Figure 10: Examples of ECA with a single initial cell: (A) rule 0 from class I, (B) rule 1 from class II, (C) rule 110 from class IV, and (D) rule 30 from class III. 100 steps are shown except for (A), only 20 steps. Images obtained from http://wolframalpha.com . tend quickly to a steady state, such as class I ECA, exhibit maximum self-organization. Shalizi et al. (2004) define self-organization as an internal increase of statistical complexity. This measure would give systems with a high C maximum self-organization, while we con- sider a maximum S for static systems. We believe that a frozen state is more organized than a complex pattern, in line with thermodynamics and information theory. However, a system with a minimum S most probably is not organizing . We believe that the measure of Shalizi et al. is more appropriate for studying the dynamics of self-organizing systems (Gershen- son and Heylighen, 2003; Gershenson, 2007a), while S is intended as a general measure of self-organization. From our simplified definitions, self-organization can be seen as the opposite of emergence: self-organization is high for ordered dynamics, emergence is high for chaotic dynamics (at all scales). Having self-organization and emergence as opposites might seem counterintuitive, since there are several systems which seem to have a high emergence and self-organization. Precisely these systems seem to be those most interesting to science. We agree, and these 22 A B C D