Andrew J. Collins, Ph.D., is an assistant professor at Old Dominion University in the Department of Engineering Management and Systems Engineering. He has a Ph.D. in Operations Research from the University of Southampton, and his undergraduate degree in Mathematics was from the University of Oxford. He has published over 80 peer-review articles. He has been the Principal Investigator on projects funded to the amount of approximately $7 million. Dr. Collins has developed several research simulations including an award-winning investigation into the foreclosure contagion that incorporated social networks.
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.
Adapting Agents on Evolving Networks: An evolutionary game theory approach
ABM researches on the theory of social systems. For example, the formation of a community, the origin of politics, nation, and state.
I study he role of biologically-based motivations in the formation of socio-political phenomena using agent-based modelling techniques. In particular I look at how behaviour inhibition and activation, as well as interpersonal attitudes can shape the emergence of complex polities.
I am interested in the study of small-group decision-making using agent-based simulation of models grounded in sociological social psychology. I am also interested in a particular kind of small-group decision-making: peer review.
-Use of models, including agent-based models, in understanding the formation of surface archaeological deposits in arid Australia
-Individual-based modelling of resource use on marginal islands in Polynesian prehistory
-Individual-based modelling of the influence of serial voyaging events on body proportions in Remote Oceania
-Discrete event simulation of early horticultural production in New Zealand