Displaying 10 of 72 results for "Jingjing Cai" clear search
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.
Charlotte is an International PhD graduate originally from New Zealand who first came to ASU to pursue her PhD in Anthropology in Aug 2013, thanks to receiving a Science and Innovation Scholarship through the Fulbright Program. She holds a BS majoring in Genetics and a BA majoring in Anthropology from Otago University, New Zealand. She received her Masters in Anthropology in May 2015 and her PhD in Anthropology in 2022 both from ASU. Her main areas of interest are Human Migration, Migration Decision Making, and Environmental Perceptions.
At present she is an Assistant Research Scientist with the School of Complex Adaptive Systems at ASU where she is primarily focused on her roles as the administrative coordinator for CoMSES.NET and The Open Modeling Foundation. She is also adjunct Anthropology faculty at Phoenix College, and Chandler-Gilbert Community College teaching various undergraduate anthropology courses. She is deeply interested in how computational tools and technologies can be used to explore complex adaptive systems, explore possible futures, and better inform policy and decision makers at the leading edge of change.
In my research I focus on understanding human behaviour in group(s) as a part of a complex (social) system. My research can be characterised by the overall question: ‘How does group or collective behaviour arise or change given its social and physical context?‘ More specifically, I have engaged with: ‘How is (individual) human behaviour affected by being in a crowd?’, ‘Why do some groups (cooperatively) use their resources sustainably, whereas others do not?‘, ‘What is the role of (often implicit simplistic) assumptions regarding human behaviour for science and/or management?’
To address these questions, I use computational simulations to integrate and reflect synthesised knowledge from literature, empirics and experts. Models, simulation and data analysis are my tools for gaining a deeper understanding of the mechanisms underlying such systems. More specifically, I work with agent-based modelling (ABM), simulation experiments and data analysis of large datasets. Apart from crowd modelling and social-ecological modelling, I also develop methodological tools to analyse social simulation data and combining ABM with other methods, such as behavioural experiments.
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.
Moira Zellner’s academic background lies at the intersection of Urban and Regional Planning, Environmental Science, and Complexity. She 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 socio-ecological systems problems, where interaction effects make responsibilities, burdens, and future pathways unclear. Her research also examines how participatory complex systems modeling with stakeholders and decision-makers can support collaborative policy exploration, social learning, and system-wide transformation. Moira has taught a variety of courses and workshops on complexity-based modeling of socio-ecological systems, for training of researchers, practitioners, and decision-makers in the US and abroad. She has served the academic community spanning across the social and natural sciences, as reviewer of journals and grants and as a member of various scientific organizations. She is dedicated to serving the public through her engaged research and activism.
Applications of agent-based modeling to urban and environmental planning
Participatory modeling
I have a strong background in building and incorporating agent-based simulations for learning. Throughout my graduate career, I have worked at the Center for Connected Learning and Computer Based Modeling (CCL), developing modeling and simulation tools for learning. In particular, we develop NetLogo, the gold standard agent-based modeling environment for learners around the world. In my dissertation work, I marry biology and computer science to teach the emergent principles of ant colonies foraging for food and expanding. The work builds on more than a decade of experience in ABM. I now work at the Center for the Science and the Schools as an Assistant Professor. We delivered a curriculum to teach about COVID-19, where I incorporated ABMs into the curriculum.
You can keep up with my work at my webpage: https://kitcmartin.com
Studying the negative externalities of networks, and the ways in which those negatives feedback and support the continuities.
My core research interest is to understand how humans and other living creature perceive and behave; respond and act upon their environment and how this dynamic interplay shapes us into who we are. In recognition of the broad scope of this question I am a strong believer in the need for inter- and multi-disciplinary approaches and have worked at research groups in a wide range of departments and institutions, including university departments of Physics as well as Psychology, a bio-medical research lab, a robotics research laboratory and most recently the RIKEN Brain Science Institute. Though my work has primarily taken the form of computational neuroscience I have also performed psychophysical experiments with healthy human subjects, been involved in neural imaging experiments and contributed towards the development of a humanoid robot.
Based on the philosophy of ‘understanding through creating’ I believe that bio-mimetic and biologically inspired computational and robotic engineering can teach us not only how to build more flexible and robust tools but also how actual living creatures deal with their environment. I am therefore a strong believer in the fertile information exchange between scientific as well as engineering research disciplines.
Kenneth D. Aiello is a postdoctoral research scholar with the Global BioSocial Complexity Initiative at ASU. Kenneth’s research contributes to cross disciplinary conversations on how historical developments in biological, social, and cultural knowledge systems are governed by processes that transform the structure, dynamics, and function of complex systems. Applying computational historical analysis and epistemology to question what scientific knowledge is and how we can analyze changes in knowledge, he uses text analysis, social network analysis, and machine learning to measure similarities and differences between the knowledge claims of individual agents and groups. His work builds on how to assess contested knowledge claims and measure the evolution of knowledge across complex systems and multiple dimensions of scale. This approach also engages in dynamic new debates about global and local structures of knowledge shaped by technological innovation within microbiology related to public policy, shrinking resources given to biomedical ideas as opposed to “translation”, and the ethics of scientific discovery. Using interdisciplinary methods for understanding historical content and context rich narratives contributes to understanding new domains and major transitions in science and provides a richer understanding of how knowledge emerges.
Modeling land use change from smallholder agricultural intensification
Agricultural expansion in the rural tropics brings much needed economic and social development in developing countries. On the other hand, agricultural development can result in the clearing of biologically-diverse and carbon-rich forests. To achieve both development and conservation objectives, many government policies and initiatives support agricultural intensification, especially in smallholdings, as a way to increase crop production without expanding farmlands. However, little is understood regarding how different smallholders might respond to such investments for yield intensification. It is also unclear what factors might influence a smallholder’s land-use decision making process. In this proposed research, I will use a bottom-up approach to evaluate whether investments in yield intensification for smallholder farmers would really translate to sustainable land use in Indonesia. I will do so by combining socioeconomic and GIS data in an agent-based model (Land-Use Dynamic Simulator multi-agent simulation model). The outputs of my research will provide decision makers with new and contextualized information to assist them in designing agricultural policies to suit varying socioeconomic, geographic and environmental contexts.
My primary research interests lie at the intersection of two fields: evolutionary computation and multi-agent systems. I am specifically interested in how evolutionary search algorithms can be used to help people understand and analyze agent-based models of complex systems (e.g., flocking birds, traffic jams, or how information diffuses across social networks). My secondary research interests broadly span the areas of artificial life, multi-agent robotics, cognitive/learning science, design of multi-agent modeling environments. I enjoy interdisciplinary research, and in pursuit of the aforementioned topics, I have been involved in application areas from archeology to zoology, from linguistics to marketing, and from urban growth patterns to materials science. I am also very interested in creative approaches to computer science and complex systems education, and have published work on the use of multi-agent simulation as a vehicle for introducing students to computer science.
It is my philosophy that theoretical research should be inspired by real-world problems, and conversely, that theoretical results should inform and enhance practice in the field. Accordingly, I view tool building as a vital practice that is complementary to theoretical and methodological research. Throughout my own work I have contributed to the research community by developing several practical software tools, including BehaviorSearch (http://www.behaviorsearch.org/)
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