|dc.description.abstract||Emerging cyber-infrastructure tools are enabling scientists to transparently codevelop,
share, and communicate in real-time diverse forms of knowledge artifacts.
In this research, these collaborative environments are modeled as complex adaptive
systems using collective action theory as a basis. Communication preferences of scientists
are posited as an important factor a ecting innovation capacity and resilience
of social and knowledge network structures. Using agent-based modeling, a complex
adaptive social communication network model is developed. By examining the Open
Biomedical Ontologies (OBO) Foundry data and drawing conclusions from observing
the Open Source Software communities, a conceptually grounded model mimicking
the dynamics in what is called Global Participatory Science (GPS), is presented.
Social network metrics and knowledge production patterns are used as proxy metrics
to infer innovation potential of emergent knowledge and collaboration networks.
Robust communication strategies with regard to innovation potential are questioned
by exploring di erent parameter and mechanism con gurations. The objective is
to present the underlying dynamics of GPS in a form of computational model that
enables analyzing the impacts of various communication preferences of scientists on
innovation potential of the collaboration network. The ultimate goal is to further our
understanding of the dynamics in GPS and facilitate developing informed policies
fostering innovation capacity.||en_US