Displaying 10 of 102 results Agent-Based Modeling clear search
I am an Associate Professor of Industrial Engineering with over two decades of experience in teaching, research, and supervision in data-driven decision making, operations research, and computational modeling. My research integrates Multi-Criteria Decision Analysis (MCDA), Agent-Based Modeling (ABM), and Reinforcement Learning (RL) to support strategic decision systems in sustainability, investment, and industrial operations. My recent work explores human-centric and multi-actor systems, leveraging simulation-based optimization and AI-driven analytics to enhance resilience, efficiency, and sustainability in complex socio-technical environments. I have published extensively in international journals, reviewed over 75 manuscripts, and am an active member of INFORMS and the System Dynamics Society. My long-term goal is to bridge industrial systems modeling with intelligent decision support, aligning academic research with real-world sustainability and innovation challenges.
š¹ Experience
Associate Professor ā Industrial Engineering, University of Engineering and Technology, Taxila (2018 ā Present)ā¢ Teach graduate and undergraduate courses in Operations Research, Data Mining, Advanced Statistics, System Simulation, and Soft Computing.ā¢ Conduct funded research in agent-based and reinforcement learning models for sustainable and data-driven decision systems.ā¢ Supervise doctoral students in decision analytics, multi-agent modeling, and MCDM applications.ā¢ Reviewer for international journals including Neural Computing and Applications, the Journal of Cleaner Production, Annals of Operations Research, Environment, Development and Sustainability, Energy for Sustainable Development, Scientific Reports, IEEE Access, Cleaner Energy Systems, Utilities Policy, and Sustainable Futures
š¹ Research Interestsā¢
Data-Driven Decision Makingā¢ Agent-Based Modeling (ABM)ā¢ Reinforcement Learning (RL)ā¢ Multi-Criteria Decision Analysis (MCDA / MAMCA)ā¢ Sustainable Supply Chainsā¢ System Dynamics; Simulationā¢ E-Health and Humanitarian Systems
š¹ Selected Achievementsā¢
30+ peer-reviewed publications; ~360+ citationsā¢ Reviewer for 75+ international journal papersā¢ Completed Coursera Specializations in Machine Learning, Deep Learning, and Reinforcement Learningā¢ 20+ years of experience integrating data science with sustainability modeling
Data-Driven Decision Making | Agent-Based & Reinforcement Learning Models | Multi-Criteria Decision Analysis | Sustainable Systems | Operations Research | Netlogo | R
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.
Associate Professor
School of Management Science and Engineering, Shandong Technology and Business University (Yantai 264005, P. R. China)
Ph. D. Degree, 09/2009 ā 07/2015
School of Economics and Management, Beihang University (P. R. China)
M. A. Degree, 09/2003 ā 02/2006
The Institute of Systems Engineering, Dalian University of Technology (P. R. China)
B. A. Degree, 09/1999 ā 07/2003
Department of Information and Control Engineering, Zhengzhou University of Light Industry (P. R. China)
Visiting Scholar at GECS ā Research Group of Experimental and Computational Sociology (March, 2017 ā February, 2018)
ļ UniversitĆ degli Studi di Brescia (Italy)
ļ Co-supervisor: Professor Flaminio Squazzoni
Summer school in āAgent-based modeling for social scientistsā (September 4-8, 2017)
ļ University of Brescia, Italy
ļ 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 ā September 8, 2017)
ļ The Santa Fe Institute, Complexity Explore Web: abm.complexityexploer.org
ļ Instructors: Bill Rand
Summer school in āComplex systems and managementā (July 2-12, 2012)
ļ National Defense University, P. R. China
ļ Instructors: Xinjun Mao, Yongfang Liu, Dinghua Shi, Qiyue Cheng
Routine dynamics, Agent-based modeling, Computational social/organization science, Industrial systems engineering, etc.
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.
I am Professor of Management at Paris School of Business and have held positions at the University of Southern Denmark, Bournemouth University (UK), University of Wisconsin (US), and at the University of Insubria (Italy). My current research efforts are on socially-based decision making, agent-based modeling, cognitive processes in organizations and socially responsible behavior in organizations. With a coauthor network of 50 colleagues located in over 10 different countries, I have published 126 (as of 2025) among articles, book chapters, and books. The monograph Computational organizational cognition (2021, Emerald), and the edited Agent-Based Simulation of Organizational Behavior with M. Neumann (2016, Springer Nature) specifically target computational simulation research in the social sciences. The book How do I Develop an Agent-Based Model? (2022, Elgar) is the first specifically written for business and management scholars.
My simulation research focuses on the applications of ABM to organizational behavior studies. I study socially-distributed decision makingāi.e., the process of exploiting external resources in a social environmentāand I work to develop its theoretical underpinnings in order to to test it. A second stream of research is on how group dynamics affect individual perceptions of social responsibility and on the definition and measurement of individual social responsibility (I-SR).
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,
I am an environmental archaeologist, specializing in charcoal analysis, computational and analytical proxy modeling, and quantitative methods to understand the dynamic relationship between fire, humans, and long-term environmental change. I work primarily in the Western United States and the Western Mediterranean. I am passionate about our public lands and ensuring that everyone has access and opportunity to experience them.
Envrionmental Archaeology, Fire Ecology, GIS, Agent-based modeling, Geoarchaeology
Andrew J. Collins, Ph.D., is an associate professor at Old Dominion University in the Department of Engineering Management and Systems Engineering. He has a Ph.D. in Operations Research from the University of Southampton, and his undergraduate degree in Mathematics was from the University of Oxford. He has published over 80 peer-review articles. He has been the Principal Investigator on projects funded to the amount of approximately $5 million. Dr. Collins has developed several research simulations including an award-winning investigation into the foreclosure contagion that incorporated social networks.
Agent-based Modeling
Agent-based simulation
Cooperative Game Theory
Behavior modeling
Social network analysis has an especially long tradition in the social science. In recent years, a dramatically increased visibility of SNA, however, is owed to statistical physicists. Among many, Barabasi-Albert model (BA model) has attracted particular attention because of its mathematical properties (i.e., obeying power-law distribution) and its appearance in a diverse range of social phenomena. BA model assumes that nodes with more links (i.e., āpopular nodesā) are more likely to be connected when new nodes entered a system. However, significant deviations from BA model have been reported in many social networks. Although numerous variants of BA model are developed, they still share the key assumption that nodes with more links were more likely to be connected. I think this line of research is problematic since it assumes all nodes possess the same preference and overlooks the potential impacts of agent heterogeneity on network formation. When joining a real social network, people are not only driven by instrumental calculation of connecting with the popular, but also motivated by intrinsic affection of joining the like. The impact of this mixed preferential attachment is particularly consequential on formation of social networks. I propose an integrative agent-based model of heterogeneous attachment encompassing both instrumental calculation and intrinsic similarity. Particularly, it emphasizes the way in which agent heterogeneity affects social network formation. This integrative approach can strongly advance our understanding about the formation of various networks.
Tenured researcher @ government think-tank (IPEA) and CNPq (productivity grant - since 2014), complex modeler interested, data fan, transitional Python user, PhD. Background in urban analysis, economics, geography. From twitter.com/furtadobb
Agent-based modeling, urban policy, urban economics. Metropolis and municipalities analyses.
Displaying 10 of 102 results Agent-Based Modeling clear search