Variations on the Gambler s Ruin Problem

Similar documents
6.042/18.062J Mathematics for Computer Science December 12, 2006 Tom Leighton and Ronitt Rubinfeld. Random Walks

Stat 134 Fall 2011: Gambler s ruin

1 Gambler s Ruin Problem

Pythagorean Triples and Rational Points on the Unit Circle

Price Elasticity of Demand MATH 104 and MATH 184 Mark Mac Lean (with assistance from Patrick Chan) 2011W

Week 4: Gambler s ruin and bold play

Coordinate Transformation

The Cubic Formula. The quadratic formula tells us the roots of a quadratic polynomial, a polynomial of the form ax 2 + bx + c. The roots (if b 2 b+

Double Integrals in Polar Coordinates

3.2 Roulette and Markov Chains

How To Understand The Difference Between A Bet And A Bet On A Draw Or Draw On A Market

Ch. 13.2: Mathematical Expectation

Stat 20: Intro to Probability and Statistics

The fast Fourier transform method for the valuation of European style options in-the-money (ITM), at-the-money (ATM) and out-of-the-money (OTM)

Complex Conjugation and Polynomial Factorization

Risk and Return. Sample chapter. e r t u i o p a s d f CHAPTER CONTENTS LEARNING OBJECTIVES. Chapter 7

C-Bus Voltage Calculation

A Simple Model of Pricing, Markups and Market. Power Under Demand Fluctuations

13.0 Central Limit Theorem

6.3 Conditional Probability and Independence

Synopsys RURAL ELECTRICATION PLANNING SOFTWARE (LAPER) Rainer Fronius Marc Gratton Electricité de France Research and Development FRANCE

PRIME NUMBERS AND THE RIEMANN HYPOTHESIS

Betting systems: how not to lose your money gambling

The Normal Approximation to Probability Histograms. Dice: Throw a single die twice. The Probability Histogram: Area = Probability. Where are we going?

Math 728 Lesson Plan

QUEUING THEORY. 1. Introduction

Index Numbers OPTIONAL - II Mathematics for Commerce, Economics and Business INDEX NUMBERS

Statistics and Random Variables. Math 425 Introduction to Probability Lecture 14. Finite valued Random Variables. Expectation defined

Binomial Random Variables. Binomial Distribution. Examples of Binomial Random Variables. Binomial Random Variables

פרויקט מסכם לתואר בוגר במדעים )B.Sc( במתמטיקה שימושית

Elementary Statistics and Inference. Elementary Statistics and Inference. 16 The Law of Averages (cont.) 22S:025 or 7P:025.

Mathematical goals. Starting points. Materials required. Time needed

Frequentist vs. Bayesian Statistics

Elementary Statistics and Inference. Elementary Statistics and Inference. 17 Expected Value and Standard Error. 22S:025 or 7P:025.

Expected Value and the Game of Craps

SOME ASPECTS OF GAMBLING WITH THE KELLY CRITERION. School of Mathematical Sciences. Monash University, Clayton, Victoria, Australia 3168

Ready, Set, Go! Math Games for Serious Minds

FREQUENCIES OF SUCCESSIVE PAIRS OF PRIME RESIDUES

25 Integers: Addition and Subtraction

Normal distribution. ) 2 /2σ. 2π σ

Sue Fine Linn Maskell

First Law, Heat Capacity, Latent Heat and Enthalpy

Worldwide Casino Consulting Inc.

Computational Finance The Martingale Measure and Pricing of Derivatives

Math Games For Skills and Concepts

Monitoring Frequency of Change By Li Qin

How To Understand And Solve A Linear Programming Problem

Fugacity, Activity, and Standard States

36 Odds, Expected Value, and Conditional Probability

Question 1 Formatted: Formatted: Formatted: Formatted:

The Online Freeze-tag Problem

Pressure Drop in Air Piping Systems Series of Technical White Papers from Ohio Medical Corporation

6.042/18.062J Mathematics for Computer Science. Expected Value I

The Mathematics of Gambling

SECTION 10-2 Mathematical Induction

Probability and Expected Value

The Impact of a Finite Bankroll on an Even-Money Game

Unit 19: Probability Models

Stochastic Derivation of an Integral Equation for Probability Generating Functions

Session 8 Probability

Introduction to Discrete Probability. Terminology. Probability definition. 22c:19, section 6.x Hantao Zhang

V. RANDOM VARIABLES, PROBABILITY DISTRIBUTIONS, EXPECTED VALUE

An important observation in supply chain management, known as the bullwhip effect,

Gaming the Law of Large Numbers

Law of Large Numbers. Alexandra Barbato and Craig O Connell. Honors 391A Mathematical Gems Jenia Tevelev

Probabilistic Strategies: Solutions

Large firms and heterogeneity: the structure of trade and industry under oligopoly

Situation Based Strategic Positioning for Coordinating a Team of Homogeneous Agents

$ ( $1) = 40

More Properties of Limits: Order of Operations

Chapter 4 Lecture Notes

Abstract: We describe the beautiful LU factorization of a square matrix (or how to write Gaussian elimination in terms of matrix multiplication).

Current California Math Standards Balanced Equations

POISSON PROCESSES. Chapter Introduction Arrival processes

CRITICAL AVIATION INFRASTRUCTURES VULNERABILITY ASSESSMENT TO TERRORIST THREATS

CABRS CELLULAR AUTOMATON BASED MRI BRAIN SEGMENTATION

Introduction to Matrices

(SEE IF YOU KNOW THE TRUTH ABOUT GAMBLING)

Grade 7/8 Math Circles Sequences and Series

SOME PROPERTIES OF EXTENSIONS OF SMALL DEGREE OVER Q. 1. Quadratic Extensions

Betting on Excel to enliven the teaching of probability

Softmax Model as Generalization upon Logistic Discrimination Suffers from Overfitting

The Binomial Distribution

That s Not Fair! ASSESSMENT #HSMA20. Benchmark Grades: 9-12

Brain Game. 3.4 Solving and Graphing Inequalities HOW TO PLAY PRACTICE. Name Date Class Period. MATERIALS game cards

Chapter 4. Probability and Probability Distributions

Introduction to Hypothesis Testing

Introduction to NP-Completeness Written and copyright c by Jie Wang 1

14.4. Expected Value Objectives. Expected Value

Stochastic Processes and Advanced Mathematical Finance. Ruin Probabilities

Homework 20: Compound Probability

Point Location. Preprocess a planar, polygonal subdivision for point location queries. p = (18, 11)

Math Quizzes Winter 2009

Applying the Kelly criterion to lawsuits

As we have seen, there is a close connection between Legendre symbols of the form

Section 1.3 P 1 = 1 2. = P n = 1 P 3 = Continuing in this fashion, it should seem reasonable that, for any n = 1, 2, 3,..., =

Transcription:

Variations on the Gambler s Ruin Problem Mat Willmott December 6, 2002 Abstract. This aer covers the history and solution to the Gambler s Ruin Problem, and then exlores the odds for each layer to win in three variations on the roblem: the attrition variation, a variation in which one layer has an infinite number of oints, and a three layer variation, in which two layers lay against a house layer. 1. Introduction. Throughout the years, the Gambler s Ruin Problem has been rominent in alied mathematics. With differing levels of comlexity, variations on the roblem arise in all tyes of games, from a child s board games to comlicated casino games such as cras and blackjack. The theory behind the roblem is also used in horse racing and dog racing, generally by the track to be sure that it makes a rofit. There are many variations on the roblem, and as fast as older ones are being solved, newer ones are being formulated. Four-layer and five-layer variations, for examle, have already been roosed, and some have even been solved. The Gambler s Ruin Problem is clearly an imortant and growing toic in discrete alied mathematics; it was relevant in the 1600s and is still relevant today. Section 2 covers the history of the Gambler s Ruin Problem. Then Section 3 discusses and gives a solution to the most common form of the roblem, closely following the solution resented by DeGroot [2,. 71 74]. Sections 4 through 6 discuss some of the more common variations on the roblem. Section 4 resents and analyzes a game where the layers do not win oints on successful trials, but only lose them on unsuccessful ones. Section 5 discusses a variation in which one layer has an infinite money suly. Section 6 concludes the aer with a discussion of a three layer variation on the roblem. 2. History. The Gambler s Ruin Problem is one of the oldest roblems in robability theory. According to Edwards [3,. 73], Pascal osed a roblem similar to the Gambler s Ruin Problem in 1656 in a letter to Fermat. Caravaci later summarizes the letter: Let two men lay with three dice, the first layer scoring a oint whenever 11 is thrown, and the second whenever 14 is thrown. But instead of the oints accumulating in the ordinary way, let a oint 1

be added to a layer s score only if his oonent s score is nil, but otherwise let it be subtracted from his oonent s score. It is as if oosing oints form airs, and annihilate each other, so that the trailing layer always has zero oints. The winner is the first to reach twelve oints; what are the relative chances of each layer winning? This roblem, however, is not the Gambler s Ruin Problem in its common form. Edwards goes on to exlain that the common form comes from Huygens, who read and restated the roblem. Originally, Huygens looked at the above roblem as if oints were accumulated normally, and the winner were the first layer to lead by 12 oints. Later, he again restated the roblem as follows: Problem 2-1 Each layer starts with 12 oints, and a successful roll of the three dice for a layer getting an 11 for the first layer or a 14 for the second adds one to that layer s score and subtracts one from the other layer s score; the loser of the game is the first to reach zero oints. What is the robability of victory for each layer? The above roblem is known as the Gambler s Ruin Problem because one of the layers will run out of money be ruined at the end of the game. This form is the form most commonly used today. 3. The Basic Form of the Gambler s Ruin Problem. To solve Problem 2-1, we look at the game from the oint of view of one of the layers, say Player A. We name Player A s oonent Player B. Say Player A starts with i oints, and Player B starts with k i oints, so that the total number of oints in the game is k. For examle, in Section 2, we see that i = 12 and k = 24. Player A wins when he has k oints, and loses when he has zero oints. Also, say that the robability of A winning the next oint is, so the robability of B winning the next oint is q = 1. Finally, say that a i is the the robability of A reaching k oints before reaching 0 oints when starting with i oints; so a 0 = 0 and a k = 1. Let us refer to the event that A reaches k oints before B as W, the event of A winning the first oint as A 1, and the event of A losing the first oint B winning the first oint as B 1. Then P W = P A 1 P W A 1 + P B 1 P W B 1. 3-1 Substituting in the robability definitions made above, we get the following system of equations: 2

a 1 = a 2 + qa 0 = a 2 a 2 = a 3 + qa 1. 3-2 a k 2 = a k 1 + qa k 3 a k 1 = a k + qa k 2 = + qa k 2. Since + q = 1, we can relace each a i with a i + qa i. Then Equations 3-2 become a 2 a 1 = q a 1 a 3 a 2 = q q 2a1 a 2 a 1 = a 4 a 3 = q q 3a1 a 3 a 2 = 3-3 a k 1 a k 2 =. q k 2a1 a k 2 a k 3 = 1 a k 1 = q q k 1a1 a k 1 a k 2 =. And finally, we can sum Equations 3-3 to get k 1 q i. 1 a 1 = a 1 3-4 For a fair game, = q = 1/2, and so Equation 3-4 becomes 1 a 1 = k 1a 1, that is, a 1 = 1/k. From Equations 3-3, we can see that for = q, we have a 2 = 2a 1 and a 3 = 3a 1 and so on, so that i=1 a i = i/k. 3-5 For an unfair game, q, and so Equation 3-4 becomes This exression can be simlified to 1 a 1 = a 1 q k q q 1. a 1 = q 1 q k 1. 3

Using Equations 3-2, this formula can be generalized to a i = q i 1 q k 1. 3-6 We can use Equation 3-6 to solve Problem 2-1. Assume Player A is rolling for an 11. Since the chances of rolling a 14 on three dice is 15/216 and the chances of rolling an 11 is 27/216, we have q/ = 5/9. Plugging this ratio into Equation 3-6 and using i = 12 and k = 24, we get a i = 0.999136316, which is about 1156/1157, the same solution that Pascal came u with. The Gambler s Ruin Problem can be modified and generalized to aly to many different tyes of games with different numbers of layers, different tyes of layers, and different rules. 4. The Attrition Variation. One of the most common variations on the Gambler s Ruin Problem is called attrition. In attrition, one layer does not win a oint from the other layer on a successful lay, such as a roll of an 11 for Player A in Problem 2-1. Instead, the losing layer simly discards a oint. As W. D. Kaigh describes it in [4,.22], this variation alies to many situations from oular board games like Risk to best-of-seven sorts series like the World Series or the Stanley Cu finals. The robability that Player A wins the game, and B is ruined, can be found by examining A s total score at the end of the game. We denote the number of oints that A has lost at the end of the game by L A. We define the event W, the robabilities a i,, and q, and the variables i and k as in Section 3. We now see that 0 L A i 1. As a result, we see that i 1 P W = P W L A = x. 4-1 x=0 For simlicity s sake, let us refer to Player B s starting score as b, where b = k i. Then for A to win while losing x oints in the rocess, we want Player A to win b oints in the same amount of time that it takes for Player B to win x oints. Using basic binomial robability [2,.84 85], we rewrite this condition as b + x 1 P W L A = x = b q x. 4-2 x Combining Equation 4-1 with Equation 4-2, we obtain the robability that A wins the game, or that B is ruined, as i 1 b + x 1 a i = b q x. 4-3 x x=0 4

5. One Player with Limited Points vs. One Player with Infinite Points. In another common variation on the Gambler s Ruin Problem, one layer, B say, has an infinite oint suly. Obviously, this hyothesis eliminates the game art of the roblem because B can never lose. However, we can still examine the numerical consequences of having a layer with an infinite oint suly. For < 1/2, we have q/ > 1, and as k goes to infinity in Equation 3-6, we see that a i always aroaches zero. For > 1/2, we have q/ < 1, and as k goes to infinity in Equation 3-6, we see that the chance that A is not ruined is q i. a i = 1 Player A can never win because B has an infinite number of oints and therefore can not be ruined, so this equation only gives the robability that A will continue to lay forever. For = 1/2, we use Equation 3-5 to find the chance that A is not ruined. Clearly, as k aroaches infinity, i/k aroaches zero, so Player A loses all of his oints. We can conclude that A is always ruined eventually for 1/2, and has a robability of a i = 1 q/ i of laying forever for > 1/2. 6. A Generalization of the Problem to Three Players. We can generalize the Gambler s Ruin Problem to three layers. Player A and Player B lay games against each other, but they also lay a combined game against a searate layer, called C. In this variation, however, A and B lace two half-oint bets on every lay, rather than one full-oint bet. In the game between A and B, say A wins the half-oint bet with robability 1, and B wins with robability q 1 = 1 1. Also, A and B both contribute a half-oint to a combined full-oint bet against C. Say A and B win this bet with robability 2, and C wins with robability q 2 = 1 2. Now suose that A, B, and C start with i, j, and l oints resectively. Set k = i + j + l. Then k is the total number of oints in the game; it is the number of oints the winning layer has at the end of the game when the other two layers are ruined. We denote the event that Player X gains a oint by G X, and the event that Player X loses a oint by L X, where X = A, B, or C. Then we define the four ossibilities on each lay as := P G A and L C = 1 2, q := P L A and G C = q 1 q 2, 6-1 r := P G B and L C = q 1 2, s := P L B and G C = 1 q 2. 5

Let x and y be the total scores for A and B resectively. Player A is ruined when x = 0; Player B is ruined when y = 0; Player C is ruined when x + y = k. Therefore, the first ortion of the game, that is, the three layer ortion before one layer is ruined, can be described as a two dimensional random walk within the triangle bounded by x = 0, y = 0, and x+y = k. Once one of the boundaries of the triangle is reached, the three layer ortion of the game ends and the game becomes a standard two layer Gambler s Ruin Problem. This two dimensional walk is illustrated in Figure 6-1. 0,k B wins C ruined A ruined r q * s 0,0 C wins B ruined k,0 A wins Figure 6-1. Reresentation of the two dimensional random walk created by the three layer game. The oint may move u, down, left, or right according to the robabilities given. As Barnett describes in [1,. 322 324], there are six ossible ways for the game to lay out: any one of the three layers may be ruined first, and then either of the two remaining layers may be ruined, leaving a single victorious layer. Thus, each layer has two ways of winning. For examle, A may win by first ruining B in the three layer game, and then ruining C in the two layer game or by ruining C in the three layer game, and then ruining B in the two layer game. Informally, this organization of the game is described as: 6

P A wins = P B is ruined first P A goes on to ruin C + P C is ruined first P A goes on to ruin B, P B wins = P A is ruined first P B goes on to ruin C 6-2 + P C is ruined first P B goes on to ruin A, P C wins = P A is ruined first P C goes on to ruin B + P B is ruined first P C goes on to ruin A. Converting these equations into more useful ones, however, is more difficult than it first sounds. The robability of a layer winning the two layer ortion of the game deends on the starting number of oints of the two layer ortion, that is, the ending number of oints of the three layer ortion. Therefore, we must sum over all the ossible scores for the start of the two layer ortion of the game. Define the function ux, y to be the robability that the three layer ortion of the game ends at the oint x, y. Thus, we combine Equation 3-6 with Equations 6-2 and simlify to get the robabilites that A, B, or C resectively win the game: P A = P B = P C = k 2 q 2 2 n 1 k 1 q 1 q 2 n=1 2 k un, 0 + 1 n 1 1 q 1 n=1 1 k un, k n, 1 k 2 q 2 2 n 1 k 1 1 q q 2 n=1 2 k u0, n + 1 k n 1 1 1 n=1 q 1 k un, k n, 6-3 1 k 2 2 q 2 k n 1 un, 2 0 + u0, n. q 2 k 1 n=1 Barnett continues to show that we can break down the robability of ultimate victory for each layer by defining a better ux, y. Define E i,j x, y to be the average number of times the two dimensional walk starting at i, j leaves the oint x, y. Then we see that the ux, y that we used in defining the robabilities is written as follows: un, 0 = se i,j n, 1 for n = 1, 2,..., k 2; u0, n = qe i,j 1, n for n = 1, 2,..., k 2; 6-4 un, k n = re i,j n, k n 1 + E i,j n 1, k n for n = 1, 2,..., k 1. Finally, we can ut Equations 6-4 into Equations 6-3 to obtain formulas for P A, P B, and P C. 7

References [1] Barnett, V. D., A three-layer extension of the gambler s ruin roblem, Journal of Alied Probability 1 1964, 321 334. [2] DeGroot, Morris H., Probability and Statistics, Addison-Wesley Publishing Co., 1975. [3] Edwards, A. W. F., Pascal s roblem: the gambler s ruin, International Statistical Review 51 1983, 73 79. [4] Kaigh, W. D., An attrition roblem of gambler s ruin, Mathematics Magazine 52 1979, 22 25. 8