The Emergence of Animal Social Complexity: theoretical and biobehavioral evidence

This paper will introduce a theory of emergent animal social complexity using various results from computational models and empirical results. These results will be organized into a vertical model of social complexity. This will support the perspective that social complexity is in essence an emergent phenomenon while helping to answer of analysis larger than the individual organism. The second involves placing aggregate social events into the context of processes occurring within individual organisms over time (e.g. genomic and physiological processes). By using a complex systems perspective, five principles of social complexity can be identified. These principles suggest that lower-level mechanisms give rise to high-level mechanisms, ultimately resulting in metastable networks of social relations. These network structures then constrain lower-level phenomena ranging from transient, collective social groups to physiological regulatory mechanisms within individual organisms. In conclusion, the broader implications and drawbacks of applying the theory to a diversity of natural populations will be discussed. Introduction Animal social complexity, like many other multifaceted processes, is a complex system. As a complex system, extracting causal relationships from the multitude of interactions can be a daunting proposition. Drawing parallels with social media platforms (Churchill and Halverson, 2005), not only is animal social complexity rich with interactions, but is also centered around the concept of social networks (Pinter-Wollman et.al, 2013). Yet these parallels reveal that behavior alone is not enough to explain the dynamics of social organization over time. In this paper, I will present a theory that links the emergence and dynamics of social complexity to genomic and physiological mechanisms. Such a vertical approach reveals many opportunities for transient phenomena to emerge (e.g. collective behaviors, phenotypes linked to social status), which in turn serve as feedback and other regulatory mechanisms. This theory will also require support derived from complex systems principles, various behavioral models, and empirical examples, to which we will now turn. Principles From observations and theoretical insights into complexity across various types of complex systems (Allen and Starr, 1982, Kelso, 1995, Kauffman, 1993), five principles of animal social complexity can be identified (Figure 1). The first (Figure 1, I) suggests that systems are composed of multiple hierarchical layers, all of which interact with each other. This leads to the second principle (Figure 1, II), which is that interactions at each Abstract This paper will introduce a theory of emergent ani al social co plexity si various results from computational models and empirical results. These results ill organized into a vertical model of social complexity. This ill support t e r ti that social complexity is in essence an emergent phenome on while helping to answer two interrelated questions. The first f the e involve h w behavior is integrated at units f nalysis larger than the individual organism. The second i volves placing aggregate social events into the context of pro esses curring within i dividual organism over time (e.g. genomic and phy iologi al rocesses). By using a complex system perspective, fiv principles of social complexity can be identified. The e principles suggest that lower-level mechanisms give ise t high-l vel mechani ms, ltimat ly resulti g in metastable etworks of social relatio . These network structures then constrain low r-level phenomena ranging from transient, collective social gr ups to physiological regulatory mechanisms within i dividual ganisms. In conclusion, the broader implications and rawbacks of applying the theory to a diversity of natural populations will be discussed.