The Axiom of Choice and Some Equivalences: a Senior Exercise
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How Many Borel Sets Are There? Object. This Series of Exercises Is
How Many Borel Sets are There? Object. This series of exercises is designed to lead to the conclusion that if BR is the σ- algebra of Borel sets in R, then Card(BR) = c := Card(R): This is the conclusion of problem 4. As a bonus, we also get some insight into the \structure" of BR via problem 2. This just scratches the surface. If you still have an itch after all this, you want to talk to a set theorist. This treatment is based on the discussion surrounding [1, Proposition 1.23] and [2, Chap. V x10 #31]. For these problems, you will need to know a bit about well-ordered sets and transfinite induction. I suggest [1, x0.4] where transfinite induction is [1, Proposition 0.15]. Note that by [1, Proposition 0.18], there is an uncountable well ordered set Ω such that for all x 2 Ω, Ix := f y 2 Ω: y < x g is countable. The elements of Ω are called the countable ordinals. We let 1 := inf Ω. If x 2 Ω, then x + 1 := inff y 2 Ω: y > x g is called the immediate successor of x. If there is a z 2 Ω such that z + 1 = x, then z is called the immediate predecessor of x. If x has no immediate predecessor, then x is called a limit ordinal.1 1. Show that Card(Ω) ≤ c. (This follows from [1, Propositions 0.17 and 0.18]. Alternatively, you can use transfinite induction to construct an injective function f :Ω ! R.)2 ANS: Actually, this follows almost immediately from Folland's Proposition 0.17. -
1 Elementary Set Theory
1 Elementary Set Theory Notation: fg enclose a set. f1; 2; 3g = f3; 2; 2; 1; 3g because a set is not defined by order or multiplicity. f0; 2; 4;:::g = fxjx is an even natural numberg because two ways of writing a set are equivalent. ; is the empty set. x 2 A denotes x is an element of A. N = f0; 1; 2;:::g are the natural numbers. Z = f:::; −2; −1; 0; 1; 2;:::g are the integers. m Q = f n jm; n 2 Z and n 6= 0g are the rational numbers. R are the real numbers. Axiom 1.1. Axiom of Extensionality Let A; B be sets. If (8x)x 2 A iff x 2 B then A = B. Definition 1.1 (Subset). Let A; B be sets. Then A is a subset of B, written A ⊆ B iff (8x) if x 2 A then x 2 B. Theorem 1.1. If A ⊆ B and B ⊆ A then A = B. Proof. Let x be arbitrary. Because A ⊆ B if x 2 A then x 2 B Because B ⊆ A if x 2 B then x 2 A Hence, x 2 A iff x 2 B, thus A = B. Definition 1.2 (Union). Let A; B be sets. The Union A [ B of A and B is defined by x 2 A [ B if x 2 A or x 2 B. Theorem 1.2. A [ (B [ C) = (A [ B) [ C Proof. Let x be arbitrary. x 2 A [ (B [ C) iff x 2 A or x 2 B [ C iff x 2 A or (x 2 B or x 2 C) iff x 2 A or x 2 B or x 2 C iff (x 2 A or x 2 B) or x 2 C iff x 2 A [ B or x 2 C iff x 2 (A [ B) [ C Definition 1.3 (Intersection). -
Biography Paper – Georg Cantor
Mike Garkie Math 4010 – History of Math UCD Denver 4/1/08 Biography Paper – Georg Cantor Few mathematicians are house-hold names; perhaps only Newton and Euclid would qualify. But there is a second tier of mathematicians, those whose names might not be familiar, but whose discoveries are part of everyday math. Examples here are Napier with logarithms, Cauchy with limits and Georg Cantor (1845 – 1918) with sets. In fact, those who superficially familier with Georg Cantor probably have two impressions of the man: First, as a consequence of thinking about sets, Cantor developed a theory of the actual infinite. And second, that Cantor was a troubled genius, crippled by Freudian conflict and mental illness. The first impression is fundamentally true. Cantor almost single-handedly overturned the Aristotle’s concept of the potential infinite by developing the concept of transfinite numbers. And, even though Bolzano and Frege made significant contributions, “Set theory … is the creation of one person, Georg Cantor.” [4] The second impression is mostly false. Cantor certainly did suffer from mental illness later in his life, but the other emotional baggage assigned to him is mostly due his early biographers, particularly the infamous E.T. Bell in Men Of Mathematics [7]. In the racially charged atmosphere of 1930’s Europe, the sensational story mathematician who turned the idea of infinity on its head and went crazy in the process, probably make for good reading. The drama of the controversy over Cantor’s ideas only added spice. 1 Fortunately, modern scholars have corrected the errors and biases in older biographies. -
Epsilon Substitution for Transfinite Induction
Epsilon Substitution for Transfinite Induction Henry Towsner May 20, 2005 Abstract We apply Mints’ technique for proving the termination of the epsilon substitution method via cut-elimination to the system of Peano Arith- metic with Transfinite Induction given by Arai. 1 Introduction Hilbert introduced the epsilon calculus in [Hilbert, 1970] as a method for proving the consistency of arithmetic and analysis. In place of the usual quantifiers, a symbol is added, allowing terms of the form xφ[x], which are interpreted as “some x such that φ[x] holds, if such a number exists.” When there is no x satisfying φ[x], we allow xφ[x] to take an arbitrary value (usually 0). Then the existential quantifier can be defined by ∃xφ[x] ⇔ φ[xφ[x]] and the universal quantifier by ∀xφ[x] ⇔ φ[x¬φ[x]] Hilbert proposed a method for transforming non-finitistic proofs in this ep- silon calculus into finitistic proofs by assigning numerical values to all the epsilon terms, making the proof entirely combinatorial. The difficulty centers on the critical formulas, axioms of the form φ[t] → φ[xφ[x]] In Hilbert’s method we consider the (finite) list of critical formulas which appear in a proof. Then we define a series of functions, called -substitutions, each providing values for some of the of the epsilon terms appearing in the list. Each of these substitutions will satisfy some, but not necessarily all, of the critical formulas appearing in proof. At each step we take the simplest unsatisfied critical formula and update it so that it becomes true. -
Determinacy in Linear Rational Expectations Models
Journal of Mathematical Economics 40 (2004) 815–830 Determinacy in linear rational expectations models Stéphane Gauthier∗ CREST, Laboratoire de Macroéconomie (Timbre J-360), 15 bd Gabriel Péri, 92245 Malakoff Cedex, France Received 15 February 2002; received in revised form 5 June 2003; accepted 17 July 2003 Available online 21 January 2004 Abstract The purpose of this paper is to assess the relevance of rational expectations solutions to the class of linear univariate models where both the number of leads in expectations and the number of lags in predetermined variables are arbitrary. It recommends to rule out all the solutions that would fail to be locally unique, or equivalently, locally determinate. So far, this determinacy criterion has been applied to particular solutions, in general some steady state or periodic cycle. However solutions to linear models with rational expectations typically do not conform to such simple dynamic patterns but express instead the current state of the economic system as a linear difference equation of lagged states. The innovation of this paper is to apply the determinacy criterion to the sets of coefficients of these linear difference equations. Its main result shows that only one set of such coefficients, or the corresponding solution, is locally determinate. This solution is commonly referred to as the fundamental one in the literature. In particular, in the saddle point configuration, it coincides with the saddle stable (pure forward) equilibrium trajectory. © 2004 Published by Elsevier B.V. JEL classification: C32; E32 Keywords: Rational expectations; Selection; Determinacy; Saddle point property 1. Introduction The rational expectations hypothesis is commonly justified by the fact that individual forecasts are based on the relevant theory of the economic system. -
Domain and Range of a Function
4.1 Domain and Range of a Function How can you fi nd the domain and range of STATES a function? STANDARDS MA.8.A.1.1 MA.8.A.1.5 1 ACTIVITY: The Domain and Range of a Function Work with a partner. The table shows the number of adult and child tickets sold for a school concert. Input Number of Adult Tickets, x 01234 Output Number of Child Tickets, y 86420 The variables x and y are related by the linear equation 4x + 2y = 16. a. Write the equation in function form by solving for y. b. The domain of a function is the set of all input values. Find the domain of the function. Domain = Why is x = 5 not in the domain of the function? 1 Why is x = — not in the domain of the function? 2 c. The range of a function is the set of all output values. Find the range of the function. Range = d. Functions can be described in many ways. ● by an equation ● by an input-output table y 9 ● in words 8 ● by a graph 7 6 ● as a set of ordered pairs 5 4 Use the graph to write the function 3 as a set of ordered pairs. 2 1 , , ( , ) ( , ) 0 09321 45 876 x ( , ) , ( , ) , ( , ) 148 Chapter 4 Functions 2 ACTIVITY: Finding Domains and Ranges Work with a partner. ● Copy and complete each input-output table. ● Find the domain and range of the function represented by the table. 1 a. y = −3x + 4 b. y = — x − 6 2 x −2 −10 1 2 x 01234 y y c. -
PROBLEM SET 1. the AXIOM of FOUNDATION Early on in the Book
PROBLEM SET 1. THE AXIOM OF FOUNDATION Early on in the book (page 6) it is indicated that throughout the formal development ‘set’ is going to mean ‘pure set’, or set whose elements, elements of elements, and so on, are all sets and not items of any other kind such as chairs or tables. This convention applies also to these problems. 1. A set y is called an epsilon-minimal element of a set x if y Î x, but there is no z Î x such that z Î y, or equivalently x Ç y = Ø. The axiom of foundation, also called the axiom of regularity, asserts that any set that has any element at all (any nonempty set) has an epsilon-minimal element. Show that this axiom implies the following: (a) There is no set x such that x Î x. (b) There are no sets x and y such that x Î y and y Î x. (c) There are no sets x and y and z such that x Î y and y Î z and z Î x. 2. In the book the axiom of foundation or regularity is considered only in a late chapter, and until that point no use of it is made of it in proofs. But some results earlier in the book become significantly easier to prove if one does use it. Show, for example, how to use it to give an easy proof of the existence for any sets x and y of a set x* such that x* and y are disjoint (have empty intersection) and there is a bijection (one-to-one onto function) from x to x*, a result called the exchange principle. -
The Axiom of Choice and Its Implications
THE AXIOM OF CHOICE AND ITS IMPLICATIONS KEVIN BARNUM Abstract. In this paper we will look at the Axiom of Choice and some of the various implications it has. These implications include a number of equivalent statements, and also some less accepted ideas. The proofs discussed will give us an idea of why the Axiom of Choice is so powerful, but also so controversial. Contents 1. Introduction 1 2. The Axiom of Choice and Its Equivalents 1 2.1. The Axiom of Choice and its Well-known Equivalents 1 2.2. Some Other Less Well-known Equivalents of the Axiom of Choice 3 3. Applications of the Axiom of Choice 5 3.1. Equivalence Between The Axiom of Choice and the Claim that Every Vector Space has a Basis 5 3.2. Some More Applications of the Axiom of Choice 6 4. Controversial Results 10 Acknowledgments 11 References 11 1. Introduction The Axiom of Choice states that for any family of nonempty disjoint sets, there exists a set that consists of exactly one element from each element of the family. It seems strange at first that such an innocuous sounding idea can be so powerful and controversial, but it certainly is both. To understand why, we will start by looking at some statements that are equivalent to the axiom of choice. Many of these equivalences are very useful, and we devote much time to one, namely, that every vector space has a basis. We go on from there to see a few more applications of the Axiom of Choice and its equivalents, and finish by looking at some of the reasons why the Axiom of Choice is so controversial. -
Module 1 Lecture Notes
Module 1 Lecture Notes Contents 1.1 Identifying Functions.............................1 1.2 Algebraically Determining the Domain of a Function..........4 1.3 Evaluating Functions.............................6 1.4 Function Operations..............................7 1.5 The Difference Quotient...........................9 1.6 Applications of Function Operations.................... 10 1.7 Determining the Domain and Range of a Function Graphically.... 12 1.8 Reading the Graph of a Function...................... 14 1.1 Identifying Functions In previous classes, you should have studied a variety basic functions. For example, 1 p f(x) = 3x − 5; g(x) = 2x2 − 1; h(x) = ; j(x) = 5x + 2 x − 5 We will begin this course by studying functions and their properties. As the course progresses, we will study inverse, composite, exponential, logarithmic, polynomial and rational functions. Math 111 Module 1 Lecture Notes Definition 1: A relation is a correspondence between two variables. A relation can be ex- pressed through a set of ordered pairs, a graph, a table, or an equation. A set containing ordered pairs (x; y) defines y as a function of x if and only if no two ordered pairs in the set have the same x-coordinate. In other words, every input maps to exactly one output. We write y = f(x) and say \y is a function of x." For the function defined by y = f(x), • x is the independent variable (also known as the input) • y is the dependent variable (also known as the output) • f is the function name Example 1: Determine whether or not each of the following represents a function. Table 1.1 Chicken Name Egg Color Emma Turquoise Hazel Light Brown George(ia) Chocolate Brown Isabella White Yvonne Light Brown (a) The set of ordered pairs of the form (chicken name, egg color) shown in Table 1.1. -
17 Axiom of Choice
Math 361 Axiom of Choice 17 Axiom of Choice De¯nition 17.1. Let be a nonempty set of nonempty sets. Then a choice function for is a function f sucFh that f(S) S for all S . F 2 2 F Example 17.2. Let = (N)r . Then we can de¯ne a choice function f by F P f;g f(S) = the least element of S: Example 17.3. Let = (Z)r . Then we can de¯ne a choice function f by F P f;g f(S) = ²n where n = min z z S and, if n = 0, ² = min z= z z = n; z S . fj j j 2 g 6 f j j j j j 2 g Example 17.4. Let = (Q)r . Then we can de¯ne a choice function f as follows. F P f;g Let g : Q N be an injection. Then ! f(S) = q where g(q) = min g(r) r S . f j 2 g Example 17.5. Let = (R)r . Then it is impossible to explicitly de¯ne a choice function for . F P f;g F Axiom 17.6 (Axiom of Choice (AC)). For every set of nonempty sets, there exists a function f such that f(S) S for all S . F 2 2 F We say that f is a choice function for . F Theorem 17.7 (AC). If A; B are non-empty sets, then the following are equivalent: (a) A B ¹ (b) There exists a surjection g : B A. ! Proof. (a) (b) Suppose that A B. -
The Church-Turing Thesis in Quantum and Classical Computing∗
The Church-Turing thesis in quantum and classical computing∗ Petrus Potgietery 31 October 2002 Abstract The Church-Turing thesis is examined in historical context and a survey is made of current claims to have surpassed its restrictions using quantum computing | thereby calling into question the basis of the accepted solutions to the Entscheidungsproblem and Hilbert's tenth problem. 1 Formal computability The need for a formal definition of algorithm or computation became clear to the scientific community of the early twentieth century due mainly to two problems of Hilbert: the Entscheidungsproblem|can an algorithm be found that, given a statement • in first-order logic (for example Peano arithmetic), determines whether it is true in all models of a theory; and Hilbert's tenth problem|does there exist an algorithm which, given a Diophan- • tine equation, determines whether is has any integer solutions? The Entscheidungsproblem, or decision problem, can be traced back to Leibniz and was successfully and independently solved in the mid-1930s by Alonzo Church [Church, 1936] and Alan Turing [Turing, 1935]. Church defined an algorithm to be identical to that of a function computable by his Lambda Calculus. Turing, in turn, first identified an algorithm to be identical with a recursive function and later with a function computed by what is now known as a Turing machine1. It was later shown that the class of the recursive functions, Turing machines, and the Lambda Calculus define the same class of functions. The remarkable equivalence of these three definitions of disparate origin quite strongly supported the idea of this as an adequate definition of computability and the Entscheidungsproblem is generally considered to be solved. -
Elements of Set Theory
Elements of set theory April 1, 2014 ii Contents 1 Zermelo{Fraenkel axiomatization 1 1.1 Historical context . 1 1.2 The language of the theory . 3 1.3 The most basic axioms . 4 1.4 Axiom of Infinity . 4 1.5 Axiom schema of Comprehension . 5 1.6 Functions . 6 1.7 Axiom of Choice . 7 1.8 Axiom schema of Replacement . 9 1.9 Axiom of Regularity . 9 2 Basic notions 11 2.1 Transitive sets . 11 2.2 Von Neumann's natural numbers . 11 2.3 Finite and infinite sets . 15 2.4 Cardinality . 17 2.5 Countable and uncountable sets . 19 3 Ordinals 21 3.1 Basic definitions . 21 3.2 Transfinite induction and recursion . 25 3.3 Applications with choice . 26 3.4 Applications without choice . 29 3.5 Cardinal numbers . 31 4 Descriptive set theory 35 4.1 Rational and real numbers . 35 4.2 Topological spaces . 37 4.3 Polish spaces . 39 4.4 Borel sets . 43 4.5 Analytic sets . 46 4.6 Lebesgue's mistake . 48 iii iv CONTENTS 5 Formal logic 51 5.1 Propositional logic . 51 5.1.1 Propositional logic: syntax . 51 5.1.2 Propositional logic: semantics . 52 5.1.3 Propositional logic: completeness . 53 5.2 First order logic . 56 5.2.1 First order logic: syntax . 56 5.2.2 First order logic: semantics . 59 5.2.3 Completeness theorem . 60 6 Model theory 67 6.1 Basic notions . 67 6.2 Ultraproducts and nonstandard analysis . 68 6.3 Quantifier elimination and the real closed fields .