My research is focused on autonomous agents and multiagent systems. Specifically: Trust and reputation models, cognitive architectures, cognitive models and social simulation.
Sae Schatz, Ph.D., is an applied human–systems researcher, professional facilitator, and cognitive scientist. Her work focuses on human–systems integration (HSI), with an emphasis on human cognition and learning, instructional technologies, adaptive systems, human performance assessment, and modeling and simulation (M&S). Frequently, her work seeks to enhance individual’s higher-order cognitive skills (i.e., the mental, emotional, and relational skills associated with “cognitive readiness”).
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.
GIS enthusiast and ABM practitioner
Social Network Analysis
social-ecological modelling; cognitive modelling; agent-based modeling&simulation; data science; smart city modelling; artificial intelligence; large-scale simulation
Mathematical modeling and simulation in social sciences, biology, physics, and signal processing.
Modeling and simulation of future impacts of information and communication technologies on environmental sustainability using agent based modeling and system dynamics
Social Simulation using MABS. At present, research to expand SocLab in order to model emotions and morality.
Others: Organisations, Soft Systems, Planning methodologies.
I studied Mathematics at Oxford (1979-1983) then did youth work in inner city areas for the Educational Charity. After teaching in Grenada in the West Indies we came back to the UK, where the first job I could get was in a 6th form college (ages 16-18). They sent me to do post16 PCGE, which was so boring that I also started a part-time PhD. The PhD was started in 1992 and was on the meaning and definition of the idea of “complexity”, which I had been pondering for a few years. Given the growth of the field of complexity from that time, I had great fun reading almost anything in the library but I did finally finish it in 1999. Fortunately I got a job at the Centre for Policy Modelling (CfPM) in 1994 with its founder and direction, Scott Moss. We were doing agent-based social simulation then, but did not know it was called this and did not meet other such simulators for a few years. With Scott Moss we built the CfPM into one of the leading research centres in agent-based social simulation in the world. I became director of the CfPM just before Scott retired, and later became Professor of Social Simulation in 2013. For more about me see http://bruce.edmonds.name or http://cfpm.org.
All aspects of social simulation including: techniques, tools, applications, philosophy, methodology and interesting examples. Understanding complex social systems. Context-dependency and how it affects interaction and cognition. Complexity and how this impacts upon simulation modelling. Social aspects of cognition - or to put it another way - the social embedding of intelligence. Simulating how science works. Integrating qualitative evidence better into ABMs. And everything else.