The Mathematics Of 1950’s Dating: Who wins the battle of the sexes? Presentation by Shuchi Chawla with some modifications.

Slides:



Advertisements
Similar presentations
The Mathematics Of 1950s Dating: Who wins the battle of the sexes? Great Theoretical Ideas In Computer Science Steven Rudich CS Spring 2003 Lecture.
Advertisements

Piyush Kumar (Lecture 3: Stable Marriage) Welcome to COT5405.
Marriage, Honesty, & Stability N ICOLE I MMORLICA, N ORTHWESTERN U NIVERSITY.
1 Overview of Graph Theory Addendum “The Stable Marriage Problem” Instructor: Carlos Pomalaza-Ráez Fall 2003 University of Oulu, Finland.
Tutorial 5 of CSCI2110 Eulerian Path & Hamiltonian Cycle Tutor: Zhou Hong ( 周宏 )
Matching Theory.
Lecture 2: Greedy Algorithms II Shang-Hua Teng Optimization Problems A problem that may have many feasible solutions. Each solution has a value In maximization.
Matching problems Toby Walsh NICTA and UNSW. Motivation Agents may express preferences for issues other than a collective decision  Preferences for a.
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.
Social Networks 101 P ROF. J ASON H ARTLINE AND P ROF. N ICOLE I MMORLICA.
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.
Stable Marriage Problem William Kozma Jr ECE 443/543.
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.
1 Bipartite Matching Polytope, Stable Matching Polytope x1 x2 x3 Lecture 10: Feb 15.
Piyush Kumar (Lecture 1: Introduction) Welcome to COT5405.
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.
Welcome to CSE Applied Algorithms Anna R. Karlin Office Hours: Wed 9: :00 pm and by appointment TA: Ashish Sabharwal
Complexity & Analysis of Data Structures & Algorithms Piyush Kumar (Lecture 1: Introduction) Welcome to COP4531 Based on slides from J. Edmonds, S. Rudich,
The Mathematics Of Dating and Marriage: Who wins the battle of the sexes? The Mathematics Of Dating and Marriage: Who wins the battle of the sexes? (adapted.
The Mathematics Of 1950’s Dating: Who wins the battle of the sexes? Adapted from a presentation by Stephen Rudich.
결혼문제로 본 조합론.
Section 2.1 “Matching in bipartite graphs” in Graph Theory Handout for reading seminar.
Advanced Algorithms Piyush Kumar (Lecture 1: Introduction) Welcome to COT5405.
Marriage Survey Socratic Seminar 2011 Honors English 9 Periods 5 & 7 Mr. Bernstein.
Stable Matchings a.k.a. the Stable Marriage Problem
Stable Marriage Problem Introductory talk Yosuke KIKUCHI Dept Comp. and Info. Eng. Tsuyama College of Tech OKAYAMA, JAPAN.
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.
Lecture 23: Stable Marriage ( Based on Lectures of Steven Rudich of CMU and Amit Sahai of Princeton) Shang-Hua Teng.
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.
Stable Marriages (Lecture modified from Carnegie Mellon University course Great Theoretical Ideas in Computer Science)
Complexity & Analysis of Data Structures & Algorithms Piyush Kumar (Lecture 1: Introduction) Welcome to COP4531 Based on slides from J. Edmonds, S. Rudich,
Design & Co-design of Embedded Systems Final Project: “The Match Maker” Second Phase Oral Presentation Safa S. Mahmoodian.
Bipartite Matching. Unweighted Bipartite Matching.
The Stable Marriage Problem
Great Theoretical Ideas in Computer Science for Some.
Incentive compatibility in 2-sided matching markets
Matching Lecture 19: Nov 23.
Sep 29, 2014 Lirong Xia Matching. Report your preferences over papers soon! –deadline this Thursday before the class Drop deadline Oct 17 Catalan independence.
Fair Shares.
The Mathematics Of 1950’s Dating: Who wins the battle of the sexes? CS Lecture 2.
Graph Algorithms Maximum Flow - Best algorithms [Adapted from R.Solis-Oba]
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.
18.1 CompSci 102 Today’s topics Impaired wanderingImpaired wandering Instant InsanityInstant Insanity 1950s TV Dating1950s TV Dating Final Exam reviewFinal.
Stable marriages Competitive programming and problem solving Yoram Meijaard.
Dating Advice from Mathematics
מתמטיקה בדידה Discrete Math Lecture 12 1 TexPoint fonts used in EMF.
Matching Boys Girls A B C D E
Cybernetica AS & Tartu University
Stable Matching.
The Stable Marriage Problem
The Mathematics Of 1950’s Dating: Who wins the battle of the sexes?
CSE 421: Introduction to Algorithms
The Mathematics Of 1950’s Dating: Who wins The Battle of The Sexes?
The Mathematics Of 1950’s Dating: Who wins the battle of the sexes?
S. Raskhodnikova; based on slides by K. Wayne
Matching Lirong Xia March 8, Matching Lirong Xia March 8, 2016.
Computational Processes II
CSE 421: Introduction to Algorithms
Discrete Math for CS CMPSC 360 LECTURE 9 Last time: Strong induction
Who does better, boys or girls?
Computational Processes II
Piyush Kumar (Lecture 3: Stable Marriage)
Presentation transcript:

The Mathematics Of 1950’s Dating: Who wins the battle of the sexes? Presentation by Shuchi Chawla with some modifications

Steven Rudich: Dating Scenario There are n boys and n girls Each girl has her own ranked preference list of all the boys Each boy has his own ranked preference list of the girls The lists have no ties Question: How do we pair them off?

Steven Rudich: C,B,A,D 1 B,A,D,C 2 D,C,A,B 3 A,B,C,D 4 A 3,4,1,2 B 1,2,3,4 C 4,3,2,1 D 1,3,4,2

Steven Rudich: Rogue Couples Suppose we pair off all the boys and girls. Now suppose that some boy and some girl prefer each other to the people to whom they are paired. They will be called a rogue couple.

Steven Rudich: Why be with them when we can be with each other?

Steven Rudich: Stable Pairings A pairing of boys and girls is called stable if it contains no rogue couples. C,B,A,D 1 B,A,D,C D,C,A,B 3 A,B,C,D 4 A 3,4,1,2 B 1,2,3,4 C 4,3,2,1 D 1,3,4,2 2

Steven Rudich: Given a set of preference lists, how do we find a stable pairing? Wait! We don’t even know that such a pairing always exists!

Steven Rudich: Idea: Allow the pairs to keep breaking up and reforming until they become stable.

Steven Rudich: Can you argue that the couples will not continue breaking up and reforming forever?

Female Steven Rudich: The Traditional Marriage Algorithm String Worshipping males

Steven Rudich: Traditional Marriage Algorithm For each day that some boy gets a “No” do: MorningMorning –Each girl stands on her balcony –Each boy proposes under the balcony of the best girl whom he has not yet crossed off Afternoon (for those girls with at least one suitor)Afternoon (for those girls with at least one suitor) “Maybe, come back tomorrow” –To today’s best suitor: “Maybe, come back tomorrow” “No, I will never marry you” –To any others: “No, I will never marry you” EveningEvening –Any rejected boy crosses the girl off his list Each girl marries the boy to whom she last said “maybe”

Steven Rudich: Traditional Marriage Algorithm While there is an unmatched boy, do: Some unmatched boy proposes to next girl on his listSome unmatched boy proposes to next girl on his list If girl is unmatched:If girl is unmatched: –boy & girl get engaged If girl is matched but prefers boy to her current fiancé:If girl is matched but prefers boy to her current fiancé: –boy & girl get engaged –previous fiancé becomes unmatched If girl is matched and prefers fiancé to proposerIf girl is matched and prefers fiancé to proposer –proposer is rejected Each girl marries the boy to whom she is last engaged

Steven Rudich: Does the Traditional Marriage Algorithm always produce a stable pairing?

Steven Rudich: Does the Traditional Marriage Algorithm always produce a stable pairing? Wait! There is a more primary question!

Steven Rudich: Does TMA always terminate? It might encounter a situation where algorithm does not specify what to do next (core dump error) It might keep on going for an infinite number of days

Steven Rudich: Traditional Marriage Algorithm While there is an unmatched boy, do: Some unmatched boy proposes to next girl on his listSome unmatched boy proposes to next girl on his list If girl is unmatched:If girl is unmatched: –boy & girl get engaged If girl is matched but prefers boy to her current fiancé:If girl is matched but prefers boy to her current fiancé: –boy & girl get engaged –previous fiancé becomes unmatched If girl is matched and prefers fiancé to proposerIf girl is matched and prefers fiancé to proposer –proposer is rejected Each girl marries the boy to whom she is last engaged

Steven Rudich: Improvement Lemma: If a girl is engaged to a boy, then she will always be engaged (or married) to someone at least as good. She would only let go of him in order to get engaged to someone better She would only let go of that guy for someone even better AND SO ON

Steven Rudich: Improvement Lemma: If a girl is engaged to a boy, then she will always be engaged (or married) to someone at least as good. She would only let go of him in order to get engaged to someone better She would only let go of that guy for someone even better AND SO ON

Steven Rudich: Lemma: No boy can be rejected by all the girls Proof by contradiction. Suppose boy b is rejected by all the girls. At that point: Each girl must have a suitor other than b (By Improvement Lemma, once a girl has a suitor she will always have at least one) The n girls have n suitors, b not among them. Thus, there are at least n+1 boys Contradiction

Steven Rudich: Theorem: The TMA always terminates in at most n 2 days A “master list” of all n of the boys lists starts with a total of n X n = n 2 girls on it. Each day that at least one girl gets crossed off the master list. Therefore, the number of days is bounded by the original size of the master list. In fact, since each list never drops below 1, the number of days is bounded by n(n-1) <= n 2.

Steven Rudich: Corollary: Each girl will marry her absolute favorite of the boys who visit her during the TMA

Steven Rudich: Great! We know that TMA will terminate and produce a pairing. But is it stable?

Steven Rudich: Theorem: Let T be the pairing produced by TMA. T is stable. g b g*g*g*g*

Steven Rudich: Theorem: Let T be the pairing produced by TMA. T is stable. g b I rejected you when you came to my balcony, now I got someone better. g*g*g*g*

Steven Rudich: Theorem: Let T be the pairing produced by TMA. T is stable. Let b and g be any couple in T. Suppose b prefers g* to g. We will argue that g* prefers her husband to b. During TMA, b proposed to g* before he proposed to g. Hence, at some point g* rejected b for someone she preferred. By the Improvement lemma, the person she married was also preferable to b. Thus, every boy will be rejected by any girl he prefers to his wife. T is stable.

Steven Rudich: Opinion Poll Who is better off in traditional dating, the boys or the girls?

Steven Rudich: Forget TMA for a moment How should we define what we mean when we say “the optimal girl for boy b”? Flawed Attempt: “The girl at the top of b’s list”

Steven Rudich: The Optimal Girl A boy’s optimal girl is the highest ranked girl for whom there is some stable pairing in which the boy gets her. She is the best girl he can conceivably get in a stable world. Presumably, she might be better than the girl he gets in the stable pairing output by TMA.

Steven Rudich: The Pessimal Girl A boy’s pessimal girl is the lowest ranked girl for whom there is some stable pairing in which the boy gets her. She is the worst girl he can conceivably get in a stable world.

Steven Rudich: Dating Heaven and Hell A pairing is male-optimal if every boy gets his optimal mate. This is the best of all possible stable worlds for every boy simultaneously. A pairing is male-pessimal if every boy gets his pessimal mate. This is the worst of all possible stable worlds for every boy simultaneously.

Steven Rudich: Dating Heaven and Hell Does a male-optimal pairing always exist? If so, is it stable?

Steven Rudich: The Mathematical Truth! The Traditional Marriage Algorithm always produces a male-optimal, female- pessimal pairing.

Steven Rudich: Theorem: TMA produces a male-optimal pairing Suppose, for a contradiction, that some boy gets rejected by his optimal girl during TMA. Let t be the earliest time at which this happened. In particular, at time t, some boy b got rejected by his optimal girl g* because she said “maybe” to a preferred b *. By the definition of t, b * had not yet been rejected by his optimal girl. Therefore, b * likes g* at least as much as his optimal say g’.

Steven Rudich: Some boy b got rejected by his optimal girl g* because she said “maybe” to a preferred b *. b* likes g* at least as much as his optimal girl g’. There must exist a stable paring S in which b and g* are married. b..,g*,.. b*.,g*,..,g’,...,b*,..,b,. g*..,b*,.. g SS.,g*,..,g’,..,g,. Contradiction to the stability of S.

Steven Rudich: Some boy b got rejected by his optimal girl g* because she said “maybe” to a preferred b *. b* likes g* at least as much as his optimal girl. There must exist a stable paring S in which b and g* are married. b* wants g* more than his wife in S –g* is at least as good as his best and he does not have her in stable pairing S g* wants b* more than her husband in S –b is her husband in S and she rejects him for b * in TMA

Steven Rudich: There must exist a stable paring S in which b and g are married. b* wants g* more than his wife in S –g* is as at least as good as his best and he does not have her in stable pairing S g* wants b* more than her husband in S –b is her husband in S and she rejects him for b * in TMA Some boy b got rejected by his optimal girl g* because she said “maybe” to a preferred b *. b* likes g* at least as much as his optimal girl. Contradiction to the stability of S.

Steven Rudich: Theorem: The TMA pairing, T, is female-pessimal. We know it is male-optimal. Suppose there is a stable pairing S where some girl g does worse than in T. Let b be her mate in T. Let b * be her mate in S. By assumption, g likes b better than her mate in S b likes g better than his mate in S –We already know that g is his optimal girl Therefore, S is not stable. Contradicti on

Steven Rudich: A,C,B 1 C,A,B 2 A,B,C 3 A 2,1,3 B 1,2,3 C What if people lie?

A,B,C 1 C,A,B 2 A,B,C 3 A 2,1,3 B 1,2,3 C What if people lie?

Roth (1982) No stable matching procedure exists for which truthful revelation of preferences is a dominant strategy for all agents.No stable matching procedure exists for which truthful revelation of preferences is a dominant strategy for all agents. Other Issues: What if the lists are partial?What if the lists are partial? What about same-sex couples, or pairing roommates?What about same-sex couples, or pairing roommates? Steven Rudich:

It is always better to make the first move. Conclusions…

Steven Rudich: REFERENCES D. Gale and L. S. Shapley, College admissions and the stability of marriage, American Mathematical Monthly (1962) A. Roth, The economics of matching: Stability and incentives, MOR (1982) Dan Gusfield and Robert W. Irving, The Stable Marriage Problem: Structures and Algorithms, MIT Press, 1989