Fall 2025 Course Topic: Games, Algorithms, and Social Choice (Fall 2025)
Games, Algorithms, and Social Choice is an interdisciplinary course that offers a broad overview on theoretical and algorithmic foundations of Artificial Intelligence through solutions developed within the fields of game theory and computational social choice. In the age of Internet, this field has seen a growing number of real-world applications in distributed platforms and online marketplaces. This course focuses on introducing students to the foundations of decision making in the presence of multiple intelligent—and often strategic—players. It will cover topics in game theory and social choice theory with specific focus on fair resource allocation, matching, preference aggregation, and non-cooperative games.
More information on this course can be found here.
- Semester: Fall 2025
- Instructor: Hadi Hosseini
- Who: Upper level undergraduate students (3rd or 4th year) who are interested in learning about foundations of AI decision making in multi-agent environments.
- When: MW 4:00 p.m. - 5:15 p.m. at E208 Westgate Building and W 4:00 p.m. - 5:15 p.m. via Zoom
- Where: This is a hybrid class (H2) which meets up to 50% remotely
- Monday class meetings will be held in E208 Westgate Building.
- Wednesday class meetings will be held via Zoom.
- This hybrid course (H2) counts as an in-person course for students with F-1 or J-1 visa status.
- Credits: 3.0
- Prerequisites: DS220
- LionPATH Class Number: 15385
By the end of this course, students will be able to:
- Design and analyze solutions concepts for formulating and solving AI problems involving multiple agents.
- Provide algorithmic or mathematical analysis for aggregating preference data, deciding collective decisions, and evaluating strategic behavior of agents in multi-agent settings.
- Identify open problems in the literature, provide theoretical framework, and validate the models through mathematical models or experimental simulation.
(Tentative) Course evaluation is based on:
- Homework assignments (2 x 10%)
- Paper critiques (20%)
- Participation (15%)
- Group project (45%)
- Projects should include solid and non-trivial implementations and/or novel research questions on a topic related to Agentic AI and Multiagent Systems.