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Analytical Sociology; Social Mechanisms; ABMs; Opinion Dynamics
Researcher in sustainable production and consumption, the service economy, energy markets, and electricity balancing mechanisms.
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 develop simulation tools for generating what-if scenarios for decision making. I predominantly use Agent-Based Modelling (ABM) technique as most of my simulations model complex systems. In some cases, I have extended existing tools with modifications to model the given system. Although the tools are meant for research purposes, I have followed industry friendly delivery mechanisms, such as unit-tests, automated builds and delivery on cloud platforms.
I have developed several agent-based and cellular automata applications combining agent-based modelling, geographical information systems and visualisation to understand the complex mechanisms of decision making in land use change and environmental stewardship in order to analyse:
• the role of pastoral agriculture in regional development,
• the tradeoffs between land use intensification and water quality,
• the adoption of land-based climate change mitigation practices, and
• the incorporation of cultural values into spatial futures or scenario modelling.
My research interests include policy informatics and decision making, modeling in policy analysis and management decisions, public health management and policy, and the role of public value in policy development. I am particularly interested in less mainstream approaches to modeling that account for learning, feedback, and other systems dynamics. I include Bayesian inference, agent-based models, and behavioral assumptions in both my research and teaching.
In my dissertation research, I conceptualize state Medicaid programs as complex adaptive systems characterized by diverse actors, behaviors, relationships, and objectives. These systems reproduce themselves through both strategic and emergent mechanisms of program management. I focus on the mechanism by which citizens are sorted into or out of the system: program enrollment. Using Bayesian regression and agent-based models, I explore the role of administrative practices (such as presumptive eligibility and longer continuous eligibility periods) in increasing enrollment of eligible citizens into Medicaid programs.
I am a Postdoctoral Associate in the Ecology, Evolution and Behavior department at the University of Minnesota. My research involves using agent-based models combined with lab and field research to test a broad range of hypotheses in biology. I am currently developing an agent-based model of animal cell systems to investigate the epigenetic mechanisms that influence cell behavior. For my PhD work, I created a model, B3GET, which simulates the evolution of virtual primates to better understand the relationships between growth and development, life history and reproductive strategies, mating strategies, foraging strategies, and how ecological factors drive these relationships. I have also conducted fieldwork to inform the modeled behavior of these virtual organisms. Here I am pictured with an adult male gelada in Ethiopia!
I specialize in creating agent-based models of biological systems for research and education in genetics, evolution, demography, ecology, and behavior.
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.
Corinna is a lecturer in the Department of Sociology. She joined the Centre for Research in Social Simulation at the in August 2008 as a Research Fellow. Her academic background is in Philosophy (LSE, BSc MSc) and Computer Science (KCL,PhD), where her PhD Instinct for Detection developed a logic for abductive reasoning.
Currently Corinna is the PI on an AHRC Research Grant on collective reasoning in agent-based modelling, titled Collective Reasoning as a Moral Point of View. Her research interests are decision mechanisms, in particular collective decision-making, context dependency of decisions and methodological and epistemological aspects of agent-based modelling and social simulation. She has applied collective decision making to the analysis to the weakening of the Mafia in Southern Italy within the GLODERS project and published a book Modelling Norms, co-authored with Nigel Gilbert, providing a systematic analysis of the contribution of agent-based modelling to the study of social norms and deviant behaviour. Recently Corinna has been developing a teaching stream within CRESS with a periodically running short course Agent-based Modelling for the Social Scientist and the MSc Social Science and Complexity.
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