Economics and Computation

This course includes basics and tools for algorithmic game theory, and covers several topics on mechanism design and no-regret dynamics.

Textbooks & Referenced Lecture Notes

Twenty Lectures on Algorithmic Game Theory. Tim Roughgarden. Cambridge University Press. 2016.
Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis. Second Edition. M. Mitzenmacher and E. Upfal, Cambridge University Press, 2017. [cover]
A Modern Introduction to Online Learning. Francesco Orabona. 2022. arXiv:1912.13213. DOI: 10.48550/arXiv.1912.13213 [link]

Grading policy

Attendance (10%)
Assignments (40%)
Final Paper Presentations (50%)

Lectures and assignments

Course Introduction [slides]
Game Theory Preliminaries [slides], [assignment 01]
Minimax Principles [slides], [assignment 02]
Equilibrium Concepts [slides]
Social Choice [slides]
A Sketch of Nash’s Theorem from Fixed Point Theorems [slides]
Auctions & Mechanism Design Basics [slides]
Myerson's Lemma [slides]
Algorithmic Mechanism Design (Knapsack Auctions) [slides]
Revenue Maximizing Auctions [slides]
Simple Near-Optimal Auctions [slides]
No-Regret Online Learning [slides]
Other Selected Topics
*Selected Papers for Final Presentations [slides]

Please feel free to use the slides as long as giving appropriate credit to the author!
Any question is welcome.
Please contact Joseph, Chuang-Chieh Lin (Email to: josephcclin_AT_mail_ntou_edu_tw)