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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.
I am Colombian with passion for social impact. I believe that change starts at the individual, community, local and then global level. I have set my goal in making a better experience to whatever challenges I encounter and monetary systems and governance models is what concerns me at the time.
In my path to understanding and reflecting about these issues I have found my way through “Reflexive Modeling”. Models are just limited abstractions of reality and is part of our job as researchers to dig in the stories behind our models and learn to engage in a dialogue between both worlds.
Technology empowers us to act locally, autonomously and in decentralized ways and my research objective is to, in a global context, find ways to govern, communicate and scale the impact of alternative monetary models. This with a special focus on achieving a more inclusive and community owned financial system.
As a Ph.D. fellow for the Agenda 2030 Graduate School, I expect to identify challenges and conflicting elements in the sustainability agenda, contribute with new perspectives, and create solutions for the challenges ahead
I am a geographer interested in exploring tourism system dynamics and assessing tourism’s role in environmental sustainability using agent-based modelling (ABM). My current work focus is on human complex systems interactions with the environment and on the application of tools (such as scenario analysis, network analysis and ABM) to explore topics systems adaptation, vulnerability and resilience to global change. I am also interested in looking into my PhD future research directions which pointed the potential of Big Data, social media and Volunteer Geographical Information to increase destination awareness.
I have extensive experience in GIS, quantitative and qualitative methods of research. My master thesis assessed the potential for automatic feature extraction from QuickBird imagery for municipal management purposes. During my PhD I have published and submitted several scientific papers in ISI indexed journals. I have a good research network in Portugal and I integrate an international research network on the topic “ABM meets tourism”. I am a collaborator in a recently awarded USA NCRCRD grant project “Using Agent Based Modelling to Understand and Enhance Rural Tourism Industry Collaboration” and applied for NSF funding with the project “Understanding and Enhancing the Resilience of Recreation and Tourism Dependent Communities in the Gulf”.
As a data scientist, I employ a variety of ecoinformatic tools to understand and improve the sustainability of complex social-ecological systems. I also apply Science and Technology Studies lenses to my modeling processes in order to see potential ways to make social-ecological system management more just. I prefer to work collaboratively with communities on modeling: teaching mapping and modeling skills, collaboratively building data representations and models, and analyzing and synthesizing community-held data as appropriate. At the same time, I look for ways to create space for qualitative and other forms of knowledge to reside alongside quantitative analysis, using mixed and integrative methods.
Recent projects include: 1) Studying Californian forest dynamics using Bayesian statistical models and object-based image analysis (datasets included forest inventories and historical aerial photographs); 2) Indigenous mapping and community-based modeling of agro-pastoral systems in rural Zimbabwe (methods included GPS/GIS, agent-based modeling and social network analysis); 3) Supporting Tribal science and environmental management on the Klamath River in California using historical aerial image analysis of land use/land cover change and social networks analysis of water quality management processes; 4) Bayesian statistical modeling of community-collected data on human uses of Marine Protected Areas in California.
I obtained a PhD in database information theory from the University of the West of Scotland in 2015, and have been a researcher at the James Hutton Institute ever since. My areas of research are agent-based-modelling (ABM), data curation, effective use of infrastructure as a service (IaaS), and semantic information representation and extraction using formal structures such as computerised ontologies, relational databases and any other structured or semi-structured data representations. I primarily deal with social and agricultural models and was originally taken on in the role of knowledge engineer in order to create the ontology for the H2020 project, Green Lifestyles, Alternative Models and Upscaling Regional Sustainability (GLAMURS). Subsequent work, for the Scottish Government has involved the use of IaaS, more commonly referred to as the “cloud” to create rapidly deployable and cheap alternatives to in-house high-performance computing for both ABM and Geographical Information System models.
It is the mixture of skills and interests involving modelling, data organisation and computing infrastructure expertise that I believe will be highly apposite in the duties associated with being a member of the CoMSES executive. Moreover, prior to joining academia, I spent about 25 years as a developer in commercial IT, in the agricultural, entertainment and banking sectors, and feel that such practical experience can only benefit the CoMSES network.
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., “popular nodes”) 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.
I discovered at the same time Agent-Based Modeling method and Companion Modelling approach during my master degrees (engeenering and artificial intelligence and decision) internship at CIRAD in 2005 and 2006 where I had the opportunity to participate as a modeller to a ComMod process (Farolfi et al., 2010).
Then, during my PhD in computer Science applied to Modeling and Simulation, I learned the Theory of Modeling and Simulation and the Discrete EVent System specification formalism and proposed a conceptual, formal and operational framework to evaluate simulation models based on the way models are used instead of their ability to reproduce the target system behavior (Bonté et al., 2012). Applied to the surveillance of Epidemics, this work was rather theoritical but very educative and structuring to formulate my further models and research questions about modeling and simulation.
From 2011 to 2013, I worked on viability theory applied to forest management at the Compex System Lab of Irstea (now Inrae) and learned about the interest of agregated models for analytical results (Bonté et al, 2012; Mathias et al, 2015).
Since 2013, I’m working for Inrae at the joint The Joint Research Unit “Water Management, Actors, Territories” (UMR G-EAU) where I’m involved in highly engaging interdisciplinary researches such as:
- The Multi-plateforme International Summer School about Agent Based Modelling and Simulation (MISSABMS)
- The development of the CORMAS (COmmon Pool Resources Multi-Agents Systems) agent-based modeling and simulation Platform (Bommel et al., 2019)
- Impacts of the adaptation to global changes using computerised serious games (Bonté et al., 2019; Bonté et al. , 2021)
- The use of experimentation to study social behaviors (Bonté et al. 2019b)
- The impact of information systems in SES trajectories (Paget et al., 2019a)
- Adaptation and transformations of traditional water management and infrastructures systems (Idda et al., 2017)
- Situational multi-agent approaches for collective irrigation (Richard et al., 2019)
- Combining psyhcological and economical experiments to study relations bewteen common pool resources situations, economical behaviours and psychological attitudes.
My research is about modelling and simulation of complex systems. My work is to use, and participate to the development of, integrative tools at the formal level (based on the Discrete EVent System Specification (DEVS) formalism), at the conceptual level (based on integrative paradigms of different forms such as Multi-Agents Systems paradigm (MAS), SES framework or viability theory), and at the level of the use of modelling and simulation for collective decision making (based on the Companion Modelling approach (ComMod)). Since 2013 and my integration in the G-EAU mixt research units, my object of studies were focused on multi-scale social and ecological systems, applied to water resource management and adaptation of territories to global change and I added experimentation to my research interest, developping methods combining agent-based model and human subjects actions.
I am a marine environmental scientist by training (U Oldenburg, 2001) with a PhD in atmospheric physics (U Wuppertal, 2005) and a strong modeling focus throughout my career.
I have built models (C, C++) for understanding the regional transitions from hunting-gathering subsistence to agropastoral life styles throughout the world. The fundamental principle of these models is to consider aggregate traits of populations, such as the preference for a subsistence style. I applied these models to the European “Wave of Advance”, to the disintegration of the urban Indus civilisation and to the differential emergence of agropastoralism in the Americas versus Europe, but also globally. An interesting outcome of these models are global and reginoally resolved prehistoric CO2 emissions caused by the land use transitions.
I have built and applied models for understanding the ecological relations and biogeochemical flows through the North Sea ecosystem. Also for this research I apply trait-based models, looking at traits such as vertical positioning or energy allocation. As an outcome, I have, e.g., estimated the biomass of blue mussels in the North Sea and quantified the effect of Offshore Wind Farm biofouling on the sea’s produtivity.
I led the development of the Earth System coupler MOSSCO, leveraging ESMF technologies. I like to rip legacy models apart and reconstruct them with interoperability and reusability by design. I contribute to building the next-generation modular hurricane forecasting system.
As a member of the Open Modeling Foundation (OMF), I am an evangelist of good scientific software practices, and educate and publish about improving underlying assumptions, stating clear purposes, keeping models simple and aquiring tools to further good practices.
To tackle the scientific challenges proposed by landscape dynamics and cooperation processes, I have developed a research methodology based on field work and companion modelling (ComMod) combined with the formalisation of the observed processes and agents based models.
This approach offers the possibility to understand : spatial, social, cultural and / or economic conditions that take place on territories, and to provide prospective scenarios.
These methods have been applied in various contexts: steep slope vineyards landscapes (2011), water resource management cooperation (2015), vegetation cover in dry climate (2017). The established research networks are still active through sustained collaborations and activities.
My technical expertise grew and evolved through investment in several workgroups: MAPS Team (Modelling Applied to Space Phenomena), OSGeo (president of the OSGeo’s French chapter between 2013 and 2016, member of the OSGeo-international chapter since 2015), various initiatives around modelling, exploration and sensibility analysis of spatial patterns behaviours, and more generally in Free Software communities.
I am interested in the socio-environmental conditions for the emergence of cooperation and mutual aid in social systems and mainly with regard to renewable resources. I consider in this context that Commons are a spatial manifestation of mutual aid.
From a technical point of view, I am very interested in the questions of model exploration (HPC), which led me to integrate the OpenMole community and to contribute to discussions about heuristic exploration.
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
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