Interested in how technology innovation impacts people’s lives.

Modelling of socio-ecological systems and management of common property resources in artisanal fisheries. Population dynamics of coastal marine invertebrates exploited by artisanal fisheries.

My academic interests involve public choice and the development of social norms for cooperation in the marketplace and the behavior of voting blocks. Recent work looks at the emergence of property rights “norms” among zero intelligence agents in an evolutionary context, and the dynamics of legislative party creation in an environment of stochastically voting voters.

Currently working on agent-based modeling of wealth and income distributions; formalizing some of Luhmann’s theories of communication; modeling social norms; and modeling generative mechanisms of status hierarchies.

fraud, occupational fraud, agent-based modeling, behavioural decision-making processes, social norms

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.

I work in the field of complex adaptive systems, specializing in multi-agent systems, simulation, machine learning, collective intelligence, self-organization, and self-adaptation. I am interested in contributing to innovative projects and research in these domains.

My experience spans across multiple large-scale international research projects in areas such as green urban logistics, blockchain for nuclear applications, autonomous robotics systems and simulation of biological neural networks.

Currently doing a program evaluation of a GIZ reforestation project in the north of Mato Grosso state, Brazil (transition area from savannah to Amazon forest). Adoption of Agroforestry Systems by lower income farmers was the goal.

Our overriding approach has been to advance the state-of-the-art in conducting large-scale simulation studies, by developing and disseminating experimental designs that facilitate the exploration of complex simulation models

I have been studying (1) applied discrete choice modelling, (2) consumer choices of seafood, (3) international seafood trade, (4) marine habitat and fishery management, (5) China’s international relation, (6) environment and health, and (7) experimental auctions.

I’m starting to learn ABM and hope to apply the method into my research.