Here you will find an evolving list of researchers around the world who do agent-based modeling or related work. This list is intended as a resource for those interested in discovering people doing interesting research in ABM or related fields in different disciplines. Where ever possible, I try to link a picture of the person, which is directly linked to their website.
Total Researchers Listed on this Page: 30
Ozge Dilaver Kalkan’s research interests predominantly relate to evolutionary economics and computational social science. While investigating issues like product value, history dependence of demand, interdependencies between consumers and technology-induced social change Ozge aims to accommodate social construction of reality in computational models of social behaviour.
František Kalvas research include agent-based simulation, agent-based modeling, applied research, and communication.
Shivaram is interested in practical applications of reinforcement learning. In particular, he has used robot soccer as a test domain for much of his research, which has resulted in two Best Student Paper awards at RoboCup. During an internship at the Honda Research Institute, Shivaram applied machine learning to the problem of humanoid fall prediction. His other interests include history, literature, cricket, geography, and theatre.
George (György) Kampis’ research focuses on theoretical biology, evolution (Darwin, modeling, foundations, philosophy, technology), cognitive science (intentionality, agency, consciousness, biological intelligence), complex systems (itinerancies, agent-based modeling, mechanisms), philosophy of science (causality, time, explanation), analyitical philosopy, and philosophy of mind.
Jean-Daniel Kant’s research interests include modelling and simulation of complex systems and the contributions of Information Technologies in the Humanities and Social Sciences. He is currently developing research in the context of multi-agent social simulation and agent-based computational economics (ACE approach) to model the French labor market or the labor organization in firms, but also in sociology (social networks, dynamic opinions) and marketing (diffusion of innovation). His approach is multidisciplinary and relies heavily on modelling of individual and social behaviors, from theories derived from economics, sociology, cognitive and social psychology, Multi-Agent Systems, and learning.
Daniel Katz’ research interests include network analysis and law, the complexity of the law as well as analysis of various constitutional, statutory and administrative law making processes.
Stuart Kauffman’s recent research interests include the development of an inference algorithm, IADGRIN, to infer the structure and logic of noisy Boolean networks; foundational work on the linkages among work, constraint, information, in the propagating organization occurring in cells in the biosphere; the relation between maximizing mutual information in Boolean networks and the dynamical criticality of such networks; the extension of IADGRIN to chemical master equation (Gillespie) network models of genetic regulatory networks; construction and implementation of Gillespie networks and study of bi-stable toggle switches and the three gene “represselator”; and the formulation of a general ensemble approach to use Gillespie nets to model genetic regulatory nets and construction of initial models.
Petros Kefalas’ research interests include formal methods in software engineering, parallel logic programming, artificial intelligence & intelligent agent systems, open & distance learning through the Internet.
Harald Kempf is currently working on my PhD thesis on the simulation of tumour reactions to different treatments using agent-based modeling.
Doug Kiel’s current research interests include how cognitive biases influence organizational change efforts, mental health in the workplace and the neuroscience of leadership and management. He is a leading authority in the application of the complexity sciences to management and organizations.
Whan-Seon Kim’s research focuses on agent-based modeling with application to Complex System (CS) theories and Agent-Based Simulation (ABS) methods in strategic management fields (e.g., supply chain management and the digital ecosystems).
Denise Kirschner’s research interests include microbial pathogenesis – TB, HIV, and H. pylori, nonlinear dynamical and complex systems, chaos, and bioinformatics.
Marek Kisiel-Dorohinicki’sfocuses on intelligent software systems, particularly utilising agent technology and evolutionary algorithms, but also other soft computing techniqes like neural networks or fuzzy systems.
Hajime Kita’s research interests include system engineering, evolutionary computation, agent-based social simulation, information education, and participatory approaches to production.
James A. Kitts (also see http://www.socdynamics.org/) is broadly interested in the dynamics of cooperation and competition among organizations and among their members. He has modeled the emergence of norms in groups, the coevolution of norms and social networks, cultural polarization and factionalism in social networks, and the demography and ecology of organizational memberships.
Adam Kleczkowski’s research interests include two main threads: parameter estimation and spatially-extended models of ecological and epidemiological systems. He is particularly interested in analysis of effects of spatial and temporal heterogeneities on dynamics of biological systems and in describing and predicting variability in various population models. Advances in in experimental techniques allow his lab to look directly at properties of single organisms, cells or even molecules, thus avoiding loss of information due to averaging. Inclusion of variability has profound consequences for modelling and predicting the behaviour of many biological systems. The variability becomes an important part of the dynamics that can and must be incorporated into the description and explained rather than removed from the analysis. It also offers us a significantly reacher insight into the mechanisms controlling behaviour of populations than a traditional approach. It is a combination of different factors (environment, nonlinearity, species interaction and dispersal) that leads to a formation and maintenance of variability and diversity. Strong emphasis is put on statistical model fitting and rigorous testing against experimental and archival data, on data collection and experimental design.
Michel Klein’s research focuses on the application of artificial intelligence techniques (including agent-based modeling) to supporting human functioning. Specifically, he focuses on technology that support people in healthcare applications, such as intelligent self-management systems for chronic patients or support systems for people with mental disorders such as depression.
Eric Klopfer is Associate Professor and the Director of the MIT Scheller Teacher Education Program (http://education.mit.edu) and the Director of the The Education Arcade (http://educationarcade.org). His research focuses on the development and use of computer games and simulations for building understanding of science and complex systems. His work combines research and development of games and simulations, from initial conceptualization, through implementation, piloting, professional development and end-user research. He is the creator of StarLogo TNG, a platform for helping kids create 3D simulations and games using a graphical programming language, as well as several mobile game platforms including location-based Augmented Reality games, and ubiquitous casual games. He is the author of Augmented Learning, a book on handheld games and learning from MIT Press, and is co-author of the book, Adventures in Modeling: Exploring Complex, Dynamic Systems with StarLogo. He is also president and co-founder of the non-profit Learning Games Network.
Franziska Klügl’s research goal is to improve methods and tools for agent-based simulation for
(1) guiding the design and implementation process so that a systematic way of developing valid and useful multi-agent simulations replaces the try-and-error procedure currently mostly applied, (2) providing rapid prototyping tools for being able to make experiences “playing around” with different models of complex systems without great effort in implementing these simulations, and (3) providing comfortable and high-level languages that allow also users without deep simulation programming experience to design and implement their simulation models – make the methods of multiagent simulation accessible for people that were not able to bridge the technology gap before. To this aim, she develops methods and tools for SeSAm.
W. Brad Brad Knox researches how to design agents that can be interactively shaped by humans. In other words, he seeks to create agents which can be effectively taught behaviors by lay people using positive and negative feedback signals (akin to reward and punishment in animal training). To this end, he developed the TAMER framework. The TAMER framework makes use of established supervised learning techniques to model a human’s reinforcement function and bases its action selection on the learned model. More recently, Brad received the Pragnesh Jay Modi Best Student Paper Award at AAMAS 2010 for an investigation of how to learn both from human reinforcement and MDP reward signals.
Matt Knudson research interests focus on autonomous agents and multi-agent systems.
Chris Koliba uses the “governance network” as the unit of analysis to generate agent based model simulated environments. He and his colleagues are interested in developing a set of governance informatics premised on the flow of resources between policy actors and the hybridized accountability regimes that persist between. Agent based models are used to simulate the strategy spaces within regional planning and watershed governance networks. The goal is to develop integrated models that may be used to drive policy design and policy implementation strategies.
Tim Kohler’s research involves quantitative analysis of archaeological data, including agent-based simulation of settlement and subsistence behavior in prehispanic societies of the US Southwest (as in the long-running Village Ecodyamics Project). He is interested in problems involving the operation and evolution of coupled natural and human systems, cooperative behavior including reciprocity and public-goods, and warfare in Neolithic (early village) societies around the world.
Sven Koenig is interested in intelligent systems that have to operate in large, nondeterministic, nonstationary or only partially known domains. Most of my research centers around techniques for decision making (planning and learning) that enable single situated agents (such as robots or decision-support systems) and teams of agents to act intelligently in their environments and exhibit goal-directed behavior in real-time, even if they have only incomplete knowledge of their environment, imperfect abilities to manipulate it, limited or noisy perception or insufficient reasoning speed. He believes that finding good solutions to these problems requires approaches that cut across many different fields and, consequently, my research draws on areas such as artificial intelligence, decision theory, and operations research. Applications of his research include planetary exploration, supply-chain management, medicine, crisis management (such as oil-spill containment), robotics and real-time games (entertainment, serious games, training and simulation).
George Konidaris’ research focuses on building intelligent, autonomous agents that display open-ended learning and broad competence. To this end he is developing new algorithms for autonomous skill acquisition with the aim of creating agents that can acquire general purpose skill hierarchies through interaction with their environments, and thereby become able to solve harder and harder control problems over time.
Daniel Kornhauser’s research focuses on designing tools to improve NetLogo’s visualization and guidelines for agent-based modeling visualization.
Ryszard Kowalczyk’s research interests include complex agent negotiations and decision-making
Distributed learning and adaptation in multi-agent systems; agent coordination and dynamic virtual organisations; agent-based service composition and adaptation; and intelligent agent applications (adaptive SLA and QoS management in service-oriented environments, collaborative e-business and virtual organisations, smart environments and pervasive computing, and complex adaptive systems)
Kerstin Kowarik’s research addresses complex behavior in European Bronze Age and Early Iron Age societies, especially focusing on the prehistoric salt mines of Hallstatt. The use of computer-based modeling techniques (i.e., agent-based simulation and system dynamics) for economic and socio-historical issues represents an important part in her research. She is especially interested in questions of man-environment interaction, environmental impact on early human societies, adaptation and resilience as well as complex economic behavior in prestate societies (mining economy, economy of salt).
Andreas Krause’s current research combines market microstructure theory and agent-based computational economics with the aim of developing models that are able to replicate realistic time series properties of financial assets. By combining approaches developed in agent-based computational economics, in particular zero-intelligence trading, with market microstructure elements, I am also investigating the implications for the optimal design of trading rules in financial markets. As part of the decision-making process in financial markets furthermore explore the formation and evolution of social networks and how decision-making in such networks affects trading decisions.
Trond Kristiansen’s research focuses on individual-based models (IBM) for simulating the early life history of larval and juvenile Atlantic cod. The models are mechanistic and can if be used for any given geographical location in the North Atlantic. They can also be used for species other than cod by changing the biological characteristics (such as e.g. metabolic rate and growth rates).
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