Integrating social and natural science to study coupled human-natural systems, and particularly the interactions of society with the physical environment under conditions of environmental stress.

I am interested in using agent-based model to understand the behavior of financial markets

Current main research interests are concerned on diffusion of ICT among social actors of territorial systems: citizens(individuals and households), enterprises and governmental bodies. Most used methodological tools are , so far, multivariate statistics and Social Network Analysis.
I’d like to apply an ABM approach in the context of my PhD research project, aimed to observe the different modes of collaboration among universities and enterprises and tehir different effectiveness in terms of creation and spread of new knowledge.

My interests are focused on the development of new methodologies capable of exploring the complex relations between time, space and human behavior. Simulation, game theory and spatial analysis are some of the techniques that I use to explore different research questions, from the relation between environment and culture to the evolution of warfare.
I’m also the project manager of Pandora, an open-source ABM platform specifically designed for executing large scale simulations in High-Performance Computing environments.

I study human dimensions of natural resource management and resource use by under-represented populations—often in developing nations—to enhance our understanding of conflicts involving land use, natural resources, and conservation from an interdisciplinary, systematic lens. My research spans subjects such as common pool resource management and policy, decentralization, and land use/land cover change drivers and trends relating to population rise and environmental change.

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 focuses on building a systemic understanding of coupled human-natural systems. In particular, I am interested in understanding how patterns of land-use and land-cover change emerge from human alterations of natural processes and the resulting feedbacks. Study systems of interest include those undergoing agricultural to urban conversion, typically known as urban sprawl, and those in which protective measures, such as wildfire suppression or flood/storm impact controls, can lead to long-term instability.

Dynamic agent- and process-based simulation models are my primary tools for studying human and natural systems, respectively. My past work includes the creation of dynamic, process-based simulation models of the wildland fires along the urban-wildland interface (UWI), and artificial dune construction to protect coastal development along a barrier island coastline. My current research involves the testing, refinement, extension of an economic agent-based model of coupled housing and land markets (CHALMS), and a new project developing a generalized agent-based model of land-use change to explore local human-environmental interactions globally.

Community assembly after intervention by coral transplantation

The potential of transplantation of scleractinian corals in restoring degraded reefs has been widely recognized. Levels of success of coral transplantation have been highly variable due to variable environmental conditions and interactions with other reef organisms. The community structure of the area being restored is an emergent outcome of the interaction of its components as well as of processes at the local level. Understanding the
coral reef as a complex adaptive system is essential in understanding how patterns emerge from processes at local scales. Data from a coral transplantation experiment will be used to develop an individual-based model of coral community development. The objectives of the model are to develop an understanding of assembly rules, predict trajectories and discover unknown properties in the development of coral reef communities in the context of reef restoration. Simulation experiments will be conducted to derive insights on community trajectories under different disturbance regimes as well as initial transplantation configurations. The model may also serve as a decision-support tool for reef restoration.