Thursday, December 16, 2021

Disaster through the Lens of Complex Adaptive Systems: Exploring Emergent Groups Utilizing Agent Based Modeling and Social Networks

For those interested in my PhD work, I'm posting the link to my dissertation in ProQuest: dissertation-link

In addition to the work I've published on the complex adaptive systems in disaster and how computational social science can contribute to disaster science and emergency management, my dissertation details the results of an agent-based model (ABM) of emergent groups in a disaster. The model shows how agent-based modeling can be linked with GIS and SNA and be used to measure the effects of emergent social networks. These emergent networks are a manifestation of the latent social capital that are available in times of crisis. When properly implemented such models can be used to both educate and inform researchers and policy-makers. 

As an example of the possible results in an ABM of social networks in disaster, the model shows that emergent group sizes follow exponential scaling patterns characteristic of complex adaptive systems. 

Number of Nodes and Edges of Emergent Groups in a Disaster Response

Dissertation Abstract:

Disasters have become more frequent and intense in the last decades and are a significant challenge to the health and well-being of local communities and regions. As a potential solution to this problem attention has been drawn to community resilience and the building of social networks that support or hinder local response and recovery. Research on disasters and community resilience has shown how the ability to leverage social capital through a community’s social networks is fundamental to the ability of individuals and communities to respond to disaster events, but there is little understanding of how the evolution of social networks can impact disaster response and recovery. A computational framework and agent-based model of disasters can provide a virtual laboratory for testing social network effects and uncover their role, function and underlying mechanisms in community resilience. Agent-based models are suited to test bottom-up dynamics and the interactions of variables that lead to the nonlinear relationships in disasters. To what extent can an agent-based model characterize the social networks that emerge in response to a no-warning disaster event such as a Nuclear Weapon of Mass Destruction impacting Manhattan Island? To explore this question this research reviews theories of disaster, primarily from sociological and anthropological research, and builds a conceptual model of disasters from which to develop an agent-based model. The agent-based model represents social networks relevant in both the normal commuting patterns of New York City and the emergent social networks responding to a Nuclear Weapon of Mass Destruction impacting Manhattan Island. Network representations of social groups along with physical representations of the community shows how individuals adapt and respond to the disaster in the initial response. Integrating agent-based models with social network analysis provides new spaces for scientific inquiry into disasters, the dynamics of social networks in resilient communities, and those areas of complexity most often explored today with qualitative methodologies.