An introduction to the course; course logistics, and overview of the field of social choice and mechanism design.
This topics provides an introduction to social choice theory and voting rules for preference data aggregation. We discuss the non-existence of "prefect" voting rules (Arrow's impossibility theorem) and the non-existence of intuitive but desirable rules that are immune to strategic manipulation (Gibbard-Satterthwaite theorem). A brief discussion of domain restriction (median rule) and complexity of manipulation (Bartholdi, Tovey and Trick condition).
An introduction to fair allocation of divisible resources (cake cutting). We will discuss fairness concepts such as proportionality and envy-freeness, seminal algorithms that guarantee these fairness properties and go over query a model for elicitation of preference data (the Robertson-Webb model).
How should we allocate resources or goods when they are not divisible? We discuss a variety of deterministic relaxations such as envy-freeness up to one item as well as other fairness concepts such as the Maximin Share Guarantee, discuss computational challenges in achieving these properties, and explore their relation to other desirable concepts such as optimality (e.g. Pareto optimality, Nash welfare).
To Divide the Rent, Start With a Triangle [link]
More relaxations and allocations of chores.
Can we achieve fairness through randomization? We discuss randomized algorithms for allocating indivisible resources and focus on two seminal mechanisms: Random Serial Dictatorship and the Probabilistic Serial Rule. We also discuss the relation to other deterministic fairness notions such as EF1.
How should mutually relationships form when two sides have preferences over one another? We discuss two-sided matching problems, algorithms for ensuring stability of the outcome (the Gale Shapley algorithm) as well as strategic manipulation in two-sided settings.
Discussions on AI ethics, philosophical aspects of fairness and algorithmic decision making.
Discussions on AI ethics, philosophical aspects of fairness and algorithmic decision making.
October 21, 2026
Paper 1: Nash Learning from Human Feedback - Xinran / Patrice / Jenn
Project Disucssions
PageRank: A trillion dollar algorithm (video) [link]
October 28, 2026
Paper 2: Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences? - Tara / Leona
Paper 3: Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment - Ishan / Zhilin
November 4, 2026
Paper 4: A Little Charity Guarantees Almost Envy-Freeness - Umar / Xiyuan
Paper 5: Judging LLM-as-a-judge with MT-bench and Chatbot Arena - Charles / Hins
November 11, 2026
Paper 6: AI can help humans find common ground in democratic deliberation - Rich / Howie / Shafa
Project Discussions
November 18, 2026
Paper 7: Colosseum: Auditing Collusion in Cooperative Multi-Agent Systems -Sourajit / Christopher
Paper 8: Manipulating Gale-Shapley Algorithm: Preserving Stability and Remaining Inconspicuous - William / Xinnian / Rui
December 2, 2026
Project Discussions
Project Presentations
December 9, 2026
Project Presentations: