Algorithmic Game Theory & Learning
ALGORITHMIC GAME THEORY & LEARNING applies computer science to game theory, focusing on algorithmic design for strategic interactions, equilibria, and mechanism design.
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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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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.
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"requirements_status": "needs_review",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}