Displaying 10 of 71 results for "Jingjing Cai" clear search
I have been researching in synchronization between agent-based-models (ABM) and multi robot systems used in logistic and manufacturing. I use Netlogo as ABM.
I develop and agile methodology to use the same ABM as supervisory control and data aquisition (SCADA). The framework works fine and I test it in two SCADAs, which you can see in my youtube channel (http://www.youtube.com/channel/UCJIb_UL-ak98F5OZxOHL0FQ).
Interested in numerical models and new conceptual ideas, applications from industry to medicine.
I focus on numerical modeling of mechanics of solid materials and cell mechanics. The models that I developed so far address granular matters, bio-fluids, cellular tissues, and individual cells.
I further develop Agent-based Models, which are methods to predict collective behavior from individual dynamics controlled by rules or differential equations. Examples: tumor growth, swarms, crowd movement.
The methods I used are Particle-based methods which offer great flexibility within physical modeling, and can operate in a large range of scales, from atomistic scales (e.g. Molecular Dynamics) to continuum approaches (e.g. Smoothed Particle Hydrodynamics).
Dr. Aaron Bramson is principal investigator of the AI Strategy Center of GA technologies in Tokyo, Japan, as well as an Affiliate Researcher in the Department of General Economics of Ghent University in Belgium. His research specialty is complexity science, especially methodologies for modeling complex systems. Research topics span across disciplines: measures of polarization and diversity, belief measure interoperability, integrating geospatial and network analyses for measuring walkability and neighborhood identification, and myriad applications in artificial intelligence and data visualization. He received his Ph.D. from the University of Michigan in a joint program with the departments of Political Science and Philosophy as well as an M.S. in Mathematics from Northeastern University.
Complex systems, agent-based modeling, social simulation, computational models, network models, network theory, methodology, philosophy of science, ontology, epistemology, ethics, artificial intelligence, big data analysis, geospatial data analysis,
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Volker Grimm currently works at the Department of Ecological Modelling, Helmholtz-Zentrum für Umweltforschung. Volker does research in ecology and biodiversity research.
How to model it: Ecological models, in particular simulation models, often seem to be formulated ad hoc and only poorly analysed. I am therefore interested in strategies and methods for making ecological modelling more coherent and efficient. The ultimate aim is to develop preditive models that provide mechanstic understanding of ecological systems and that are transparent and structurally realistic enough to support environmental decision making.
Pattern-oriented modelling: This is a general strategy of using multiple patterns observed in real systems as multiple criteria for chosing model structure, selecting among alternative submodels, and inversely determining entire sets of unknown model parameters.
Individual-based and agent-based modelling: For many, if not most, ecological questions individual-level aspects can be decisive for explaining system-level behavior. IBM/ABMs allow to represent individual heterogeneity, local interactions, and/or adaptive behaviour
Ecological theory and concepts: I am particularly interested in exploring stability properties like resilience and persistence.
Modelling for ecological applications: Pattern-oriented modelling allows to develop structurally realistic models, which can be used to support decision making and the management of biodiversity and natural resources. Currently, I am involved in the EU project CREAM, where a suite of population models is developed for pesticide risk assessment.
Standards for model communication and formulation: In 2006, we published a general protocol for describing individual- and agent-based models, called the ODD protocol (Overview, Design concepts, details). ODD turned out to be more useful (and needed) than we expected.
I am a computational archaeologist interested in how individuals and groups respond to both large scale processes such as climate change and local processes such as violence and wealth inequality. I am currently a PhD Candidate in the Department of Anthropology at Washington State University.
My dissertation research focuses on experimenting with paleoecological data (e.g., pollen) to assess whether or not different approaches are feasible for paleoclimatic field reconstructions. In addition, I will also use pollen data to generate vegetation (biome) reconstructions. By using tree-ring and pollen data, we can gain a better understanding of the paleoclimate and the spatial distribution of vegetation communities and how those changed over time. These data can be used to better understand changes in demography and how people responded to environmental change.
In Summer 2019, I attended the Santa Fe Institute’s Complex Systems Summer School, where I got to work in a highly collaborative and interdisciplinary international scientific community. For one of my projects, I got to merry my love of Sci-fi with complexity and agent-based modeling. Sci-fi agent-based modeling is an anthology and we wanted to build a community of collaborators for exploring sci-fi worlds. We also have an Instagram page (@Scifiabm).
I am a Professor in the School of Sustainability and the Director of the Center for Behavior, Institutions and the Environment. I want to understand how people solve collective problems at different levels of scale, especially those problems related to sustainability of our environment. Our society experience unprecedented challenged to sustain common resource for future generations at a scale we have never experienced before. What makes groups cooperate? What is the role of information? How does the ecological context affect the social fabric? How do they deal with a changing environment? How can we use these insight to address global challenges? To do this research I combine behavioral experiments, agent-based modeling and case study analysis.
Ken Buetow is a human genetics and genomics researcher who leverages computational tools to understand complex traits such as cancer, liver disease, and obesity. He currently serves as director of Computational Sciences and Informatics program for Complex Adaptive Systems at Arizona State University (CAS@ASU), is a professor in the School of Life Sciences in ASU’s College of Liberal Arts and Sciences; is a core faculty in the Center for Evolution and Medicine in the Biodesign Institute at ASU; and is director of bioinformatics and data management for the National Biomarker Development Alliance.
Professor Buetow previously served as the Founding Director of the Center for Biomedical Informatics and Information Technology within the National Institutes of Health’s National Cancer Institute.
My research uses modeling to understand complex coupled human and natural systems, and can be generally described as computational social science. I am especially interested in modeling water management systems, in both archaeological and contemporary contexts. I have previously developed a framework for modeling general archaeological complex systems, and applied this to the specific case of the Hohokam in southern Arizona. I am currently engaged in research in data mining to understand contemporary water management strategies in the U.S. southwest and in several locations in Alaska. I am also a developer for the Repast HPC toolkit, an agent-based modeling toolkit specifically for high-performance computing platforms, and maintain an interest in the philosophy of science underlying our use of models as a means to approach complex systems. I am currently serving as Communications Officer for the Computational Social Science Society of the Americas.
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