One of my research areas is agent-based modelling of land change in Brazil. I have worked with ABM in frontier areas of the Brazilian Amazon. I am also part of the team that develops TerraME, an OSS toolkit for ABM in cellular spaces.

In this paper, we explore the dynamic of stock prices over time by developing an agent-based market. The developed artificial market comprises of heterogeneous agents occupied with various behaviors and trading strategies. To be specific, the agents in the market may expose to overconfidence, conservatism or loss aversion biases. Additionally, they may employ fundamental, technical, adaptive (neural network) strategies or simply being arbitrary agents (zero intelligence agents). The market has property of direct interaction. The environment takes the form of network structure, namely, it takes the manifestation of scale-free network. The information will flow between the agents through the linkages that connect them. Furthermore, the tax imposed by the regulator is investigated. The model is subjected to goodness of fit to the empirical observations of the S\&P500. The fitting of the model is refined by calibrating the model parameters through heuristic approach, particularly, scatter search. Conclusively, the parameters are validated against normality, absence of correlations, volatility cluster and leverage effect using statistical tests.

My research interests stand between natural resource management and ecological economics. The aim of my PhD project responds to the increasing demand for cross-disciplinary agent-based models that examine the disjunction between economic growth and more sustainable use of natural resources.

My research attempts to test the effectiveness of different governance and economic frameworks on managing natural resources sustainably at both regional and national levels. The goal is to simulate how communities and institutions manage the commons in complex socio-ecological systems through several case-studies, e.g. rainforest management in Australia. It is hoped that the models will highlight which combination of variables lead to positive trends in both economic and environmental indicators, which could stimulate more sustainable practices by governments, private sectors and civil society.

1. Dynamics of socio-ecological resource use systems
   • Pasture use in dryland grazing systems under change, effects of new technologies and policy instruments, emergence of polarization between pastoralists (e.g. in terms of livestock numbers).
   • Thresholds of disaster management performance under change, loss of manpower, the role of information as critical resource.
2. Human decision making in agent-based models.
3. Remote sensing and GIS.

All aspects of social simulation including: techniques, tools, applications, philosophy, methodology and interesting examples. Understanding complex social systems. Context-dependency and how it affects interaction and cognition. Complexity and how this impacts upon simulation modelling. Social aspects of cognition - or to put it another way - the social embedding of intelligence. Simulating how science works. Integrating qualitative evidence better into ABMs. And everything else.

I specialize in creating agent-based models of biological systems for research and education in genetics, evolution, demography, ecology, and behavior.