I am a modeler scientist at CIRAD. As member of the Green Research Unit, I contribute to promote the Companion Modeling approach (http://www.commod.org). Through the development of CORMAS, a Framework for Agent-Based Models (http://cormas.cirad.fr), I have been focusing on the development and the use of multi-agent simulations for renewable resource management issues. I have been based several years in Brazil, at the University of Brasilia and at the PUC-Rio University, until 2014. I developed models related to environmental management, such as breeding adaptation to drought in the Uruguay or as breeding and deforestation in the Amazon. I am currently based in Costa Rica, firstly at the University of Costa Rica working on adaptation of agriculture and livestock to Climate Changes, and now at CATIE, working on coffe rust.

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.