Computational Social Psychology

Overview

Computational social psychology is a new frontier in the field of social psychology. Computational social psychology has two pillars: (a) computational modeling for theory building and hypothesis generation and (b) computational analysis for pattern discovery and hypothesis testing. This course combines a seminar-style survey of computational approaches to social psychological research with hands-on workshops on methods, tools, and software frameworks. Throughout the course, emphasis is placed on complex systems theory and its application to the study of psychological mechanisms underlying social phenomena.

Objectives

At the conclusion of the course, you will be able to:

1. Know the basic concepts and have skills of computational social psychology

   • Describe computational social psychological research by referencing classic literature, landmark studies, historical developments, and contemporary research

   • Build computational models of social psychological phenomena grounded in empirically established psychological mechanisms

   • Simulate computational experiments to generate causal explanations of complex social systems

   • Conduct computational analyses to observe, measure, and interpret patterns in data

2. Become familiar with the programming language of NetLogo and Python

3. Communicate your own research effectively to both academic and public audiences