Exploring the Impact of Socio-technical Communication Styles on the Robustness and Innovation Potential of Global Participatory Science

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.