A complex adaptive model of information foraging and preferential attachment dynamics in Global Participatory Science

Abstract

Recent developments in cyber-infrastructure and emerging virtual science collaboratories are enabling scientists to transparently co-develop, share, and communicate diverse forms of knowledge artifacts in real-time. Using collective action theory as a basis, we introduce an agent-based model of such collaborative environments as complex adaptive social communication systems. By examining empirical data from the Open Biomedical Ontologies (OBO) Foundry, we present a conceptually grounded agent-based model of what we call Global Participatory Science (GPS). The model represents the dynamics of GPS in terms of the information foraging, social exposure, and preferential attachment mechanisms. We monitor social network metrics and activity patterns as proxy metrics to infer innovation potential of collaboration networks. In this paper, we introduce our CollectiveInnoSim model and demonstrate the impact of foraging and preferential attachment mechanisms on emergent social network structures. The objective is to further our understanding of the dynamics of GPS and facilitate developing informed policies fostering innovation potential.