systems are those with a highest C , but if one goes to the extremes of E or S , one finds extreme disorder or extreme order, respectively. Information (and emergence) can be seen as the balance of zeros and ones ( P (0) = 1 − P (1); max ( I ) ⇐⇒ P (0) = P (1) = 0 . 5). Complexity can be seen as the balance of emergence and self-organization (if I$_{in}$ = 0, S = 1 − E ; max ( C ) ⇐⇒ S = E = 0 . 5). This has already been noted as complexity being maximal when there is a tradeoff between persistence of information (memory) and its variability (exploration, computation) (Langton, 1990; Kauffman, 1993). It has also been argued that there is a tendency for systems to self- organize or evolve to states of increased balance, i.e. high C (at multiple scales) (Bak et al., 1987; Kauffman, 2000; Gershenson, 2007b). This is in agreement with recent work by Escalona-Mor´ an et al. (2012). Even when the mathematical formulation is different, C is strongly related with Crutch- field et al.’s statistical complexity (Crutchfield and Young, 1989), which measures how much computation is required to produce information. Ordered and random strings require com- putation, while complex strings require more computation. This statistical complexity can also be defined as the minimal information required to predict the future of a process (Shalizi et al., 2004). For our C , low I implies strings which are easy to predict, while high I implies strings where there is not much to be predicted. Both cases give a low C and statistical complexity. High C occurs when I is medium, which corresponds with more computation required to produce or predict such a pattern. Even when C and statistical complexity exhibit similar behaviors, C is much simpler to calculate and explain. Complexity has been confused with chaos, i.e. a high entropy (Shannon information) (Prokopenko et al., 2009). For example, Bar-Yam’s complexity profile measures complexity as the amount of information required to describe a system at different scales (Bar-Yam, 2004b). From our perspective, this could be better named “information profile” or “emergence profile”. Inspired by Bar-Yam’s complexity (information) profile, the σ profile was proposed to measure organization at different scales (Gershenson, 2011). Following the approach of 24