Social systems are complex and adaptive. The advent of large language models (LLMs) affords the opportunity to model such systems with unprecedented realism, complexity, and depth. This course explores classic social scientific questions - such as the emergence of social coordination, durable inequality, and science as a social system - through the lens of cutting-edge AI. Leveraging LLM-based multi-agent simulations, students will revisit and reimagine foundational social science theories by modeling complex social interactions in silico. The course combines theoretical inquiry with computational experimentation, and seeks to push the frontier in social scientific modeling and theorizing. This course is a graduate level seminar serving multiple programs and asks students to read challenging texts and reflect on them in class and in writing. Students are also asked to develop multiagent simulations related to course topics.
3 units · GSB Letter Graded
Social systems are complex and adaptive. The advent of large language models (LLMs) affords the opportunity to model such systems with unprecedented realism, complexity, and depth. This course explores classic social scientific questions - such as the emergence of social coordination, durable inequality, and science as a social system - through the lens of cutting-edge AI. Leveraging LLM-based multi-agent simulations, students will revisit and reimagine foundational social science theories by modeling complex social interactions in silico. The course combines theoretical inquiry with computational experimentation, and seeks to push the frontier in social scientific modeling and theorizing. This course is a graduate level seminar serving multiple programs and asks students to read challenging texts and reflect on them in class and in writing. Students are also asked to develop multiagent simulations related to course topics.
Offered in Autumn 2025 at Stanford University.