Supplement for In Search of the Holy Grail: Policy Convergence, Experimentation and Economic Performance Sharun W. Mukand and Dani Rodrik

Size: px
Start display at page:

Download "Supplement for In Search of the Holy Grail: Policy Convergence, Experimentation and Economic Performance Sharun W. Mukand and Dani Rodrik"

Transcription

1 Supplement or In Search o the Holy Grail: Policy Convergence, Experimentation and Economic Perormance Sharun W. Mukand and Dani Rodrik In what ollows we sketch out the proos or the lemma and propositions I and II in the paper. Lemma I: We solve or a ixed-point δ1, such that all countries in the leader cohort with δ δ 1 preer a corrupt policy, while all countries with a δ>δ1 preer to experiment. Consider irst the citizen s voting decision: in period 1, the incumbent is retained with probability π = prob(c i >ψ 1 R)= c ψr c, since c U[0, c]. Now suppose that there exists a δ 1 such that ψ 1 = prob(δ <δ 1 )=G(δ 1 ). This implies that rom the citizen s perspective the probability that the government will be retained is given by the relationship π 1 =1 RG(δ 1) c, such that associated with every δ is a corresponding π 1. Notice that this implies a negative relationship between π and δ. We now turn to the government s problem. Here a government will be indierent between experimentation or corruption i V xpmt = V corr. This latter equality is true i θση + π X r = θδ + π C [(φ 1)R + r]. Simpliying, we obtain π 1 = θδ θση (φ 1)R. Observe that the government s optimization gives a positive relationship between δ and π. For there to be a ixed-point equilibrium, we need a δ1 that simultaneously solves both the government s policy choice rule as well as the citizen s optimal voting rule. Given the continuity o the underlying unctions, since the relationship between δ and π described by the citizen s problem has a negative slope while that given by the government s problem has a positive slope, there exists a unique cut-o δ1. Proposition I: We are interested in inding whether there exist parameters such that our proposed equilibrium is satisied, where or a given history h 1 the government s decision to experiment, imitate or choose a corrupt policy is a unction o (,δ). For expositional convenience, we reproduce the payos to government in ollower cohort (z,δ ) rom imitating (V imit ), experimenting (V xpmt )or choosing a corrupt policy (V corr ), (see also Figure 1) θ L + πi [r] i a A h L (i) V k = θση + π X [r] i a j A h (ii) θδc + πc [(φ 1)R + r] i a i A c. (iii) The opportunity cost o the government s choice o a corrupt policy is increasing in δ. Thereore, in our proposed equilibrium, the probability that an observed policy (conditional on no imitation) is corrupt, is decreasing in δ. The set o all possible period one histories H 1 are divided three ways - the set o period one histories where (a) there is a sole successul leader (n L = 1), i.e. y 1 > ȳ = θδl R>y j, j 1, (b) there are two or more successul leaders, i.e n L, (c) there are no successul leaders, i.e. n L = 0. In what ollows we ocus on (a) and sketch out (b) and (c). 1

2 dv xpmt We now begin by mapping V corr and V xpmt on (V,δ) space. Observe that = r dπx < 0 dv corr and that = θδ +[(φ 1)R + r] dπc < 0. Since the citizen-voter cannot distinguish between a corrupt and an honest policy, we have π X = π C dv xpmt dv corr = π which implies that <. In order to ensure that both the downward sloping curves intersect in the relevant range on (V,δ) space (recollect that δ is bounded between δ L and δ H ), we assume (φ 1)R >θ(δl σ η). This ensures conditions on the end-points o (i) (iii) such that: V xpmt (δ = δ L )=r θση <V corr (δ = δ L )= θδ + r +(φ 1)R <V imit = r. Given the continuity o the payos and the restrictions on the end-points that we have imposed, we have an intersection between the curves V corr and V xpmt in the relevant (V,δ) space - where we denote the point o intersection ( ˆ, ˆδ). Further, notice that V imit ((i) above) is independent o δ and hence can be depicted as a horizontal line. Thereore we are now in a position to graph (i) (iii) on(v,δ) space - as illustrated in Figure 1. We are interested in obtaining the optimal policy choice or a government at z as a unction o (,δ). This is simple to obtain by mapping or each δ [δ L,δ H ] the policy a j corresponding to the V j that constitutes the upper envelope o the payos, i.e. or any δ, a j = argmax a j {V j (a j): a j {A c, A h, A h L }}(see urther below or a caveat). Keeping this in mind, observe that the payo rom imitation decreases as L increases i.e. dv imit /d L = θ L < 0. A successul leader s neighbors are deined as the set o countries whose eective distance is suiciently small such that they would preer to imitate quite irrespective o the payo rom alternative policy choices or all δ, i.e. θ L θδ +(φ 1)R + r. This simpliies to a distance L 1 = δ L 1 θ [(φ 1)R + r]. Thereore, all such neighbors o the leader imitate its policy. On the other extreme consider the cut-o or the ar-periphery. First, observe rom Figure 1 that i distance rom the leader is such that V imit V xpmt (δ = δ H ) (i.e. i θ L + r = θσ η), we either have V cor >V xpmt >V imit or δ<ˆδ or we have V xpmt >V corr >V imit or δ ˆδ. Given the continuity o the payos over the relevant space, this establishes the existence o a cut-o distance L, such that or all δ [δ L,δ H ], governments located beyond this cut-o no longer imitate - giving us the ar-periphery. Countries that lie at some intermediate distance between the neighbors and the ar-periphery (as deined above), constitute the near-periphery. As can be readily observed rom Figure 1, countries located in this region, may choose (as a unction o their δ) imitation, experimentation or corruption as their policy choices. We next check the optimality o the citizen-voter s re-election rule. Here the citizen compares the realized cost o replacing the incumbent c i, with the expected gain in rents i the policy adopted by the government is corrupt. Given the uniorm distribution or c, the re-election probability π is continuous. This is because the citizen s payo u i is decreasing continuosly in the probability that the policy is corrupt since π = prob(c i ψ R). For instance, i a A h L, then rom Bayes rule the

3 citizen-voter knows or sure that the policy is honest - and hence ψ 0 π I = 1. Furthermore since the voter observes the location o the government s policy choice a j, he can make an assessment o the likelihood o whether the policy is corrupt or honest. In particular, i a government who s distance rom the leader is ˆ (the corresponding to ˆδ in igure 1), chooses to not imitate, it will perceived by the voter to have chosen a corrupt policy with probability one. This in turn will imply that π 0 (since c R). Thereore, or a relatively small L, we have V corr >V imit since θδ θ L + r. In contrast, when L is suiciently large the inequality will reverse itsel. Now we sketch out alternative histories with two or more successul leaders, i.e. n L > 1. Without loss o generality, suppose there are two leaders z L1 and z L. There are two possible sub-cases. First, is the possibility that none o the countries in the ollower cohort lies simultaneously within a distance L o both the leaders z Li. In this case, the preceding analysis carries over and z s decision problem is as i there is a single leader. However, we note that L (n L =)> L (n L = 1) since higher δ countries are more likely to imitate, thereby lowering the average δ (and hence π) o those who do no imitate. In diagrammatic terms, this is equivalent to a downward shit o the V corr,v xpmt curves - increasing L. Observe that or any F (z), we can always choose a θ large enough so as to ensure that this downward shit o the V corr and V xpmt is not so large so as to make imitation the dominant strategy or all countries in F (z). Second, is the possibility that z lies between and within a distance L o two or more countries z L1 and z L, i.e. z L1 z L < L. In this case, a ollower country s payo is maximized by imitating the leader with whom it has the least eective distance and there is no experimentation by any o the countries located between these two successul leaders. Finally, i no successul leaders emerged in the leader cohort, then decision making in the ollower cohort is identical to that in the leader cohort. Proposition II: In what ollows we begin by considering the history h 1, with n L = 1. Eicient discipline implies that imitation o a leader enhances private sector national income, either by reducing the costs o corruption or the cost o experimentation. Thereore, or disciplining to be eicient or any country z (δ, ), it is the case that V imit θδ c V corr. The latter is true when, θ il + πi + π C[(φ 1)R + r] L {(δ 1 θ (πc [(φ 1)R + r]+r)}. Now recollect that eicient imitation occurs when L σ η, where σ η <δ L. Thereore, or the inormational externality to cause eicient disciplining, the ollowing two conditions must be met:(a) there exists a non-empty set o countries which are disciplined into imitating a leader or political reasons, but would not do so otherwise, and (b) or a subset o these countries, private-sector income increases due to the reduced corruption that results because o greater disciplining. Now or (a) to hold, parameters must be such that V imit >V corr i.e. i δ + 1 θ {r(1 πc ) π C (φ 1)R} L. On the other hand, or (b) to 3

4 hold, parameters must be such that θ L > θδ R. It is easy to check that both (a) and (b) are satisied or a suiciently low bound on δ L and r. We now demonstrate that there exist parameters in the near-periphery, that display ineicient disciplining. There are two possibilities: (a) countries that would otherwise have preerred to experiment with an honest policy (i.e. those with high δ) are ineiciently disciplined into imitating a leader primarily or political reasons; (b) countries that would have chosen corrupt policies, are disciplined into adopting honest policies, but the loss in imitating a policy o a distant country is greater than the gain due to a reduction in corruption. First, we describe parameters such that (a) is satisied: i.e. V imit V xpmt i ση + r θ (πi π X ) L. Since πi = 1 and π X < 1, we have ineicient imitation or a large enough r (recollect that e <σ η ). (b) Now consider the possibility o ineicient disciplining o corrupt governments. For this to be true, distance L has to be such that V imit V corr, even though θ L < θδ R. To see that parameters exist that satisy these conditions, observe that V imit V corr δ + 1 θ [r(πi π C ) π C (φ 1)R] L. Imposing the above condition implies that a suicient condition is i δ + 1 θ [r(πi π C ) π C (φ 1)R] L >δ + R θ. Simpliying we obtain the ollowing r(π I π C ) >R(1 + p C (φ 1)) where π I = 1 and π C < 1. It is easy to check that this is true or ego rents that are suiciently large - a restriction consistent with others in the paper. In order to show that expected income in the near-periphery is lower than in the ar-periphery, examine Figure 1. From the preceding paragraph, there is ineicient imitation i V imit >V xpmt, even though such imitation results in a decrease in private national income as compared to experimenting, i.e. even though θ < θση. Recollect that imitation is or political reasons (i.e. r) when a government imitates despite the eective distance L being greater than e = σ η. Now suppose that such a government lies at some > e. Observe that or any country located at such a, political considerations ensure that the country ineiciently imitates, i.e. V imit ( ) >V xpmt ( ). Then there is ineicient imitation by all countries with δ [ δ, δ H ]. Observe that all countries with δ in this range but with L > L will witness an increase in income as all governments in this region preer to experiment. Since L >σ η or countries in this region, private national income rom experimentation is greater than imitation. This implies a higher expected income or countries in this region. Finally, it is easy to check that or countries in the ar-periphery, none o the countries are disciplined into enacting honest policies. Those with a high δ, such that V xpmt >V corr, would have chosen honest policies in any case. However, since experimentation is being carried out in this region, some countries will achieve a very high income, while others will do poorly, where the variance in incomes is given by ση. 4

5 Government Payos V imit ( L =0)=r Deadweight loss V cor (δ L ) =-θδ + (φ-1) R+r δ^ V xpm (δ L ) = -θσ + r V corr V xpmt V imit ( ) = -θ σ η -θδ Figure 1: Equilibrium Policy Choices o the Incumbent

Basic mathematics of economic models. 3. Maximization

Basic mathematics of economic models. 3. Maximization John Riley 1 January 16 Basic mathematics o economic models 3 Maimization 31 Single variable maimization 1 3 Multi variable maimization 6 33 Concave unctions 9 34 Maimization with non-negativity constraints

More information

Online Appendix: The Continuous-type Model of Competitive Nonlinear Taxation and Constitutional Choice by Massimo Morelli, Huanxing Yang, and Lixin Ye

Online Appendix: The Continuous-type Model of Competitive Nonlinear Taxation and Constitutional Choice by Massimo Morelli, Huanxing Yang, and Lixin Ye Online Appendix: The Continuous-type Model o Competitive Nonlinear Taxation and Constitutional Choice by Massimo Morelli, Huanxing Yang, and Lixin Ye For robustness check, in this section we extend our

More information

Supplementary material for Continuous-action planning for discounted infinite-horizon nonlinear optimal control with Lipschitz values

Supplementary material for Continuous-action planning for discounted infinite-horizon nonlinear optimal control with Lipschitz values Supplementary material or Continuous-action planning or discounted ininite-horizon nonlinear optimal control with Lipschitz values List o main notations x, X, u, U state, state space, action, action space,

More information

Essential Microeconomics : EQUILIBRIUM AND EFFICIENCY WITH PRODUCTION Key ideas: Walrasian equilibrium, first and second welfare theorems

Essential Microeconomics : EQUILIBRIUM AND EFFICIENCY WITH PRODUCTION Key ideas: Walrasian equilibrium, first and second welfare theorems Essential Microeconomics -- 5.2: EQUILIBRIUM AND EFFICIENCY WITH PRODUCTION Key ideas: Walrasian equilibrium, irst and second welare teorems A general model 2 First welare Teorem 7 Second welare teorem

More information

THE CAUCHY PROBLEM VIA THE METHOD OF CHARACTERISTICS

THE CAUCHY PROBLEM VIA THE METHOD OF CHARACTERISTICS THE CAUCHY PROBLEM VIA THE METHOD OF CHARACTERISTICS ARICK SHAO In this short note, we solve the Cauchy, or initial value, problem or general ully nonlinear irst-order PDE. Throughout, our PDE will be

More information

9.3 Graphing Functions by Plotting Points, The Domain and Range of Functions

9.3 Graphing Functions by Plotting Points, The Domain and Range of Functions 9. Graphing Functions by Plotting Points, The Domain and Range o Functions Now that we have a basic idea o what unctions are and how to deal with them, we would like to start talking about the graph o

More information

Curve Sketching. The process of curve sketching can be performed in the following steps:

Curve Sketching. The process of curve sketching can be performed in the following steps: Curve Sketching So ar you have learned how to ind st and nd derivatives o unctions and use these derivatives to determine where a unction is:. Increasing/decreasing. Relative extrema 3. Concavity 4. Points

More information

Math 2412 Activity 1(Due by EOC Sep. 17)

Math 2412 Activity 1(Due by EOC Sep. 17) Math 4 Activity (Due by EOC Sep. 7) Determine whether each relation is a unction.(indicate why or why not.) Find the domain and range o each relation.. 4,5, 6,7, 8,8. 5,6, 5,7, 6,6, 6,7 Determine whether

More information

On High-Rate Cryptographic Compression Functions

On High-Rate Cryptographic Compression Functions On High-Rate Cryptographic Compression Functions Richard Ostertág and Martin Stanek Department o Computer Science Faculty o Mathematics, Physics and Inormatics Comenius University Mlynská dolina, 842 48

More information

Web Appendix for The Value of Switching Costs

Web Appendix for The Value of Switching Costs Web Appendix or The Value o Switching Costs Gary Biglaiser University o North Carolina, Chapel Hill Jacques Crémer Toulouse School o Economics (GREMAQ, CNRS and IDEI) Gergely Dobos Gazdasági Versenyhivatal

More information

Circuit Complexity / Counting Problems

Circuit Complexity / Counting Problems Lecture 5 Circuit Complexity / Counting Problems April 2, 24 Lecturer: Paul eame Notes: William Pentney 5. Circuit Complexity and Uniorm Complexity We will conclude our look at the basic relationship between

More information

PROBLEM SET 1 (Solutions) (MACROECONOMICS cl. 15)

PROBLEM SET 1 (Solutions) (MACROECONOMICS cl. 15) PROBLEM SET (Solutions) (MACROECONOMICS cl. 5) Exercise Calculating GDP In an economic system there are two sectors A and B. The sector A: - produces value added or a value o 50; - pays wages or a value

More information

Roberto s Notes on Differential Calculus Chapter 8: Graphical analysis Section 1. Extreme points

Roberto s Notes on Differential Calculus Chapter 8: Graphical analysis Section 1. Extreme points Roberto s Notes on Dierential Calculus Chapter 8: Graphical analysis Section 1 Extreme points What you need to know already: How to solve basic algebraic and trigonometric equations. All basic techniques

More information

Algebra II Notes Inverse Functions Unit 1.2. Inverse of a Linear Function. Math Background

Algebra II Notes Inverse Functions Unit 1.2. Inverse of a Linear Function. Math Background Algebra II Notes Inverse Functions Unit 1. Inverse o a Linear Function Math Background Previously, you Perormed operations with linear unctions Identiied the domain and range o linear unctions In this

More information

Online Appendix for Lerner Symmetry: A Modern Treatment

Online Appendix for Lerner Symmetry: A Modern Treatment Online Appendix or Lerner Symmetry: A Modern Treatment Arnaud Costinot MIT Iván Werning MIT May 2018 Abstract Tis Appendix provides te proos o Teorem 1, Teorem 2, and Proposition 1. 1 Perect Competition

More information

Section 3.4: Concavity and the second Derivative Test. Find any points of inflection of the graph of a function.

Section 3.4: Concavity and the second Derivative Test. Find any points of inflection of the graph of a function. Unit 3: Applications o Dierentiation Section 3.4: Concavity and the second Derivative Test Determine intervals on which a unction is concave upward or concave downward. Find any points o inlection o the

More information

ON STRATEGY-PROOF SOCIAL CHOICE BETWEEN TWO ALTERNATIVES

ON STRATEGY-PROOF SOCIAL CHOICE BETWEEN TWO ALTERNATIVES Discussion Paper No. 1013 ON STRATEGY-PROOF SOCIAL CHOICE BETWEEN TWO ALTERNATIVES Ahinaa Lahiri Anup Pramanik Octoer 2017 The Institute o Social and Economic Research Osaka University 6-1 Mihogaoka, Iaraki,

More information

THE use of radio frequency channels assigned to primary. Traffic-Aware Channel Sensing Order in Dynamic Spectrum Access Networks

THE use of radio frequency channels assigned to primary. Traffic-Aware Channel Sensing Order in Dynamic Spectrum Access Networks EEE JOURNAL ON SELECTED AREAS N COMMUNCATONS, VOL. X, NO. X, X 01X 1 Traic-Aware Channel Sensing Order in Dynamic Spectrum Access Networks Chun-Hao Liu, Jason A. Tran, Student Member, EEE, Przemysław Pawełczak,

More information

STAT 801: Mathematical Statistics. Hypothesis Testing

STAT 801: Mathematical Statistics. Hypothesis Testing STAT 801: Mathematical Statistics Hypothesis Testing Hypothesis testing: a statistical problem where you must choose, on the basis o data X, between two alternatives. We ormalize this as the problem o

More information

arxiv: v1 [math.gt] 20 Feb 2008

arxiv: v1 [math.gt] 20 Feb 2008 EQUIVALENCE OF REAL MILNOR S FIBRATIONS FOR QUASI HOMOGENEOUS SINGULARITIES arxiv:0802.2746v1 [math.gt] 20 Feb 2008 ARAÚJO DOS SANTOS, R. Abstract. We are going to use the Euler s vector ields in order

More information

Do new competitors, new customers, new suppliers,... sustain, destroy or create competitive advantage?

Do new competitors, new customers, new suppliers,... sustain, destroy or create competitive advantage? Do new competitors, new customers, new suppliers,... sustain, destroy or create competitive advantage? Glenn MacDonald Olin School o Business Washington University MichaelD.Ryall Melbourne Business School

More information

Example: When describing where a function is increasing, decreasing or constant we use the x- axis values.

Example: When describing where a function is increasing, decreasing or constant we use the x- axis values. Business Calculus Lecture Notes (also Calculus With Applications or Business Math II) Chapter 3 Applications o Derivatives 31 Increasing and Decreasing Functions Inormal Deinition: A unction is increasing

More information

UNIVERSITY OF MARYLAND Department of Economics Economics 754 Topics in Political Economy Fall 2005 Allan Drazen. Exercise Set I

UNIVERSITY OF MARYLAND Department of Economics Economics 754 Topics in Political Economy Fall 2005 Allan Drazen. Exercise Set I UNIVERSITY OF MARYLAND Department of Economics Economics 754 Topics in Political Economy Fall 005 Allan Drazen Exercise Set I The first four exercises are review of what we did in class on 8/31. The next

More information

Review D: Potential Energy and the Conservation of Mechanical Energy

Review D: Potential Energy and the Conservation of Mechanical Energy MSSCHUSETTS INSTITUTE OF TECHNOLOGY Department o Physics 8. Spring 4 Review D: Potential Energy and the Conservation o Mechanical Energy D.1 Conservative and Non-conservative Force... D.1.1 Introduction...

More information

On the Girth of (3,L) Quasi-Cyclic LDPC Codes based on Complete Protographs

On the Girth of (3,L) Quasi-Cyclic LDPC Codes based on Complete Protographs On the Girth o (3,L) Quasi-Cyclic LDPC Codes based on Complete Protographs Sudarsan V S Ranganathan, Dariush Divsalar and Richard D Wesel Department o Electrical Engineering, University o Caliornia, Los

More information

( x) f = where P and Q are polynomials.

( x) f = where P and Q are polynomials. 9.8 Graphing Rational Functions Lets begin with a deinition. Deinition: Rational Function A rational unction is a unction o the orm ( ) ( ) ( ) P where P and Q are polynomials. Q An eample o a simple rational

More information

A Consistent Generation of Pipeline Parallelism and Distribution of Operations and Data among Processors

A Consistent Generation of Pipeline Parallelism and Distribution of Operations and Data among Processors ISSN 0361-7688 Programming and Computer Sotware 006 Vol. 3 No. 3 pp. 166176. Pleiades Publishing Inc. 006. Original Russian Text E.V. Adutskevich N.A. Likhoded 006 published in Programmirovanie 006 Vol.

More information

EXISTENCE OF ISOPERIMETRIC SETS WITH DENSITIES CONVERGING FROM BELOW ON R N. 1. Introduction

EXISTENCE OF ISOPERIMETRIC SETS WITH DENSITIES CONVERGING FROM BELOW ON R N. 1. Introduction EXISTECE OF ISOPERIMETRIC SETS WITH DESITIES COVERGIG FROM BELOW O R GUIDO DE PHILIPPIS, GIOVAI FRAZIA, AD ALDO PRATELLI Abstract. In this paper, we consider the isoperimetric problem in the space R with

More information

3. Several Random Variables

3. Several Random Variables . Several Random Variables. Two Random Variables. Conditional Probabilit--Revisited. Statistical Independence.4 Correlation between Random Variables. Densit unction o the Sum o Two Random Variables. Probabilit

More information

Maximum Flow. Reading: CLRS Chapter 26. CSE 6331 Algorithms Steve Lai

Maximum Flow. Reading: CLRS Chapter 26. CSE 6331 Algorithms Steve Lai Maximum Flow Reading: CLRS Chapter 26. CSE 6331 Algorithms Steve Lai Flow Network A low network G ( V, E) is a directed graph with a source node sv, a sink node tv, a capacity unction c. Each edge ( u,

More information

MEAN VALUE THEOREM. Section 3.2 Calculus AP/Dual, Revised /30/2018 1:16 AM 3.2: Mean Value Theorem 1

MEAN VALUE THEOREM. Section 3.2 Calculus AP/Dual, Revised /30/2018 1:16 AM 3.2: Mean Value Theorem 1 MEAN VALUE THEOREM Section 3. Calculus AP/Dual, Revised 017 viet.dang@humbleisd.net 7/30/018 1:16 AM 3.: Mean Value Theorem 1 ACTIVITY A. Draw a curve (x) on a separate sheet o paper within a deined closed

More information

Root Arrangements of Hyperbolic Polynomial-like Functions

Root Arrangements of Hyperbolic Polynomial-like Functions Root Arrangements o Hyperbolic Polynomial-like Functions Vladimir Petrov KOSTOV Université de Nice Laboratoire de Mathématiques Parc Valrose 06108 Nice Cedex France kostov@mathunicer Received: March, 005

More information

RATIONAL FUNCTIONS. Finding Asymptotes..347 The Domain Finding Intercepts Graphing Rational Functions

RATIONAL FUNCTIONS. Finding Asymptotes..347 The Domain Finding Intercepts Graphing Rational Functions RATIONAL FUNCTIONS Finding Asymptotes..347 The Domain....350 Finding Intercepts.....35 Graphing Rational Functions... 35 345 Objectives The ollowing is a list o objectives or this section o the workbook.

More information

Categories and Natural Transformations

Categories and Natural Transformations Categories and Natural Transormations Ethan Jerzak 17 August 2007 1 Introduction The motivation or studying Category Theory is to ormalise the underlying similarities between a broad range o mathematical

More information

RESOLUTION MSC.362(92) (Adopted on 14 June 2013) REVISED RECOMMENDATION ON A STANDARD METHOD FOR EVALUATING CROSS-FLOODING ARRANGEMENTS

RESOLUTION MSC.362(92) (Adopted on 14 June 2013) REVISED RECOMMENDATION ON A STANDARD METHOD FOR EVALUATING CROSS-FLOODING ARRANGEMENTS (Adopted on 4 June 203) (Adopted on 4 June 203) ANNEX 8 (Adopted on 4 June 203) MSC 92/26/Add. Annex 8, page THE MARITIME SAFETY COMMITTEE, RECALLING Article 28(b) o the Convention on the International

More information

Logarithm of a Function, a Well-Posed Inverse Problem

Logarithm of a Function, a Well-Posed Inverse Problem American Journal o Computational Mathematics, 4, 4, -5 Published Online February 4 (http://www.scirp.org/journal/ajcm http://dx.doi.org/.436/ajcm.4.4 Logarithm o a Function, a Well-Posed Inverse Problem

More information

The 2nd Texas A&M at Galveston Mathematics Olympiad. September 24, Problems & Solutions

The 2nd Texas A&M at Galveston Mathematics Olympiad. September 24, Problems & Solutions nd Math Olympiad Solutions Problem #: The nd Teas A&M at Galveston Mathematics Olympiad September 4, 00 Problems & Solutions Written by Dr. Lin Qiu A runner passes 5 poles in 30 seconds. How long will

More information

SIO 211B, Rudnick. We start with a definition of the Fourier transform! ĝ f of a time series! ( )

SIO 211B, Rudnick. We start with a definition of the Fourier transform! ĝ f of a time series! ( ) SIO B, Rudnick! XVIII.Wavelets The goal o a wavelet transorm is a description o a time series that is both requency and time selective. The wavelet transorm can be contrasted with the well-known and very

More information

Documents de Travail du Centre d Economie de la Sorbonne

Documents de Travail du Centre d Economie de la Sorbonne Documents de Travail du Centre d Economie de la Sorbonne On equilibrium payos in wage bargaining with discount rates varying in time Ahmet OZKARDAS, Agnieszka RUSINOWSKA 2014.11 Maison des Sciences Économiques,

More information

3.5 Analysis of Members under Flexure (Part IV)

3.5 Analysis of Members under Flexure (Part IV) 3.5 Analysis o Members under Flexure (Part IV) This section covers the ollowing topics. Analysis o a Flanged Section 3.5.1 Analysis o a Flanged Section Introduction A beam can have langes or lexural eiciency.

More information

Universidad Carlos III de Madrid

Universidad Carlos III de Madrid Universidad Carlos III de Madrid Exercise 3 5 6 Total Points Department de Economics Mathematicas I Final Exam January 0th 07 Exam time: hours. LAST NAME: FIRST NAME: ID: DEGREE: GROUP: () Consider the

More information

FIXED POINTS OF RENORMALIZATION.

FIXED POINTS OF RENORMALIZATION. FIXED POINTS OF RENORMALIZATION. XAVIER BUFF Abstract. To study the geometry o a Fibonacci map o even degree l 4, Lyubich [Ly2] deined a notion o generalized renormalization, so that is renormalizable

More information

Function Operations. I. Ever since basic math, you have been combining numbers by using addition, subtraction, multiplication, and division.

Function Operations. I. Ever since basic math, you have been combining numbers by using addition, subtraction, multiplication, and division. Function Operations I. Ever since basic math, you have been combining numbers by using addition, subtraction, multiplication, and division. Add: 5 + Subtract: 7 Multiply: (9)(0) Divide: (5) () or 5 II.

More information

2. ETA EVALUATIONS USING WEBER FUNCTIONS. Introduction

2. ETA EVALUATIONS USING WEBER FUNCTIONS. Introduction . ETA EVALUATIONS USING WEBER FUNCTIONS Introduction So ar we have seen some o the methods or providing eta evaluations that appear in the literature and we have seen some o the interesting properties

More information

Online Appendixes for \A Theory of Military Dictatorships"

Online Appendixes for \A Theory of Military Dictatorships May 2009 Online Appendixes for \A Theory of Military Dictatorships" By Daron Acemoglu, Davide Ticchi and Andrea Vindigni Appendix B: Key Notation for Section I 2 (0; 1): discount factor. j;t 2 f0; 1g:

More information

Finite Dimensional Hilbert Spaces are Complete for Dagger Compact Closed Categories (Extended Abstract)

Finite Dimensional Hilbert Spaces are Complete for Dagger Compact Closed Categories (Extended Abstract) Electronic Notes in Theoretical Computer Science 270 (1) (2011) 113 119 www.elsevier.com/locate/entcs Finite Dimensional Hilbert Spaces are Complete or Dagger Compact Closed Categories (Extended bstract)

More information

Math 216A. A gluing construction of Proj(S)

Math 216A. A gluing construction of Proj(S) Math 216A. A gluing construction o Proj(S) 1. Some basic deinitions Let S = n 0 S n be an N-graded ring (we ollows French terminology here, even though outside o France it is commonly accepted that N does

More information

The Importance of the Median Voter

The Importance of the Median Voter The Importance of the Median Voter According to Duncan Black and Anthony Downs V53.0500 NYU 1 Committee Decisions utility 0 100 x 1 x 2 x 3 x 4 x 5 V53.0500 NYU 2 Single-Peakedness Condition The preferences

More information

1 Relative degree and local normal forms

1 Relative degree and local normal forms THE ZERO DYNAMICS OF A NONLINEAR SYSTEM 1 Relative degree and local normal orms The purpose o this Section is to show how single-input single-output nonlinear systems can be locally given, by means o a

More information

On the Flow-level Dynamics of a Packet-switched Network

On the Flow-level Dynamics of a Packet-switched Network On the Flow-level Dynamics o a Packet-switched Network Ciamac C. Moallemi and Devavrat Shah Abstract: The packet is the undamental unit o transportation in modern communication networks such as the Internet.

More information

Secure Communication in Multicast Graphs

Secure Communication in Multicast Graphs Secure Communication in Multicast Graphs Qiushi Yang and Yvo Desmedt Department o Computer Science, University College London, UK {q.yang, y.desmedt}@cs.ucl.ac.uk Abstract. In this paper we solve the problem

More information

Statistical Mechanics and Combinatorics : Lecture II

Statistical Mechanics and Combinatorics : Lecture II Statistical Mechanics and Combinatorics : Lecture II Kircho s matrix-tree theorem Deinition (Spanning tree) A subgraph T o a graph G(V, E) is a spanning tree i it is a tree that contains every vertex in

More information

Review of Prerequisite Skills for Unit # 2 (Derivatives) U2L2: Sec.2.1 The Derivative Function

Review of Prerequisite Skills for Unit # 2 (Derivatives) U2L2: Sec.2.1 The Derivative Function UL1: Review o Prerequisite Skills or Unit # (Derivatives) Working with the properties o exponents Simpliying radical expressions Finding the slopes o parallel and perpendicular lines Simpliying rational

More information

Probabilistic Model of Error in Fixed-Point Arithmetic Gaussian Pyramid

Probabilistic Model of Error in Fixed-Point Arithmetic Gaussian Pyramid Probabilistic Model o Error in Fixed-Point Arithmetic Gaussian Pyramid Antoine Méler John A. Ruiz-Hernandez James L. Crowley INRIA Grenoble - Rhône-Alpes 655 avenue de l Europe 38 334 Saint Ismier Cedex

More information

Fisher Consistency of Multicategory Support Vector Machines

Fisher Consistency of Multicategory Support Vector Machines Fisher Consistency o Multicategory Support Vector Machines Yueng Liu Department o Statistics and Operations Research Carolina Center or Genome Sciences University o North Carolina Chapel Hill, NC 7599-360

More information

Math-3 Lesson 8-5. Unit 4 review: a) Compositions of functions. b) Linear combinations of functions. c) Inverse Functions. d) Quadratic Inequalities

Math-3 Lesson 8-5. Unit 4 review: a) Compositions of functions. b) Linear combinations of functions. c) Inverse Functions. d) Quadratic Inequalities Math- Lesson 8-5 Unit 4 review: a) Compositions o unctions b) Linear combinations o unctions c) Inverse Functions d) Quadratic Inequalities e) Rational Inequalities 1. Is the ollowing relation a unction

More information

Intro Prefs & Voting Electoral comp. Political Economics. Ludwig-Maximilians University Munich. Summer term / 37

Intro Prefs & Voting Electoral comp. Political Economics. Ludwig-Maximilians University Munich. Summer term / 37 1 / 37 Political Economics Ludwig-Maximilians University Munich Summer term 2010 4 / 37 Table of contents 1 Introduction(MG) 2 Preferences and voting (MG) 3 Voter turnout (MG) 4 Electoral competition (SÜ)

More information

Math 754 Chapter III: Fiber bundles. Classifying spaces. Applications

Math 754 Chapter III: Fiber bundles. Classifying spaces. Applications Math 754 Chapter III: Fiber bundles. Classiying spaces. Applications Laurențiu Maxim Department o Mathematics University o Wisconsin maxim@math.wisc.edu April 18, 2018 Contents 1 Fiber bundles 2 2 Principle

More information

Problem Set. Problems on Unordered Summation. Math 5323, Fall Februray 15, 2001 ANSWERS

Problem Set. Problems on Unordered Summation. Math 5323, Fall Februray 15, 2001 ANSWERS Problem Set Problems on Unordered Summation Math 5323, Fall 2001 Februray 15, 2001 ANSWERS i 1 Unordered Sums o Real Terms In calculus and real analysis, one deines the convergence o an ininite series

More information

Math Review and Lessons in Calculus

Math Review and Lessons in Calculus Math Review and Lessons in Calculus Agenda Rules o Eponents Functions Inverses Limits Calculus Rules o Eponents 0 Zero Eponent Rule a * b ab Product Rule * 3 5 a / b a-b Quotient Rule 5 / 3 -a / a Negative

More information

Objectives. By the time the student is finished with this section of the workbook, he/she should be able

Objectives. By the time the student is finished with this section of the workbook, he/she should be able FUNCTIONS Quadratic Functions......8 Absolute Value Functions.....48 Translations o Functions..57 Radical Functions...61 Eponential Functions...7 Logarithmic Functions......8 Cubic Functions......91 Piece-Wise

More information

GENERALIZED ABSTRACT NONSENSE: CATEGORY THEORY AND ADJUNCTIONS

GENERALIZED ABSTRACT NONSENSE: CATEGORY THEORY AND ADJUNCTIONS GENERALIZED ABSTRACT NONSENSE: CATEGORY THEORY AND ADJUNCTIONS CHRIS HENDERSON Abstract. This paper will move through the basics o category theory, eventually deining natural transormations and adjunctions

More information

1. Definition: Order Statistics of a sample.

1. Definition: Order Statistics of a sample. AMS570 Order Statistics 1. Deinition: Order Statistics o a sample. Let X1, X2,, be a random sample rom a population with p.d.. (x). Then, 2. p.d.. s or W.L.O.G.(W thout Loss o Ge er l ty), let s ssu e

More information

8.4 Inverse Functions

8.4 Inverse Functions Section 8. Inverse Functions 803 8. Inverse Functions As we saw in the last section, in order to solve application problems involving eponential unctions, we will need to be able to solve eponential equations

More information

Feedback Linearization

Feedback Linearization Feedback Linearization Peter Al Hokayem and Eduardo Gallestey May 14, 2015 1 Introduction Consider a class o single-input-single-output (SISO) nonlinear systems o the orm ẋ = (x) + g(x)u (1) y = h(x) (2)

More information

Supplement To: Search for Tensor, Vector, and Scalar Polarizations in the Stochastic Gravitational-Wave Background

Supplement To: Search for Tensor, Vector, and Scalar Polarizations in the Stochastic Gravitational-Wave Background Supplement To: Search or Tensor, Vector, and Scalar Polarizations in the Stochastic GravitationalWave Background B. P. Abbott et al. (LIGO Scientiic Collaboration & Virgo Collaboration) This documents

More information

Calculators are NOT permitted.

Calculators are NOT permitted. THE 0-0 KEESW STTE UIVERSITY HIGH SHOOL THETIS OETITIO RT II In addition to scoring student responses based on whether a solution is correct and complete, consideration will be given to elegance, simplicity,

More information

Feedback Linearization Lectures delivered at IIT-Kanpur, TEQIP program, September 2016.

Feedback Linearization Lectures delivered at IIT-Kanpur, TEQIP program, September 2016. Feedback Linearization Lectures delivered at IIT-Kanpur, TEQIP program, September 216 Ravi N Banavar banavar@iitbacin September 24, 216 These notes are based on my readings o the two books Nonlinear Control

More information

Published in the American Economic Review Volume 102, Issue 1, February 2012, pages doi: /aer

Published in the American Economic Review Volume 102, Issue 1, February 2012, pages doi: /aer Published in the American Economic Review Volume 102, Issue 1, February 2012, pages 594-601. doi:10.1257/aer.102.1.594 CONTRACTS VS. SALARIES IN MATCHING FEDERICO ECHENIQUE Abstract. Firms and workers

More information

Estimation and detection of a periodic signal

Estimation and detection of a periodic signal Estimation and detection o a periodic signal Daniel Aronsson, Erik Björnemo, Mathias Johansson Signals and Systems Group, Uppsala University, Sweden, e-mail: Daniel.Aronsson,Erik.Bjornemo,Mathias.Johansson}@Angstrom.uu.se

More information

Computing proximal points of nonconvex functions

Computing proximal points of nonconvex functions Mathematical Programming manuscript No. (will be inserted by the editor) Warren Hare Claudia Sagastizábal Computing proximal points o nonconvex unctions the date o receipt and acceptance should be inserted

More information

SEPARATED AND PROPER MORPHISMS

SEPARATED AND PROPER MORPHISMS SEPARATED AND PROPER MORPHISMS BRIAN OSSERMAN Last quarter, we introduced the closed diagonal condition or a prevariety to be a prevariety, and the universally closed condition or a variety to be complete.

More information

8. INTRODUCTION TO STATISTICAL THERMODYNAMICS

8. INTRODUCTION TO STATISTICAL THERMODYNAMICS n * D n d Fluid z z z FIGURE 8-1. A SYSTEM IS IN EQUILIBRIUM EVEN IF THERE ARE VARIATIONS IN THE NUMBER OF MOLECULES IN A SMALL VOLUME, SO LONG AS THE PROPERTIES ARE UNIFORM ON A MACROSCOPIC SCALE 8. INTRODUCTION

More information

Physics 5153 Classical Mechanics. Solution by Quadrature-1

Physics 5153 Classical Mechanics. Solution by Quadrature-1 October 14, 003 11:47:49 1 Introduction Physics 5153 Classical Mechanics Solution by Quadrature In the previous lectures, we have reduced the number o eective degrees o reedom that are needed to solve

More information

The Clifford algebra and the Chevalley map - a computational approach (detailed version 1 ) Darij Grinberg Version 0.6 (3 June 2016). Not proofread!

The Clifford algebra and the Chevalley map - a computational approach (detailed version 1 ) Darij Grinberg Version 0.6 (3 June 2016). Not proofread! The Cliord algebra and the Chevalley map - a computational approach detailed version 1 Darij Grinberg Version 0.6 3 June 2016. Not prooread! 1. Introduction: the Cliord algebra The theory o the Cliord

More information

Equidistant Polarizing Transforms

Equidistant Polarizing Transforms DRAFT 1 Equidistant Polarizing Transorms Sinan Kahraman Abstract arxiv:1708.0133v1 [cs.it] 3 Aug 017 This paper presents a non-binary polar coding scheme that can reach the equidistant distant spectrum

More information

Robust Residual Selection for Fault Detection

Robust Residual Selection for Fault Detection Robust Residual Selection or Fault Detection Hamed Khorasgani*, Daniel E Jung**, Gautam Biswas*, Erik Frisk**, and Mattias Krysander** Abstract A number o residual generation methods have been developed

More information

Figure 1: Bayesian network for problem 1. P (A = t) = 0.3 (1) P (C = t) = 0.6 (2) Table 1: CPTs for problem 1. (c) P (E B) C P (D = t) f 0.9 t 0.

Figure 1: Bayesian network for problem 1. P (A = t) = 0.3 (1) P (C = t) = 0.6 (2) Table 1: CPTs for problem 1. (c) P (E B) C P (D = t) f 0.9 t 0. Probabilistic Artiicial Intelligence Problem Set 3 Oct 27, 2017 1. Variable elimination In this exercise you will use variable elimination to perorm inerence on a bayesian network. Consider the network

More information

A solid-fluid mixture theory of porous media

A solid-fluid mixture theory of porous media A solid-luid mixture theory o porous media I-Shih Liu Instituto de Matemática, Universidade Federal do Rio de Janeiro Abstract The theories o mixtures in the ramework o continuum mechanics have been developed

More information

Extreme Values of Functions

Extreme Values of Functions Extreme Values o Functions When we are using mathematics to model the physical world in which we live, we oten express observed physical quantities in terms o variables. Then, unctions are used to describe

More information

STRAIGHT LINE GRAPHS. Lesson. Overview. Learning Outcomes and Assessment Standards

STRAIGHT LINE GRAPHS. Lesson. Overview. Learning Outcomes and Assessment Standards STRAIGHT LINE GRAPHS Learning Outcomes and Assessment Standards Lesson 15 Learning Outcome : Functions and Algebra The learner is able to investigate, analse, describe and represent a wide range o unctions

More information

9.1 The Square Root Function

9.1 The Square Root Function Section 9.1 The Square Root Function 869 9.1 The Square Root Function In this section we turn our attention to the square root unction, the unction deined b the equation () =. (1) We begin the section

More information

Rigorous pointwise approximations for invariant densities of non-uniformly expanding maps

Rigorous pointwise approximations for invariant densities of non-uniformly expanding maps Ergod. Th. & Dynam. Sys. 5, 35, 8 44 c Cambridge University Press, 4 doi:.7/etds.3.9 Rigorous pointwise approximations or invariant densities o non-uniormly expanding maps WAEL BAHSOUN, CHRISTOPHER BOSE

More information

BANDELET IMAGE APPROXIMATION AND COMPRESSION

BANDELET IMAGE APPROXIMATION AND COMPRESSION BANDELET IMAGE APPOXIMATION AND COMPESSION E. LE PENNEC AND S. MALLAT Abstract. Finding eicient geometric representations o images is a central issue to improve image compression and noise removal algorithms.

More information

On a Closed Formula for the Derivatives of e f(x) and Related Financial Applications

On a Closed Formula for the Derivatives of e f(x) and Related Financial Applications International Mathematical Forum, 4, 9, no. 9, 41-47 On a Closed Formula or the Derivatives o e x) and Related Financial Applications Konstantinos Draais 1 UCD CASL, University College Dublin, Ireland

More information

Theorem Let J and f be as in the previous theorem. Then for any w 0 Int(J), f(z) (z w 0 ) n+1

Theorem Let J and f be as in the previous theorem. Then for any w 0 Int(J), f(z) (z w 0 ) n+1 (w) Second, since lim z w z w z w δ. Thus, i r δ, then z w =r (w) z w = (w), there exist δ, M > 0 such that (w) z w M i dz ML({ z w = r}) = M2πr, which tends to 0 as r 0. This shows that g = 2πi(w), which

More information

SEPARATED AND PROPER MORPHISMS

SEPARATED AND PROPER MORPHISMS SEPARATED AND PROPER MORPHISMS BRIAN OSSERMAN The notions o separatedness and properness are the algebraic geometry analogues o the Hausdor condition and compactness in topology. For varieties over the

More information

The achievable limits of operational modal analysis. * Siu-Kui Au 1)

The achievable limits of operational modal analysis. * Siu-Kui Au 1) The achievable limits o operational modal analysis * Siu-Kui Au 1) 1) Center or Engineering Dynamics and Institute or Risk and Uncertainty, University o Liverpool, Liverpool L69 3GH, United Kingdom 1)

More information

Constant overhead quantum fault-tolerance with quantum expander codes

Constant overhead quantum fault-tolerance with quantum expander codes 018 IEEE 59th Annual Symposium on Foundations o Computer Science Constant overhead quantum ault-tolerance with quantum expander codes Omar Fawzi Univ Lyon, ENS de Lyon, CNRS, UCBL, LIP UMR 5668 F-69007

More information

CHOW S LEMMA. Matthew Emerton

CHOW S LEMMA. Matthew Emerton CHOW LEMMA Matthew Emerton The aim o this note is to prove the ollowing orm o Chow s Lemma: uppose that : is a separated inite type morphism o Noetherian schemes. Then (or some suiciently large n) there

More information

A Simple Explanation of the Sobolev Gradient Method

A Simple Explanation of the Sobolev Gradient Method A Simple Explanation o the Sobolev Gradient Method R. J. Renka July 3, 2006 Abstract We have observed that the term Sobolev gradient is used more oten than it is understood. Also, the term is oten used

More information

Comments on Problems. 3.1 This problem offers some practice in deriving utility functions from indifference curve specifications.

Comments on Problems. 3.1 This problem offers some practice in deriving utility functions from indifference curve specifications. CHAPTER 3 PREFERENCES AND UTILITY These problems provide some practice in eamining utilit unctions b looking at indierence curve maps and at a ew unctional orms. The primar ocus is on illustrating the

More information

SOME CHARACTERIZATIONS OF HARMONIC CONVEX FUNCTIONS

SOME CHARACTERIZATIONS OF HARMONIC CONVEX FUNCTIONS International Journal o Analysis and Applications ISSN 2291-8639 Volume 15, Number 2 2017, 179-187 DOI: 10.28924/2291-8639-15-2017-179 SOME CHARACTERIZATIONS OF HARMONIC CONVEX FUNCTIONS MUHAMMAD ASLAM

More information

Max-Weight Scheduling in Queueing Networks with. Heavy-Tailed Traffic

Max-Weight Scheduling in Queueing Networks with. Heavy-Tailed Traffic Max-Weight Scheduling in Queueing Networks with Heavy-Tailed Traic arxiv:1108.0370v1 [cs.ni] 1 Aug 011 Mihalis G. Markakis, Eytan H. Modiano, and John N. Tsitsiklis Abstract We consider the problem o packet

More information

CS 361 Meeting 28 11/14/18

CS 361 Meeting 28 11/14/18 CS 361 Meeting 28 11/14/18 Announcements 1. Homework 9 due Friday Computation Histories 1. Some very interesting proos o undecidability rely on the technique o constructing a language that describes the

More information

Fluctuationlessness Theorem and its Application to Boundary Value Problems of ODEs

Fluctuationlessness Theorem and its Application to Boundary Value Problems of ODEs Fluctuationlessness Theorem and its Application to Boundary Value Problems o ODEs NEJLA ALTAY İstanbul Technical University Inormatics Institute Maslak, 34469, İstanbul TÜRKİYE TURKEY) nejla@be.itu.edu.tr

More information

Additional exercises in Stationary Stochastic Processes

Additional exercises in Stationary Stochastic Processes Mathematical Statistics, Centre or Mathematical Sciences Lund University Additional exercises 8 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *

More information

2.6 Two-dimensional continuous interpolation 3: Kriging - introduction to geostatistics. References - geostatistics. References geostatistics (cntd.

2.6 Two-dimensional continuous interpolation 3: Kriging - introduction to geostatistics. References - geostatistics. References geostatistics (cntd. .6 Two-dimensional continuous interpolation 3: Kriging - introduction to geostatistics Spline interpolation was originally developed or image processing. In GIS, it is mainly used in visualization o spatial

More information

VALUATIVE CRITERIA FOR SEPARATED AND PROPER MORPHISMS

VALUATIVE CRITERIA FOR SEPARATED AND PROPER MORPHISMS VALUATIVE CRITERIA FOR SEPARATED AND PROPER MORPHISMS BRIAN OSSERMAN Recall that or prevarieties, we had criteria or being a variety or or being complete in terms o existence and uniqueness o limits, where

More information

COMP 408/508. Computer Vision Fall 2017 PCA for Recognition

COMP 408/508. Computer Vision Fall 2017 PCA for Recognition COMP 408/508 Computer Vision Fall 07 PCA or Recognition Recall: Color Gradient by PCA v λ ( G G, ) x x x R R v, v : eigenvectors o D D with v ^v (, ) x x λ, λ : eigenvalues o D D with λ >λ v λ ( B B, )

More information