I’m a PhD researcher at the University of Glasgow working on modelling national identity polarisation on social media platforms using ABMs.
agent-based models, social networks, python, R, NetLogo
I like developing models to support the design of bottom-up policies that can make communities more resilient and foster the coordination among formal and informal actors in crisis response. In my PhD, I specifically focus on disaster information management as a mean to support community resilience and crisis coordination.
I am an Assistant Professor at the School of Computer Science, University of Nottingham, UK.
My main research interest is the application of computer simulation to study human-centric complex adaptive systems. I am a strong advocate of Object Oriented Agent-Based Social Simulation. This is a novel and highly interdisciplinary research field, involving disciplines like Social Science, Economics, Psychology, Operations Research, Geography, and Computer Science. My current research focusses on Urban Sustainability and I am a co-investigator in several related projects and a member of the university’s “Sustainable and Resilient Cities” Research Priority Area management team.
My name is Roberto and I am a graduate student at The Pennsylvania State University. I am in the “Information Sciences - Cybersecurity and Information Assurance program”, through which I discovered my interest in ABM. I am conducting my capstone research project on how to make ABM more effective in the disaster recovery planning process of IT companies. I am currently looking for interview candidates to conduct my research. If you or anyone you know have experience using ABM for disaster recovery planning in IT or tech, please reach out!
I learned about ABM through the Intelligent Agents course at Penn State, where we modeled everything from terrorist attacks to social relationships. I was immediately interested in ABM due to the potential and capabilities that it provides in so many areas. I hope to make ABM more popular in IT disaster recovery planning through my research, while learning more about ABM myself.
Angelos Chliaoutakis received his PhD in Electronic & Computer Engineering in 2020 at Technical University of Crete (TUC), Greece. During 2005-2020 he was a research assistant at the Intelligent Systems Laboratory of TUC, participating in several research projects associated with NLP, semantic similarity and ontology based information systems. Since 2010 he is also a research assistant at the Laboratory of Geophysical - Satellite Remote Sensing and Archaeo-environment (GeoSat ReSeArch Lab) of the Institute for Mediterranean Studies of Foundation for Research and Technology (IMS-FORTH), were he is involved in various research projects related to the full-stack development of Geographical Information Systems (GIS), web-based GIS applications and Geoinformatics in the cultural and archaeological domain. This ultimately transformed his interest and research direction towards computational archaeology, in particular, agent-based modeling and simulation, while intertwining ideas and approaches from Artificial Intelligence, Multi-agent Systems and GIS.
Research activities range between Computer Science, Information Systems and Natural Language Processing (NLP), Agent-based modeling/simulation (ABM), Artificial Intelligence (AI) and Multi-Agent Systems (MAS) and Geographical Information Science (GIScience).
I am an assistant professor in the department of computer science at the Hamedan University of Technology, Hamedan, IRAN. I have completed my Ph.D. in Futures Studies (foresight) as an interdisciplinary field, a juncture of social sciences and engineering. My
background comes from computer science. For my Ph.D., I decided to pursue my education in Futures Studies; the field I thought I could apply engineering principles such as requirements engineering, analytical skills, design, modeling, planning, and, test engineering to shape the
desired futures. In PhD, I started the complex systems research field and agent-based modeling with NetLogo. In addition to several publications of papers, I published a book on complex systems titled “Futures Studies in Complex Systems” which was awarded as the book of the year by the Iranian Foresight Association.
Since May 2021, I started a research collaboration with TISSS Lab at the Johannes Gutenberg University Mainz as a project coordinator, the German Research Centre for AI, Human-Centered Multimedia, and the Centre for Research in Social Simulation. The project title is “AI for Assessment” and its objective is to understand the status quo and the future options of AI-based social assessment in public service provisions to help in the creation of improved AI technology for social welfare systems.
On the executive side, I have also various experiences, including head of the department, deputy of the Technology Incubator Center, director of the university’s research affairs, and head of the International Scientific Cooperation Office.
Complex Systems, Social Modeling and Simulation
Enginnering the Futures
Eric Kameni holds a Ph.D. in Computer Science option modeling and application from the Radboud University of Nijmegen in the Netherlands, after a Bachelor’s Degree in Computer Science in Application Development and a Diploma in Master’s degree with Thesis in Computer Science on “modeling the diffusion of trust in social networks” at the University of Yaoundé I in Cameroon. My doctoral thesis focused on developing a model-based development approach for designing ICT-based solutions to solve environmental problems (Natural Model based Design in Context (NMDC)).
The particular focus of the research is the development of a spatial and Agent-Based Model to capture the motivations underlying the decision making of the various actors towards the investments in the quality of land and institutions, or other aspects of land use change. Inductive models (GIS and statistical based) can extrapolate existing land use patterns in time but cannot include actors decisions, learning and responses to new phenomena, e.g. new crops or soil conservation techniques. Therefore, more deductive (‘theory-driven’) approaches need to be used to complement the inductive (‘data-driven’) methods for a full grip on transition processes. Agent-Based Modeling is suitable for this work, in view of the number and types of actors (farmer, sedentary and transhumant herders, gender, ethnicity, wealth, local and supra-local) involved in land use and management. NetLogo framework could be use to facilitate modeling because it portray some desirable characteristics (agent based and spatially explicit). The model develop should provide social and anthropological insights in how farmers work and learn.