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.
PhD student in the Agent Systems Research Group of the Department of Artificial Intelligence at the VU University Amsterdam. Current research focuses on Modeling Human Behavior and exploring Serious Games interactions with humans.
I’m a trained philosopher, but, besides conceptual problems, I care for conclusions based on systematic observations and I also care for the applicability of those conclusions. One might say that I wish I were a behavioral economist, or maybe an ethologist/behavioral ecologist.
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.
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.
I am broadly interested in using Agent-based Modelling, Microsimulation, Geosimulation or a hybrid of these approaches as methodology to investigate complex dynamics of systems in various domains. I am also interested in exploring the potential of simulation models as decision support and policy-informing tools.
Dr. Gravel-Miguel currently works as a Postdoctoral Research Scholar for the Institute of Human Origins at Arizona State University. She does research in Archaeology and focuses on the Upper Paleolithic of Southwest Europe. She currently works on projects ranging from cultural transmission to human-environment interactions in prehistory.
Archaeology, GIS, ABM, social networks, portable art, ornaments, data science
I develop simulation tools for generating what-if scenarios for decision making. I predominantly use Agent-Based Modelling (ABM) technique as most of my simulations model complex systems. In some cases, I have extended existing tools with modifications to model the given system. Although the tools are meant for research purposes, I have followed industry friendly delivery mechanisms, such as unit-tests, autmated builds and delivery on cloud platforms.
My profound interest in networks convinced me to work in these subjects and start my master project on an application of social network analysis for detecting organized fraud in Automobile insurance, which helps to flag groups of fraudsters. The key point of this project is simply to find fraudulent rings, while the most of traditional methods have only taken opportunistic fraud into consideration. My duty in research is to design an algorithm for identifying cyclic components, then to be compared with theoretical ones. This project showed me how networks are used in the analysis of relations.
I obtained my undergraduate degree in Mathematics at Worcester College, Oxford University. I then worked for 9 years for the UK government before returning to university to study for a MSc and PhD at UCL. On leaving UCL I started working in the insurance industry, where I develop models of cyber catastrophe events.
Key research interests are how to build models of complex human behaviour.
My PhD research project was focussed on building a model of the process by which people develop the propensity to commit acts of crime or terrorism, from which came a computer simulation of the radicalisation process.
My current research interest is on creating models of cyber threats.