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Wageningen University & Research
Currently researcher modelling at Biometris, Wageningen University & Research
modelling,
ecology, foraging, forestry, forest management, water management
scenario analysis
agent based modelling
individual based modelling
model quality, model evaluation, model governance
Business model innovation on markets for digital cultural goods.
Currently doing Agent based model in biogy
model-based policy analysis; system dynamics; agent-based modeling
I am a developer for CoMSES Net as part of the Global Biosocial Complexity Initiative at Arizona State University. I work on improving model reuse, accessibility and discoverability through the development of the comses.net website and the CoMSES bibliographic database (catalog.comses.net). I also provide data analysis and software development advice on coupling models, version control, dependency management and data analysis to researchers and modelers.
My interests include model componentization, statistics, data analysis and improving model development and resuability practices.
Agent based model for coastal settlement transitions
Interested in learning how to accurately model social power, diffusion of ideas, social exchange
Developing Disease Modeling Software - MIcro Simulation Tool (MIST). The Reference Model for Disease Progression is my main effort.
Management of Water Resources Conflicts in Halil-Rud River Basin: Application of Integrated Economic- Hydrological- Behavioral Model
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.
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