Computational modeling of collective behaviour and cognition - Detailseite

This course will introduce students to computational models of collective behaviour and cognition.
Such computational approaches can bridge the gap between the dynamics happening within and
between agents in social groups, thereby mechanistically linking the individual and collective level. This
ties together how (i) individual cognition drives social systems; (ii) the social environment drives
individual cognition, and (iii) both levels interact.
We will combine introductory lectures with practical hands-on sessions in R. We will start with a
general introduction to computational models of cognition and typical Bayesian workflows including
simulation, model fitting and parameter recovery. We then apply these methods to various computational models in social systems including models of (i) signal detection, (ii) evidence
accumulation, and (iii) reinforcement learning. We will show how these approaches can help answer
fundamental questions on social and collective decision making, such as when is it beneficial to learn
from others (as opposed to on your own), and how do social learning strategies influence collective
performance? A basic understanding of statistical theory and programming is recommended. By the
end of the course, students are expected to have an overview over key concepts of Bayesian statistics
and approaches to model decision making in social systems and will have developed step-by-step
knowledge on the process of computational modelling of social systems.

Mind and Brain students only!