 Complexity also has been a source for articulating non-reductionist epistemological accounts of biological reasoning (Wimsatt 1974, Hooker 2011). Sandra Mitchell (2003, 2009) has argued that different forms of biological complexity block standard reductionist perspectives. Multiple (idealized) causal models must be integrated from different disciplinary approaches to adequately represent this complexity, including our uncertainty in predicting its behavior, which has implications for any social policy derived from these models. She specifically draws attention to the phenomenon of self-organization, which refers to how system behaviors or patterns emerge from non-specific interactions among lower-level components (Camazine et al. 2001, Kauffman 1993), such as the collective behavior of social insects. (Self-organizing phenomena are also relevant to reductionism in physical science; see, e.g., Goldenfeld and Kadanoff 1999, Whitesides and Ismagilov 1999.) Although decomposing a system into parts is important for claims about self-organization, there is no localization of causal responsibility to individual components (Bechtel and Richardson 1993, ch. 9). Instead, the system behavior is a function of generic patterns of component interaction, which foregrounds how complexity emerges from dynamics rather than constitution (see  Section 4.3).  For example, gene networks and human social networks may exhibit isomorphic patterns of self-organization due to their interactive structure, which is unrelated to whether genes or humans are the components. Thus, self-organizing phenomena occupy an odd place in the reductionism discussion because even though components are used to explain the behavior of the system, it is the nature of their interactions (not their specific characteristics) that generate patterns of behavior. These patterns are often referred to as  emergent properties  of the system (see also Grantham 2007, Mitchell 2012).