Algorithmic Game Theory & Learning
Recorded 2026-10-14 Spring 2027
Algorithmic Game Theory & Learning
Algorithmic Game Theory & Learning covers non-cooperative and cooperative game theory, mechanism design, and equilibrium computation using tools from TCS, optimization, and ML.
offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed
Summary
What you’ll be able to do
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).
Compute/approximate NE, CE, and CCE using techniques such as the indifference lemma, linear programming, best response dynamics, and no-regret online (machine) learning.
Apply game-theoretic frameworks to machine learning problems, such as image generation and curriculum learning, by modeling and analyzing strategic behavior.
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.
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.
Source: UW Guide
Grade history
↗ average GPA
00 letter grades
No recorded grade history.
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Prerequisites
Course map(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
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Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|---|---|---|
| LEC 001 | Classroom Instruction | 0 / 50 | 0 |
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.
Meeting source records
Student experience
the class
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.
difficulty & workload
No workload feedback recorded.
Topics
- Non-cooperative and cooperative game theory, and mechanism design.
- Theoretical computer science, optimization, probability, and machine learning applications.
- Fair value distribution through cooperation.
Skills
- Analyze strategic interactions using normal form games and apply solution concepts like Nash and correlated equilibria.
- Compute or approximate game-theoretic equilibria using linear programming, best response dynamics, and online learning.
- Design mechanisms for resource allocation, matching, and auctions while evaluating efficiency and fairness tradeoffs.
- Apply game-theoretic frameworks to machine learning problems like image generation and curriculum learning.
Grades
Instructor
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Sources & history
UW–Madison
Catalog & offerings
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Selected offering source records
Raw records
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"title": "Algorithmic Game Theory & Learning",
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"typically_offered": "Not Applicable"
}
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View the dataset on Hugging Face Recorded 2026-10-14
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "bcf434c98f9e8ff3e183526adc25c21bbe514980c2decc383a90e4b60e84c873",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}