ALGORITHMIC GAME THEORY & LEARNING
Game theory is a mathematical lens for studying interactions among strategic agents, modeling these situations as games with the goal of understanding and influencing outcomes. Core topics include non-cooperative game theory, mechanism design, and cooperative game theory, examined via the lens of a computer scientist, drawing on tools from theoretical computer science, optimization, probability, and machine learning. Themes include the impact of self-interested behavior on others and on societal outcomes, the emergence of equilibria under such behavior, how such equilibria can be computed or approximated, designing mechanisms to ensure fair and efficient outcomes, and how cooperation can lead to fair value distribution. Particular emphasis is placed on studying, designing, and implementing algorithms.
Topics
- non-cooperative game theory
- mechanism design
- and cooperative game theory
- examined via the lens of a computer scientist
- drawing on tools from theoretical computer science
- optimization
- probability
- and machine learning
Topics explicitly listed in official catalogue description
Prerequisites & course information
Requisites: ( COMP SCI 300 or 320 ), ( MATH/COMP SCI 240 or STAT/COMP SCI/MATH 475 ), and ( MATH 320 , 340 , 341 , 345 or 375 ), or graduate/professional standing Course Designation: Core GenEd - Mathematics & Quantitative Reasoning Level - Advanced L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Learning Outcomes: 1. Analyze strategic interactions using normal form games, including general-sum and zero-sum formulations, and apply solution concepts such as dominant strategy equilibria, Nash equilibria (NE), correlated equilibria (CE), and coarse correlated equilibria (CCE). Audience: Undergraduate 2. Compute/approximate NE, CE, and CCE using techniques such as the indifference lemma, linear programming, best response dynamics, and no-regret online (machine) learning. Audience: Undergraduate 3. Apply game-theoretic frameworks to machine learning problems, such as image generation and curriculum learning, by modeling and analyzing strategic behavior. Audience: Undergraduate 4. Design mechanisms that promote socially desirable outcomes among strategic agents, apply them to domains such as compute resource allocation, kidney exchange, residency matching, and auctions, and evaluate tradeoffs between game-theoretic desiderata and computational complexity. Audience: Undergraduate 5. Describe and evaluate foundational and computational aspects of cooperative game theory, including bargaining solutions and coalitional game concepts, and develop efficient approximation algorithms for computing them. Audience: Undergraduate Learning Outcomes: 1. Analyze strategic interactions using normal form games, including general-sum and zero-sum formulations, and apply solution concepts such as dominant strategy equilibria, Nash equilibria (NE), correlated equilibria (CE), and coarse correlated equilibria (CCE). Audience: Undergraduate 2. Compute/approximate NE, CE, and CCE using techniques such as the indifference lemma, linear programming, best response dynamics, and no-regret online (machine) learning. Audience: Undergraduate 3. Apply game-theoretic frameworks to machine learning problems, such as image generation and curriculum learning, by modeling and analyzing strategic behavior. Audience: Undergraduate 4. Design mechanisms that promote socially desirable outcomes among strategic agents, apply them to domains such as compute resource allocation, kidney exchange, residency matching, and auctions, and evaluate tradeoffs between game-theoretic desiderata and computational complexity. Audience: Undergraduate 5. Describe and evaluate foundational and computational aspects of cooperative game theory, including bargaining solutions and coalitional game concepts, and develop efficient approximation algorithms for computing them. Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027