MA Game Theory 2005, LSE
|
|
- Grace Parker
- 5 years ago
- Views:
Transcription
1 MA. Game Theor, LSE Problem Set The problems in our third and final homework will serve two purposes. First, the will give ou practice in studing games with incomplete information. Second (unless indicated below) the are formatted like our eam questions. M part of our eam will consist of four such questions, and ou will have to answer three of them. Notice that the questions ma look long, but that is onl to introduce the problem. The answers that I ask for are generall quite short. I do not epect, nor encourage, long answers, just answers that are to the point. However, ou must eplain wh ou get what ou do, otherwise ou will not get full credit. Some of the problems below are adapted from Martin Osborne s tetbook. [] Two persons are bidding on an object for auction. Each person has independent, private valuations for the object that are drawn from a uniform distribution over the interval [, ]. The auction is sealed-bid, first price. (a) Formall describe this situation as a Baesian game, taking care to define states and strategies in particular. (b) Find the smmetric equilibrium of this game, in which each plaer uses the same bid function. (c) Using the equilibrium bid function, prove that the outcome of the auction ields the same epected revenue as a second price auction in which the equilibrium in weakl dominant strategies is plaed. [In answering this question, ou can simpl state what the equilibrium in weakl dominant strategies is for the second-price auction, without an proof.] [] In this question, we stud a problem of public goods provision with incomplete information. Suppose that a well is to be dug in a village with n people. It costs c to get the well running. Each person has a valuation for the well. Each person i knows her true valuation v i. But when i j, i onl knows that j s valuation is drawn independentl from a uniform distribution (the same one for everbod) over the range [v, v]. Assume that v < c < v. The following mechanism is to determine whether the well will be dug. All the individuals simultaneousl submit sealed envelopes to an arbitrator. The envelopes can either contain a contribution of c or contain nothing (no intermediate contributions are allowed). If everone submits nothing, the well is not dug and everone gets. If at least one person submits c, then each person i who did contribute c gets v i c (no ecess contributions are returned), and each person i who contributed nothing gets free use of the well, with paoff v i. (a) B comparing the costs and benefits of contributing c, find a smmetric Baesian Nash equilibrium in which each person contributes if and onl if her valuation is above some
2 threshold v. Prove that v is the unique solution to the equation ( v () v ) v n = c. (b) The probabilit that the good is provided is obviousl equal to ( v ) v n (because this is the chance that at least one person has a valuation eceeding v ). Using equation (), prove that ( v ) v n = c F (v ) v, and using this equation, what can ou sa about the probabilit of the public good being provided as the number of individuals n increases? Does it rise? Fall? Sta the same? Eplain our answer. [] A defendant in a case is either Guilt G or Innocent I. The judge doesn t knows which, but has a prior that the defendant is G with probabilit π. We summarize the progress of the case b saing that the judge receives one of two signals. A i-signal indicates innocence, a g-signal guilt. If the defendant is trul guilt, the g-signal is received with probabilit p > / (but the i-signal can also be received, with probabilit p). Similarl, if the defendant is trul innocent, the i-signal is received with probabilit q > / (but the g-signal can also be received, with probabilit q). That is, the signals are nois but indicative of true guilt or innocence. The judge s paoffs are: = if a trul guilt person is convicted or a trul blameless person is acquitted. = z if an innocent person is convicted. = ( z) if a guilt person is acquitted. (a) Show that z can be viewed as a measure of resistance to conviction in the following sense: if the judge assesses the defendant to be guilt with probabilit r, then acquital is weakl better than conviction iff r z. (b) Prove that the judge makes different decisions depending on her received signal (i.e., convict if she sees g, acquit if she sees i) whenever () [You will have to use Baes Rule.] ( p)π ( p)π + q( π) < z < pπ pπ + ( q)( π). [] [Too long to be an eam question but good practice.] Now we replace the judge b n identical jurors, all with the same signal structure and preferences as the judge in the previous question. But the receive their signals independentl (in particular, two jurors could get different signals). Each juror looks onl at her own signal no one shares signals in this problem and votes to acquit or convict.
3 Conviction occurs if and onl everone votes to convict, otherwise the defendant is acquitted. Assume that the condition in equation () is satisfied, so that if each juror were to be full in charge of the case, she would vote to convict on seeing a g-signal and to acquit on seeing an i-signal. In this question, we want to see whether this voting according to signal can continue to be an equilibrium when there are several jurors, instead of just one. (a) Assuming that each juror votes according to her signal. Prove that an individual s vote onl makes a difference when all other jurors have received a g-signal. (b) B part (a), it suffices to look at an individual juror s behavior in the event that everone else gets a g signal. Now suppose the juror does receive an i-signal. Show that she will use it i.e., vote to acquit in this case onl if z + q π p π ( ) n q p (c) Notice there is something odd about the condition in (b). Show that its right hand side converges to as n becomes large. This means that for a given resistance to conviction (see the interpretation of z in the previous question) each juror cannot vote according to signal as n becomes large! (d) Interpret this result. Recall part (a) to do so. (e) Just for fun, and to see how quickl signals ma start getting ignored under these presumptions, get a calculator and calculate the value of the RHS when n =, π =., and p = q =.8 (prett reasonable parameters). (f) So for n large, there is no Nash equilibrium in which ever juror votes strictl according to her signal. What about miing? Prove that there is no smmetric Nash equilibrium in which jurors mi if the get a g-signal. [Hint: if the do so, the must be indifferent when the get a g-signal, so must strictl prefer to acquit when the get a b-signal. Now show that same argument as in part (c) applies.] (g) [Etra credit; consult Osborne s tet for help if needed.] So the onl kind of smmetric Nash equilibrium involves a juror voting to convict with some probabilit β when the signal is i and voting to convict for sure when the signal is g. Compute this equilibrium and show that it must have the propert that ( ) q + qβ n = p + ( p)β ( p)π( z). q( π)z [To show this, note that we must have indifference when a b-signal is received. But use part (a): this means that we must have indifference in the pivotal case when everone else is voting to convict. Pr(G signal is b and n votes for C ) = z. Now epand this epression. You will have to use Baes Rule again.] [] Consider the following three-stage version of the centipede game:
4 (a) Carefull describe the subgame perfect equilibrium of this game. (b) Now amend this game as follows. With probabilit ɛ plaer s preferences are as shown in the diagram (call this the rational tpe of ). But with probabilit ɛ, he is the sort who alwas likes to pass (move horizontall in the diagram) without regard to his paoff. Plaer is unchanged. Prove that in an Baesian Nash equilibrium, it cannot be the case that the rational tpe of plaer ends the game for sure in the first stage. (c) On the other hand, prove that if ɛ < /, it also cannot be an equilibrium strateg for the rational tpe of plaer to pass for sure. This part, combined with part (b), means that the rational tpe of plaer must mi between grabbing and passing at the ver first date. (d) [etra credit, great practice] Tr and solve the mied strateg Baesian Nash equilibrium of this game. [6] This is a formalization of the idea of conspicuous consumption. Suppose that Jim s wealth is either high H or low L, with H > L >. Jim knows which one it is; the world at large doesn t. But Jim would like people to think that he has high wealth; this makes him happier. Assume that if people think he has high wealth with probabilit q, his paoff equals q. Initiall the world has a prior on Jim: it thinks Jim is rich with probabilit p. Now, suppose that Jim has the option to bu flash things to signal his wealth. Let c be the conspicuous consumption of these flash items and suppose that the bring no intrinsic pleasure to Jim. There is a cost, though. Let the cost of c units be c/w, where w is Jim s wealth, which can take on one of two values. The world observes c and updates its priors on Jim. Jim s final net paoff is q c/w, where q is the updated posterior and w is Jim s private wealth. (a) Are there separating sequential equilibria in this model? (b) Are there pooling sequential equilibria in this model? (c) Are there hbrid equilibria? (d) Describe what happens if the intuitive criterion is applied to these equilibria. [7] Finall, a little puzzle with the sequential equilibrium definition. Look at this game:
5 u a b (a) Find all the sequential equilibria of this game. Now look at this game: u r a b (b) Find all the sequential equilibria of this game. (c) Notice that plaing u can be part of a sequential equilibrium in (a) but not in (b). Wh are these two games different? [Think of our discussion in class regarding off-equilibrium moves as errors.]
SF2972 Game Theory Problem set on extensive form games
SF2972 Game Theor Problem set on etensive form games Mark Voorneveld There are five eercises, to be handed in at the final lecture (March 0). For a bonus point, please answer all questions; at least half
More informationFebruary 11, JEL Classification: C72, D43, D44 Keywords: Discontinuous games, Bertrand game, Toehold, First- and Second- Price Auctions
Eistence of Mied Strateg Equilibria in a Class of Discontinuous Games with Unbounded Strateg Sets. Gian Luigi Albano and Aleander Matros Department of Economics and ELSE Universit College London Februar
More information5. Zeros. We deduce that the graph crosses the x-axis at the points x = 0, 1, 2 and 4, and nowhere else. And that s exactly what we see in the graph.
. Zeros Eample 1. At the right we have drawn the graph of the polnomial = ( 1) ( 2) ( 4). Argue that the form of the algebraic formula allows ou to see right awa where the graph is above the -ais, where
More informationEconomics 3012 Strategic Behavior Andy McLennan October 20, 2006
Economics 301 Strategic Behavior Andy McLennan October 0, 006 Lecture 11 Topics Problem Set 9 Extensive Games of Imperfect Information An Example General Description Strategies and Nash Equilibrium Beliefs
More informationLESSON #12 - FORMS OF A LINE COMMON CORE ALGEBRA II
LESSON # - FORMS OF A LINE COMMON CORE ALGEBRA II Linear functions come in a variet of forms. The two shown below have been introduced in Common Core Algebra I and Common Core Geometr. TWO COMMON FORMS
More informationLESSON #11 - FORMS OF A LINE COMMON CORE ALGEBRA II
LESSON # - FORMS OF A LINE COMMON CORE ALGEBRA II Linear functions come in a variet of forms. The two shown below have been introduced in Common Core Algebra I and Common Core Geometr. TWO COMMON FORMS
More informationTheory of Auctions. Carlos Hurtado. Jun 23th, Department of Economics University of Illinois at Urbana-Champaign
Theory of Auctions Carlos Hurtado Department of Economics University of Illinois at Urbana-Champaign hrtdmrt2@illinois.edu Jun 23th, 2015 C. Hurtado (UIUC - Economics) Game Theory On the Agenda 1 Formalizing
More information6.896 Topics in Algorithmic Game Theory February 8, Lecture 2
6.896 Topics in Algorithmic Game Theor Februar 8, 2010 Lecture 2 Lecturer: Constantinos Daskalakis Scribe: Yang Cai, Debmala Panigrahi In this lecture, we focus on two-plaer zero-sum games. Our goal is
More informationLecture 4. 1 Examples of Mechanism Design Problems
CSCI699: Topics in Learning and Game Theory Lecture 4 Lecturer: Shaddin Dughmi Scribes: Haifeng Xu,Reem Alfayez 1 Examples of Mechanism Design Problems Example 1: Single Item Auctions. There is a single
More informationMsc Micro I exam. Lecturer: Todd Kaplan.
Msc Micro I 204-205 exam. Lecturer: Todd Kaplan. Please answer exactly 5 questions. Answer one question from each of sections: A, B, C, and D and answer one additional question from any of the sections
More informationLecture 1.1: The Minimax Theorem
Algorithmic Game Theor, Compleit and Learning Jul 17, 2017 Lecture 1.1: The Mini Theorem Lecturer: Constantinos Daskalakis Scribe: Constantinos Daskalakis In these notes, we show that a Nash equilibrium
More information11.4 Polar Coordinates
11. Polar Coordinates 917 11. Polar Coordinates In Section 1.1, we introduced the Cartesian coordinates of a point in the plane as a means of assigning ordered pairs of numbers to points in the plane.
More informationMATH 021 UNIT 1 HOMEWORK ASSIGNMENTS
MATH 01 UNIT 1 HOMEWORK ASSIGNMENTS General Instructions You will notice that most of the homework assignments for a section have more than one part. Usuall, the part (A) questions ask for eplanations,
More informationPh.D. Preliminary Examination MICROECONOMIC THEORY Applied Economics Graduate Program May 2012
Ph.D. Preliminary Examination MICROECONOMIC THEORY Applied Economics Graduate Program May 2012 The time limit for this exam is 4 hours. It has four sections. Each section includes two questions. You are
More informationIntroduction. Introduction
Bayesian Persuasion Emir Kamenica and Matthew Gentzkow (2010) presented by Johann Caro Burnett and Sabyasachi Das March 4, 2011 Emir Kamenica and Matthew Gentzkow (2010) (presentedbayesian by Johann Persuasion
More informationIntroduction to Vector Spaces Linear Algebra, Spring 2011
Introduction to Vector Spaces Linear Algebra, Spring 2011 You probabl have heard the word vector before, perhaps in the contet of Calculus III or phsics. You probabl think of a vector like this: 5 3 or
More informationLESSON #48 - INTEGER EXPONENTS COMMON CORE ALGEBRA II
LESSON #8 - INTEGER EXPONENTS COMMON CORE ALGEBRA II We just finished our review of linear functions. Linear functions are those that grow b equal differences for equal intervals. In this unit we will
More informationLecture 10: Mechanism Design
Computational Game Theory Spring Semester, 2009/10 Lecture 10: Mechanism Design Lecturer: Yishay Mansour Scribe: Vera Vsevolozhsky, Nadav Wexler 10.1 Mechanisms with money 10.1.1 Introduction As we have
More informationLecture 6 Games with Incomplete Information. November 14, 2008
Lecture 6 Games with Incomplete Information November 14, 2008 Bayesian Games : Osborne, ch 9 Battle of the sexes with incomplete information Player 1 would like to match player 2's action Player 1 is unsure
More informationLESSON #28 - POWER FUNCTIONS COMMON CORE ALGEBRA II
1 LESSON #8 - POWER FUNCTIONS COMMON CORE ALGEBRA II Before we start to analze polnomials of degree higher than two (quadratics), we first will look at ver simple functions known as power functions. The
More informationCh 5 Alg 2 L2 Note Sheet Key Do Activity 1 on your Ch 5 Activity Sheet.
Ch Alg L Note Sheet Ke Do Activit 1 on our Ch Activit Sheet. Chapter : Quadratic Equations and Functions.1 Modeling Data With Quadratic Functions You had three forms for linear equations, ou will have
More informationSection 2: Wave Functions and Probability Solutions
Phsics 43a: Quantum Mechanics I Section : Wave Functions and Probabilit Solutions Spring 5, Harvard Here is a summar of the most important points from the second week with a few of m own tidbits), relevant
More informationUniversity of Warwick, Department of Economics Spring Final Exam. Answer TWO questions. All questions carry equal weight. Time allowed 2 hours.
University of Warwick, Department of Economics Spring 2012 EC941: Game Theory Prof. Francesco Squintani Final Exam Answer TWO questions. All questions carry equal weight. Time allowed 2 hours. 1. Consider
More informationPh.D. Preliminary Examination MICROECONOMIC THEORY Applied Economics Graduate Program June 2016
Ph.D. Preliminary Examination MICROECONOMIC THEORY Applied Economics Graduate Program June 2016 The time limit for this exam is four hours. The exam has four sections. Each section includes two questions.
More information6.207/14.15: Networks Lecture 24: Decisions in Groups
6.207/14.15: Networks Lecture 24: Decisions in Groups Daron Acemoglu and Asu Ozdaglar MIT December 9, 2009 1 Introduction Outline Group and collective choices Arrow s Impossibility Theorem Gibbard-Satterthwaite
More information1.7 Inverse Functions
71_0107.qd 1/7/0 10: AM Page 17 Section 1.7 Inverse Functions 17 1.7 Inverse Functions Inverse Functions Recall from Section 1. that a function can be represented b a set of ordered pairs. For instance,
More informationDeceptive Advertising with Rational Buyers
Deceptive Advertising with Rational Buyers September 6, 016 ONLINE APPENDIX In this Appendix we present in full additional results and extensions which are only mentioned in the paper. In the exposition
More informationMath 107: Calculus II, Spring 2015: Midterm Exam II Monday, April Give your name, TA and section number:
Math 7: Calculus II, Spring 25: Midterm Exam II Monda, April 3 25 Give our name, TA and section number: Name: TA: Section number:. There are 5 questions for a total of points. The value of each part of
More information6. Linear transformations. Consider the function. f : R 2 R 2 which sends (x, y) (x, y)
Consider the function 6 Linear transformations f : R 2 R 2 which sends (x, ) (, x) This is an example of a linear transformation Before we get into the definition of a linear transformation, let s investigate
More informationCS 598RM: Algorithmic Game Theory, Spring Practice Exam Solutions
CS 598RM: Algorithmic Game Theory, Spring 2017 1. Answer the following. Practice Exam Solutions Agents 1 and 2 are bargaining over how to split a dollar. Each agent simultaneously demands share he would
More informationChapter 5: Systems of Equations
Chapter : Sstems of Equations Section.: Sstems in Two Variables... 0 Section. Eercises... 9 Section.: Sstems in Three Variables... Section. Eercises... Section.: Linear Inequalities... Section.: Eercises.
More informationOPMT 5701 Term Project 2013
OPMT 570 Term Project 03 Selected Answers. Willingness to Pa versus Equivalent Compensation Skipp and Mrtle are friends who consume the same goods: oga classes (X) and Timbits (Y ). Skipp has the utilit
More informationCrowdsourcing contests
December 8, 2012 Table of contents 1 Introduction 2 Related Work 3 Model: Basics 4 Model: Participants 5 Homogeneous Effort 6 Extensions Table of Contents 1 Introduction 2 Related Work 3 Model: Basics
More informationWars of Attrition with Budget Constraints
Wars of Attrition with Budget Constraints Gagan Ghosh Bingchao Huangfu Heng Liu October 19, 2017 (PRELIMINARY AND INCOMPLETE: COMMENTS WELCOME) Abstract We study wars of attrition between two bidders who
More informationIntroduction to Game Theory
COMP323 Introduction to Computational Game Theory Introduction to Game Theory Paul G. Spirakis Department of Computer Science University of Liverpool Paul G. Spirakis (U. Liverpool) Introduction to Game
More informationLab 5 Forces Part 1. Physics 211 Lab. You will be using Newton s 2 nd Law to help you examine the nature of these forces.
b Lab 5 Forces Part 1 Phsics 211 Lab Introduction This is the first week of a two part lab that deals with forces and related concepts. A force is a push or a pull on an object that can be caused b a variet
More informationA. Real numbers greater than 2 B. Real numbers less than or equal to 2. C. Real numbers between 1 and 3 D. Real numbers greater than or equal to 2
39 CHAPTER 9 DAY 0 DAY 0 Opportunities To Learn You are what ou are when nobod Is looking. - Ann Landers 6. Match the graph with its description. A. Real numbers greater than B. Real numbers less than
More information15.2 Graphing Logarithmic
_ - - - - - - Locker LESSON 5. Graphing Logarithmic Functions Teas Math Standards The student is epected to: A.5.A Determine the effects on the ke attributes on the graphs of f () = b and f () = log b
More informationCourse 15 Numbers and Their Properties
Course Numbers and Their Properties KEY Module: Objective: Rules for Eponents and Radicals To practice appling rules for eponents when the eponents are rational numbers Name: Date: Fill in the blanks.
More informationAlgebra II Notes Unit Six: Polynomials Syllabus Objectives: 6.2 The student will simplify polynomial expressions.
Algebra II Notes Unit Si: Polnomials Sllabus Objectives: 6. The student will simplif polnomial epressions. Review: Properties of Eponents (Allow students to come up with these on their own.) Let a and
More informationProblems and results for the ninth week Mathematics A3 for Civil Engineering students
Problems and results for the ninth week Mathematics A3 for Civil Engineering students. Production line I of a factor works 0% of time, while production line II works 70% of time, independentl of each other.
More informationGame theory lecture 4. September 24, 2012
September 24, 2012 Finding Nash equilibrium Best-response or best-reply functions. We introduced Nash-equilibrium as a profile of actions (an action for each player) such that no player has an incentive
More informationGame Theory: Spring 2017
Game Theory: Spring 2017 Ulle Endriss Institute for Logic, Language and Computation University of Amsterdam Ulle Endriss 1 Plan for Today In this second lecture on mechanism design we are going to generalise
More informationLinear Equation Theory - 2
Algebra Module A46 Linear Equation Theor - Copright This publication The Northern Alberta Institute of Technolog 00. All Rights Reserved. LAST REVISED June., 009 Linear Equation Theor - Statement of Prerequisite
More informationCPS 173 Mechanism design. Vincent Conitzer
CPS 173 Mechanism design Vincent Conitzer conitzer@cs.duke.edu edu Mechanism design: setting The center has a set of outcomes O that she can choose from Allocations of tasks/resources, joint plans, Each
More informationChapter 2. Equilibrium. 2.1 Complete Information Games
Chapter 2 Equilibrium Equilibrium attempts to capture what happens in a game when players behave strategically. This is a central concept to these notes as in mechanism design we are optimizing over games
More informationMath 123 Summary of Important Algebra & Trigonometry Concepts Chapter 1 & Appendix D, Stewart, Calculus Early Transcendentals
Math Summar of Important Algebra & Trigonometr Concepts Chapter & Appendi D, Stewart, Calculus Earl Transcendentals Function a rule that assigns to each element in a set D eactl one element, called f (
More informationLecture Notes on Game Theory
Lecture Notes on Game Theory Levent Koçkesen 1 Bayesian Games So far we have assumed that all players had perfect information regarding the elements of a game. These are called games with complete information.
More informationMathematics 309 Conic sections and their applicationsn. Chapter 2. Quadric figures. ai,j x i x j + b i x i + c =0. 1. Coordinate changes
Mathematics 309 Conic sections and their applicationsn Chapter 2. Quadric figures In this chapter want to outline quickl how to decide what figure associated in 2D and 3D to quadratic equations look like.
More informationIntroduction to Game Theory
Introduction to Game Theory Part 3. Static games of incomplete information Chapter 2. Applications Ciclo Profissional 2 o Semestre / 2011 Graduação em Ciências Econômicas V. Filipe Martins-da-Rocha (FGV)
More informationLESSON #42 - INVERSES OF FUNCTIONS AND FUNCTION NOTATION PART 2 COMMON CORE ALGEBRA II
LESSON #4 - INVERSES OF FUNCTIONS AND FUNCTION NOTATION PART COMMON CORE ALGEBRA II You will recall from unit 1 that in order to find the inverse of a function, ou must switch and and solve for. Also,
More informationSection 3.1. ; X = (0, 1]. (i) f : R R R, f (x, y) = x y
Paul J. Bruillard MATH 0.970 Problem Set 6 An Introduction to Abstract Mathematics R. Bond and W. Keane Section 3.1: 3b,c,e,i, 4bd, 6, 9, 15, 16, 18c,e, 19a, 0, 1b Section 3.: 1f,i, e, 6, 1e,f,h, 13e,
More informationLAB 05 Projectile Motion
LAB 5 Projectile Motion CONTENT: 1. Introduction. Projectile motion A. Setup B. Various characteristics 3. Pre-lab: A. Activities B. Preliminar info C. Quiz 1. Introduction After introducing one-dimensional
More informationChapter 6. Self-Adjusting Data Structures
Chapter 6 Self-Adjusting Data Structures Chapter 5 describes a data structure that is able to achieve an epected quer time that is proportional to the entrop of the quer distribution. The most interesting
More informationPhysics Gravitational force. 2. Strong or color force. 3. Electroweak force
Phsics 360 Notes on Griffths - pluses and minuses No tetbook is perfect, and Griffithsisnoeception. Themajorplusisthat it is prett readable. For minuses, see below. Much of what G sas about the del operator
More informationFACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures AB = BA = I,
FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING Lectures MODULE 7 MATRICES II Inverse of a matri Sstems of linear equations Solution of sets of linear equations elimination methods 4
More informationLESSON #24 - POWER FUNCTIONS COMMON CORE ALGEBRA II
1 LESSON #4 - POWER FUNCTIONS COMMON CORE ALGEBRA II Before we start to analze polnomials of degree higher than two (quadratics), we first will look at ver simple functions known as power functions. The
More information0.24 adults 2. (c) Prove that, regardless of the possible values of and, the covariance between X and Y is equal to zero. Show all work.
1 A socioeconomic stud analzes two discrete random variables in a certain population of households = number of adult residents and = number of child residents It is found that their joint probabilit mass
More informationThe Condorcet Jur(ies) Theorem
The Condorcet Jur(ies) Theorem David S. Ahn University of California, Berkeley Santiago Oliveros Haas School of Business, UC Berkeley September 2010 Abstract Two issues can be decided in a single election
More informationThe Revenue Equivalence Theorem 1
John Nachbar Washington University May 2, 2017 The Revenue Equivalence Theorem 1 1 Introduction. The Revenue Equivalence Theorem gives conditions under which some very different auctions generate the same
More informationCh 3 Alg 2 Note Sheet.doc 3.1 Graphing Systems of Equations
Ch 3 Alg Note Sheet.doc 3.1 Graphing Sstems of Equations Sstems of Linear Equations A sstem of equations is a set of two or more equations that use the same variables. If the graph of each equation =.4
More informationDomain, Range, and End Behavior
Locker LESSON 1.1 Domain, Range, and End Behavior Common Core Math Standards The student is epected to: F-IF.5 Relate the domain of a function to its graph and, where applicable, to the quantitative relationship
More informationCostly Expertise. Dino Gerardi and Leeat Yariv yz. Current Version: December, 2007
Costly Expertise Dino Gerardi and Leeat Yariv yz Current Version: December, 007 In many environments expertise is costly. Costs can manifest themselves in numerous ways, ranging from the time that is required
More informationRoberto s Notes on Integral Calculus Chapter 3: Basics of differential equations Section 3. Separable ODE s
Roberto s Notes on Integral Calculus Chapter 3: Basics of differential equations Section 3 Separable ODE s What ou need to know alread: What an ODE is and how to solve an eponential ODE. What ou can learn
More information2.1 Rates of Change and Limits AP Calculus
. Rates of Change and Limits AP Calculus. RATES OF CHANGE AND LIMITS Limits Limits are what separate Calculus from pre calculus. Using a it is also the foundational principle behind the two most important
More informationModels of Reputation with Bayesian Updating
Models of Reputation with Bayesian Updating Jia Chen 1 The Tariff Game (Downs and Rocke 1996) 1.1 Basic Setting Two states, A and B, are setting the tariffs for trade. The basic setting of the game resembles
More informationGame Theory Lecture 10+11: Knowledge
Game Theory Lecture 10+11: Knowledge Christoph Schottmüller University of Copenhagen November 13 and 20, 2014 1 / 36 Outline 1 (Common) Knowledge The hat game A model of knowledge Common knowledge Agree
More informationGame Theory. Monika Köppl-Turyna. Winter 2017/2018. Institute for Analytical Economics Vienna University of Economics and Business
Monika Köppl-Turyna Institute for Analytical Economics Vienna University of Economics and Business Winter 2017/2018 Static Games of Incomplete Information Introduction So far we assumed that payoff functions
More informationLaurie s Notes. Overview of Section 3.5
Overview of Section.5 Introduction Sstems of linear equations were solved in Algebra using substitution, elimination, and graphing. These same techniques are applied to nonlinear sstems in this lesson.
More informationProbability Theory Refresher
Machine Learning WS24 Module IN264 Sheet 2 Page Machine Learning Worksheet 2 Probabilit Theor Refresher Basic Probabilit Problem : A secret government agenc has developed a scanner which determines whether
More information14.3 Constructing Exponential Functions
Name Class Date 1.3 Constructing Eponential Functions Essential Question: What are discrete eponential functions and how do ou represent them? Resource Locker Eplore Understanding Discrete Eponential Functions
More informationSection 1.2: A Catalog of Functions
Section 1.: A Catalog of Functions As we discussed in the last section, in the sciences, we often tr to find an equation which models some given phenomenon in the real world - for eample, temperature as
More informationUnit 26 Solving Inequalities Inequalities on a Number Line Solution of Linear Inequalities (Inequations)
UNIT Solving Inequalities: Student Tet Contents STRAND G: Algebra Unit Solving Inequalities Student Tet Contents Section. Inequalities on a Number Line. of Linear Inequalities (Inequations). Inequalities
More informationSystems of Linear Equations
Sstems of Linear Equations Monetar Sstems Overload Lesson 3-1 Learning Targets: Use graphing, substitution, and elimination to solve sstems of linear equations in two variables. Formulate sstems of linear
More informationBiostatistics in Research Practice - Regression I
Biostatistics in Research Practice - Regression I Simon Crouch 30th Januar 2007 In scientific studies, we often wish to model the relationships between observed variables over a sample of different subjects.
More informationSolutions to the Math 1051 Sample Final Exam (from Spring 2003) Page 1
Solutions to the Math 0 Sample Final Eam (from Spring 00) Page Part : Multiple Choice Questions. Here ou work out the problems and then select the answer that matches our answer. No partial credit is given
More informationChapter 5: Quadratic Equations and Functions 5.1 Modeling Data With Quadratic Functions Quadratic Functions and Their Graphs
Ch 5 Alg Note Sheet Ke Chapter 5: Quadratic Equations and Functions 5.1 Modeling Data With Quadratic Functions Quadratic Functions and Their Graphs Definition: Standard Form of a Quadratic Function The
More informationLinear Equations and Arithmetic Sequences
CONDENSED LESSON.1 Linear Equations and Arithmetic Sequences In this lesson, ou Write eplicit formulas for arithmetic sequences Write linear equations in intercept form You learned about recursive formulas
More informationEC319 Economic Theory and Its Applications, Part II: Lecture 2
EC319 Economic Theory and Its Applications, Part II: Lecture 2 Leonardo Felli NAB.2.14 23 January 2014 Static Bayesian Game Consider the following game of incomplete information: Γ = {N, Ω, A i, T i, µ
More informationIntroduction to Game Theory Lecture Note 2: Strategic-Form Games and Nash Equilibrium (2)
Introduction to Game Theory Lecture Note 2: Strategic-Form Games and Nash Equilibrium (2) Haifeng Huang University of California, Merced Best response functions: example In simple games we can examine
More informationx y plane is the plane in which the stresses act, yy xy xy Figure 3.5.1: non-zero stress components acting in the x y plane
3.5 Plane Stress This section is concerned with a special two-dimensional state of stress called plane stress. It is important for two reasons: () it arises in real components (particularl in thin components
More informationGetting ready for Exam 1 - review
Getting read for Eam - review For Eam, stud ALL the homework, including supplements and in class activities from sections..5 and.,.. Good Review Problems from our book: Pages 6-9: 0 all, 7 7 all (don t
More informationGame Theory. Solutions to Problem Set 4
1 Hotelling s model 1.1 Two vendors Game Theory Solutions to Problem Set 4 Consider a strategy pro le (s 1 s ) with s 1 6= s Suppose s 1 < s In this case, it is pro table to for player 1 to deviate and
More informationEconS Microeconomic Theory II Midterm Exam #2 - Answer Key
EconS 50 - Microeconomic Theory II Midterm Exam # - Answer Key 1. Revenue comparison in two auction formats. Consider a sealed-bid auction with bidders. Every bidder i privately observes his valuation
More informationAnswers to Spring 2014 Microeconomics Prelim
Answers to Spring 204 Microeconomics Prelim. To model the problem of deciding whether or not to attend college, suppose an individual, Ann, consumes in each of two periods. She is endowed with income w
More informationPower in voting games: axiomatic and probabilistic approaches
: axiomatic and probabilistic approaches a a Department of Mathematics Technical Universit of Catalonia Summer School, Campione d Italia Game Theor and Voting Sstems Outline Power indices, several interpretations
More informationPersuasion Under Costly Lying
Persuasion Under Costly Lying Teck Yong Tan Columbia University 1 / 43 Introduction Consider situations where agent designs learning environment (i.e. what additional information to generate) to persuade
More information1 + x 1/2. b) For what values of k is g a quasi-concave function? For what values of k is g a concave function? Explain your answers.
Questions and Answers from Econ 0A Final: Fall 008 I have gone to some trouble to explain the answers to all of these questions, because I think that there is much to be learned b working through them
More information2.5 CONTINUITY. a x. Notice that Definition l implicitly requires three things if f is continuous at a:
SECTION.5 CONTINUITY 9.5 CONTINUITY We noticed in Section.3 that the it of a function as approaches a can often be found simpl b calculating the value of the function at a. Functions with this propert
More informationLab 5 Forces Part 1. Physics 225 Lab. You will be using Newton s 2 nd Law to help you examine the nature of these forces.
b Lab 5 orces Part 1 Introduction his is the first week of a two part lab that deals with forces and related concepts. A force is a push or a pull on an object that can be caused b a variet of reasons.
More information3.0 PROBABILITY, RANDOM VARIABLES AND RANDOM PROCESSES
3.0 PROBABILITY, RANDOM VARIABLES AND RANDOM PROCESSES 3.1 Introduction In this chapter we will review the concepts of probabilit, rom variables rom processes. We begin b reviewing some of the definitions
More information3.7 InveRSe FUnCTIOnS
CHAPTER functions learning ObjeCTIveS In this section, ou will: Verif inverse functions. Determine the domain and range of an inverse function, and restrict the domain of a function to make it one-to-one.
More information2.1 Rates of Change and Limits AP Calculus
.1 Rates of Change and Limits AP Calculus.1 RATES OF CHANGE AND LIMITS Limits Limits are what separate Calculus from pre calculus. Using a it is also the foundational principle behind the two most important
More information5.6 RATIOnAl FUnCTIOnS. Using Arrow notation. learning ObjeCTIveS
CHAPTER PolNomiAl ANd rational functions learning ObjeCTIveS In this section, ou will: Use arrow notation. Solve applied problems involving rational functions. Find the domains of rational functions. Identif
More informationTwo hours UNIVERSITY OF MANCHESTER SCHOOL OF COMPUTER SCIENCE. Date: Thursday 17th May 2018 Time: 09:45-11:45. Please answer all Questions.
COMP 34120 Two hours UNIVERSITY OF MANCHESTER SCHOOL OF COMPUTER SCIENCE AI and Games Date: Thursday 17th May 2018 Time: 09:45-11:45 Please answer all Questions. Use a SEPARATE answerbook for each SECTION
More informationDerivatives of Multivariable Functions
Chapter 0 Derivatives of Multivariable Functions 0. Limits Motivating Questions In this section, we strive to understand the ideas generated b the following important questions: What do we mean b the limit
More informationReview of topics since what was covered in the midterm: Topics that we covered before the midterm (also may be included in final):
Review of topics since what was covered in the midterm: Subgame-perfect eqms in extensive games with perfect information where players choose a number (first-order conditions, boundary conditions, favoring
More informationPolitical Economy of Institutions and Development: Problem Set 1. Due Date: Thursday, February 23, in class.
Political Economy of Institutions and Development: 14.773 Problem Set 1 Due Date: Thursday, February 23, in class. Answer Questions 1-3. handed in. The other two questions are for practice and are not
More informationLecture 2: Separable Ordinary Differential Equations
Lecture : Separable Ordinar Differential Equations Dr. Michael Doughert Januar 8, 00 Some Terminolog: ODE s, PDE s, IVP s The differential equations we have looked at so far are called ordinar differential
More information10.1 Inverses of Simple Quadratic and Cubic Functions
COMMON CORE Locker LESSON 0. Inverses of Simple Quadratic and Cubic Functions Name Class Date 0. Inverses of Simple Quadratic and Cubic Functions Essential Question: What functions are the inverses of
More information