Sep 29, 2014 Lirong Xia Matching. Report your preferences over papers soon! –deadline this Thursday before the class Drop deadline Oct 17 Catalan independence.

Slides:



Advertisements
Similar presentations
Piyush Kumar (Lecture 3: Stable Marriage) Welcome to COT5405.
Advertisements

Marriage, Honesty, & Stability N ICOLE I MMORLICA, N ORTHWESTERN U NIVERSITY.
Announcement Paper presentation schedule online Second stage open
Naveen Garg, CSE, IIT Delhi
Matching Theory.
Mechanism Design without Money Lecture 1 Avinatan Hassidim.
Sep. 8, 2014 Lirong Xia Introduction to MD (mooncake design or mechanism design)
Men Cheating in the Gale-Shapley Stable Matching Algorithm Chien-Chung Huang Dartmouth College.
Matching problems Toby Walsh NICTA and UNSW. Motivation Agents may express preferences for issues other than a collective decision  Preferences for a.
Aug 25, 2014 Lirong Xia Computational Social Choice.
1 Stable Marriage Problem. 2 Consider a society with n men (denoted by capital letters) and n women (denoted by lower case letters). A marriage M is a.
1 Discrete Structures & Algorithms Graphs and Trees: IV EECE 320.
Centralized Matching. Preview Many economic problems concern the need to match members of one group of agents with one or more members of a second group.
Stabile Marriage Thanks to Mohammad Mahdian Lab for Computer Science, MIT.
Lecture 6 CSE 331 Sep 14. A run of the GS algorithm Mal Wash Simon Inara Zoe Kaylee.
CSE 421 Algorithms Richard Anderson Lecture 2. Announcements Office Hours –Richard Anderson, CSE 582 Monday, 10:00 – 11:00 Friday, 11:00 – 12:00 –Yiannis.
Lecture 4 CSE 331 Sep 9, Blog posts for lectures Starts from today See Sep 8 post on the blog.
1 Bipartite Matching Polytope, Stable Matching Polytope x1 x2 x3 Lecture 10: Feb 15.
Inefficiency of equilibria, and potential games Computational game theory Spring 2008 Michal Feldman.
The Stable Marriage Problem
1 Stable Matching Problem Goal. Given n men and n women, find a "suitable" matching. n Participants rate members of opposite sex. n Each man lists women.
L3 #1 The Hospitals / Residents Problem and Some Extensions David Manlove University of Glasgow Department of Computing Science Supported by EPSRC grant.
August 12, 2010Online Mechanisms without Money On-line Mechanisms without Money Sujit Gujar E-Commerce Lab Dept of Computer Science and Automation Indian.
The Mathematics Of 1950’s Dating: Who wins the battle of the sexes? Adapted from a presentation by Stephen Rudich.
결혼문제로 본 조합론.
The Mathematics Of 1950’s Dating: Who wins the battle of the sexes? Presentation by Shuchi Chawla with some modifications.
Introduction to Matching Theory E. Maskin Jerusalem Summer School in Economic Theory June 2014.
Stable Matchings a.k.a. the Stable Marriage Problem
Great Theoretical Ideas in Computer Science.
1 The Stable Marriage Problem Algorithms and Networks 2014/2015 Hans L. Bodlaender Johan M. M. van Rooij.
Marriage and Mathematics Lau Ting Sum Samson Suen Wai.
Stable Matching Lecture 7: Oct 3. Matching A B C DE Boys Girls Today’s goal: to “match” the boys and the girls in a “good” way.
CSE 421 Algorithms Richard Anderson Winter 2009 Lecture 1.
Stable Marriages (Lecture modified from Carnegie Mellon University course Great Theoretical Ideas in Computer Science)
CSE 421 Algorithms Richard Anderson (for Anna Karlin) Winter 2006 Lecture 2.
The Stable Marriage Problem
By: Eric Zhang.  Indivisible items from multiple categories are allocated to agents without monetary transfer  Example – How paper presentations are.
Great Theoretical Ideas in Computer Science for Some.
Incentive compatibility in 2-sided matching markets
Matching Lecture 19: Nov 23.
Fair Shares.
Graph Algorithms Maximum Flow - Best algorithms [Adapted from R.Solis-Oba]
Instructor: Shengyu Zhang 1. Resource allocation General goals:  Maximize social welfare.  Fairness.  Stability. 2.
AEA Continuing Education in Game Theory Avinash Dixit and David Reiley Session 6: Market Design and Algorithms David Reiley Yahoo! Research January 2011.
Negotiating Socially Optimal Allocations of Resources U. Endriss, N. Maudet, F. Sadri, and F. Toni Presented by: Marcus Shea.
CSCI 256 Data Structures and Algorithm Analysis Lecture 2 Some slides by Kevin Wayne copyright 2005, Pearson Addison Wesley all rights reserved, and some.
Market Design and Analysis Lecture 2 Lecturer: Ning Chen ( 陈宁 )
Market Design and Analysis Lecture 1 Lecturer: Ning Chen ( 陈宁 )
18.1 CompSci 102 Today’s topics 1950s TV Dating1950s TV Dating.
Algorithms used by CDNs Stable Marriage Algorithm Consistent Hashing.
Stable marriages Competitive programming and problem solving Yoram Meijaard.
Matching Boys Girls A B C D E
Stable Matching.
Stable Marriage Problem
The Stable Marriage Problem
Fair division Lirong Xia Oct 7, 2013.
The Mathematics Of 1950’s Dating: Who wins The Battle of The Sexes?
Richard Anderson (for Anna Karlin) Winter 2006 Lecture 1
Matching Lirong Xia March 8, Matching Lirong Xia March 8, 2016.
Richard Anderson Autumn 2006 Lecture 1
CSE 421: Introduction to Algorithms
Matching and Resource Allocation
Richard Anderson Autumn 2016 Lecture 2
Introduction to Mechanism Design
Introduction to Mechanism Design
Richard Anderson Winter 2009 Lecture 2
Piyush Kumar (Lecture 3: Stable Marriage)
Introduction to Mechanism Design
Richard Anderson Autumn 2015 Lecture 2
Presentation transcript:

Sep 29, 2014 Lirong Xia Matching

Report your preferences over papers soon! –deadline this Thursday before the class Drop deadline Oct 17 Catalan independence referendum –Nov 9, News and announcements

"for the theory of stable allocations and the practice of market design." 2 Nobel prize in Economics 2013 Alvin E. Roth Lloyd Shapley

3 Two-sided one-one matching Boys Girls Stan Eric Kenny Kyle Kelly Rebecca Wendy Applications: student/hospital, National Resident Matching Program

Two groups: B and G Preferences: –members in B : full ranking over G ∪ {nobody} –members in G : full ranking over B ∪ {nobody} Outcomes: a matching M: B ∪ G→B ∪ G ∪ {nobody} –M( B ) ⊆ G ∪ {nobody} –M( G ) ⊆ B ∪ {nobody} –[M( a )=M( b )≠nobody] ⇒ [ a = b ] –[M( a )= b ] ⇒ [M( b )= a ] 4 Formal setting

5 Example of a matching nobody Boys Girls Stan Eric Kenny Kyle Kelly Rebecca Wendy

Does a matching always exist? –apparently yes Which matching is the best? –utilitarian: maximizes “total satisfaction” –egalitarian: maximizes minimum satisfaction –but how to define utility? 6 Good matching?

Given a matching M, ( b, g ) is a blocking pair if – g > b M( b ) – b > g M( g ) –ignore the condition for nobody A matching is stable, if there is no blocking pair –no (boy,girl) pair wants to deviate from their currently matches 7 Stable matchings

8 Example Boys Girls Stan Eric Kenny Kyle Kelly Rebecca Wendy >>>N : >>>N : >>>N : >>>N : >>>N : > >>>N : > >>>N : >

9 A stable matching Boys Girls Stan Eric Kenny Kyle Kelly Rebecca Wendy no link = matched to “nobody”

10 An unstable matching Boys Girls Stan Eric Kenny Kyle Kelly Rebecca Wendy Blocking pair: ( ) Stan Wendy

Yes: Gale-Shapley’s deferred acceptance algorithm (DA) Men-proposing DA: each girl starts with being matched to “nobody” –each boy proposes to his top-ranked girl (or “nobody”) who has not rejected him before –each girl rejects all but her most-preferred proposal –until no boy can make more proposals In the algorithm –Boys are getting worse –Girls are getting better 11 Does a stable matching always exist?

12 Men-proposing DA (on blackboard) Boys Girls Stan Eric Kenny Kelly Rebecca Wendy >>>N : >>>N : >>>N : >>>N : > >>>N : > >>>N : > Kyle >>>N :

13 Round 1 Boys Girls Stan Eric Kenny Kyle Kelly Rebecca Wendy nobody reject

14 Round 2 Boys Girls Stan Eric Kenny Kyle Kelly Rebecca Wendy nobody

15 Women-proposing DA (on blackboard) Boys Girls Stan Eric Kenny Kelly Rebecca Wendy >>>N : >>>N : >>>N : >>>N : > >>>N : > >>>N : > Kyle >>>N :

16 Round 1 Boys Girls Stan Eric Kenny Kyle Kelly Rebecca Wendy nobody reject

17 Round 2 Boys Girls Stan Eric Kenny Kyle Kelly Rebecca Wendy nobody reject

18 Round 3 Boys Girls Stan Eric Kenny Kyle Kelly Rebecca Wendy nobody

19 Women-proposing DA with slightly different preferences Boys Girls Stan Eric Kenny Kelly Rebecca Wendy >>>N : >>>N : >>>N : >>>N : > >>>N : > >>>N : > Kyle >>>N :

20 Round 1 Boys Girls Stan Eric Kenny Kyle Kelly Rebecca Wendy nobody reject

21 Round 2 Boys Girls Stan Eric Kenny Kyle Kelly Rebecca Wendy nobody reject

22 Round 3 Boys Girls Stan Eric Kenny Kyle Kelly Rebecca Wendy nobody reject

23 Round 4 Boys Girls Stan Eric Kenny Kyle Kelly Rebecca Wendy nobody reject

24 Round 5 Boys Girls Stan Eric Kenny Kyle Kelly Rebecca Wendy nobody

Can be computed efficiently Outputs a stable matching –The “best” stable matching for boys, called men-optimal matching –and the worst stable matching for girls Strategy-proof for boys 25 Properties of men-proposing DA

For each boy b, let g b denote his most favorable girl matched to him in any stable matching A matching is men-optimal if each boy b is matched to g b Seems too strong, but… 26 The men-optimal matching

Theorem. The output of men-proposing DA is men- optimal Proof: by contradiction –suppose b is the first boy not matched to g ≠ g b in the execution of DA, –let M be an arbitrary matching where b is matched to g b –Suppose b ’ is the boy whom g b chose to reject b, and M( b’ )= g ’ – g ’ > b ’ g b, which means that g ’ rejected b ’ in a previous round 27 Men-proposing DA is men-optimal b’b’ bg gbgb g’ DA b’b’ bg gbgb g’ M

Theorem. Truth-reporting is a dominant strategy for boys in men-proposing DA 28 Strategy-proofness for boys

Proof. If (S,W) and (K,R) then If (S,R) and (K,W) then 29 No matching mechanism is strategy-proof and stable Boys Girls Stan Rebecca Wendy > : > : > : Kyle > : Stan > : > N Wendy > : > N

Men-proposing deferred acceptance algorithm (DA) –outputs the men-optimal stable matching –runs in polynomial time –strategy-proof on men’s side 30 Recap: two-sided 1-1 matching

Indivisible goods: one-sided 1-1 or 1- many matching (papers, apartments, etc.) Divisible goods: cake cutting 31 Next class: Fair division