In recent years, the focus of social network theory in behavioral ecology and the social sciences has shifted to understanding the dynamics of social networks. Data analytical methods such as relational state models and others have been used to address patterns of network change over time as agents gain or lose ties and how network structure coevolves with the attributes of agents in real-world networks. Network models are beginning to incorporate data at multiple scales and multiple types of interactions. New technologies have facilitated collection of large quantities of data in many systems allowing increasingly sophisticated analyses of changes in social structure over time.
MBI Auditorium, Jennings Hall 355 Mathematical Biosciences Institute mbi-webmaster@osu.edu America/New_York publicIn recent years, the focus of social network theory in behavioral ecology and the social sciences has shifted to understanding the dynamics of social networks. Data analytical methods such as relational state models and others have been used to address patterns of network change over time as agents gain or lose ties and how network structure coevolves with the attributes of agents in real-world networks. Network models are beginning to incorporate data at multiple scales and multiple types of interactions. New technologies have facilitated collection of large quantities of data in many systems allowing increasingly sophisticated analyses of changes in social structure over time.
Mathematical and empirical challenges arise because social networks are complex systems that emerge from, as well as influence, the interacting decisions of multiple, autonomous, objective-maximizing or goal-oriented agents. Agents often have multiple types of relations, resulting in multilayer (multiplex) networks. Current techniques for data analysis of dynamic networks are best suited to address enduring relationships, rather than momentary interactions, but many social interactions are better described by the latter. Consequences of agent decisions to pursue interactions can depend on attributes at multiple levels, and decisions that maximize agent objectives may be in conflict with those of others or with beneficial outcomes for the network as a whole. In humans and non-human animals, opportunities for interaction are constrained by factors such as location and mobility. Social networks frequently involve a small number of agents, and stochastic processes are likely to be important influences on network dynamics. Key emerging problems include how to incorporate multilayer and momentary data into network models, the roles of feedbacks between space use and network processes, how individual decisions interact with the evolution of network attributes, and the fitness or other consequences of such behaviors.
This workshop will consider these emerging problems with an interdisciplinary approach incorporating modeling and empirical work from the social sciences, behavioral biology, mathematics, and statistics. In addition, because many of the challenges inherent to the study of social network dynamics are not unique to such networks, this workshop aims to include perspectives from other areas of network research.
This MBI workshop is being co-sponsored by the National Institute of Statistical Sciences.
