Quiz on Deterministic part of course October 22, 2002

Size: px
Start display at page:

Download "Quiz on Deterministic part of course October 22, 2002"

Transcription

1 Engneerng ystems Analyss for Desgn Quz on Determnstc part of course October 22, 2002 Ths s a closed book exercse. You may use calculators Grade Tables There are 90 ponts possble for the regular test, or 1 pont per mnute. Ponts assocated wth each queston correspond to the estmated tme t mght take to answer them. There are also a possble 10 ponts of extra credt. Fnal scores wll be based on 90 possble ponts. Item Max Your Name (provded we can read t) 1 Concepts 6 What's the best desgn? 20 Money, Money, Money 1 hp t out! 20 Extra Credt 10 Total 100 Percentage grade on bass of 90 maxmum: core Yours I have completed ths test farly, wthout copyng from others or from a textbook Please sgn your name legbly 1

2 Concepts (6 ponts -- 2 ponts per part) Wrte a short defnton or descrpton explanng the followng: Producton Functon A physcal functon represents the techncally effcent transformaton of physcal resources nto products. Techncal Effcency Techncal effcency represents the maxmum product that can be obtaned from any gven set of resources. Economc Effcency Economc effcency represents the best desgn economcally. Not the same as Techncal Effcency. Margnal Product A margnal product s the change n output due to a unt change n a specfc nput. Isoquant An soquant s a locus on the producton functon of all equal specfc levels of product. Returns to cale Returns to scale s the rato of the rate of change n output due to a proportonal change n all nputs smultaneously. Economes of cale Economes of scale exst for a producton process when t s cheaper to produce n quantty. How are the prevous two related (or not)? Both refer to the dea of somehow gettng proportonately more as the scale of producton ncreases. Ther prncpal dfference arses from the fact that the noton of economes of scale ncorporates nformaton about the nput cost functon. hadow Prces A shadow prce s the rate of change of the obectve functon wth respect to a partcular constrant. 2

3 Opportunty Costs An opportunty cost s the rate of degradaton of the optmum per unt use of a non-optmal varable n the desgn. Lagrangean Multplers For the problem: optmze ( ) g subect to h ( ) = b and 0, Lagrangean s L = g( ) λ [ h ( ) b ]. The parameters of λ are known as Lagrangean multplers, and actually they are the shadow prces. Optmalty Crtera n Margnal Analyss MP MC = 1 λ for all Complementary lackness λ = 0 for all so that ether lambda or s s equal to zero s Expanson Path The expanson path s the locus of all the optmum desgns for every level of output Y. Cost Functon A cost functon descrbes the optmal, the least cost of producng any level of product Y. Actvtes An actvty s a specfc way of combnng basc materals or resources to acheve some obectve or output. Fxed Charge Problem A fxed charge problem s a specfc amount, typcally a cost, assocated wth any level of a decson varable. For example, cost of = c 0 + c. It cannot ordnarly be handled by LP software. Data Tables An Excel tool to execute senstvty analyss automatcally by examnng the consequences of varyng one, two, and sometmes three varables at once.

4 What's the best desgn? (20 ponts) You are gven a producton functon: And the cost of the resources as: 0.6 R 0.8 6R + 10 a) What can you say by mmedately, by nspecton, about the returns to scale? About the economes of scale? Justfy your answer. ( ponts) Returns to scale are decreasng because < 1. Economes of scale can not be told by nspecton. b) What s the optmal relatonshp between the resources R and? (10 ponts) MP R = 1.8R MP = 0.9R (oluton usng Y n the expresson s good also = (0.6/R ) Y ; = (/) Y) MC R =.8R MC = 12 Applyng Optmalty Crtera n Margnal Analyss: MP MC R R MP = MC R.8R R = = R 0.8 c) What s the assocated cost functon? (6 ponts) Z = R 0.6 C = 6 5 = ( ) = 0 = 5 so the cost functon s 0 C = Z. 99Z and has no economes of scale. (5 )

5 Money, Money, Money (1 ponts) What s Net present value? ( Ponts) Net Present Value = Present Value Revenues Present Value Costs Present value s the dscounted value of future sums of money usng the approprate dscount rate. What are the maor advantages and dsadvantages of the Beneft/Cost rato as a crteron of evaluaton? (6 Ponts) Advantages: It compares proects on a common scale It provdes a drect ndcaton of whether a proect s worthwhle (the rato exceeds 1) It provdes an easy means to rank proects n order of relatve mert Dsadvantages: It requres all benefts to be assgned a monetary value Ambguty of the treatment of recurrng costs Bas n favor of captal-ntensve proects Relatve rank of proects can depend on dscount rate used Why mght the rank order of proects change when you calculate ther beneft-cost ratos usng dfferent dscount rates? ( Ponts) Lower dscount rates favor proects wth longer-term benefts. Thus, proects wth longer-term benefts can appear better than proects wth benefts accrung sooner f a lower rate s used n the analyss. 5

6 hp t out! (20 ponts) A plant manager wants to mnmze the cost of shpments from plants A and B (capacty of 1000 and 500) to markets K, L, M (requrements of 00, 800, 200, respectvely). The shppng costs are as n the table From To K L M A B a) et up the Lnear Program. You may use a vector notaton f convenent. (7 ponts) Let be the shpment from plant to market. Mnmze ubect to ,, + + = 00 = 800 = 200, ,, 0 b) uppose the results gave the followng results: (9 ponts) Requrement at hadow Prce Range A 0 (0. 800) B 0 (00, 600) K 20 (200, 00) L a M 0 (250, 50) What s the meanng of the shadow prce on producton at B? The rate of cost reducton f capacty of B ncreases. What can you say about the shadow prce on requrements at K f these rse to 50 unts? Greater than 20 because as a constrant tghtened, the shadow prce ncreases beyond the range What can you say about the shadow prce at L? a > 0 because f I change requrement that wll change amount shpped and ths has a cost 6

7 c) The manager thnks t mght be a good dea to set up a faclty between B and M at a cost of throughput. The obect would be to reduce the shppng costs on ths route. How does the LP handle such an extenson to the basc formulaton? ( ponts) Ths s a fxed charge problem. We can solve two problems: - one wthout the new faclty - one wth the new faclty, but drop off the fxed charge (20) off when solvng the LP, and add the fxed charge (20) back to get the fnal result Comparng the optmal solutons of the above two problems, whchever lower s the fnal soluton. The above s vald only for trval problems. The better answer s that standard LP does not know how to deal wth ths problem. For 10 ponts extra credt: d) uppose that the actual cost of shppng between B and M s not but 80 ( volume _ on _ route), a ( volume _ on _ route). Under what condtons could you ncorporate ths feature nto the LP? ( ponts) If a 1, then the feasble regon s convex (the feasble regon s above the curve because we can always spend more) and we can ncorporate t nto the LP. If a=1, we can solve LP drectly; f a>1, we need pecewse approxmaton of the curve. If a<1, we cannot deal wth ths problem usng LP. How would ncludng ths feature change the LP? how specfc equatons. (6 ponts) Ths s for the case that a > 1. If a = 1, we can ncorporate the feature drectly nto the LP. Let denote volume_on_route. We have the formulaton related to ths feature: 1, Defne breakponts on that defne the new varables ', ", "', etc. and then '< V 1, ",< V 2, etc for as many segments as relevant, also > 0. a ' '' ''' 2, Defne ( volume _ on _ route) as c 1 + c2 + c wth c determned by the breakponts selected, and change the obectve functon to ' '' ''' ( + + ), Insert the new defntons of n the constrants for shpments out of B and nto M. 7

- contrast so-called first-best outcome of Lindahl equilibrium with case of private provision through voluntary contributions of households

- contrast so-called first-best outcome of Lindahl equilibrium with case of private provision through voluntary contributions of households Prvate Provson - contrast so-called frst-best outcome of Lndahl equlbrum wth case of prvate provson through voluntary contrbutons of households - need to make an assumpton about how each household expects

More information

Solution of periodic review inventory model with general constrains

Solution of periodic review inventory model with general constrains Soluton of perodc revew nventory model wth general constrans Soluton of perodc revew nventory model wth general constrans Prof Dr J Benkő SZIU Gödöllő Summary Reasons for presence of nventory (stock of

More information

AC : THE DIAGRAMMATIC AND MATHEMATICAL APPROACH OF PROJECT TIME-COST TRADEOFFS

AC : THE DIAGRAMMATIC AND MATHEMATICAL APPROACH OF PROJECT TIME-COST TRADEOFFS AC 2008-1635: THE DIAGRAMMATIC AND MATHEMATICAL APPROACH OF PROJECT TIME-COST TRADEOFFS Kun-jung Hsu, Leader Unversty Amercan Socety for Engneerng Educaton, 2008 Page 13.1217.1 Ttle of the Paper: The Dagrammatc

More information

Least Cost Strategies for Complying with New NOx Emissions Limits

Least Cost Strategies for Complying with New NOx Emissions Limits Least Cost Strateges for Complyng wth New NOx Emssons Lmts Internatonal Assocaton for Energy Economcs New England Chapter Presented by Assef A. Zoban Tabors Caramans & Assocates Cambrdge, MA 02138 January

More information

ISyE 2030 Summer Semester 2004 June 30, 2004

ISyE 2030 Summer Semester 2004 June 30, 2004 ISyE 030 Summer Semester 004 June 30, 004 1. Every day I must feed my 130 pound dog some combnaton of dry dog food and canned dog food. The cost for the dry dog food s $0.50 per cup, and the cost of a

More information

Economic Design of Short-Run CSP-1 Plan Under Linear Inspection Cost

Economic Design of Short-Run CSP-1 Plan Under Linear Inspection Cost Tamkang Journal of Scence and Engneerng, Vol. 9, No 1, pp. 19 23 (2006) 19 Economc Desgn of Short-Run CSP-1 Plan Under Lnear Inspecton Cost Chung-Ho Chen 1 * and Chao-Yu Chou 2 1 Department of Industral

More information

THE ECONOMICS OF TAXATION

THE ECONOMICS OF TAXATION THE ECONOMICS OF TAXATION Statc Ramsey Tax School of Economcs, Xamen Unversty Fall 2015 Overvew of Optmal Taxaton Combne lessons on ncdence and effcency costs to analyze optmal desgn of commodty taxes.

More information

c slope = -(1+i)/(1+π 2 ) MRS (between consumption in consecutive time periods) price ratio (across consecutive time periods)

c slope = -(1+i)/(1+π 2 ) MRS (between consumption in consecutive time periods) price ratio (across consecutive time periods) CONSUMPTION-SAVINGS FRAMEWORK (CONTINUED) SEPTEMBER 24, 2013 The Graphcs of the Consumpton-Savngs Model CONSUMER OPTIMIZATION Consumer s decson problem: maxmze lfetme utlty subject to lfetme budget constrant

More information

Elements of Economic Analysis II Lecture VI: Industry Supply

Elements of Economic Analysis II Lecture VI: Industry Supply Elements of Economc Analyss II Lecture VI: Industry Supply Ka Hao Yang 10/12/2017 In the prevous lecture, we analyzed the frm s supply decson usng a set of smple graphcal analyses. In fact, the dscusson

More information

OPERATIONS RESEARCH. Game Theory

OPERATIONS RESEARCH. Game Theory OPERATIONS RESEARCH Chapter 2 Game Theory Prof. Bbhas C. Gr Department of Mathematcs Jadavpur Unversty Kolkata, Inda Emal: bcgr.umath@gmal.com 1.0 Introducton Game theory was developed for decson makng

More information

CHAPTER 9 FUNCTIONAL FORMS OF REGRESSION MODELS

CHAPTER 9 FUNCTIONAL FORMS OF REGRESSION MODELS CHAPTER 9 FUNCTIONAL FORMS OF REGRESSION MODELS QUESTIONS 9.1. (a) In a log-log model the dependent and all explanatory varables are n the logarthmc form. (b) In the log-ln model the dependent varable

More information

/ Computational Genomics. Normalization

/ Computational Genomics. Normalization 0-80 /02-70 Computatonal Genomcs Normalzaton Gene Expresson Analyss Model Computatonal nformaton fuson Bologcal regulatory networks Pattern Recognton Data Analyss clusterng, classfcaton normalzaton, mss.

More information

Chapter 10 Making Choices: The Method, MARR, and Multiple Attributes

Chapter 10 Making Choices: The Method, MARR, and Multiple Attributes Chapter 0 Makng Choces: The Method, MARR, and Multple Attrbutes INEN 303 Sergy Butenko Industral & Systems Engneerng Texas A&M Unversty Comparng Mutually Exclusve Alternatves by Dfferent Evaluaton Methods

More information

15-451/651: Design & Analysis of Algorithms January 22, 2019 Lecture #3: Amortized Analysis last changed: January 18, 2019

15-451/651: Design & Analysis of Algorithms January 22, 2019 Lecture #3: Amortized Analysis last changed: January 18, 2019 5-45/65: Desgn & Analyss of Algorthms January, 09 Lecture #3: Amortzed Analyss last changed: January 8, 09 Introducton In ths lecture we dscuss a useful form of analyss, called amortzed analyss, for problems

More information

CS 286r: Matching and Market Design Lecture 2 Combinatorial Markets, Walrasian Equilibrium, Tâtonnement

CS 286r: Matching and Market Design Lecture 2 Combinatorial Markets, Walrasian Equilibrium, Tâtonnement CS 286r: Matchng and Market Desgn Lecture 2 Combnatoral Markets, Walrasan Equlbrum, Tâtonnement Matchng and Money Recall: Last tme we descrbed the Hungaran Method for computng a maxmumweght bpartte matchng.

More information

EDC Introduction

EDC Introduction .0 Introducton EDC3 In the last set of notes (EDC), we saw how to use penalty factors n solvng the EDC problem wth losses. In ths set of notes, we want to address two closely related ssues. What are, exactly,

More information

Taxation and Externalities. - Much recent discussion of policy towards externalities, e.g., global warming debate/kyoto

Taxation and Externalities. - Much recent discussion of policy towards externalities, e.g., global warming debate/kyoto Taxaton and Externaltes - Much recent dscusson of polcy towards externaltes, e.g., global warmng debate/kyoto - Increasng share of tax revenue from envronmental taxaton 6 percent n OECD - Envronmental

More information

Facility Location Problem. Learning objectives. Antti Salonen Farzaneh Ahmadzadeh

Facility Location Problem. Learning objectives. Antti Salonen Farzaneh Ahmadzadeh Antt Salonen Farzaneh Ahmadzadeh 1 Faclty Locaton Problem The study of faclty locaton problems, also known as locaton analyss, s a branch of operatons research concerned wth the optmal placement of facltes

More information

Applications of Myerson s Lemma

Applications of Myerson s Lemma Applcatons of Myerson s Lemma Professor Greenwald 28-2-7 We apply Myerson s lemma to solve the sngle-good aucton, and the generalzaton n whch there are k dentcal copes of the good. Our objectve s welfare

More information

Multiobjective De Novo Linear Programming *

Multiobjective De Novo Linear Programming * Acta Unv. Palack. Olomuc., Fac. rer. nat., Mathematca 50, 2 (2011) 29 36 Multobjectve De Novo Lnear Programmng * Petr FIALA Unversty of Economcs, W. Churchll Sq. 4, Prague 3, Czech Republc e-mal: pfala@vse.cz

More information

Tree-based and GA tools for optimal sampling design

Tree-based and GA tools for optimal sampling design Tree-based and GA tools for optmal samplng desgn The R User Conference 2008 August 2-4, Technsche Unverstät Dortmund, Germany Marco Balln, Gulo Barcarol Isttuto Nazonale d Statstca (ISTAT) Defnton of the

More information

Finance 402: Problem Set 1 Solutions

Finance 402: Problem Set 1 Solutions Fnance 402: Problem Set 1 Solutons Note: Where approprate, the fnal answer for each problem s gven n bold talcs for those not nterested n the dscusson of the soluton. 1. The annual coupon rate s 6%. A

More information

Optimal Service-Based Procurement with Heterogeneous Suppliers

Optimal Service-Based Procurement with Heterogeneous Suppliers Optmal Servce-Based Procurement wth Heterogeneous Supplers Ehsan Elah 1 Saf Benjaafar 2 Karen L. Donohue 3 1 College of Management, Unversty of Massachusetts, Boston, MA 02125 2 Industral & Systems Engneerng,

More information

Consumption Based Asset Pricing

Consumption Based Asset Pricing Consumpton Based Asset Prcng Mchael Bar Aprl 25, 208 Contents Introducton 2 Model 2. Prcng rsk-free asset............................... 3 2.2 Prcng rsky assets................................ 4 2.3 Bubbles......................................

More information

Appendix for Solving Asset Pricing Models when the Price-Dividend Function is Analytic

Appendix for Solving Asset Pricing Models when the Price-Dividend Function is Analytic Appendx for Solvng Asset Prcng Models when the Prce-Dvdend Functon s Analytc Ovdu L. Caln Yu Chen Thomas F. Cosmano and Alex A. Hmonas January 3, 5 Ths appendx provdes proofs of some results stated n our

More information

Equilibrium in Prediction Markets with Buyers and Sellers

Equilibrium in Prediction Markets with Buyers and Sellers Equlbrum n Predcton Markets wth Buyers and Sellers Shpra Agrawal Nmrod Megddo Benamn Armbruster Abstract Predcton markets wth buyers and sellers of contracts on multple outcomes are shown to have unque

More information

Elton, Gruber, Brown, and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 9

Elton, Gruber, Brown, and Goetzmann. Modern Portfolio Theory and Investment Analysis, 7th Edition. Solutions to Text Problems: Chapter 9 Elton, Gruber, Brown, and Goetzmann Modern Portfolo Theory and Investment Analyss, 7th Edton Solutons to Text Problems: Chapter 9 Chapter 9: Problem In the table below, gven that the rskless rate equals

More information

INTRODUCTION TO MACROECONOMICS FOR THE SHORT RUN (CHAPTER 1) WHY STUDY BUSINESS CYCLES? The intellectual challenge: Why is economic growth irregular?

INTRODUCTION TO MACROECONOMICS FOR THE SHORT RUN (CHAPTER 1) WHY STUDY BUSINESS CYCLES? The intellectual challenge: Why is economic growth irregular? INTRODUCTION TO MACROECONOMICS FOR THE SHORT RUN (CHATER 1) WHY STUDY BUSINESS CYCLES? The ntellectual challenge: Why s economc groth rregular? The socal challenge: Recessons and depressons cause elfare

More information

Benefit-Cost Analysis

Benefit-Cost Analysis Chapter 12 Beneft-Cost Analyss Utlty Possbltes and Potental Pareto Improvement Wthout explct nstructons about how to compare one person s benefts wth the losses of another, we can not expect beneft-cost

More information

IND E 250 Final Exam Solutions June 8, Section A. Multiple choice and simple computation. [5 points each] (Version A)

IND E 250 Final Exam Solutions June 8, Section A. Multiple choice and simple computation. [5 points each] (Version A) IND E 20 Fnal Exam Solutons June 8, 2006 Secton A. Multple choce and smple computaton. [ ponts each] (Verson A) (-) Four ndependent projects, each wth rsk free cash flows, have the followng B/C ratos:

More information

Economics 1410 Fall Section 7 Notes 1. Define the tax in a flexible way using T (z), where z is the income reported by the agent.

Economics 1410 Fall Section 7 Notes 1. Define the tax in a flexible way using T (z), where z is the income reported by the agent. Economcs 1410 Fall 2017 Harvard Unversty Yaan Al-Karableh Secton 7 Notes 1 I. The ncome taxaton problem Defne the tax n a flexble way usng T (), where s the ncome reported by the agent. Retenton functon:

More information

We consider the problem of scheduling trains and containers (or trucks and pallets)

We consider the problem of scheduling trains and containers (or trucks and pallets) Schedulng Trans and ontaners wth Due Dates and Dynamc Arrvals andace A. Yano Alexandra M. Newman Department of Industral Engneerng and Operatons Research, Unversty of alforna, Berkeley, alforna 94720-1777

More information

2) In the medium-run/long-run, a decrease in the budget deficit will produce:

2) In the medium-run/long-run, a decrease in the budget deficit will produce: 4.02 Quz 2 Solutons Fall 2004 Multple-Choce Questons ) Consder the wage-settng and prce-settng equatons we studed n class. Suppose the markup, µ, equals 0.25, and F(u,z) = -u. What s the natural rate of

More information

Tests for Two Correlations

Tests for Two Correlations PASS Sample Sze Software Chapter 805 Tests for Two Correlatons Introducton The correlaton coeffcent (or correlaton), ρ, s a popular parameter for descrbng the strength of the assocaton between two varables.

More information

Problem Set 6 Finance 1,

Problem Set 6 Finance 1, Carnege Mellon Unversty Graduate School of Industral Admnstraton Chrs Telmer Wnter 2006 Problem Set 6 Fnance, 47-720. (representatve agent constructon) Consder the followng two-perod, two-agent economy.

More information

3: Central Limit Theorem, Systematic Errors

3: Central Limit Theorem, Systematic Errors 3: Central Lmt Theorem, Systematc Errors 1 Errors 1.1 Central Lmt Theorem Ths theorem s of prme mportance when measurng physcal quanttes because usually the mperfectons n the measurements are due to several

More information

Lecture 7. We now use Brouwer s fixed point theorem to prove Nash s theorem.

Lecture 7. We now use Brouwer s fixed point theorem to prove Nash s theorem. Topcs on the Border of Economcs and Computaton December 11, 2005 Lecturer: Noam Nsan Lecture 7 Scrbe: Yoram Bachrach 1 Nash s Theorem We begn by provng Nash s Theorem about the exstance of a mxed strategy

More information

Ch Rival Pure private goods (most retail goods) Non-Rival Impure public goods (internet service)

Ch Rival Pure private goods (most retail goods) Non-Rival Impure public goods (internet service) h 7 1 Publc Goods o Rval goods: a good s rval f ts consumpton by one person precludes ts consumpton by another o Excludable goods: a good s excludable f you can reasonably prevent a person from consumng

More information

Financial mathematics

Financial mathematics Fnancal mathematcs Jean-Luc Bouchot jean-luc.bouchot@drexel.edu February 19, 2013 Warnng Ths s a work n progress. I can not ensure t to be mstake free at the moment. It s also lackng some nformaton. But

More information

4: SPOT MARKET MODELS

4: SPOT MARKET MODELS 4: SPOT MARKET MODELS INCREASING COMPETITION IN THE BRITISH ELECTRICITY SPOT MARKET Rchard Green (1996) - Journal of Industral Economcs, Vol. XLIV, No. 2 PEKKA SULAMAA The obect of the paper Dfferent polcy

More information

General Examination in Microeconomic Theory. Fall You have FOUR hours. 2. Answer all questions

General Examination in Microeconomic Theory. Fall You have FOUR hours. 2. Answer all questions HARVARD UNIVERSITY DEPARTMENT OF ECONOMICS General Examnaton n Mcroeconomc Theory Fall 2010 1. You have FOUR hours. 2. Answer all questons PLEASE USE A SEPARATE BLUE BOOK FOR EACH QUESTION AND WRITE THE

More information

II. Random Variables. Variable Types. Variables Map Outcomes to Numbers

II. Random Variables. Variable Types. Variables Map Outcomes to Numbers II. Random Varables Random varables operate n much the same way as the outcomes or events n some arbtrary sample space the dstncton s that random varables are smply outcomes that are represented numercally.

More information

Explaining Movements of the Labor Share in the Korean Economy: Factor Substitution, Markups and Bargaining Power

Explaining Movements of the Labor Share in the Korean Economy: Factor Substitution, Markups and Bargaining Power Explanng Movements of the abor Share n the Korean Economy: Factor Substtuton, Markups and Barganng ower Bae-Geun, Km January 2, 26 Appendx A. Dervaton of the dervatve of et us start from eq. (). For notatonal

More information

Multifactor Term Structure Models

Multifactor Term Structure Models 1 Multfactor Term Structure Models A. Lmtatons of One-Factor Models 1. Returns on bonds of all maturtes are perfectly correlated. 2. Term structure (and prces of every other dervatves) are unquely determned

More information

MgtOp 215 Chapter 13 Dr. Ahn

MgtOp 215 Chapter 13 Dr. Ahn MgtOp 5 Chapter 3 Dr Ahn Consder two random varables X and Y wth,,, In order to study the relatonshp between the two random varables, we need a numercal measure that descrbes the relatonshp The covarance

More information

Parallel Prefix addition

Parallel Prefix addition Marcelo Kryger Sudent ID 015629850 Parallel Prefx addton The parallel prefx adder presented next, performs the addton of two bnary numbers n tme of complexty O(log n) and lnear cost O(n). Lets notce the

More information

references Chapters on game theory in Mas-Colell, Whinston and Green

references Chapters on game theory in Mas-Colell, Whinston and Green Syllabus. Prelmnares. Role of game theory n economcs. Normal and extensve form of a game. Game-tree. Informaton partton. Perfect recall. Perfect and mperfect nformaton. Strategy.. Statc games of complete

More information

FORD MOTOR CREDIT COMPANY SUGGESTED ANSWERS. Richard M. Levich. New York University Stern School of Business. Revised, February 1999

FORD MOTOR CREDIT COMPANY SUGGESTED ANSWERS. Richard M. Levich. New York University Stern School of Business. Revised, February 1999 FORD MOTOR CREDIT COMPANY SUGGESTED ANSWERS by Rchard M. Levch New York Unversty Stern School of Busness Revsed, February 1999 1 SETTING UP THE PROBLEM The bond s beng sold to Swss nvestors for a prce

More information

Two Period Models. 1. Static Models. Econ602. Spring Lutz Hendricks

Two Period Models. 1. Static Models. Econ602. Spring Lutz Hendricks Two Perod Models Econ602. Sprng 2005. Lutz Hendrcks The man ponts of ths secton are: Tools: settng up and solvng a general equlbrum model; Kuhn-Tucker condtons; solvng multperod problems Economc nsghts:

More information

Price and Quantity Competition Revisited. Abstract

Price and Quantity Competition Revisited. Abstract rce and uantty Competton Revsted X. Henry Wang Unversty of Mssour - Columba Abstract By enlargng the parameter space orgnally consdered by Sngh and Vves (984 to allow for a wder range of cost asymmetry,

More information

EXAMINATIONS OF THE HONG KONG STATISTICAL SOCIETY

EXAMINATIONS OF THE HONG KONG STATISTICAL SOCIETY EXAMINATIONS OF THE HONG KONG STATISTICAL SOCIETY HIGHER CERTIFICATE IN STATISTICS, 2013 MODULE 7 : Tme seres and ndex numbers Tme allowed: One and a half hours Canddates should answer THREE questons.

More information

Resource Allocation with Lumpy Demand: To Speed or Not to Speed?

Resource Allocation with Lumpy Demand: To Speed or Not to Speed? Resource Allocaton wth Lumpy Demand: To Speed or Not to Speed? Track: Operatons Plannng, Schedulng and Control Abstract Gven multple products wth unque lumpy demand patterns, ths paper explores the determnaton

More information

The evaluation method of HVAC system s operation performance based on exergy flow analysis and DEA method

The evaluation method of HVAC system s operation performance based on exergy flow analysis and DEA method The evaluaton method of HVAC system s operaton performance based on exergy flow analyss and DEA method Xng Fang, Xnqao Jn, Yonghua Zhu, Bo Fan Shangha Jao Tong Unversty, Chna Overvew 1. Introducton 2.

More information

Cyclic Scheduling in a Job shop with Multiple Assembly Firms

Cyclic Scheduling in a Job shop with Multiple Assembly Firms Proceedngs of the 0 Internatonal Conference on Industral Engneerng and Operatons Management Kuala Lumpur, Malaysa, January 4, 0 Cyclc Schedulng n a Job shop wth Multple Assembly Frms Tetsuya Kana and Koch

More information

Random Variables. b 2.

Random Variables. b 2. Random Varables Generally the object of an nvestgators nterest s not necessarly the acton n the sample space but rather some functon of t. Techncally a real valued functon or mappng whose doman s the sample

More information

Pasinetti s Structural Change and Economic Growth: a conceptual excursus

Pasinetti s Structural Change and Economic Growth: a conceptual excursus MPRA Munch Personal RePEc Archve Pasnett s Structural Change and Economc Growth: a conceptual excursus Nada Garbelln and Arel Lus Wrkerman Unverstà Cattolca del Sacro Cuore, Gruppo PRIN 2007 8. July 2010

More information

Bid-auction framework for microsimulation of location choice with endogenous real estate prices

Bid-auction framework for microsimulation of location choice with endogenous real estate prices Bd-aucton framework for mcrosmulaton of locaton choce wth endogenous real estate prces Rcardo Hurtuba Mchel Berlare Francsco Martínez Urbancs Termas de Chllán, Chle March 28 th 2012 Outlne 1) Motvaton

More information

Analysis of the Influence of Expenditure Policies of Government on Macroeconomic behavior of an Agent- Based Artificial Economic System

Analysis of the Influence of Expenditure Policies of Government on Macroeconomic behavior of an Agent- Based Artificial Economic System Analyss of the Influence of Expendture olces of Government on Macroeconomc behavor of an Agent- Based Artfcal Economc System Shgeak Ogbayash 1 and Kouse Takashma 1 1 School of Socal Systems Scence Chba

More information

Random Variables. 8.1 What is a Random Variable? Announcements: Chapter 8

Random Variables. 8.1 What is a Random Variable? Announcements: Chapter 8 Announcements: Quz starts after class today, ends Monday Last chance to take probablty survey ends Sunday mornng. Next few lectures: Today, Sectons 8.1 to 8. Monday, Secton 7.7 and extra materal Wed, Secton

More information

Supplier Selection And Evaluation. Through Activity-Based Costing Approach

Supplier Selection And Evaluation. Through Activity-Based Costing Approach Suppler Selecton nd Evaluaton Through ctvty-based Costng pproach Bran Korea Logstcs Team Industral Engneerng / Pusan Natonal Unversty uthor : Han Lee (Economc System nalyss Lab., Industral Engneerng, Pusan

More information

ISE High Income Index Methodology

ISE High Income Index Methodology ISE Hgh Income Index Methodology Index Descrpton The ISE Hgh Income Index s desgned to track the returns and ncome of the top 30 U.S lsted Closed-End Funds. Index Calculaton The ISE Hgh Income Index s

More information

MODELLING FARMS PRODUCTION DECISIONS UNDER EXPENDITURE CONSTRAINTS RAUSHAN BOKUSHEVA AND SUBAL KUMBHAKAR

MODELLING FARMS PRODUCTION DECISIONS UNDER EXPENDITURE CONSTRAINTS RAUSHAN BOKUSHEVA AND SUBAL KUMBHAKAR MODELLING FARMS PRODUCTION DECISIONS UNDER EXPENDITURE CONSTRAINTS RAUSHAN BOKUSHEVA AND SUBAL KUMBHAKAR ETH Zurch (Swss Federal Insttute of Technology) bokushev@ethz.ch State Unversty of New York n Bnghamton

More information

New Distance Measures on Dual Hesitant Fuzzy Sets and Their Application in Pattern Recognition

New Distance Measures on Dual Hesitant Fuzzy Sets and Their Application in Pattern Recognition Journal of Artfcal Intellgence Practce (206) : 8-3 Clausus Scentfc Press, Canada New Dstance Measures on Dual Hestant Fuzzy Sets and Ther Applcaton n Pattern Recognton L Xn a, Zhang Xaohong* b College

More information

Problems to be discussed at the 5 th seminar Suggested solutions

Problems to be discussed at the 5 th seminar Suggested solutions ECON4260 Behavoral Economcs Problems to be dscussed at the 5 th semnar Suggested solutons Problem 1 a) Consder an ultmatum game n whch the proposer gets, ntally, 100 NOK. Assume that both the proposer

More information

Stochastic optimal day-ahead bid with physical future contracts

Stochastic optimal day-ahead bid with physical future contracts Introducton Stochastc optmal day-ahead bd wth physcal future contracts C. Corchero, F.J. Hereda Departament d Estadístca Investgacó Operatva Unverstat Poltècnca de Catalunya Ths work was supported by the

More information

Understanding Annuities. Some Algebraic Terminology.

Understanding Annuities. Some Algebraic Terminology. Understandng Annutes Ma 162 Sprng 2010 Ma 162 Sprng 2010 March 22, 2010 Some Algebrac Termnology We recall some terms and calculatons from elementary algebra A fnte sequence of numbers s a functon of natural

More information

Lecture Note 2 Time Value of Money

Lecture Note 2 Time Value of Money Seg250 Management Prncples for Engneerng Managers Lecture ote 2 Tme Value of Money Department of Systems Engneerng and Engneerng Management The Chnese Unversty of Hong Kong Interest: The Cost of Money

More information

Efficient Project Portfolio as a Tool for Enterprise Risk Management

Efficient Project Portfolio as a Tool for Enterprise Risk Management Effcent Proect Portfolo as a Tool for Enterprse Rsk Management Valentn O. Nkonov Ural State Techncal Unversty Growth Traectory Consultng Company Enterprse Rsk Management Symposum Socety of Actuares Chcago,

More information

Answers to exercises in Macroeconomics by Nils Gottfries 2013

Answers to exercises in Macroeconomics by Nils Gottfries 2013 . a) C C b C C s the ntercept o the consumpton uncton, how much consumpton wll be at zero ncome. We can thnk that, at zero ncome, the typcal consumer would consume out o hs assets. The slope b s the margnal

More information

Teaching Note on Factor Model with a View --- A tutorial. This version: May 15, Prepared by Zhi Da *

Teaching Note on Factor Model with a View --- A tutorial. This version: May 15, Prepared by Zhi Da * Copyrght by Zh Da and Rav Jagannathan Teachng Note on For Model th a Ve --- A tutoral Ths verson: May 5, 2005 Prepared by Zh Da * Ths tutoral demonstrates ho to ncorporate economc ves n optmal asset allocaton

More information

ECE 586GT: Problem Set 2: Problems and Solutions Uniqueness of Nash equilibria, zero sum games, evolutionary dynamics

ECE 586GT: Problem Set 2: Problems and Solutions Uniqueness of Nash equilibria, zero sum games, evolutionary dynamics Unversty of Illnos Fall 08 ECE 586GT: Problem Set : Problems and Solutons Unqueness of Nash equlbra, zero sum games, evolutonary dynamcs Due: Tuesday, Sept. 5, at begnnng of class Readng: Course notes,

More information

Uniform Output Subsidies in Economic Unions versus Profit-shifting Export Subsidies

Uniform Output Subsidies in Economic Unions versus Profit-shifting Export Subsidies nform Output Subsdes n Economc nons versus Proft-shftng Export Subsdes Bernardo Moreno nversty of Málaga and José L. Torres nversty of Málaga Abstract Ths paper focuses on the effect of output subsdes

More information

Stochastic ALM models - General Methodology

Stochastic ALM models - General Methodology Stochastc ALM models - General Methodology Stochastc ALM models are generally mplemented wthn separate modules: A stochastc scenaros generator (ESG) A cash-flow projecton tool (or ALM projecton) For projectng

More information

Domestic Savings and International Capital Flows

Domestic Savings and International Capital Flows Domestc Savngs and Internatonal Captal Flows Martn Feldsten and Charles Horoka The Economc Journal, June 1980 Presented by Mchael Mbate and Chrstoph Schnke Introducton The 2 Vews of Internatonal Captal

More information

Data Mining Linear and Logistic Regression

Data Mining Linear and Logistic Regression 07/02/207 Data Mnng Lnear and Logstc Regresson Mchael L of 26 Regresson In statstcal modellng, regresson analyss s a statstcal process for estmatng the relatonshps among varables. Regresson models are

More information

Final Exam. 7. (10 points) Please state whether each of the following statements is true or false. No explanation needed.

Final Exam. 7. (10 points) Please state whether each of the following statements is true or false. No explanation needed. Fnal Exam Fall 4 Econ 8-67 Closed Book. Formula Sheet Provded. Calculators OK. Tme Allowed: hours Please wrte your answers on the page below each queston. (5 ponts) Assume that the rsk-free nterest rate

More information

occurrence of a larger storm than our culvert or bridge is barely capable of handling? (what is The main question is: What is the possibility of

occurrence of a larger storm than our culvert or bridge is barely capable of handling? (what is The main question is: What is the possibility of Module 8: Probablty and Statstcal Methods n Water Resources Engneerng Bob Ptt Unversty of Alabama Tuscaloosa, AL Flow data are avalable from numerous USGS operated flow recordng statons. Data s usually

More information

Scribe: Chris Berlind Date: Feb 1, 2010

Scribe: Chris Berlind Date: Feb 1, 2010 CS/CNS/EE 253: Advanced Topcs n Machne Learnng Topc: Dealng wth Partal Feedback #2 Lecturer: Danel Golovn Scrbe: Chrs Berlnd Date: Feb 1, 2010 8.1 Revew In the prevous lecture we began lookng at algorthms

More information

Instituto de Engenharia de Sistemas e Computadores de Coimbra Institute of Systems Engineering and Computers INESC - Coimbra

Instituto de Engenharia de Sistemas e Computadores de Coimbra Institute of Systems Engineering and Computers INESC - Coimbra Insttuto de Engenhara de Sstemas e Computadores de Combra Insttute of Systems Engneerng and Computers INESC - Combra Joana Das Can we really gnore tme n Smple Plant Locaton Problems? No. 7 2015 ISSN: 1645-2631

More information

A Single-Product Inventory Model for Multiple Demand Classes 1

A Single-Product Inventory Model for Multiple Demand Classes 1 A Sngle-Product Inventory Model for Multple Demand Classes Hasan Arslan, 2 Stephen C. Graves, 3 and Thomas Roemer 4 March 5, 2005 Abstract We consder a sngle-product nventory system that serves multple

More information

Members not eligible for this option

Members not eligible for this option DC - Lump sum optons R6.1 Uncrystallsed funds penson lump sum An uncrystallsed funds penson lump sum, known as a UFPLS (also called a FLUMP), s a way of takng your penson pot wthout takng money from a

More information

Spring 2010 Social Sciences 7418 University of Wisconsin-Madison. The Financial and Economic Crisis Interpreted in a CC-LM Model

Spring 2010 Social Sciences 7418 University of Wisconsin-Madison. The Financial and Economic Crisis Interpreted in a CC-LM Model Publc Affars 854 Menze D. Chnn Sprng 2010 Socal Scences 7418 Unversty of Wsconsn-Madson The Fnancal and Economc Crss Interpreted n a CC-LM Model 1. Background: Typcal Fnancal Crss Source: Mshkn 2. Theory:

More information

YORK UNIVERSITY Faculty of Science Department of Mathematics and Statistics MATH A Test #2 November 03, 2014

YORK UNIVERSITY Faculty of Science Department of Mathematics and Statistics MATH A Test #2 November 03, 2014 Famly Name prnt): YORK UNIVERSITY Faculty of Scence Department of Mathematcs and Statstcs MATH 2280.00 A Test #2 November 0, 2014 Solutons Gven Name: Student No: Sgnature: INSTRUCTIONS: 1. Please wrte

More information

Analysis of Variance and Design of Experiments-II

Analysis of Variance and Design of Experiments-II Analyss of Varance and Desgn of Experments-II MODULE VI LECTURE - 4 SPLIT-PLOT AND STRIP-PLOT DESIGNS Dr. Shalabh Department of Mathematcs & Statstcs Indan Insttute of Technology Kanpur An example to motvate

More information

OCR Statistics 1 Working with data. Section 2: Measures of location

OCR Statistics 1 Working with data. Section 2: Measures of location OCR Statstcs 1 Workng wth data Secton 2: Measures of locaton Notes and Examples These notes have sub-sectons on: The medan Estmatng the medan from grouped data The mean Estmatng the mean from grouped data

More information

A MODEL OF COMPETITION AMONG TELECOMMUNICATION SERVICE PROVIDERS BASED ON REPEATED GAME

A MODEL OF COMPETITION AMONG TELECOMMUNICATION SERVICE PROVIDERS BASED ON REPEATED GAME A MODEL OF COMPETITION AMONG TELECOMMUNICATION SERVICE PROVIDERS BASED ON REPEATED GAME Vesna Radonć Đogatovć, Valentna Radočć Unversty of Belgrade Faculty of Transport and Traffc Engneerng Belgrade, Serba

More information

Welfare Aspects in the Realignment of Commercial Framework. between Japan and China

Welfare Aspects in the Realignment of Commercial Framework. between Japan and China Prepared for the 13 th INFORUM World Conference n Huangshan, Chna, July 3 9, 2005 Welfare Aspects n the Realgnment of Commercal Framework between Japan and Chna Toshak Hasegawa Chuo Unversty, Japan Introducton

More information

A MODEL FOR OPTIMIZING ENTERPRISE S INVENTORY COSTS. A FUZZY APPROACH

A MODEL FOR OPTIMIZING ENTERPRISE S INVENTORY COSTS. A FUZZY APPROACH OPERATIONS RESEARCH AND DECISIONS No. 4 2013 DOI: 10.5277/ord130404 Wtold KOSIŃSKI Rafał MUNIAK Wtold Konrad KOSIŃSKI A MODEL FOR OPTIMIZING ENTERPRISE S INVENTORY COSTS. A FUZZY APPROACH Applcablty of

More information

iii) pay F P 0,T = S 0 e δt when stock has dividend yield δ.

iii) pay F P 0,T = S 0 e δt when stock has dividend yield δ. Fnal s Wed May 7, 12:50-2:50 You are allowed 15 sheets of notes and a calculator The fnal s cumulatve, so you should know everythng on the frst 4 revews Ths materal not on those revews 184) Suppose S t

More information

Introduction. Chapter 7 - An Introduction to Portfolio Management

Introduction. Chapter 7 - An Introduction to Portfolio Management Introducton In the next three chapters, we wll examne dfferent aspects of captal market theory, ncludng: Brngng rsk and return nto the pcture of nvestment management Markowtz optmzaton Modelng rsk and

More information

Discrete Dynamic Shortest Path Problems in Transportation Applications

Discrete Dynamic Shortest Path Problems in Transportation Applications 17 Paper No. 98-115 TRANSPORTATION RESEARCH RECORD 1645 Dscrete Dynamc Shortest Path Problems n Transportaton Applcatons Complexty and Algorthms wth Optmal Run Tme ISMAIL CHABINI A soluton s provded for

More information

Mathematical Thinking Exam 1 09 October 2017

Mathematical Thinking Exam 1 09 October 2017 Mathematcal Thnkng Exam 1 09 October 2017 Name: Instructons: Be sure to read each problem s drectons. Wrte clearly durng the exam and fully erase or mark out anythng you do not want graded. You may use

More information

Likelihood Fits. Craig Blocker Brandeis August 23, 2004

Likelihood Fits. Craig Blocker Brandeis August 23, 2004 Lkelhood Fts Crag Blocker Brandes August 23, 2004 Outlne I. What s the queston? II. Lkelhood Bascs III. Mathematcal Propertes IV. Uncertantes on Parameters V. Mscellaneous VI. Goodness of Ft VII. Comparson

More information

Production and Supply Chain Management Logistics. Paolo Detti Department of Information Engeneering and Mathematical Sciences University of Siena

Production and Supply Chain Management Logistics. Paolo Detti Department of Information Engeneering and Mathematical Sciences University of Siena Producton and Supply Chan Management Logstcs Paolo Dett Department of Informaton Engeneerng and Mathematcal Scences Unversty of Sena Convergence and complexty of the algorthm Convergence of the algorthm

More information

SIMPLE FIXED-POINT ITERATION

SIMPLE FIXED-POINT ITERATION SIMPLE FIXED-POINT ITERATION The fed-pont teraton method s an open root fndng method. The method starts wth the equaton f ( The equaton s then rearranged so that one s one the left hand sde of the equaton

More information

-~~?.~~.!.i.':':'.~.~-~-~~~~':':'.~~~.::.~~~~~~~--~-~-~~.::."!}.~!.~.. ~.~~-~...

-~~?.~~.!.i.':':'.~.~-~-~~~~':':'.~~~.::.~~~~~~~--~-~-~~.::.!}.~!.~.. ~.~~-~... -~~?.~~.!..':':'.~.~-~-~~~~':':'.~~~.::.~~~~~~~--~-~-~~.::."!}.~!.~.. ~.~~-~.... Part 1: Defnng schedules (10 Descrbe the followng terms as used n schedulng projects. 1.1 Crtcal path 1.2 Slack tme or float

More information

Global Optimization in Multi-Agent Models

Global Optimization in Multi-Agent Models Global Optmzaton n Mult-Agent Models John R. Brge R.R. McCormck School of Engneerng and Appled Scence Northwestern Unversty Jont work wth Chonawee Supatgat, Enron, and Rachel Zhang, Cornell 11/19/2004

More information

Note on Cubic Spline Valuation Methodology

Note on Cubic Spline Valuation Methodology Note on Cubc Splne Valuaton Methodology Regd. Offce: The Internatonal, 2 nd Floor THE CUBIC SPLINE METHODOLOGY A model for yeld curve takes traded yelds for avalable tenors as nput and generates the curve

More information

Tests for Two Ordered Categorical Variables

Tests for Two Ordered Categorical Variables Chapter 253 Tests for Two Ordered Categorcal Varables Introducton Ths module computes power and sample sze for tests of ordered categorcal data such as Lkert scale data. Assumng proportonal odds, such

More information

An Example (based on the Phillips article)

An Example (based on the Phillips article) An Eample (based on the Phllps artcle) Suppose ou re the hapless MBA, and ou haven t been fred You decde to use IP to fnd the best N-product soluton, for N = to 56 Let be 0 f ou don t produce product,

More information