700950700001500967700001400982700001500996856004101011 2004 eng d00aIntroduction to Random {Boolean} Networks0 aIntroduction to Random Boolean Networks aBoston, MA a160–1733 aThe goal of this tutorial is to promote interest in the study of random Boolean networks (RBNs). These can be very interesting models, since one does not have to assume any functionality or particular connectivity of the networks to study their generic properties. Like this, RBNs have been used for exploring the configurations where life could emerge. The fact that RBNs are a generalization of cellular automata makes their research a very important topic. The tutorial, intended for a broad audience, presents the state of the art in RBNs, spanning over several lines of research carried out by different groups. We focus on research done within artificial life, as we cannot exhaust the abundant research done over the decades related to RBNs.1 aGershenson, Carlos1 aBedau, M.1 aHusbands, P.1 aHutton, T.1 aKumar, S.1 aSuzuki, H. uhttp://arxiv.org/abs/nlin.AO/0408006