Sharing Perspectives Across Disciplines

 

The social world that we observe reflects a web of interdependent processes, with macro-level structures of organizations, communities, and societies both emerging from and constraining the micro-level interactions of individuals. Most social science research has focused on finding statistical relationships in cross-sectional data – such as correlations of individuals’ age with political attitudes, administrative structure with organizational performance, or law enforcement policies with municipal crime rates – while assuming that the objects of study are independent. This focus may describe typical patterns, but gives us limited insight into the underlying generative processes or the dynamic consequences of statistical relationships. Furthermore, many social phenomena are inherently time varying and depend on interactions between entities within a social system, such as in the spread of epidemics, the rise of political insurgency, or the dissolution of formal organizations.

 

Understanding the link between micro-level interactions and macro-level dynamics could have profound impact on the ways we engage basic social science research. Toward this end, an increasing number of scientists are using mathematical and computational models to elucidate theoretical problems in social dynamics, often by applying general theories or methods from the natural and physical sciences. Population ecology models are applied to the evolution of industries, gene sequence analysis is applied to the study of career trajectories; neural networks are used to model the origins of religious beliefs. Although these links are promising, their impact is limited by conventional disciplinary institutions that fail to promote broad diffusion of ideas and methods.

 

We are organizing a meeting to foster intellectual exchange among complementary experts from mathematics, biology, physics, computer science, and engineering, as well as social and behavioral scientists. Participants are either studying models of social dynamics directly or are developing theoretical methods that would be applicable to this problem. The meeting has three goals. First, it will provide a forum where scholars may compare and coordinate lines of research, helping the NSF priority area in Human and Social Dynamics to identify and nurture emerging research agendas.

 

Second, we hope the interdisciplinary conversations among diverse experts in this workshop will inspire future collaborative projects of high impact in areas of social dynamics, such as:

1.      Evolution of social networks (including social tie formation and attrition, robustness of information flow, etc.)

2.      Evolutionary and ecological models of social behavior, population dynamics and demography

3.      Organizational dynamics (including agent-based and multilevel models of individuals’ behavior in interaction with broader social institutions)

 

We will discuss ways in which fruitful interdisciplinary collaboration may extend research in specific disciplines, integrate various lines of research, and advance the state-of-the-art in modeling human and social dynamics.

Our third goal is to encourage applications of these models to real-world problems – such as epidemics, distributed failures due to natural disasters, and general consequences of environmental change. Such applications will directly demonstrate the promise of understanding human and social dynamics beyond the basic research community.