Summary

This course will explore the intersection of machine learning (ML) and human behavior. The format of the course will be a mix of paper presentations, lectures, and a final project. We will cover three units:

Unit I: Modeling Human Behavior with ML. Predicting or simulating human behaviors is useful when behaviors are difficult to observe (e.g., for cost or privacy reasons) or cannot be observed (e.g., future or counterfactual behaviors). In this unit, we'll begin with recent efforts to simulate behaviors with LLMs, including survey responses, experimental results, and social interactions. We will discuss challenges in this domain, such as bias, diversity, steerability, generalization, and scalability, along with approaches to address these challenges. We will start from the individual-level and scale up to entire networks and societies, exploring both LLM-based and more traditional approaches to modeling human societies.

Unit II: Algorithmically Infused Societies. In this unit, we'll discuss the interplay between algorithms and social systems: how algorithms shape human behaviors and human behaviors feed back into algorithms, resulting in feedback loops. We will study these loops in different contexts, such as recommender systems and social media feeds, and explore human awareness of algorithms and strategic shifts in behavior. We will end with a discussion about automating human decision-making, including how to compare human vs. algorithmic decisions under selective labels and the validity and risks of such automation.

Unit III: Adapting AI to Human Behavior. In this final unit, we will focus on how humans behave in interaction with AI systems and how we should adapt generative AI systems to work more effectively with humans. We will begin with analyses of human-AI interactions in the wild and discuss how to evaluate human-AI interactions. We will then explore various ways in which AI needs to adapt to humans in order to improve human-AI outcomes, such as how to understand human intents (e.g., given ambiguous user queries), learn individual preferences and personalize models, mitigate overreliance and provide explanations, and strive for complementarity.

Schedule

The schedule is tentative and will be finalized after the first class. More readings will be added soon.

Date	Lecture
8/28 Thu	Introduction to course
Unit I. Modeling Human Behaviors with ML
09/02 Tue	Introduction to LLM social simulation Anthis et al (ICML 2025): "Position: LLM Social Simulations Are a Promising Research Method" Hewitt*, Ashokkumar* et al (2024): "Predicting results of social science experiments using large language models"
09/04 Thu	Bias and diversity
09/09 Tue	Steerability: fine-tuning, steering vectors
09/11 Thu	Foundation model of individuals, generalization
09/16 Tue	GUEST LECTURE: Joon Sung Park Generative agent simulations of individuals and societies
09/18 Thu	Limits to prediction
09/23 Tue	Social interactions and networks
09/25 Thu	Mobility patterns, interactions with the physical environment
09/30 Tue	Agent-based modeling, policy planning
Unit II. Algorithmically Infused Societies
10/02 Thu	Introduction to algorithmically infused societies Wagner et al (Nature 2021): "Measuring algorithmically infused societies" Brinkmann et al (Nature Human Behaviour 2023): "Machine culture"
10/07 Tue	GUEST LECTURE: Jonathan Stray Social media algorithms and polarization
10/09 Thu	Recommender systems, diversity
10/14 Tue	Algorithmic awareness & strategic behavior
10/16 Thu	Algorithmic decision-making
10/21 Tue	GUEST LECTURE: Diag Davenport Automating automaticity: algorithms trained on automatic human behaviors
Unit III. Adapting AI to Human Behavior
10/23 Thu	Human-AI conversations in the wild
10/28 Tue	Evaluating human-AI interactions
10/30 Thu	Understanding human intents
11/04 Tue	Learning individual preferences, personalization
11/06 Thu	GUEST LECTURE: Katie Collins
11/11 Tue	Academic and Administrative Holiday
11/13 Thu	Human-AI decision-making, overreliance
11/18 Tue	Towards human-AI complementarity
11/20 Thu	Project presentations
11/25 Tue	Project presentations
11/27 Thu	Thanksgiving Break
12/02 Tue	GUEST LECTURE: Jessy Lin Decision-oriented dialogues and human-AI collaboration
12/04 Thu	Course wrap-up

Acknowledgements

Course topics were inspired in part by Johan Ugander's course on Social Algorithms and Joon Sung Park's course on AI Agents and Simulations. The course form was adapted from Sewon Min's course on Data-Centric Large Language Models.