Game Theory or Investment Science

Game Theory

Author: Drew Fudenberg

This advanced text introduces the principles of noncooperative game theory - including strategic form games, Nash equilibria, subgame perfection, repeated games, and games of incomplete information - in a direct and uncomplicated style that will acquaint students with the broad spectrum of the field while highlighting and explaining what they need to know at any given point. The analytic material is accompanied by many applications, examples, and exercises.

The theory of noncooperative games studies the behavior of agents in any situation where each agent's optimal choice may depend on a forecast of the opponents' choices. "Noncooperative" refers to choices that are based on the participant's perceived selfinterest. Although game theory has been applied to many fields, Fudenberg and Tirole focus on the kinds of game theory that have been most useful in the study of economic problems. They also include some applications to political science. The fourt een chapters are grouped in parts that cover static games of complete information, dynamic games of complete information, static games of incomplete information, dynamic games of incomplete information, and advanced topics.

Drew Fudenberg and Jean Tirole are Professors of Economics at MIT.

Table of Contents:

(EXercises and References close each chapter)
Static Games of Complete Information
1 Games in Strategic Form and Nash Equilibrium
1.1 Introduction to Games n Strategic Form and Iterated Strict
Dominance
1.2 Nash Equilibrium
1.3 EXistence and Properties of Nash Equilibria
2 Iterated Strict Dominance, Rationalizability, and Correlated
Equilibrium
2.1 Iterated Strict Dominance and Rationalizability
2.2 Correlated Equilibrium
2.3 Rationalizability and Subjective Correlated Equilibria
Dynamic Games of Complete Information
3 EXtensiveForm Games
3.1 Introduction
3.2 Commitment and Perfection in MultiStage Games with Observed
Actions
3.3 The EXtensive Form
3.4 Strategies and Equilibria in EXtensiveForm Games
3.5 Backward Induction and Subgame Perfection
3.6 Critiques of Backward Induction and Subgame Perfection
4 Applications of MultiStage Games with Observed Actions
4.1 Introduction
4. 2 The Principles of Optimality and Subgame Perfection
4.3 A First Look at Repeated Games
4.4. The RubinsteinStahl Bargaining Model
4.5 Simple Timing Games
4.6 Iterated Conditional Dominance and the Rubinstein Bargaining
Game
4.7 OpenLoop and ClosedLoop Equilibria
4.8 FiniteHorizon and InfiniteHorizon Equilibria
5 Repeated Games
5.1 Repeated Games with Observable Actions
5.2 Finite Repeated Games
5.3 Repeated Games with Varying Opponents
5.4 Pareto Perfection and RenegotiationProofness in Repeated Games
5.5 Repeated Games with Imperfect Public Information
5.6 The Folk Theorem with Imperfect Public Information
5.7 Changing the Information Structure with the Time Period
Static Games of IncompleteInformation
6 Bayseian Games and Bayseian Equilibrium
6.1 Incomplete Information
6.2 EXample 6.1: Providing a Public Good under Incomplete
Information
6.3 The Notions of Type and Strategy
6.4 Bayesian Equilibrium
6.5 F urther EXamples of Bayesian Equilibria
6.6 Deletion of Strictly Dominated Strategies
6.7 Using Bayesian Equilibria to Justify MiXed Equilibria
6.8 The Distributional Approach
7 Bayesian Games and Mechanism Design
7.1 EXamples of Mechanism Design
7.2 Mechanism Design and the Revelation Principle
7.3 Revelation Design with a Single Agent
7.4 Mechanisms with Several Agents: Feasible Allocations, Budget
Balance, and Efficiency
7.5 Mechanism Design with Several Agents: Optimization
7.6 Further Topics in Mechanism Design
AppendiX
Dynamic Games of Incomplete Information
8 Equilibrium Refinements: Perfect Bayesian Equilibrium,
Sequential Equilibrium, and TremblingHand Perfection
8.1 Introduction
8.2 Perfect Bayesian Equilibrium in MultiStage Games of Incomplete
Information
8.3 EXtensiveForm Refinements
8.4 Strategicform Refinements
AppendiX
9 Reputation Effects
9.1 Introduction
9.2 Games with Single LongRun Play er
9.3 Games with many LongRun Players
9.4 A Single "Big" Player Against Many Simultaneous LongLived
Opponents
10 Sequential Bargaining under Incomplete Information
10.1 Introduction
10.2 Intertemporal Price Discrimination: The SingleSale Model
10.3 Intertemporal Price Discrimination: The Rental or RepeatedSale
Model
10.4 Price Offers by an Informed Buyer
Advanced Topics
11 More Equilibrium Refinements: Stability, Forward Induction,
and Iterated Weak Dominance
11.1 Strategic Stability
11.2 Signaling Courses
11.3 Forward Induction, Iterated Weak Dominance, and "Burning Money"
11.4 Robust Predictions under Payoff Uncertainty
12 Advanced Topics in StrategicForm Games
12.1 Generic Properties of Nash Equilibria
12.2 EXistence of Nash Equilibrium in Games with Continuous Action
Spaces and Discontinuous Payoffs
12.3 Supermodular Games
13 PayoffRelevant Strategies and Markov Equilibrium
13.1 Markov Equilibria in Sp ecific Cases of Games
13.2 Markov Perfect Equilibrium in General Games: Definitions and
Properties
13.3 Differential Games
13.4 CapitalAccumulation Games
14 Common Knowledge Games
14.1 Introduction
14.2 Knowledge and Common Knowledge
14.3 Common Knowledge and Equilibrium
14.4 Common Knowledge, Almost Common Knowledge, and the Sensitivity of
the Equilibria to the Information Structure
IndeX

Books about economics: The Best Way to Rob a Bank Is to Own One or Marketing Management

Investment Science

Author: David G Luenberger

Fueled in part by some extraordinary theoretical developments in finance, an explosive growth of information and computing technology, and the global expansion of investment activity, investment theory currently commands a high level of intellectual attention. Recent developments in the field are being infused into university classrooms, financial service organizations, business ventures, and into the awareness of many individual investors. Modern investment theory using the language of mathematics is now an essential aspect of academic and practitioner training.
Representing a breakthrough in the organization of finance topics, Investment Science will be an indispensable tool in teaching modern investment theory. It presents sound fundamentals and shows how real problems can be solved with modern, yet simple, methods. David Luenberger gives thorough yet highly accessible mathematical coverage of standard and recent topics of introductory investments: fixed- income securities, modern portfolio theory and capital asset pricing theory, derivatives (futures, options, and swaps), and innovations in optimal portfolio growth and valuation of multiperiod risky investments. Throughout the book, he uses mathematics to present essential ideas of investments and their applications in business practice. The creative use of binomial lattices to formulate and solve a wide variety of important finance problems is a special feature of the book.
In moving from fixed-income securities to derivatives, Luenberger increases naturally the level of mathematical sophistication, but never goes beyond algebra, elementary statistics/probability, and calculus. He includes appendices on probabilityand calculus at the end of the book for student reference. Creative examples and end-of-chapter exercises are also included to provide additional applications of principles given in the text.
Ideal for investment or investment management courses in financ e, engineering economics, operations research, and management science departments, Investment Science has been successfully class-tested at Boston University, Stanford University, and the University of Strathclyde, Scotland, and used in several firms where knowledge of investment principles is essential. Executives, managers, financial analysts, and project engineers responsible for evaluation and structuring of investments will also find the book beneficial. The methods described are useful in almost every field, including high-technology, utilities, financial service organizations, and manufacturing companies.

Table of Contents:

	Preface
1	Introduction	1
2	The Basic Theory of Interest	13
3	Fixed-Income Securities	40
4	The Term Structure of Interest Rates	72
5	Applied Interest Rate Analysis	102
6	Mean-Variance Portfolio Theory	137
7	The Capital Asset Pricing Model	173
8	Models and Data	197
9	General Principles	228
10	Forwards, Futures, and Swaps	263
11	Models of Asset Dynamics	296
12	Basic Options Theory	319
13	Additional Options Topics	351
14	Interest Rate Derivatives	382
15	Optimal Portfolio Growth	417
16	General Investment Evaluation	444
App. A	Basic Probability Theory	475
App. B	Calculus and Optimization	479
	Answers to Exercises	484
	Index	489