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Water scarcity generated by climate change and mismanagement, affects individual at microlevel and the society and the system at a more general level. The research focuses on irrigation system and their robustness and adaptation capacity to uncertainty. In particular it investigates the evolution of farmers interactions and the effectiveness of policies by means of dynamic game theory and incorporate the results into an Agent Based Model to explore farmers emergent behaviors and the role of an agency in defining policies. Early knowledge of individual decision makers could help the agency to design more acceptable solutions.
Currently working on agent-based modeling of wealth and income distributions; formalizing some of Luhmann’s theories of communication; modeling social norms; and modeling generative mechanisms of status hierarchies.
The goal of my research program is to improve our understanding about highly integrated natural and human processes. Within the context of Land-System Science, I seek to understand how natural and human systems interact through feedback mechanisms and affect land management choices among humans and ecosystem (e.g., carbon storage) and biophysical processes (e.g., erosion) in natural systems. One component of this program involves finding novel methods for data collection (e.g., unmanned aerial vehicles) that can be used to calibrate and validate models of natural systems at the resolution of decision makers. Another component of this program involves the design and construction of agent-based models to formalize our understanding of human decisions and their interaction with their environment in computer code. The most exciting, and remaining part, is coupling these two components together so that we may not only quantify the impact of representing their coupling, but more importantly to assess the impacts of changing climate, technology, and policy on human well-being, patterns of land use and land management, and ecological and biophysical aspects of our environment.
To achieve this overarching goal, my students and I conduct fieldwork that involves the use of state-of-the-art unmanned aerial vehicles (UAVs) in combination with ground-based light detection and ranging (LiDAR) equipment, RTK global positioning system (GPS) receivers, weather and soil sensors, and a host of different types of manual measurements. We bring these data together to make methodological advancements and benchmark novel equipment to justify its use in the calibration and validation of models of natural and human processes. By conducting fieldwork at high spatial resolutions (e.g., parcel level) we are able to couple our representation of natural system processes at the scale at which human actors make decisions and improve our understanding about how they react to changes and affect our environment.
land use; land management; agricultural systems; ecosystem function; carbon; remote sensing; field measurements; unmanned aerial vehicle; human decision-making; erosion, hydrological, and agent-based modelling
I currently work on an agent-based model on energy-efficient renovation decisions.
Associate Professor
School of Management Science and Engineering, Shandong Technology and Business University (Yantai 264005, P. R. China)
Ph. D. Degree, 09/2009 – 07/2015
School of Economics and Management, Beihang University (P. R. China)
M. A. Degree, 09/2003 – 02/2006
The Institute of Systems Engineering, Dalian University of Technology (P. R. China)
B. A. Degree, 09/1999 – 07/2003
Department of Information and Control Engineering, Zhengzhou University of Light Industry (P. R. China)
Visiting Scholar at GECS – Research Group of Experimental and Computational Sociology (March, 2017 – February, 2018)
Università degli Studi di Brescia (Italy)
Co-supervisor: Professor Flaminio Squazzoni
Summer school in ‘Agent-based modeling for social scientists’ (September 4-8, 2017)
University of Brescia, Italy
Instructors: Flaminio Squazzoni, Simone Gabbriellini, Nicolas Payette, Federico Bianchi
The Santa Fe Institute’s Massive Open Online Course: Introduction to Agent-Based Modeling (Jun 5 – September 8, 2017)
The Santa Fe Institute, Complexity Explore Web: abm.complexityexploer.org
Instructors: Bill Rand
Summer school in ‘Complex systems and management’ (July 2-12, 2012)
National Defense University, P. R. China
Instructors: Xinjun Mao, Yongfang Liu, Dinghua Shi, Qiyue Cheng
Routine dynamics, Agent-based modeling, Computational social/organization science, Industrial systems engineering, etc.
Agent based modelling in water management, especially focused in extreme phenomena such floods and droughts.
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.
Industrial Engineering, Multi-criteria Decision Making, Optimization Techniques, Global/International Facility Location, Agent-based Modeling
Agent-based modeling in political science
My interests lie in the intersection of economics, networks, and computation. I am currently studying labour dynamics as a process where people flow throughout the economy by moving from one firm to another. I study these flows by looking at detailed data about employment histories of each individual and every firm in entire economies. Using this information, I construct networks of firms in order to map the roads that people take throughout their careers. This allows to study labour markets at an unprecedented fine-grained level of detail. I employ agent-based computing methods to understand how economic shocks and policies alter labour flows, which eventually translate into unemployment and other related problems.
Displaying 10 of 237 results agent based clear search