I have a backround in computer science, worked in natural resource management, and ended up with a PhD in Sustainability Sciences!
My interests are to explore aspects of sustainability, resilience, and adaptive management in social-ecological systems using agent-based models and other simulation models.
My current interests include: agent-based modeling, simulating social complexity, land use, dynamic networks, social and cultural anthropology, HIV transmission dynamics, socio-political conflicts and social movements
Aquatic ecology, Socio-ecological fisheries systems
Archaeological Simulation of Social Interactions, mainly between hunter gatherers societies.
Mainly interested in studying social networks of learners, teachers, and innovators. Uses Social Network Analysis, but also sentiment analysis, data mining, and recommender system techniques.
Modeling of spatialized social-ecological-agrosystems
Social network analysis has an especially long tradition in the social science. In recent years, a dramatically increased visibility of SNA, however, is owed to statistical physicists. Among many, Barabasi-Albert model (BA model) has attracted particular attention because of its mathematical properties (i.e., obeying power-law distribution) and its appearance in a diverse range of social phenomena. BA model assumes that nodes with more links (i.e., “popular nodes”) are more likely to be connected when new nodes entered a system. However, significant deviations from BA model have been reported in many social networks. Although numerous variants of BA model are developed, they still share the key assumption that nodes with more links were more likely to be connected. I think this line of research is problematic since it assumes all nodes possess the same preference and overlooks the potential impacts of agent heterogeneity on network formation. When joining a real social network, people are not only driven by instrumental calculation of connecting with the popular, but also motivated by intrinsic affection of joining the like. The impact of this mixed preferential attachment is particularly consequential on formation of social networks. I propose an integrative agent-based model of heterogeneous attachment encompassing both instrumental calculation and intrinsic similarity. Particularly, it emphasizes the way in which agent heterogeneity affects social network formation. This integrative approach can strongly advance our understanding about the formation of various networks.