About

The Computational Summer school on Modeling Social and collective behavior (COSMOS) is designed to provide attendees (COSMOnauts) from diverse fields (e.g., psychology, economics, neuroscience, biology, computer science, etc…) and career stages (master, PhD, postdoc, and junior PI) with the computational skills required to tackle emerging challenges in understanding social learning and collective behaviour. Taking advantage of the close collaboration networks and interdisciplinary expertise available at the Centre for the Advanced Study of Collective Behaviour (CASCB) in Konstanz, COSMOS will invite leading researchers from diverse disciplines as guest instructors) who will mentor attendees’ own modelling projects during the School. In addition to providing hands-on instruction and mentorship for in-person attendees, we will publish openly accessible teaching materials) and recorded talks. The goal of the students will be to develop a foundational understanding about how computational modeling can be a strong thread that unifies and advances the study of a wide range of social and collective phenomena.

Objective

Today, we live in a massively interconnected and digitalized society that allows us to rapidly exchange information among huge numbers of genetically unrelated individuals. This level of connectivity is made possible by our capacity for integrating socially acquired information with our individual experiences in order to inform our reasoning and decision-making processes. Human social learning and our capacity for cumulatively innovating upon past solutions thus plays a central role in our ability to thrive in a wide range of environmental conditions. Yet, in addition to the benefits, there are also emerging dangers and pitfalls of social learning mechanisms gone awry, such as the spread of misinformation and formation of echo-chambers, which are phenomena we are only beginning to understand.

While human social learning is certainly distinct, there has also been a long-standing tradition of researchers studying collective behavior in non-human animals, with their own sophisticated vocabulary of theories and powerful predictive models. Yet, superficial gaps in terminologies and concepts used across human and non-human domains have made cross-disciplinary collaborations difficult. To better understand ourselves as human social learners, what is required is a unified language that allows us to engage in the quantitative and comparative study of social learning and collective intelligence observed across species.

To address this gap, we propose a summer school dedicated to developing the skills needed to communicate with researchers of social and collective behavior from diverse fields, using computational models as a common language. Computational modeling is becoming both more important and widely used in behavioural sciences. However, the mathematical framework and computational skills necessary to tackle the interdisciplinary challenges of modeling collective behaviour are not always readily accessible to early career researchers.

For our second iteration in 2023, we have invited an exciting new line-up of speakers across a diverse spectrum of approaches for modeling social and collective behavior in humans and animals. Some new concepts include communication (McCarthy et al,. 2021), coordination (Hawkins et al., 2022), the geometry of collective decision-making (Sridhar et al., 2021), and both agent-based models (Acerbi et al., 2023) and population-level dynamics of cultural transmission (Kandler & Crema, 2019).

Assignments and Mentorship

At the start of the summer school, students will give short poster presentations, during which they present their current work and receive feedback. Additionally, all participants will be matched with a faculty member as a mentor, where mentorship sessions provide opportunities for hands-on feedback. And new for 2023, we are introducing hands-on project work sessions that will provide participants with the opportunity to directly apply the skills that they have learned under the close supervision of instructors, using a number of prepared projects with open source data and code.

Resources

Course materials and exercises will be made available as interactive code notebooks in the R programming language (see Materials). Participants will also have access to a browser-based programming environment, which will accessible to all operating systems and avoid any issues with dependencies.

Applications

Submit your application by April 14th. Visit the application page. page for details.