Joint Modeling of Multiple Social Networks to Elucidate Primate Social Dynamics: Maximum Entropy Principle and Network-Based Interactions

• Activity Number: 439
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Stephanie Chan*+
• Companies: UC Davis
• Keywords: maximum entropy ; joint modeling ; social dynamics ; inter-behavioral relationship ; primate ; rhesus macaque

Abstract

In a complex behavioral system, such as an animal society, the dynamics of the system as a whole represent the synergistic interaction among multiple aspects of the society. We constructed multiple single-behavior social networks to approximate from multiple aspects a single complex behavioral system of interest: rhesus macaque society. We describe a new method for jointly analyzing the single-behavior networks in order to understand the system dynamics as a whole and to study the interaction among multiple aspects of any system. We develop a bottom-up, iterative modeling approach based upon the maximum entropy principle. This principle is applied to a multi-dimensional link-based distributional framework, which combines the directed single-behavioral social network data, for extracting patterns of synergistic inter-behavioral relationships. We jointly modeled and analyzed four different social behavioral networks at two time points from a rhesus macaque group housed at the California National Primate Research Center (CNPRC). We report and discuss the inter-behavioral dynamics uncovered by our joint modeling approach with respect to social stability.