My general research interest is on modeling of complex natural and human systems systems. Specifically, I am interested in modeling agricultural production systems, that blends the complexity, multiplicity of scales and feedbacks of biophysical interactions in natural ecosystems with the additional intricacies of human decision-making. During last years I have coordinated the development and evaluation of an agent-based of agricultural production systems in the Argentinean Pampas.

I use agent-based systems, stochastic process, mass balance models and computational statistics in exploring human exposure assessment.

I have been researching in synchronization between agent-based-models (ABM) and multi robot systems used in logistic and manufacturing. I use Netlogo as ABM.
I develop and agile methodology to use the same ABM as supervisory control and data aquisition (SCADA). The framework works fine and I test it in two SCADAs, which you can see in my youtube channel (http://www.youtube.com/channel/UCJIb_UL-ak98F5OZxOHL0FQ).

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.

Christian Reynolds is a Public Health Research Fellow at the Rowett Institute of Nutrition and Health, University of Aberdeen, and an adjunct Research Fellow at the Barbara Hardy Institute for Sustainable Environments and Technologies, University of South Australia. Christian’s research examines the economic and environmental impacts of food consumption; with focus upon food waste, sustainable diets, and the political power of food in international relations.
Christian has experience in economic input-output, material flow and environmental (Life Cycle Analysis) modelling and has published peer reviewed articles on these topics.

Enhancing Athena visualizations and internals, see https://github.com/AthenaModel.