Elections and Strategic Voting: Condorcet and Borda. P. Dasgupta E. Maskin

Size: px
Start display at page:

Download "Elections and Strategic Voting: Condorcet and Borda. P. Dasgupta E. Maskin"

Transcription

1 Electons and Strategc Votng: ondorcet and Borda P. Dasgupta E. Maskn

2 votng rule (socal choce functon) method for choosng socal alternatve (canddate) on bass of voters preferences (rankngs, utlt functons) promnent eamples Pluralt Rule (MPs n Brtan, members of ongress n U.S.) choose alternatve ranked frst b more voters than an other Majort Rule (ondorcet Method) choose alternatve preferred b majort to each other alternatve 2

3 Run-off Votng (presdental electons n France) choose alternatve ranked frst b more voters than an other, unless number of frst-place rankngs less than majort among top 2 alternatves, choose alternatve preferred b majort Rank-Order Votng (Borda ount) alternatve assgned 1 pont ever tme some voter ranks t frst, 2 ponts ever tme ranked second, etc. choose alternatve wth lowest pont total Utltaran Prncple choose alternatve that mamzes sum of voters utltes 3

4 Whch votng rule to adopt? Answer depends on what one wants n votng rule can specf crtera (aoms) votng rule should satsf see whch rules best satsf them One mportant crteron: nonmanpulablt voters shouldn t have ncentve to msrepresent preferences,.e., vote strategcall otherwse not mplementng ntended votng rule decson problem for voters ma be hard 4

5 But basc negatve result Gbbard-Satterthwate (GS) theorem f 3 or more alternatves, no votng rule s alwas nonmanpulable (ecept for dctatoral rules - - where one voter has all the power) Stll, GS overl pessmstc requres that votng rule never be manpulable but some crcumstances where manpulaton can occur ma be unlkel In an case, natural queston: Whch (reasonable) votng rule(s) nonmanpulable most often? Paper tres to answer queston 5

6 X = fnte set of socal alternatves socet conssts of a contnuum of voters [0,1] tpcal voter 0,1 reason for contnuum clear soon utlt functon for voter U : X R restrct attenton to strct utlt functons f, then U( ) U( ) = set of strct utlt functons U X profle U [ ] - - specfcaton of each ndvdual's utlt functon 6

7 votng rule (generalzed socal choce functon) F for all proflesu and all Y X, F U, Y Y ( ) F U, Y = optmal alternatve n Y f profle s U defnton sn t qute rght - - gnores tes wth pluralt rule, mght be two alternatves that are both ranked frst the most wth rank-order votng, mght be two alternatves that each get lowest number of ponts But eact tes unlkel wth man voters wth contnuum, tes are nongenerc so, correct defnton: for generc profle U and all Y X F U Y Y (, ) ( ) 7

8 pluralt rule: { ( ) = μ ( ) ( ) μ U a U b for all b for all a majort rule: rank-order votng: utltaran prncple: { } { ( ) ( ) } } P f U, Y a U a U b for all b ( ) μ ( ) ( ) { } U U ( ) = ( ) μ( ) ( ) μ( ) { } B f U, Y a r a d r b d for all b, ( ) = ( ) ( ) where r a # bu b U a U { } ( ) = ( ) μ() ( ) μ() U f U, Y a U a d U b d for all b 1 { { } 2 } f U, Y = a U a U b for all b 8

9 What propertes should reasonable votng rule satsf? ( ) ( ) for all Pareto Propert (P): f U > U and Y, then F( U, Y) f everbod prefers to, should not be chosen Anonmt (A): suppose [ ] [ ] π : 0,1 0,1 measure-preservng π permutaton. If U = U for all, then π () ( π ) = ( ) F U, Y F U, Y for all Y alternatve chosen depends onl on voters preferences and not who has those preferences voters treated smmetrcall 9

10 Neutralt (N): then Suppose ρ : Y Y permutaton. ρ Y ( ( )) ( ) alternatves treated smmetrcall ( ) ( ) ( ) ρ, Y, If U ρ > U ρ U > U for all,,, ρ, Y ( ) = ρ ( ) ( ) F U, Y F U, Y. All four votng rules pluralt, majort, rank-order, utltaran satsf P, A, N Net aom most controversal stll has qute compellng justfcaton nvoked b both Arrow (1951) and Nash (1950) 10

11 Independence of Irrelevant Alternatves (I): then ( ) f = F U, Y and Y Y (, ) F U Y = f chosen and some non-chosen alternatves removed, stll chosen Nash formulaton (rather than Arrow) no spolers (e.g. Nader n 2000 U.S. presdental electon, Le Pen n 2002 French presdental electon) 11

12 Majort rule and utltaransm satsf I, but others don t: pluralt rule.35 z.33 z.32 z (, {,, }) P f U z = (, {, }) P f U = rank-order votng.55 z.45 z (, {,, }) ( ) = B f U z B,{, } = f U 12

13 Fnal Aom: Nonmanpulablt (NM): then ( ) ( ) U = U j [ ] f = F U, Y and = F U, Y, where for all 0,1 j ( ) ( ) for some j U > U the members of coalton can t all gan from msrepresentng utlt functons as U 13

14 NM mples votng rule must be ordnal (no cardnal nformaton used) F s ordnal f whenever, for profles U and U, U > U U > U (*) F U, Y F U =, Y for all Y Lemma: If F satsfes NM and I, F ordnal ( ) ( ) ( ) ( ) for all,, ( ) ( ) ( ) ( ) ( { }) { } suppose = F U, Y = F U, Y, where U and U same ordnall F( U U { } ) = ( { }) = NM rules out utltaransm ( ) then = F U,, = F U,,, from I suppose f,,,, then wll manpulate f F U, U,,, then wll manpulate 14

15 But majort rule also volates NM F not even alwas defned.35 z.33 z.32 z (, {,, }) F U z = eample of ondorcet ccle F must be etended to ondorcet ccles one possblt F ( U, Y), f nonempt / B F ( U, Y) = B F U, Y, otherwse ( ) (Black's method) etensons make F vulnerable to manpulaton.35 z.33 z.32 z (, {,, }) / B F U z = z (, {,, }) = / B F U z z 15

16 Theorem: There ests no votng rule satsfng P,A,N,I and NM Proof: smlar to that of GS overl pessmstc - - man cases n whch some rankngs unlkel 16

17 Lemma: Majort rule satsfes all 5 propertes f and onl f preferences restrcted to doman wth no ondorcet ccles When can we rule out ondorcet ccles? preferences sngle-peaked 2000 US electon Nader Gore Bush unlkel that man had rankng Bush Nader or Nader Bush strongl-felt canddate Gore Gore n 2002 French electon, 3 man canddates: hrac, Jospn, Le Pen voters ddn t feel strongl about hrac and Jospn felt strongl about Le Pen (ranked hm frst or last) 17

18 Votng rule Fworkswellon doman U f satsfes P,A,N,I,NM when utlt functons restrcted to U e.g., F works well when preferences sngle-peaked 18

19 Theorem 1: Suppose F works well on doman U, then F works well on U too. onversel, suppose that F works well on U. Then f there ests profle on U such that U ( ) ( ) FU, Y F U, Y for some Y, there ests doman U on whch F works well but F does not Proof: From NM and I, f F works well on U, F must be ordnal Hence result follows from Dasgupta-Maskn (2008), JEEA shows that Theorem 1 holds when NM replaced b ordnalt 19

20 To show ths D-M uses Lemma: F works well on U f and onl f U has no ondorcet ccles Suppose F works well on U If F doesn't work well on U, Lemma mples U must contan ondorcet ccle z z z 20

21 onsder 1 2 n 1 U = z z z so 1 ( { }) (*) Suppose F U,, z = z 2 U = n z z z z ( ) ( { }) ( ) 2 2 { } ( 2 2 { }) { } F U,,, z = (from I) F U,, z =, contradcts (*) F U,,, z = (from I) F U,, =, contradcts (*) (A,N) ( 2, {,, }) { } F U z = z ( ) 2 so F U, so for, z = z (I) n 3 U = z z z z ( { }) F U 3,, z = z (N) 4 ontnung n the same wa, let U = ( { }) F U 4,, z = z, contradcts (*) 1 n 1 n z z z 21

22 So F can t work well on U wth ondorcet ccle onversel, suppose that F works well on U and ( ) ( ) F U, Y F U, Y for some U and Y Then there est α wth 1 α > α and U = 1 α such that α (, {, }) and, {, } ( ) = F U = F U But not hard to show that F unque votng rule satsfng P,A,N, and NM when X = contradcton 22

23 Let s drop I most controversal no votng rule satsfes P,A,N,NM on GS agan U X F works ncel on U f satsfes P,A,N,NM on U 23

24 Theorem 2: X = 3 Suppose F works ncel on U, then B F or F works ncel on U too. onversel suppose works ncel on, where B F U F = F or F. Then, f there ests profle U on U such that * there ests doman U on whch F works ncel but F does not Proof: F works ncel on an ondorcet-ccle-free doman B F ( ) ( ) B so F and F complement each other F U, Y F U, Y for some Y, works ncel onl when U s subset of ondorcet ccle f F works ncel on U and U doesn't contan ondorcet ccle, F works ncel too f F works ncel on U and U contans ondorcet ccle, then U can't contan an other rankng (otherwse no votng rule works ncel) B so F works ncel on U. 24

25 Strkng that the 2 longest-studed votng rules (ondorcet and Borda) are also onl two that work ncel on mamal domans 25

Elections and Strategic Voting: Condorcet and Borda. P. Dasgupta E. Maskin

Elections and Strategic Voting: Condorcet and Borda. P. Dasgupta E. Maskin Elections and Strategic Voting: ondorcet and Borda P. Dasgupta E. Maskin voting rule (social choice function) method for choosing social alternative (candidate) on basis of voters preferences (rankings,

More information

ON THE EQUIVALENCE OF ORDINAL BAYESIAN INCENTIVE COMPATIBILITY AND DOMINANT STRATEGY INCENTIVE COMPATIBILITY FOR RANDOM RULES

ON THE EQUIVALENCE OF ORDINAL BAYESIAN INCENTIVE COMPATIBILITY AND DOMINANT STRATEGY INCENTIVE COMPATIBILITY FOR RANDOM RULES ON THE EQUIVALENCE OF ORDINAL BAYESIAN INCENTIVE COMPATIBILITY AND DOMINANT STRATEGY INCENTIVE COMPATIBILITY FOR RANDOM RULES Madhuparna Karmokar 1 and Souvk Roy 1 1 Economc Research Unt, Indan Statstcal

More information

Voting procedures with incomplete preferences

Voting procedures with incomplete preferences Votng procedures wth ncomplete preferences Kathrn Konczak Insttut für Informatk, Unverstät Potsdam Postfach 90 03 27, D-14439 Potsdam konczak@cs.un-potsdam.de Jerome Lang IIT- Unv. Paul Sabater Toulouse,

More information

Week 2. This week, we covered operations on sets and cardinality.

Week 2. This week, we covered operations on sets and cardinality. Week 2 Ths week, we covered operatons on sets and cardnalty. Defnton 0.1 (Correspondence). A correspondence between two sets A and B s a set S contaned n A B = {(a, b) a A, b B}. A correspondence from

More information

Group-Strategyproof Irresolute Social Choice Functions

Group-Strategyproof Irresolute Social Choice Functions Group-Strategyproof Irresolute Socal Choce Functons Felx Brandt Technsche Unverstät München Munch, Germany brandtf@n.tum.de Abstract An mportant problem n votng s that agents may msrepresent ther preferences

More information

CHAPTER 4. Vector Spaces

CHAPTER 4. Vector Spaces man 2007/2/16 page 234 CHAPTER 4 Vector Spaces To crtcze mathematcs for ts abstracton s to mss the pont entrel. Abstracton s what makes mathematcs work. Ian Stewart The man am of ths tet s to stud lnear

More information

Adjacent non-manipulability and strategy-proofness in voting domains: equivalence results

Adjacent non-manipulability and strategy-proofness in voting domains: equivalence results Adjacent non-manpulablty and strategy-proofness n votng domans: equvalence results Souvk Roy, Arunava Sen, Sonal Yadav and Huaxa Zeng February 14, 2016 Abstract A plausble ncentve compatblty requrement

More information

Axiomatizations of Pareto Equilibria in Multicriteria Games

Axiomatizations of Pareto Equilibria in Multicriteria Games ames and Economc Behavor 28, 146154 1999. Artcle ID game.1998.0680, avalable onlne at http:www.dealbrary.com on Axomatzatons of Pareto Equlbra n Multcrtera ames Mark Voorneveld,* Dres Vermeulen, and Peter

More information

Welfare Properties of General Equilibrium. What can be said about optimality properties of resource allocation implied by general equilibrium?

Welfare Properties of General Equilibrium. What can be said about optimality properties of resource allocation implied by general equilibrium? APPLIED WELFARE ECONOMICS AND POLICY ANALYSIS Welfare Propertes of General Equlbrum What can be sad about optmalty propertes of resource allocaton mpled by general equlbrum? Any crteron used to compare

More information

A NOTE ON A GROUP PREFERENCE AXIOMATIZATION WITH CARDINAL UTILITY

A NOTE ON A GROUP PREFERENCE AXIOMATIZATION WITH CARDINAL UTILITY Ths s a PDF fle of an unedted manuscrpt that has been accepted for publcaton n Decson Analyss. The manuscrpt wll undergo copyedtng, typesettng, and revew of the resultng proof before t s publshed n ts

More information

The basic point with mechanism design is that it allows a distinction between the underlying

The basic point with mechanism design is that it allows a distinction between the underlying 14 Mechansm Desgn The basc pont wth mechansm desgn s that t allows a dstncton between the underlyng economc envronment and the rules of the game. We wll take as gven some set of possble outcomes (alternatves,

More information

Solutions to Homework 7, Mathematics 1. 1 x. (arccos x) (arccos x) 1

Solutions to Homework 7, Mathematics 1. 1 x. (arccos x) (arccos x) 1 Solutons to Homework 7, Mathematcs 1 Problem 1: a Prove that arccos 1 1 for 1, 1. b* Startng from the defnton of the dervatve, prove that arccos + 1, arccos 1. Hnt: For arccos arccos π + 1, the defnton

More information

Bayesian epistemology II: Arguments for Probabilism

Bayesian epistemology II: Arguments for Probabilism Bayesan epstemology II: Arguments for Probablsm Rchard Pettgrew May 9, 2012 1 The model Represent an agent s credal state at a gven tme t by a credence functon c t : F [0, 1]. where F s the algebra of

More information

Determining Possible and Necessary Winners under Common Voting Rules Given Partial Orders

Determining Possible and Necessary Winners under Common Voting Rules Given Partial Orders Determnng Possble and Necessary Wnners under Common Votng Rules Gven Partal Orders Lrong Xa lxa@cs.duke.edu Vncent Contzer contzer@cs.duke.edu Department of Computer Scence, Duke Unversty, Durham, NC 27708,

More information

A 2D Bounded Linear Program (H,c) 2D Linear Programming

A 2D Bounded Linear Program (H,c) 2D Linear Programming A 2D Bounded Lnear Program (H,c) h 3 v h 8 h 5 c h 4 h h 6 h 7 h 2 2D Lnear Programmng C s a polygonal regon, the ntersecton of n halfplanes. (H, c) s nfeasble, as C s empty. Feasble regon C s unbounded

More information

ORDINAL VERSUS CARDINAL VOTING RULES: A MECHANISM DESIGN APPROACH. October 24, 2012

ORDINAL VERSUS CARDINAL VOTING RULES: A MECHANISM DESIGN APPROACH. October 24, 2012 ORDINAL VERSUS CARDINAL VOTING RULES: A MECHANISM DESIGN APPROACH SEMIN KIM October 4, 0 ABSTRACT. We consder the performance and ncentve compatblty of votng rules n a Bayesan envronment wth ndependent

More information

ORDINAL VERSUS CARDINAL VOTING RULES: November 22, 2013

ORDINAL VERSUS CARDINAL VOTING RULES: November 22, 2013 ORDINAL VERSUS CARDINAL VOTING RULES: A MECHANISM DESIGN APPROACH [JOB MARKET PAPER] SEMIN KIM November 22, 2013 Abstract. We consder the performance and ncentve compatblty of votng rules n a Bayesan envronment:

More information

NOTES IN SOCIAL CHOICE THEORY LEVENT KUTLU ISTANBUL BILGI UNIVERSITY INSTITUTE OF SOCIAL SCIENCES MASTER OF SCIENCE IN ECONOMICS

NOTES IN SOCIAL CHOICE THEORY LEVENT KUTLU ISTANBUL BILGI UNIVERSITY INSTITUTE OF SOCIAL SCIENCES MASTER OF SCIENCE IN ECONOMICS OTES I SOCIAL CHOICE THEORY LEVET KUTLU 103622011 ISTABUL BILGI UIVERSITY ISTITUTE OF SOCIAL SCIECES MASTER OF SCIECE I ECOOMICS UDER SUPERVISIO OF PROF. DR. M. REMZİ SAVER 2007 otes In Socal Choce Theor

More information

ORDINAL VERSUS CARDINAL VOTING RULES: November 6, 2014

ORDINAL VERSUS CARDINAL VOTING RULES: November 6, 2014 ORDINAL VERSUS CARDINAL VOTING RULES: A MECHANISM DESIGN APPROACH SEMIN KIM November 6, 2014 Abstract. We consder the performance and ncentve compatblty of votng rules n a Bayesan envronment: agents have

More information

Determining Possible and Necessary Winners under Common Voting Rules Given Partial Orders

Determining Possible and Necessary Winners under Common Voting Rules Given Partial Orders Journal of Artfcal Intellgence Research 41 (2011) pages 25 67 Submtted 09/10; publshed 05/11 Determnng Possble and Necessary Wnners under Common Votng Rules Gven Partal Orders Lrong Xa Vncent Contzer Department

More information

Perfect Competition and the Nash Bargaining Solution

Perfect Competition and the Nash Bargaining Solution Perfect Competton and the Nash Barganng Soluton Renhard John Department of Economcs Unversty of Bonn Adenauerallee 24-42 53113 Bonn, Germany emal: rohn@un-bonn.de May 2005 Abstract For a lnear exchange

More information

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Regression Analysis

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Regression Analysis Resource Allocaton and Decson Analss (ECON 800) Sprng 04 Foundatons of Regresson Analss Readng: Regresson Analss (ECON 800 Coursepak, Page 3) Defntons and Concepts: Regresson Analss statstcal technques

More information

On the Multicriteria Integer Network Flow Problem

On the Multicriteria Integer Network Flow Problem BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 5, No 2 Sofa 2005 On the Multcrtera Integer Network Flow Problem Vassl Vasslev, Marana Nkolova, Maryana Vassleva Insttute of

More information

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also

More information

Implementation in Mixed Nash Equilibrium

Implementation in Mixed Nash Equilibrium Department of Economcs Workng Paper Seres Implementaton n Mxed Nash Equlbrum Claudo Mezzett & Ludovc Renou May 2012 Research Paper Number 1146 ISSN: 0819-2642 ISBN: 978 0 7340 4496 9 Department of Economcs

More information

1 GSW Iterative Techniques for y = Ax

1 GSW Iterative Techniques for y = Ax 1 for y = A I m gong to cheat here. here are a lot of teratve technques that can be used to solve the general case of a set of smultaneous equatons (wrtten n the matr form as y = A), but ths chapter sn

More information

The Second Anti-Mathima on Game Theory

The Second Anti-Mathima on Game Theory The Second Ant-Mathma on Game Theory Ath. Kehagas December 1 2006 1 Introducton In ths note we wll examne the noton of game equlbrum for three types of games 1. 2-player 2-acton zero-sum games 2. 2-player

More information

APPENDIX A Some Linear Algebra

APPENDIX A Some Linear Algebra APPENDIX A Some Lnear Algebra The collecton of m, n matrces A.1 Matrces a 1,1,..., a 1,n A = a m,1,..., a m,n wth real elements a,j s denoted by R m,n. If n = 1 then A s called a column vector. Smlarly,

More information

Can the Market be Fixed? (II) Does the Market Need Fixing? The Beneficent Effects of Assigning Property Rights. The Coase Theorem

Can the Market be Fixed? (II) Does the Market Need Fixing? The Beneficent Effects of Assigning Property Rights. The Coase Theorem Eternaltes An eternalt occurs whenever the actvtes of one economc agent affect the actvtes of another agent n was that are not taken nto account b the operaton of the market. Eamples: One frm produces

More information

(1 ) (1 ) 0 (1 ) (1 ) 0

(1 ) (1 ) 0 (1 ) (1 ) 0 Appendx A Appendx A contans proofs for resubmsson "Contractng Informaton Securty n the Presence of Double oral Hazard" Proof of Lemma 1: Assume that, to the contrary, BS efforts are achevable under a blateral

More information

Linear, affine, and convex sets and hulls In the sequel, unless otherwise specified, X will denote a real vector space.

Linear, affine, and convex sets and hulls In the sequel, unless otherwise specified, X will denote a real vector space. Lnear, affne, and convex sets and hulls In the sequel, unless otherwse specfed, X wll denote a real vector space. Lnes and segments. Gven two ponts x, y X, we defne xy = {x + t(y x) : t R} = {(1 t)x +

More information

CHAPTER-5 INFORMATION MEASURE OF FUZZY MATRIX AND FUZZY BINARY RELATION

CHAPTER-5 INFORMATION MEASURE OF FUZZY MATRIX AND FUZZY BINARY RELATION CAPTER- INFORMATION MEASURE OF FUZZY MATRI AN FUZZY BINARY RELATION Introducton The basc concept of the fuzz matr theor s ver smple and can be appled to socal and natural stuatons A branch of fuzz matr

More information

Journal of Mathematical Economics

Journal of Mathematical Economics Journal of Mathematcal Economcs 45 (2009) 3 23 Contents lsts avalable at ScenceDrect Journal of Mathematcal Economcs journal homepage: www.elsever.com/locate/jmateco Implementaton of Pareto effcent allocatons

More information

Chapter 1. Probability

Chapter 1. Probability Chapter. Probablty Mcroscopc propertes of matter: quantum mechancs, atomc and molecular propertes Macroscopc propertes of matter: thermodynamcs, E, H, C V, C p, S, A, G How do we relate these two propertes?

More information

Incremental Mechanism Design

Incremental Mechanism Design Incremental Mechansm Desgn Vncent Contzer Duke Unversty Department of Computer Scence contzer@cs.duke.edu Abstract Mechansm desgn has tradtonally focused almost exclusvely on the desgn of truthful mechansms.

More information

Edge Isoperimetric Inequalities

Edge Isoperimetric Inequalities November 7, 2005 Ross M. Rchardson Edge Isopermetrc Inequaltes 1 Four Questons Recall that n the last lecture we looked at the problem of sopermetrc nequaltes n the hypercube, Q n. Our noton of boundary

More information

princeton univ. F 17 cos 521: Advanced Algorithm Design Lecture 7: LP Duality Lecturer: Matt Weinberg

princeton univ. F 17 cos 521: Advanced Algorithm Design Lecture 7: LP Duality Lecturer: Matt Weinberg prnceton unv. F 17 cos 521: Advanced Algorthm Desgn Lecture 7: LP Dualty Lecturer: Matt Wenberg Scrbe: LP Dualty s an extremely useful tool for analyzng structural propertes of lnear programs. Whle there

More information

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur Module Random Processes Lesson 6 Functons of Random Varables After readng ths lesson, ou wll learn about cdf of functon of a random varable. Formula for determnng the pdf of a random varable. Let, X be

More information

Graph Reconstruction by Permutations

Graph Reconstruction by Permutations Graph Reconstructon by Permutatons Perre Ille and Wllam Kocay* Insttut de Mathémathques de Lumny CNRS UMR 6206 163 avenue de Lumny, Case 907 13288 Marselle Cedex 9, France e-mal: lle@ml.unv-mrs.fr Computer

More information

Introductory Cardinality Theory Alan Kaylor Cline

Introductory Cardinality Theory Alan Kaylor Cline Introductory Cardnalty Theory lan Kaylor Clne lthough by name the theory of set cardnalty may seem to be an offshoot of combnatorcs, the central nterest s actually nfnte sets. Combnatorcs deals wth fnte

More information

Implementation and Detection

Implementation and Detection 1 December 18 2014 Implementaton and Detecton Htosh Matsushma Department of Economcs Unversty of Tokyo 2 Ths paper consders mplementaton of scf: Mechansm Desgn wth Unqueness CP attempts to mplement scf

More information

Economics 101. Lecture 4 - Equilibrium and Efficiency

Economics 101. Lecture 4 - Equilibrium and Efficiency Economcs 0 Lecture 4 - Equlbrum and Effcency Intro As dscussed n the prevous lecture, we wll now move from an envronment where we looed at consumers mang decsons n solaton to analyzng economes full of

More information

CS 3710: Visual Recognition Classification and Detection. Adriana Kovashka Department of Computer Science January 13, 2015

CS 3710: Visual Recognition Classification and Detection. Adriana Kovashka Department of Computer Science January 13, 2015 CS 3710: Vsual Recognton Classfcaton and Detecton Adrana Kovashka Department of Computer Scence January 13, 2015 Plan for Today Vsual recognton bascs part 2: Classfcaton and detecton Adrana s research

More information

The Geometry of Manipulation - a Quantitative Proof of the Gibbard Satterthwaite Theorem arxiv: v4 [math.co] 12 Apr 2010

The Geometry of Manipulation - a Quantitative Proof of the Gibbard Satterthwaite Theorem arxiv: v4 [math.co] 12 Apr 2010 The Geometry of Manpulaton - a Quanttatve Proof of the Gbbard Satterthwate Theorem arxv:0911.0517v4 [math.co] 12 Apr 2010 Marcus Isaksson Guy Kndler Elchanan Mossel Aprl 14, 2010 Abstract We prove a quanttatve

More information

2.3 Nilpotent endomorphisms

2.3 Nilpotent endomorphisms s a block dagonal matrx, wth A Mat dm U (C) In fact, we can assume that B = B 1 B k, wth B an ordered bass of U, and that A = [f U ] B, where f U : U U s the restrcton of f to U 40 23 Nlpotent endomorphsms

More information

On the Incompatibility of Efficiency and Strategyproofness in Randomized Social Choice

On the Incompatibility of Efficiency and Strategyproofness in Randomized Social Choice On the Incompatblty of Effcency and Strategyproofness n Randomzed Socal Choce Hars Azz NICTA and UNSW Sydney 2033, Australa Floran Brandl Technsche Unverstät München 85748 Garchng, Germany Felx Brandt

More information

Vickrey Auction VCG Combinatorial Auctions. Mechanism Design. Algorithms and Data Structures. Winter 2016

Vickrey Auction VCG Combinatorial Auctions. Mechanism Design. Algorithms and Data Structures. Winter 2016 Mechansm Desgn Algorthms and Data Structures Wnter 2016 1 / 39 Vckrey Aucton Vckrey-Clarke-Groves Mechansms Sngle-Mnded Combnatoral Auctons 2 / 39 Mechansm Desgn (wth Money) Set A of outcomes to choose

More information

Interactive Bi-Level Multi-Objective Integer. Non-linear Programming Problem

Interactive Bi-Level Multi-Objective Integer. Non-linear Programming Problem Appled Mathematcal Scences Vol 5 0 no 65 3 33 Interactve B-Level Mult-Objectve Integer Non-lnear Programmng Problem O E Emam Department of Informaton Systems aculty of Computer Scence and nformaton Helwan

More information

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng

More information

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could

More information

arxiv: v1 [math.ho] 18 May 2008

arxiv: v1 [math.ho] 18 May 2008 Recurrence Formulas for Fbonacc Sums Adlson J. V. Brandão, João L. Martns 2 arxv:0805.2707v [math.ho] 8 May 2008 Abstract. In ths artcle we present a new recurrence formula for a fnte sum nvolvng the Fbonacc

More information

Assortment Optimization under MNL

Assortment Optimization under MNL Assortment Optmzaton under MNL Haotan Song Aprl 30, 2017 1 Introducton The assortment optmzaton problem ams to fnd the revenue-maxmzng assortment of products to offer when the prces of products are fxed.

More information

Lecture Notes Introduction to Cluster Algebra

Lecture Notes Introduction to Cluster Algebra Lecture Notes Introducton to Cluster Algebra Ivan C.H. Ip Updated: Ma 7, 2017 3 Defnton and Examples of Cluster algebra 3.1 Quvers We frst revst the noton of a quver. Defnton 3.1. A quver s a fnte orented

More information

Which Separator? Spring 1

Which Separator? Spring 1 Whch Separator? 6.034 - Sprng 1 Whch Separator? Mamze the margn to closest ponts 6.034 - Sprng Whch Separator? Mamze the margn to closest ponts 6.034 - Sprng 3 Margn of a pont " # y (w $ + b) proportonal

More information

12 MATH 101A: ALGEBRA I, PART C: MULTILINEAR ALGEBRA. 4. Tensor product

12 MATH 101A: ALGEBRA I, PART C: MULTILINEAR ALGEBRA. 4. Tensor product 12 MATH 101A: ALGEBRA I, PART C: MULTILINEAR ALGEBRA Here s an outlne of what I dd: (1) categorcal defnton (2) constructon (3) lst of basc propertes (4) dstrbutve property (5) rght exactness (6) localzaton

More information

Non-bossy Single Object Auctions

Non-bossy Single Object Auctions Non-bossy Sngle Object Auctons Debass Mshra and Abdul Quadr January 3, 2014 Abstract We study determnstc sngle object auctons n the prvate values envronment. We show that an allocaton rule s mplementable

More information

MMA and GCMMA two methods for nonlinear optimization

MMA and GCMMA two methods for nonlinear optimization MMA and GCMMA two methods for nonlnear optmzaton Krster Svanberg Optmzaton and Systems Theory, KTH, Stockholm, Sweden. krlle@math.kth.se Ths note descrbes the algorthms used n the author s 2007 mplementatons

More information

Affine transformations and convexity

Affine transformations and convexity Affne transformatons and convexty The purpose of ths document s to prove some basc propertes of affne transformatons nvolvng convex sets. Here are a few onlne references for background nformaton: http://math.ucr.edu/

More information

Chapter Twelve. Integration. We now turn our attention to the idea of an integral in dimensions higher than one. Consider a real-valued function f : D

Chapter Twelve. Integration. We now turn our attention to the idea of an integral in dimensions higher than one. Consider a real-valued function f : D Chapter Twelve Integraton 12.1 Introducton We now turn our attenton to the dea of an ntegral n dmensons hgher than one. Consder a real-valued functon f : R, where the doman s a nce closed subset of Eucldean

More information

On the Operation A in Analysis Situs. by Kazimierz Kuratowski

On the Operation A in Analysis Situs. by Kazimierz Kuratowski v1.3 10/17 On the Operaton A n Analyss Stus by Kazmerz Kuratowsk Author s note. Ths paper s the frst part slghtly modfed of my thess presented May 12, 1920 at the Unversty of Warsaw for the degree of Doctor

More information

Game Theory Course: Jackson, Leyton-Brown & Shoham. Vickrey-Clarke-Groves Mechanisms: Definitions

Game Theory Course: Jackson, Leyton-Brown & Shoham. Vickrey-Clarke-Groves Mechanisms: Definitions Vckrey-Clarke-Groves Mechansms: Defntons Game Theory Course: Jackson, Leyton-Brown & Shoham A postve result Recall that n the quaslnear utlty settng, a drect mechansm conssts of a choce rule and a payment

More information

Discussion Papers Department of Economics University of Copenhagen

Discussion Papers Department of Economics University of Copenhagen Dscusson Papers Department of Economcs Unversty of Copenhagen No. 07-01 Cardnal Scales for Publc Health Evaluaton Charles M. Harvey Lars Peter Østerdal Studestræde 6, DK-1455 Copenhagen K., Denmark Tel.

More information

Lecture 4: November 17, Part 1 Single Buffer Management

Lecture 4: November 17, Part 1 Single Buffer Management Lecturer: Ad Rosén Algorthms for the anagement of Networs Fall 2003-2004 Lecture 4: November 7, 2003 Scrbe: Guy Grebla Part Sngle Buffer anagement In the prevous lecture we taled about the Combned Input

More information

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9 Chapter 9 Correlaton and Regresson 9. Correlaton Correlaton A correlaton s a relatonshp between two varables. The data can be represented b the ordered pars (, ) where s the ndependent (or eplanator) varable,

More information

Weighted Voting Systems

Weighted Voting Systems Weghted Votng Systems Elssa Brown Chrssy Donovan Charles Noneman June 5, 2009 Votng systems can be deceptve. For nstance, a votng system mght consst of four people, n whch three of the people have 2 votes,

More information

A FINITE TO ONE OPEN MAPPING PRESERVES SPAN ZERO

A FINITE TO ONE OPEN MAPPING PRESERVES SPAN ZERO Volume 13, 1988 Pages 181 188 http://topology.auburn.edu/tp/ A FINITE TO ONE OPEN MAPPING PRESERVES SPAN ZERO by Mara Cuervo and Edwn Duda Topology Proceedngs Web: http://topology.auburn.edu/tp/ Mal: Topology

More information

FREQUENCY DISTRIBUTIONS Page 1 of The idea of a frequency distribution for sets of observations will be introduced,

FREQUENCY DISTRIBUTIONS Page 1 of The idea of a frequency distribution for sets of observations will be introduced, FREQUENCY DISTRIBUTIONS Page 1 of 6 I. Introducton 1. The dea of a frequency dstrbuton for sets of observatons wll be ntroduced, together wth some of the mechancs for constructng dstrbutons of data. Then

More information

Lecture 12: Discrete Laplacian

Lecture 12: Discrete Laplacian Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly

More information

Density matrix. c α (t)φ α (q)

Density matrix. c α (t)φ α (q) Densty matrx Note: ths s supplementary materal. I strongly recommend that you read t for your own nterest. I beleve t wll help wth understandng the quantum ensembles, but t s not necessary to know t n

More information

Portfolios with Trading Constraints and Payout Restrictions

Portfolios with Trading Constraints and Payout Restrictions Portfolos wth Tradng Constrants and Payout Restrctons John R. Brge Northwestern Unversty (ont wor wth Chrs Donohue Xaodong Xu and Gongyun Zhao) 1 General Problem (Very) long-term nvestor (eample: unversty

More information

NP-Completeness : Proofs

NP-Completeness : Proofs NP-Completeness : Proofs Proof Methods A method to show a decson problem Π NP-complete s as follows. (1) Show Π NP. (2) Choose an NP-complete problem Π. (3) Show Π Π. A method to show an optmzaton problem

More information

More metrics on cartesian products

More metrics on cartesian products More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of

More information

Approximate Smallest Enclosing Balls

Approximate Smallest Enclosing Balls Chapter 5 Approxmate Smallest Enclosng Balls 5. Boundng Volumes A boundng volume for a set S R d s a superset of S wth a smple shape, for example a box, a ball, or an ellpsod. Fgure 5.: Boundng boxes Q(P

More information

FINITELY-GENERATED MODULES OVER A PRINCIPAL IDEAL DOMAIN

FINITELY-GENERATED MODULES OVER A PRINCIPAL IDEAL DOMAIN FINITELY-GENERTED MODULES OVER PRINCIPL IDEL DOMIN EMMNUEL KOWLSKI Throughout ths note, s a prncpal deal doman. We recall the classfcaton theorem: Theorem 1. Let M be a fntely-generated -module. (1) There

More information

find (x): given element x, return the canonical element of the set containing x;

find (x): given element x, return the canonical element of the set containing x; COS 43 Sprng, 009 Dsjont Set Unon Problem: Mantan a collecton of dsjont sets. Two operatons: fnd the set contanng a gven element; unte two sets nto one (destructvely). Approach: Canoncal element method:

More information

Introduction. 1. The Model

Introduction. 1. The Model H23, Q5 Introducton In the feld of polluton regulaton the problems stemmng from the asymmetry of nformaton between the regulator and the pollutng frms have been thoroughly studed. The semnal works by Wetzman

More information

Games of Threats. Elon Kohlberg Abraham Neyman. Working Paper

Games of Threats. Elon Kohlberg Abraham Neyman. Working Paper Games of Threats Elon Kohlberg Abraham Neyman Workng Paper 18-023 Games of Threats Elon Kohlberg Harvard Busness School Abraham Neyman The Hebrew Unversty of Jerusalem Workng Paper 18-023 Copyrght 2017

More information

Genericity of Critical Types

Genericity of Critical Types Genercty of Crtcal Types Y-Chun Chen Alfredo D Tllo Eduardo Fangold Syang Xong September 2008 Abstract Ely and Pesk 2008 offers an nsghtful characterzaton of crtcal types: a type s crtcal f and only f

More information

CS286r Assign One. Answer Key

CS286r Assign One. Answer Key CS286r Assgn One Answer Key 1 Game theory 1.1 1.1.1 Let off-equlbrum strateges also be that people contnue to play n Nash equlbrum. Devatng from any Nash equlbrum s a weakly domnated strategy. That s,

More information

arxiv: v2 [cs.cc] 28 Nov 2011

arxiv: v2 [cs.cc] 28 Nov 2011 arxv:1108.446v2 [cs.cc] 28 Nov 2011 Takng the Fnal Step to a Full Dchotomy of the Possble Wnner Problem n Pure Scorng Rules Dorothea Baumester and Jörg Rothe Insttut für Informatk Henrch-Hene-Unverstät

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

Module 17: Mechanism Design & Optimal Auctions

Module 17: Mechanism Design & Optimal Auctions Module 7: Mechansm Desgn & Optmal Auctons Informaton Economcs (Ec 55) George Georgads Examples: Auctons Blateral trade Producton and dstrbuton n socety General Setup N agents Each agent has prvate nformaton

More information

Computing Correlated Equilibria in Multi-Player Games

Computing Correlated Equilibria in Multi-Player Games Computng Correlated Equlbra n Mult-Player Games Chrstos H. Papadmtrou Presented by Zhanxang Huang December 7th, 2005 1 The Author Dr. Chrstos H. Papadmtrou CS professor at UC Berkley (taught at Harvard,

More information

Boostrapaggregating (Bagging)

Boostrapaggregating (Bagging) Boostrapaggregatng (Baggng) An ensemble meta-algorthm desgned to mprove the stablty and accuracy of machne learnng algorthms Can be used n both regresson and classfcaton Reduces varance and helps to avod

More information

Lecture 3: Probability Distributions

Lecture 3: Probability Distributions Lecture 3: Probablty Dstrbutons Random Varables Let us begn by defnng a sample space as a set of outcomes from an experment. We denote ths by S. A random varable s a functon whch maps outcomes nto the

More information

One Dimensional Axial Deformations

One Dimensional Axial Deformations One Dmensonal al Deformatons In ths secton, a specfc smple geometr s consdered, that of a long and thn straght component loaded n such a wa that t deforms n the aal drecton onl. The -as s taken as the

More information

STEINHAUS PROPERTY IN BANACH LATTICES

STEINHAUS PROPERTY IN BANACH LATTICES DEPARTMENT OF MATHEMATICS TECHNICAL REPORT STEINHAUS PROPERTY IN BANACH LATTICES DAMIAN KUBIAK AND DAVID TIDWELL SPRING 2015 No. 2015-1 TENNESSEE TECHNOLOGICAL UNIVERSITY Cookevlle, TN 38505 STEINHAUS

More information

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009 College of Computer & Informaton Scence Fall 2009 Northeastern Unversty 20 October 2009 CS7880: Algorthmc Power Tools Scrbe: Jan Wen and Laura Poplawsk Lecture Outlne: Prmal-dual schema Network Desgn:

More information

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution Department of Statstcs Unversty of Toronto STA35HS / HS Desgn and Analyss of Experments Term Test - Wnter - Soluton February, Last Name: Frst Name: Student Number: Instructons: Tme: hours. Ads: a non-programmable

More information

MATH 5707 HOMEWORK 4 SOLUTIONS 2. 2 i 2p i E(X i ) + E(Xi 2 ) ä i=1. i=1

MATH 5707 HOMEWORK 4 SOLUTIONS 2. 2 i 2p i E(X i ) + E(Xi 2 ) ä i=1. i=1 MATH 5707 HOMEWORK 4 SOLUTIONS CİHAN BAHRAN 1. Let v 1,..., v n R m, all lengths v are not larger than 1. Let p 1,..., p n [0, 1] be arbtrary and set w = p 1 v 1 + + p n v n. Then there exst ε 1,..., ε

More information

Poisson brackets and canonical transformations

Poisson brackets and canonical transformations rof O B Wrght Mechancs Notes osson brackets and canoncal transformatons osson Brackets Consder an arbtrary functon f f ( qp t) df f f f q p q p t But q p p where ( qp ) pq q df f f f p q q p t In order

More information

General theory of fuzzy connectedness segmentations: reconciliation of two tracks of FC theory

General theory of fuzzy connectedness segmentations: reconciliation of two tracks of FC theory General theory of fuzzy connectedness segmentatons: reconclaton of two tracks of FC theory Krzysztof Chrs Ceselsk Department of Mathematcs, West Vrgna Unversty and MIPG, Department of Radology, Unversty

More information

8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS

8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 493 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces you have studed thus far n the text are real vector spaces because the scalars

More information

Polynomials. 1 More properties of polynomials

Polynomials. 1 More properties of polynomials Polynomals 1 More propertes of polynomals Recall that, for R a commutatve rng wth unty (as wth all rngs n ths course unless otherwse noted), we defne R[x] to be the set of expressons n =0 a x, where a

More information

Lecture 5.8 Flux Vector Splitting

Lecture 5.8 Flux Vector Splitting Lecture 5.8 Flux Vector Splttng 1 Flux Vector Splttng The vector E n (5.7.) can be rewrtten as E = AU (5.8.1) (wth A as gven n (5.7.4) or (5.7.6) ) whenever, the equaton of state s of the separable form

More information

MAT 578 Functional Analysis

MAT 578 Functional Analysis MAT 578 Functonal Analyss John Qugg Fall 2008 Locally convex spaces revsed September 6, 2008 Ths secton establshes the fundamental propertes of locally convex spaces. Acknowledgment: although I wrote these

More information

Geometry of Müntz Spaces

Geometry of Müntz Spaces WDS'12 Proceedngs of Contrbuted Papers, Part I, 31 35, 212. ISBN 978-8-7378-224-5 MATFYZPRESS Geometry of Müntz Spaces P. Petráček Charles Unversty, Faculty of Mathematcs and Physcs, Prague, Czech Republc.

More information

Basically, if you have a dummy dependent variable you will be estimating a probability.

Basically, if you have a dummy dependent variable you will be estimating a probability. ECON 497: Lecture Notes 13 Page 1 of 1 Metropoltan State Unversty ECON 497: Research and Forecastng Lecture Notes 13 Dummy Dependent Varable Technques Studenmund Chapter 13 Bascally, f you have a dummy

More information

a b a In case b 0, a being divisible by b is the same as to say that

a b a In case b 0, a being divisible by b is the same as to say that Secton 6.2 Dvsblty among the ntegers An nteger a ε s dvsble by b ε f there s an nteger c ε such that a = bc. Note that s dvsble by any nteger b, snce = b. On the other hand, a s dvsble by only f a = :

More information

Errors for Linear Systems

Errors for Linear Systems Errors for Lnear Systems When we solve a lnear system Ax b we often do not know A and b exactly, but have only approxmatons  and ˆb avalable. Then the best thng we can do s to solve ˆx ˆb exactly whch

More information