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Applications of Learning Theory in Algorithmic Game Theory
Published on Aug 20, 20154346 Views
Algorithmic game theory is a field that uses and extends tools from economics and game theory to reason about fundamental computer science problems. The field is important both for its applications, w
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Chapter list
Two Applications of Learning Theory in Algorithmic Game Theory00:00
Two Case Studies - 100:45
Two Case Studies - 202:54
The Price of Anarchy - 103:03
The Price of Anarchy - 203:13
The Price of Anarchy - 304:25
The Price of Anarchy - 405:21
The Price of Anarchy of Health Care06:19
The Price of Anarchy in basketball06:47
POA Bounds Without Convergence08:12
Robust POA Bounds10:07
POA Bounds Without Convergence - 111:25
POA Bounds Without Convergence - 212:15
Extension Theorems - 114:11
Extension Theorems - 214:55
Extension Theorems - 315:24
The Math - 115:59
The Math - 217:36
Smooth Games -120:25
Smooth Games -222:21
Some Smoothness Bounds26:08
Canonical Example26:34
An Out-of-Equilibrium Bound26:38
No-Regret Sequences27:33
Smooth => No-Regret Bound29:07
Two Case Studies32:43
Myerson’s Auction (i.i.d.)34:49
Optimal Single-Item Auctions37:06
Motivating Question39:13
Formalism: Single Buyer42:27
Results for a Single Buyer43:52
Formalism: Multiple Buyers46:27
Positive Results46:55
Negative Results47:55
Recent Developments49:42
Open Directions53:05