Frequently Asked Questions in Mathematics
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Game Theory Lecture Notes
Game Theory: Penn State Math 486 Lecture Notes Version 2.1.1 Christopher Griffin « 2010-2021 Licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License With Major Contributions By: James Fan George Kesidis and Other Contributions By: Arlan Stutler Sarthak Shah Contents List of Figuresv Preface xi 1. Using These Notes xi 2. An Overview of Game Theory xi Chapter 1. Probability Theory and Games Against the House1 1. Probability1 2. Random Variables and Expected Values6 3. Conditional Probability8 4. The Monty Hall Problem 11 Chapter 2. Game Trees and Extensive Form 15 1. Graphs and Trees 15 2. Game Trees with Complete Information and No Chance 18 3. Game Trees with Incomplete Information 22 4. Games of Chance 24 5. Pay-off Functions and Equilibria 26 Chapter 3. Normal and Strategic Form Games and Matrices 37 1. Normal and Strategic Form 37 2. Strategic Form Games 38 3. Review of Basic Matrix Properties 40 4. Special Matrices and Vectors 42 5. Strategy Vectors and Matrix Games 43 Chapter 4. Saddle Points, Mixed Strategies and the Minimax Theorem 45 1. Saddle Points 45 2. Zero-Sum Games without Saddle Points 48 3. Mixed Strategies 50 4. Mixed Strategies in Matrix Games 53 5. Dominated Strategies and Nash Equilibria 54 6. The Minimax Theorem 59 7. Finding Nash Equilibria in Simple Games 64 8. A Note on Nash Equilibria in General 66 Chapter 5. An Introduction to Optimization and the Karush-Kuhn-Tucker Conditions 69 1. A General Maximization Formulation 70 2. Some Geometry for Optimization 72 3. -
Chapter 7 Expressions and Assignment Statements
Chapter 7 Expressions and Assignment Statements Chapter 7 Topics Introduction Arithmetic Expressions Overloaded Operators Type Conversions Relational and Boolean Expressions Short-Circuit Evaluation Assignment Statements Mixed-Mode Assignment Chapter 7 Expressions and Assignment Statements Introduction Expressions are the fundamental means of specifying computations in a programming language. To understand expression evaluation, need to be familiar with the orders of operator and operand evaluation. Essence of imperative languages is dominant role of assignment statements. Arithmetic Expressions Their evaluation was one of the motivations for the development of the first programming languages. Most of the characteristics of arithmetic expressions in programming languages were inherited from conventions that had evolved in math. Arithmetic expressions consist of operators, operands, parentheses, and function calls. The operators can be unary, or binary. C-based languages include a ternary operator, which has three operands (conditional expression). The purpose of an arithmetic expression is to specify an arithmetic computation. An implementation of such a computation must cause two actions: o Fetching the operands from memory o Executing the arithmetic operations on those operands. Design issues for arithmetic expressions: 1. What are the operator precedence rules? 2. What are the operator associativity rules? 3. What is the order of operand evaluation? 4. Are there restrictions on operand evaluation side effects? 5. Does the language allow user-defined operator overloading? 6. What mode mixing is allowed in expressions? Operator Evaluation Order 1. Precedence The operator precedence rules for expression evaluation define the order in which “adjacent” operators of different precedence levels are evaluated (“adjacent” means they are separated by at most one operand). -
The Monty Hall Problem in the Game Theory Class
The Monty Hall Problem in the Game Theory Class Sasha Gnedin∗ October 29, 2018 1 Introduction Suppose you’re on a game show, and you’re given the choice of three doors: Behind one door is a car; behind the others, goats. You pick a door, say No. 1, and the host, who knows what’s behind the doors, opens another door, say No. 3, which has a goat. He then says to you, “Do you want to pick door No. 2?” Is it to your advantage to switch your choice? With these famous words the Parade Magazine columnist vos Savant opened an exciting chapter in mathematical didactics. The puzzle, which has be- come known as the Monty Hall Problem (MHP), has broken the records of popularity among the probability paradoxes. The book by Rosenhouse [9] and the Wikipedia entry on the MHP present the history and variations of the problem. arXiv:1107.0326v1 [math.HO] 1 Jul 2011 In the basic version of the MHP the rules of the game specify that the host must always reveal one of the unchosen doors to show that there is no prize there. Two remaining unrevealed doors may hide the prize, creating the illusion of symmetry and suggesting that the action does not matter. How- ever, the symmetry is fallacious, and switching is a better action, doubling the probability of winning. There are two main explanations of the paradox. One of them, simplistic, amounts to just counting the mutually exclusive cases: either you win with ∗[email protected] 1 switching or with holding the first choice. -
Operators and Expressions
UNIT – 3 OPERATORS AND EXPRESSIONS Lesson Structure 3.0 Objectives 3.1 Introduction 3.2 Arithmetic Operators 3.3 Relational Operators 3.4 Logical Operators 3.5 Assignment Operators 3.6 Increment and Decrement Operators 3.7 Conditional Operator 3.8 Bitwise Operators 3.9 Special Operators 3.10 Arithmetic Expressions 3.11 Evaluation of Expressions 3.12 Precedence of Arithmetic Operators 3.13 Type Conversions in Expressions 3.14 Operator Precedence and Associability 3.15 Mathematical Functions 3.16 Summary 3.17 Questions 3.18 Suggested Readings 3.0 Objectives After going through this unit you will be able to: Define and use different types of operators in Java programming Understand how to evaluate expressions? Understand the operator precedence and type conversion And write mathematical functions. 3.1 Introduction Java supports a rich set of operators. We have already used several of them, such as =, +, –, and *. An operator is a symbol that tells the computer to perform certain mathematical or logical manipulations. Operators are used in programs to manipulate data and variables. They usually form a part of mathematical or logical expressions. Java operators can be classified into a number of related categories as below: 1. Arithmetic operators 2. Relational operators 1 3. Logical operators 4. Assignment operators 5. Increment and decrement operators 6. Conditional operators 7. Bitwise operators 8. Special operators 3.2 Arithmetic Operators Arithmetic operators are used to construct mathematical expressions as in algebra. Java provides all the basic arithmetic operators. They are listed in Tabled 3.1. The operators +, –, *, and / all works the same way as they do in other languages. -
ARM Instruction Set
4 ARM Instruction Set This chapter describes the ARM instruction set. 4.1 Instruction Set Summary 4-2 4.2 The Condition Field 4-5 4.3 Branch and Exchange (BX) 4-6 4.4 Branch and Branch with Link (B, BL) 4-8 4.5 Data Processing 4-10 4.6 PSR Transfer (MRS, MSR) 4-17 4.7 Multiply and Multiply-Accumulate (MUL, MLA) 4-22 4.8 Multiply Long and Multiply-Accumulate Long (MULL,MLAL) 4-24 4.9 Single Data Transfer (LDR, STR) 4-26 4.10 Halfword and Signed Data Transfer 4-32 4.11 Block Data Transfer (LDM, STM) 4-37 4.12 Single Data Swap (SWP) 4-43 4.13 Software Interrupt (SWI) 4-45 4.14 Coprocessor Data Operations (CDP) 4-47 4.15 Coprocessor Data Transfers (LDC, STC) 4-49 4.16 Coprocessor Register Transfers (MRC, MCR) 4-53 4.17 Undefined Instruction 4-55 4.18 Instruction Set Examples 4-56 ARM7TDMI-S Data Sheet 4-1 ARM DDI 0084D Final - Open Access ARM Instruction Set 4.1 Instruction Set Summary 4.1.1 Format summary The ARM instruction set formats are shown below. 3 3 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 9876543210 1 0 9 8 7 6 5 4 3 2 1 0 9 8 7 6 5 4 3 2 1 0 Cond 0 0 I Opcode S Rn Rd Operand 2 Data Processing / PSR Transfer Cond 0 0 0 0 0 0 A S Rd Rn Rs 1 0 0 1 Rm Multiply Cond 0 0 0 0 1 U A S RdHi RdLo Rn 1 0 0 1 Rm Multiply Long Cond 0 0 0 1 0 B 0 0 Rn Rd 0 0 0 0 1 0 0 1 Rm Single Data Swap Cond 0 0 0 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 Rn Branch and Exchange Cond 0 0 0 P U 0 W L Rn Rd 0 0 0 0 1 S H 1 Rm Halfword Data Transfer: register offset Cond 0 0 0 P U 1 W L Rn Rd Offset 1 S H 1 Offset Halfword Data Transfer: immediate offset Cond 0 -
Game, Set, and Match Carl W
Game, Set, and Match Carl W. Lee September 2016 Note: Some of the text below comes from Martin Gardner's articles in Scientific American and some from Mathematical Circles by Fomin, Genkin, and Itenberg. 1 1 Fifteen This is a two player game. Take a set of nine cards, numbered one (ace) through nine. They are spread out on the table, face up. Players alternately select a card to add their hands, which they keep face up in front of them. The goal is to achieve a subset of three cards in your hand so that the values of these three cards sum to exactly fifteen. (The ace counts as 1.) This is an example of a game that is isomorphic to (the \same as") a well-known game: Tic-Tac-Toe. Number the cells of a tic-tac-toe board with the integers from 1 to 9 arranged in the form of a magic square. 8 1 6 3 5 7 4 9 2 Then winning combinations correspond exactly to triples of numbers that sum to 15. This is a combinatorial two person game (but one that permits ties). There is no hidden information (e.g., cards that one player has that the other cannot see) and no random elements (e.g., rolling of dice). 2 2 Ones and Twos Ten 1's and ten 2's are written on a blackboard. In one turn, a player may erase (or cross out) any two numbers. If the two numbers erased are identical, they are replaced with a single 2. -
Subgame-Perfect Ε-Equilibria in Perfect Information Games With
Subgame-Perfect -Equilibria in Perfect Information Games with Common Preferences at the Limit Citation for published version (APA): Flesch, J., & Predtetchinski, A. (2016). Subgame-Perfect -Equilibria in Perfect Information Games with Common Preferences at the Limit. Mathematics of Operations Research, 41(4), 1208-1221. https://doi.org/10.1287/moor.2015.0774 Document status and date: Published: 01/11/2016 DOI: 10.1287/moor.2015.0774 Document Version: Publisher's PDF, also known as Version of record Document license: Taverne Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. -
Introduction to Probability 1 Monty Hall
Massachusetts Institute of Technology Course Notes, Week 12 6.042J/18.062J, Fall ’05: Mathematics for Computer Science November 21 Prof. Albert R. Meyer and Prof. Ronitt Rubinfeld revised November 29, 2005, 1285 minutes Introduction to Probability Probability is the last topic in this course and perhaps the most important. Many algo rithms rely on randomization. Investigating their correctness and performance requires probability theory. Moreover, many aspects of computer systems, such as memory man agement, branch prediction, packet routing, and load balancing are designed around probabilistic assumptions and analyses. Probability also comes up in information theory, cryptography, artificial intelligence, and game theory. Beyond these engineering applica tions, an understanding of probability gives insight into many everyday issues, such as polling, DNA testing, risk assessment, investing, and gambling. So probability is good stuff. 1 Monty Hall In the September 9, 1990 issue of Parade magazine, the columnist Marilyn vos Savant responded to this letter: Suppose you’re on a game show, and you’re given the choice of three doors. Behind one door is a car, behind the others, goats. You pick a door, say number 1, and the host, who knows what’s behind the doors, opens another door, say number 3, which has a goat. He says to you, ”Do you want to pick door number 2?” Is it to your advantage to switch your choice of doors? Craig. F. Whitaker Columbia, MD The letter roughly describes a situation faced by contestants on the 1970’s game show Let’s Make a Deal, hosted by Monty Hall and Carol Merrill. -
Tic-Tac-Toe Is Not Very Interesting to Play, Because If Both Players Are Familiar with the Game the Result Is Always a Draw
CMPSCI 250: Introduction to Computation Lecture #27: Games and Adversary Search David Mix Barrington 4 November 2013 Games and Adversary Search • Review: A* Search • Modeling Two-Player Games • When There is a Game Tree • The Determinacy Theorem • Searching a Game Tree • Examples of Games Review: A* Search • The A* Search depends on a heuristic function, which h(y) = 6 2 is a lower bound on the 23 s distance to the goal. x 4 If x is a node, and g is the • h(z) = 3 nearest goal node to x, the admissibility condition p(y) = 23 + 2 + 6 = 31 on h is that 0 ≤ h(x) ≤ d(x, g). p(z) = 23 + 4 + 3 = 30 Review: A* Search • Suppose we have taken y off of the open list. The best-path distance from the start s to the goal g through y is d(s, y) + d(y, h(y) = 6 g), and this cannot be less than 2 23 d(s, y) + h(y). s x 4 • Thus when we find a path of length k from s to y, we put y h(z) = 3 onto the open list with priority k p(y) = 23 + 2 + 6 = 31 + h(y). We still record the p(z) = 23 + 4 + 3 = 30 distance d(s, y) when we take y off of the open list. Review: A* Search • The advantage of A* over uniform-cost search is that we do not consider entries x in the closed list for which d(s, x) + h(x) h(y) = 6 is greater than the actual best- 2 23 path distance from s to g. -
PYTHON BASIC OPERATORS Rialspo Int.Co M/Pytho N/Pytho N Basic O Perato Rs.Htm Copyrig Ht © Tutorialspoint.Com
PYTHON BASIC OPERATORS http://www.tuto rialspo int.co m/pytho n/pytho n_basic_o perato rs.htm Copyrig ht © tutorialspoint.com What is an operator? Simple answer can be g iven using expression 4 + 5 is equal to 9. Here, 4 and 5 are called operands and + is called operator. Python lang uag e supports the following types of operators. Arithmetic Operators Comparison (i.e., Relational) Operators Assig nment Operators Log ical Operators Bitwise Operators Membership Operators Identity Operators Let's have a look on all operators one by one. Python Arithmetic Operators: Assume variable a holds 10 and variable b holds 20, then: [ Show Example ] Operator Description Example + Addition - Adds values on either side of the a + b will g ive 30 operator - Subtraction - Subtracts rig ht hand operand a - b will g ive -10 from left hand operand * Multiplication - Multiplies values on either side a * b will g ive 200 of the operator / Division - Divides left hand operand by rig ht b / a will g ive 2 hand operand % Modulus - Divides left hand operand by rig ht b % a will g ive 0 hand operand and returns remainder ** Exponent - Performs exponential (power) a**b will g ive 10 to the power 20 calculation on operators // Floor Division - The division of operands 9//2 is equal to 4 and 9.0//2.0 is equal to where the result is the quotient in which the 4.0 dig its after the decimal point are removed. Python Comparison Operators: Assume variable a holds 10 and variable b holds 20, then: [ Show Example ] Operator Description Example == Checks if the value of two operands are equal (a == b) is not true. -
The Monty Hall Problem
The Monty Hall Problem Richard D. Gill Mathematical Institute, University of Leiden, Netherlands http://www.math.leidenuniv.nl/∼gill 17 March, 2011 Abstract A range of solutions to the Monty Hall problem is developed, with the aim of connecting popular (informal) solutions and the formal mathematical solutions of in- troductory text-books. Under Riemann's slogan of replacing calculations with ideas, we discover bridges between popular solutions and rigorous (formal) mathematical solutions by using various combinations of symmetry, symmetrization, independence, and Bayes' rule, as well as insights from game theory. The result is a collection of intuitive and informal logical arguments which can be converted step by step into formal mathematics. The Monty Hall problem can be used simultaneously to develop probabilistic intuition and to give a deeper understanding of the paradox, not just to provide a routine exercise in computation of conditional probabilities. Simple insights from game theory can be used to prove probability inequalities. Acknowledgement: this text is the result of an evolution through texts written for three internet encyclopedias (wikipedia.org, citizendium.org, statprob.com) and each time has benefitted from the contributions of many other editors. I thank them all, for all the original ideas in this paper. That leaves only the mistakes, for which I bear responsibility. 1 Introduction Imagine you are a guest in a TV game show. The host, a certain Mr. Monty Hall, shows you three large doors and tells you that behind one of the doors there is a car while behind the other two there are goats. You want to win the car. -
Introduction to Game Theory I
Introduction to Game Theory I Presented To: 2014 Summer REU Penn State Department of Mathematics Presented By: Christopher Griffin [email protected] 1/34 Types of Game Theory GAME THEORY Classical Game Combinatorial Dynamic Game Evolutionary Other Topics in Theory Game Theory Theory Game Theory Game Theory No time, doesn't contain differential equations Notion of time, may contain differential equations Games with finite or Games with no Games with time. Modeling population Experimental / infinite strategy space, chance. dynamics under Behavioral Game but no time. Games with motion or competition Theory Generally two player a dynamic component. Games with probability strategic games Modeling the evolution Voting Theory (either induced by the played on boards. Examples: of strategies in a player or the game). Optimal play in a dog population. Examples: Moves change the fight. Chasing your Determining why Games with coalitions structure of a game brother across a room. Examples: altruism is present in or negotiations. board. The evolution of human society. behaviors in a group of Examples: Examples: animals. Determining if there's Poker, Strategic Chess, Checkers, Go, a fair voting system. Military Decision Nim. Equilibrium of human Making, Negotiations. behavior in social networks. 2/34 Games Against the House The games we often see on television fall into this category. TV Game Shows (that do not pit players against each other in knowledge tests) often require a single player (who is, in a sense, playing against The House) to make a decision that will affect only his life. Prize is Behind: 1 2 3 Choose Door: 1 2 3 1 2 3 1 2 3 Switch: Y N Y N Y N Y N Y N Y N Y N Y N Y N Win/Lose: L W W L W L W L L W W L W L W L L W The Monty Hall Problem Other games against the house: • Blackjack • The Price is Right 3/34 Assumptions of Multi-Player Games 1.