CoMSES Winter School on Agent-Based Modeling of Social-Ecological Systems

Planned for January 17-28, 2022

Purpose of the Winter School
The winter school will teach participants about the opportunities and challenges of agent-based modeling of social-ecological systems. Participants will engage intensely with a few comprehensive agent-based models, learn best practices for modeling within a team, and learn how to successfully navigate modeling challenges across the social and natural sciences.

Content of the Course
The winter school has two primary components: lectures and group project work. Participants will also have the opportunity to present their own work in lightning talks. Lectures will introduce concepts in the social and natural sciences essential to modeling social-ecological systems including human behavior, collective behavior, resilience, and land cover change. Students will also learn and apply best practices for computational modeling with respect to reproducibility, model documentation, analysis of models and how to collaborate effectively in remote teams using Git and GitHub. Participants will be introduced to various stylized agent-based models used in actual research projects on social-ecological systems. Participants will chose one of the models and form groups to adapt, expand, and analyze the model to better understand the impact of particular assumptions on the social-ecological system in question. All models are developed in NetLogo so participantsmust be comfortable with reading and writing NetLogo code.

The 2022 Winter School will be be virtual once again like the 2021 Winter School, spread out over 2 weeks from January 17-28, 2022. The online live and interactive component will be kept at four hours a day during the morning of the Arizona, USA timezone (UTC-7). The first week will focus on lectures, hands-on training in best practices and the start of group projects. The second week will focus on group projects and presentation of results.

For more information and to submit your application by October 1, please visit

Short Course on Agent-Based Modelling for Social Research


The ERC Bayesian Agent-based Population Studies project team, based at the University of Southampton and the University of Rostock, in collaboration with the ESRC Centre for Population Change and the Max Planck Institute for Demographic Research, are delighted to announce a call for applications for a short training course “Agent-based modelling for social research”, to be held at the University of Southampton, on 6–10 July 2020.

The main aims of this week-long course are to familiarise the participants with the most recent advances in building, analysing and documenting agent-based models of social processes. During the course, we will cover aspects related to the choice of modelling language and environment, tailoring models for specific research purposes, statistical analysis of model results and key principles of experimental design, inclusion of realistic cognitive assumptions in models, and documenting the modelling endeavours by using a variety of approaches. The course is aimed particularly at PhD level students and early career researchers, with some prior experience with coding and interest in computational modelling in social science.

Further details and the application requirements are included in the call below. The deadline for applications is midnight on 29 February 2020, and successful applicants will be notified about acceptance by 15 March. For specific queries, please email

A new NetLogo Tutorial by Jen Badham

Jen Badham has released a new NetLogo tutorial, available from The tutorial is intended for complete beginners and covers topics like agent-centric thinking and good programming practices as well as NetLogo. The tutorial starts with an empty model and gradually adds people, environmental interaction and behaviour, replicating the modelling process that developers go through when building their own model.

Winter School on Agent-Based Modeling and Social-Ecological Systems

CoMSES Net is hosting its annual Winter School on Agent-Based Modelling and Social-Ecological Systems January 6-10, 2020 in sunny Tempe, Arizona, USA.

Purpose of the Winter School

The overall aim of the winter school is that the participants will learn about the opportunities and challenges of agent-based modeling of social-ecological systems. Participants will engage intensely with a few comprehensive models, learn best practices in doing modeling, and learn about the different modeling challenges across the various social and natural sciences.

Course Content

The winter school has two main components: 1) lectures and 2) project work. Lectures will introduce participants to different concepts in the social and natural sciences critical for modeling social-ecological systems, such as human behavior, collective behavior, hydrology, and land cover change. Students will also learn and use best practices to do modeling (reproducibility, model documentation, analysis of models). The participants will be introduced to various stylized agent-based models of actual research projects on social-ecological systems. Groups of participants will chose one of the models and adapt, expand, and analyze the model to better understand the impact of a particular assumptions on the overall outcome of the social-ecological system. The models are written in NetLogo. Therefore, participants must be able to write NetLogo programming code.

Application deadline: August 19, 2019

More details at:<

Two Day Mini-Course in Social Simulation (USA)

Modeling Emergence: Computer Simulation in the Social Sciences

University of Massachusetts, Amherst

Tuesday, August 27, 2019 – 9:00am to 4:00pm
Wednesday, August 28, 2019 – 9:00am to 4:00pm
Dubois Library Room 1667


Much quantitative social science and behavioral research has focused on identifying statistical relationships in cross-sectional data. While rigorous and tractable, this research typically assumes the objects of study are independent of one another, and thus assumes away the complex social processes that we hope to understand. Qualitative (ethnographic and comparative-historical) lenses have allowed us to view the social world as a web of interdependent and contingent processes, with macro-level cultures, communities and organizations emerging from and constraining the micro-level interactions of individuals, relationships and families. An explosion of recent work has used computational models to think systematically and rigorously about these complex social dynamics. Simulation research can offer rich, nuanced process models similar to qualitative work, but employs a rigorous, transparent and replicable framework that can be extended to other research contexts, similar to statistical approaches.

Theorists use computer models to elucidate, extend, integrate and validate social theory. Policy analysts use computer models to predict outcomes of policy scenarios in complex and interactive domains. Managers use computer models to design efficient and robust organizational operations. Health agencies use computer models to explore strategies for population interventions. This proliferation of simulation work has generated great interest in computer modeling methods, but few disciplinary departments presently offer general training in this area. This introductory workshop will allow attendees to understand some of the overall goals and techniques of social simulation, give them hands-on experience in experimenting on computational models, and point them to resources to begin using these tools in their own work.

We will explore a range of modeling domains including social networks, social influence, individual and social learning, and social norms. We will draw substantive applications mostly from the science of organizations (e.g. models of organizational culture and turnover, dynamics of collective action in teams, and intergroup conflict) and public health (e.g. understanding and resolving health disparities, transmission of disease on contact networks, social contagion and diffusion of health or risk behaviors).


To register for the course click here.<>


James Kitts<> joined UMass in 2012 and served as Founding Director of the Computational Social Science Institute. He has previously held faculty appointments at Columbia University, Dartmouth College, and the University of Washington. He has taught computational modeling Ph.D. seminars at Columbia, the University of Arizona, and the University of Washington, and has led modeling workshops for government agencies, universities and research institutes around the world. His research recently appeared in American Journal of Sociology, American Sociological Review, Social Forces, Social Psychology Quarterly, and Demography