I am a Ph.D. candidate in Computational Social Science (CSS) program at George Mason (GMU). I hold a MAIS from GMU and a Bachelor of Economics from the University of Tasmania. My research interests are the application of ABMs, network analysis, and machine learning to financial markets. My email address and website is [email protected] and www.aussiecas.com
I am interested in using agent-based model to understand the behavior of financial markets
GIS enthusiast and ABM practitioner
Social Network Analysis
Sedar is a PhD student at the University of Leeds, department of Geography. He graduated in Computer Science at King’s College London 2018. From a very early stage of his degree, he focused on artificial intelligence planning implementations on drones in a search and rescue domain, and this was his first formal attempt to study artificial intelligence. He participated in summer school at Boğaziçi University in Istanbul working on programming techniques to reduce execution time. During his final year, he concentrated on how argumentation theory with natural language processing can be used to optimise political influence. In the midst of completing his degree, he applied to Professor Alison Heppenstall’s research proposal focusing on data analytics and society, a joint endeavour with the Alan Turing Institute and the Economic and Social Research Council. From 2018 - 2023 he will be working on his PhD at the Alan Turing Institute and Leeds Institute for Data Analytics.
Sedar will be focusing on data analytics and smart cities, developing a programming library to try simulate how policies can impact a small world of autonomous intelligent agents to try deduce positive or negative impact in the long run. If the impact is positive and this is conveyed collectively taking into consideration the agent’s health, happiness and other social characteristics then the policy can be considered. Furthermore, he will work on agent based modelling to solve and provide faster solutions to economic and social elements of society, establishing applied and theoretical answers. Some other interests are:
The big picture question driving my research is how do complex systems of interactions among individuals / agents result in emergent properties and how do those emergent properties feedback to affect individual / agent decisions. I have explored this big picture question in a number of different contexts including the evolution of cooperation, suburban sprawl, traffic patterns, financial systems, land-use and land-change in urban systems, and most recently social media. For all of these explorations, I employ the tools of complex systems, most importantly agent-based modeling.
My current research focus is on understanding the dynamics of social media, examining how concepts like information, authority, influence and trust diffuse in these new media formats. This allows us to ask questions such as who do users trust to provide them with the information that they want? Which entities have the greatest influence on social media users? How do fads and fashions arise in social media? What happens when time is critical to the diffusion process such as an in a natural disaster? I have employed agent-based modeling, machine learning, geographic information systems, and network analysis to understand and start to answer these questions.
B.S. in Fish and Wildlife from Michigan State University in 1996. M.S. in Wildlife Ecology from the University of Maine - Orono in 2001. Employed by the Michigan Department of Natural Resources since 2003, first as a field biologist (2003-2008), then statewide endangered species coordinator (2008-2012), and currently as the statewide (climate) adaptation program lead (2012-present). Also currently a graduate student in the Boone and Crockett Quantitative Wildlife Center at Michigan State University (2015-present). Father, gardener, hiker, and amateur myxomycologist.
Human-wildlife social-ecological systems, resilience and learning in complex adaptive systems, climate change, disturbance ecology, and historical ecology
Dr. Moira Zellner is an associate professor at the University of Illinois at Chicago in the Department of Urban Planning and Policy, the director of the Urban Data Visualization lab, and is a research associate professor in the Institute for Environmental Science and Policy at UIC. Dr. Zellner has served as Principal Investigator and Co-Investigator in interdisciplinary projects examining how specific policy, technological and behavioral factors influence the emergence and impacts of a range of complex environmental problems, where interaction effects make responsibilities and burdens unclear. Her research also examines the value of complexity-based modeling for participatory policy exploration and social learning with stakeholders and decision-makers. Dr. Zellner also teaches a variety of workshops on complexity-based modeling of socio-ecological systems, for training of both scientists and decision-makers.
Applications of agent-based modeling to urban and environmental planning
Ecology - Natural Resources Management (Community-based management)
I worked on natural resources management modelling in STELLA. I developed a technical and scientific model to analyze soil, climate and biological conditions to explain how Bamboo ecosystem works and how people in Cundinamarca, Colombia could focus on a sustainable model for use and manage forestry resources.
Also, I worked on the seventh framework program named: Community-based management of Environmental Challenges in Latin America -COMET-LA-. The project built a learning arena with scientists, civil society and government to identify sustainable models for governance of natural resources in social-ecological systems located in a rural context from Colombia, México and Argentina.
I am interesting in research on Modelling of governance and Community-based management of natural resources.
PhD student in economics
My research interests include policy informatics and decision making, modeling in policy analysis and management decisions, public health management and policy, and the role of public value in policy development. I am particularly interested in less mainstream approaches to modeling that account for learning, feedback, and other systems dynamics. I include Bayesian inference, agent-based models, and behavioral assumptions in both my research and teaching.
In my dissertation research, I conceptualize state Medicaid programs as complex adaptive systems characterized by diverse actors, behaviors, relationships, and objectives. These systems reproduce themselves through both strategic and emergent mechanisms of program management. I focus on the mechanism by which citizens are sorted into or out of the system: program enrollment. Using Bayesian regression and agent-based models, I explore the role of administrative practices (such as presumptive eligibility and longer continuous eligibility periods) in increasing enrollment of eligible citizens into Medicaid programs.
As a Master’s Thesis student, I am intended to apply Artificial Intelligence to an already existing model with the aim of making it more accurate.
Even though I do not have the focus point and the scope of the research clear yet, the road map is set to start from a very simple model to validate the technology and methodology used and then continue with more abitiuos projects.
I like the co-operation that I have found in this space and I think that I could both learn a lot from the community and add value with my novel trials and findings.
Of course I would be pleased to update the status of my project and I would try to help if I have the proper knowledge or different angle to other peers who seek for seconds opinions.