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Christophe Le Page currently works at the Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD). Christophe does research on participatory modelling of the interactions between agriculture and the environment, focusing more specifically on the relationships among stakeholders about the management of natural renewable resources. Christophe is designing and using interactive agent-based simulation and role-playing games. He is an active member of the Companion Modelling research group.
Agent-based simulations and role-playing games in the field of renewable resource management.
Interested in how technology innovation impacts people’s lives.
My dissertation research at the Johnson-Shoyama Graduate School of Public Policy focuses on food safety and consumer choices, using agent-based models as a novel method for investigating this policy space.
My research focuses on using generic social science in creating models of social reality, in particular self-organization of social systems.
agent-based modelling
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.
Social scientist based in Milan, Italy. Post-doctoral researcher in Sociology at the Department of Social and Political Sciences of the University of Milan (Italy), member of the Behave Lab. Adjunct professor of Social Network Analysis at the Graduate School in Social and Political Sciences of the University of Milan.
Displaying 10 of 537 results for "Ian M Hamilton" clear search