Dr. Roger Cremades is a complex systems scientist and heterodox global change economist integrating human-Earth interactions across systems and scales into modular quantitative tools, e.g. connecting drought risks in cities with land use at the river basin scale. He is elected Council member of the Complex Systems Society (2022-2025) and previously served as co-Chair of the Development Team of the Finance and Economics Knowledge-Action Network of Future Earth, the largest global research programme in global change (2020-2022). Roger coordinated research and co-production projects above €1M, and published in top journal like PNAS, Nature Climate Change, and Nature Geoscience. As a scientific modeler in the Social and Ecological Sciences, Roger integrates complex systems concepts into integrated assessment models of global change, with a focus on cities.

The future of CoMSES.Net, in Roger’s vision, is to augment its projection into a hub for discussing state-of-the-art approaches on modeling for the Social and Ecological Sciences, e.g. via bi-annual webinars, so that the Model Library becomes a lighthouse from where all communities developing, sharing, using, and reusing agent-based and other computational models also find valuable discussions to advance their research, education, and computational practice.

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.

Dr. Dawn Parker is a professor at the University of Waterloo in the School of Planning. Her research focuses on the development of integrated socio-economic and biophysical models of land-use change. Dr. Parker works with agent-based modeling, complexity theory, geographic information systems, and environmental and resource economics. Her current ongoing projects include Waterloo Area Regional Model (WARM) Urban intensification vs. suburban flight, a SSHRC funded development grant that explores the causal relationships between light rail transit and core-area intensification, and the Digging into Data MIRACLE (Mining relationships among variables in large datasets from complex systems) project.

Antônio Sousa is a biologist with a background in medical entomology, disease ecology, statistical and computational modeling. Antônio has a Ph.D. (2018) and Master (2014) in Science from the School of Public Health at the University of São Paulo, Brazil. Currently, he is a postdoctoral fellow in the same institution.

I am a computational archaeologist with a strong background in humanities and social sciences, specialising in simulating socioecological systems from the past.

My main concern has been to tackle meaningful theoretical questions about human behaviour and social institutions and their role in the biosphere, as documented by history and archaeology. My research focuses specifically on how social behaviour reflects long-term historical processes, especially those concerning food systems in past small-scale societies. Among the aspects investigated are competition for land use between sedentary farmers and mobile herders (Angourakis et al. 2014; 2017), cooperation for food storage (Angourakis et al. 2015), origins of agriculture and domestication of plants (Angourakis et al. 2022), the sustainability of subsistence strategies and resilience to climate change (Angourakis et al. 2020, 2022). He has also been actively involved in advancing data science applications in archaeology, such as multivariate statistics on archaeometric data (Angourakis et al. 2018) and the use of computer vision and machine learning to photographs of human remains (Graham et al. 2020).

As a side, but not less important interest, I had the opportunity to learn about video game development and engage with professionals in Creative Industries. In one collaborative initiative, I was able to combine my know-how in both video games and simulation models (\href{https://doi.org/10.1007/978-3-030-92843-8_15}{Szczepanska et al. 2022}).

Elizabeth Hunter received a BA in Mathematics and Economics at Boston University in 2011. She worked as a health economics researcher at Research Triangle Institute for three years where she worked on a team that developed the risk adjustment models for the US health insurance exchanges. She attended the University of Limerick and received an MSc in Mathematical Modelling in 2015. She completed a PhD at Technological University Dublin. Her PhD research focuses on agent-based simulations for infectious disease epidemiology with the goal of creating an agent-based simulation of Ireland. Elizabeth is currently working on the Precise4Q as a Postdoctoral researcher working on predictive modelling in stroke.

I’m a Research Associate in Computational Social Science at Durham University working on a project that intends to produce more realistic artificial social networks (RASN) for simulation by creating a taxonomy of existing generator papers, accessible as an interactive, open-access database, in addition to exploring the interdependencies of social network’s structural properties. I obtained my PhD from University of Glasgow in (2023) where I was working on modelling national identity polarisation on social media platforms using ABMs.

I am a Reader in the Centre for Health Economics, conducting interdisciplinary research aimed at tackling healthcare challenges and improving decision-making and implementation in healthcare policy. My research is centred around using systems thinking and modelling approaches in health economics evaluation and draws on tools and methods from mathematical epidemiology, economics, management science, and computer science, among other fields.

My main body of work involves systems modelling and simulation, and it involves integrating disease and economic models for policy impact evaluation and prioritisation. I am interested in both infectious disease and non-communicable disease modelling. From a methodological standpoint, I am particularly interested in strengthening rigour in agent-based modelling and hybrid models, which integrate modelling methods when this simplifies analyses. I have applied my research to studying and conducting knowledge-exchange activities addressing global health challenges. This includes conducting healthcare intervention and policy evaluations, studying health systems strengthening in low- and middle-income countries, studying antimicrobial resistance policy globally and in the UK, evaluating COVID-19 policy and interventions, investigating how behaviour and social structure affect health and diseases, and exploring the role of incentives in healthcare policy design.

I hold a PhD in Management Science, specialising in modelling for healthcare policy, from the University of Strathclyde and an MA in economics and BA honours economics from McGill University, in Montreal.

Mario Ureta holds a BSc in Economics from Birkbeck, University of London, a Graduate Diploma in Data Science from the London School of Economics, and an MSc in Data Science and Analytics from Brunel University London. He is currently a PhD student in Computing Science at Birkbeck, University of London. His research focuses on the economic study of individual preferences and decision-making, and on the use of agent-based models as a bridge between economic theory and computational experimentation. Through economic simulation, his work examines how heterogeneous preferences, social interaction, and firm behaviour jointly shape aggregate market outcomes, including non-linear dynamics and tipping points.