Learn · ACM Summer School on Algorithmic Game Theory,2019 - IIT Gandhinagar
Lecture 19 of 36ACM Summer School on Algorithmic Game Theory
Algorithmic for Computing Market Equilibrium
IIT Madras
Credits and licence
- From
- ACM Summer School on Algorithmic Game Theory,2019 - IIT Gandhinagar, IIT Madras (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- Teens profiles and older. How courses are placed
All the lectures
- Introduction to Stable Matchings
- Men-Optimality of the Men-Proposing Gale-Shapley Algorithm
- GS: Cheating Strategies for Men
- GS: Cheating Strategies for Women
- The Hospital Residents Problem
- Popular Matchings in the stable marriage problem
- Popularity in the House Allocation Problem - 1
- Popularity in the House Allocation Problem - 2
- Strategic Behavior in Popular Matchings
- Stable Roommates: Matchings in the Non-bipartite Setting
- An Introduction to Voting
- The Game of Trust - Nicky Case's Interactive Essay
- Arrow's Theorem
- Gibbard-Satterethwaite Theorem
- Domain Restrictions and Multiwinner Elections
- Incentive Design in Crowdsourcing Applications
- Adversarial Approaches in Deep Learning - Part 01
- Adversarial Approaches in Deep Learning - Part 02
- Algorithmic for Computing Market Equilibrium
- Tournament Fixing and Superkings
- Tournament Fixing Parameterized by FAS
- Tournament Fixing with Bribery
- An Introduction to Cake-Cutting
- Two Algorithms for Finding Proportional Allocations
- Envy-Freenes and Approximate EF
- Sperner's Lemma and Applications
- Cake Cutting with a Secret Agent
- Fairness Notions for Indivisible Goods
- Computing EF1 Allocations: Cycle Trading and Round Robin
- An Introduction to Rent Division
- Rent Division and Maximum Weight Matchings
- Hall's Theorem and Maximin Share
- Probability Review - Part 1
- Probability Review - Part 2
- Predicting Election Outcomes
- Reservoir Sampling and Preference Elicitation