The Axiom of Choice in the Foundations of Mathematics John L
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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). -
Mathematics 144 Set Theory Fall 2012 Version
MATHEMATICS 144 SET THEORY FALL 2012 VERSION Table of Contents I. General considerations.……………………………………………………………………………………………………….1 1. Overview of the course…………………………………………………………………………………………………1 2. Historical background and motivation………………………………………………………….………………4 3. Selected problems………………………………………………………………………………………………………13 I I. Basic concepts. ………………………………………………………………………………………………………………….15 1. Topics from logic…………………………………………………………………………………………………………16 2. Notation and first steps………………………………………………………………………………………………26 3. Simple examples…………………………………………………………………………………………………………30 I I I. Constructions in set theory.………………………………………………………………………………..……….34 1. Boolean algebra operations.……………………………………………………………………………………….34 2. Ordered pairs and Cartesian products……………………………………………………………………… ….40 3. Larger constructions………………………………………………………………………………………………..….42 4. A convenient assumption………………………………………………………………………………………… ….45 I V. Relations and functions ……………………………………………………………………………………………….49 1.Binary relations………………………………………………………………………………………………………… ….49 2. Partial and linear orderings……………………………..………………………………………………… ………… 56 3. Functions…………………………………………………………………………………………………………… ….…….. 61 4. Composite and inverse function.…………………………………………………………………………… …….. 70 5. Constructions involving functions ………………………………………………………………………… ……… 77 6. Order types……………………………………………………………………………………………………… …………… 80 i V. Number systems and set theory …………………………………………………………………………………. 84 1. The Natural Numbers and Integers…………………………………………………………………………….83 2. Finite induction -
The Iterative Conception of Set Author(S): George Boolos Reviewed Work(S): Source: the Journal of Philosophy, Vol
Journal of Philosophy, Inc. The Iterative Conception of Set Author(s): George Boolos Reviewed work(s): Source: The Journal of Philosophy, Vol. 68, No. 8, Philosophy of Logic and Mathematics (Apr. 22, 1971), pp. 215-231 Published by: Journal of Philosophy, Inc. Stable URL: http://www.jstor.org/stable/2025204 . Accessed: 12/01/2013 10:53 Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp . JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. Journal of Philosophy, Inc. is collaborating with JSTOR to digitize, preserve and extend access to The Journal of Philosophy. http://www.jstor.org This content downloaded on Sat, 12 Jan 2013 10:53:17 AM All use subject to JSTOR Terms and Conditions THE JOURNALOF PHILOSOPHY VOLUME LXVIII, NO. 8, APRIL 22, I97I _ ~ ~~~~~~- ~ '- ' THE ITERATIVE CONCEPTION OF SET A SET, accordingto Cantor,is "any collection.., intoa whole of definite,well-distinguished objects... of our intuitionor thought.'1Cantor alo sdefineda set as a "many, whichcan be thoughtof as one, i.e., a totalityof definiteelements that can be combinedinto a whole by a law.'2 One mightobject to the firstdefi- nitionon the groundsthat it uses the conceptsof collectionand whole, which are notionsno betterunderstood than that of set,that there ought to be sets of objects that are not objects of our thought,that 'intuition'is a termladen with a theoryof knowledgethat no one should believe, that any object is "definite,"that there should be sets of ill-distinguishedobjects, such as waves and trains,etc., etc. -
Fuzzy Logic : Introduction
Fuzzy Logic : Introduction Debasis Samanta IIT Kharagpur [email protected] 07.01.2015 Debasis Samanta (IIT Kharagpur) Soft Computing Applications 07.01.2015 1 / 69 What is Fuzzy logic? Fuzzy logic is a mathematical language to express something. This means it has grammar, syntax, semantic like a language for communication. There are some other mathematical languages also known • Relational algebra (operations on sets) • Boolean algebra (operations on Boolean variables) • Predicate logic (operations on well formed formulae (wff), also called predicate propositions) Fuzzy logic deals with Fuzzy set. Debasis Samanta (IIT Kharagpur) Soft Computing Applications 07.01.2015 2 / 69 A brief history of Fuzzy Logic First time introduced by Lotfi Abdelli Zadeh (1965), University of California, Berkley, USA (1965). He is fondly nick-named as LAZ Debasis Samanta (IIT Kharagpur) Soft Computing Applications 07.01.2015 3 / 69 A brief history of Fuzzy logic 1 Dictionary meaning of fuzzy is not clear, noisy etc. Example: Is the picture on this slide is fuzzy? 2 Antonym of fuzzy is crisp Example: Are the chips crisp? Debasis Samanta (IIT Kharagpur) Soft Computing Applications 07.01.2015 4 / 69 Example : Fuzzy logic vs. Crisp logic Yes or No Crisp answer True or False Milk Yes Water A liquid Crisp Coca No Spite Is the liquid colorless? Debasis Samanta (IIT Kharagpur) Soft Computing Applications 07.01.2015 5 / 69 Example : Fuzzy logic vs. Crisp logic May be May not be Fuzzy answer Absolutely Partially etc Debasis Samanta (IIT Kharagpur) Soft Computing Applications -
Sets in Homotopy Type Theory
Predicative topos Sets in Homotopy type theory Bas Spitters Aarhus Bas Spitters Aarhus Sets in Homotopy type theory Predicative topos About me I PhD thesis on constructive analysis I Connecting Bishop's pointwise mathematics w/topos theory (w/Coquand) I Formalization of effective real analysis in Coq O'Connor's PhD part EU ForMath project I Topos theory and quantum theory I Univalent foundations as a combination of the strands co-author of the book and the Coq library I guarded homotopy type theory: applications to CS Bas Spitters Aarhus Sets in Homotopy type theory Most of the presentation is based on the book and Sets in HoTT (with Rijke). CC-BY-SA Towards a new design of proof assistants: Proof assistant with a clear (denotational) semantics, guiding the addition of new features. E.g. guarded cubical type theory Predicative topos Homotopy type theory Towards a new practical foundation for mathematics. I Modern ((higher) categorical) mathematics I Formalization I Constructive mathematics Closer to mathematical practice, inherent treatment of equivalences. Bas Spitters Aarhus Sets in Homotopy type theory Predicative topos Homotopy type theory Towards a new practical foundation for mathematics. I Modern ((higher) categorical) mathematics I Formalization I Constructive mathematics Closer to mathematical practice, inherent treatment of equivalences. Towards a new design of proof assistants: Proof assistant with a clear (denotational) semantics, guiding the addition of new features. E.g. guarded cubical type theory Bas Spitters Aarhus Sets in Homotopy type theory Formalization of discrete mathematics: four color theorem, Feit Thompson, ... computational interpretation was crucial. Can this be extended to non-discrete types? Predicative topos Challenges pre-HoTT: Sets as Types no quotients (setoids), no unique choice (in Coq), .. -
Mathematics Is Megethology
17 Mathematics is megethology Mereology is the theory of the relation of part to whole, and kindred notions. Megethology is the result of adding plural quantification, as advocated by George Boolos in [1] and [2], to the language of mere ology. It is so-called because it turns out to have enough expressive power to let us express interesting hypotheses about the size of Reality. It also has the power, as John P. Burgess and A. P. Hazen have shown in [3], to simulate quantification over relations. It is generally accepted that mathematics reduces to set theory. In my book Parts of Classes, [6], I argued that set theory in turn reduces, with the aid of mereology, to the theory of singleton functions. I also argued (somewhat reluctantly) for a 'structuralist' approach to the theory of singleton functions. We need not think we have somehow achieved a primitive grasp of some one special singleton function. Rather, we can take the theory of singleton functions, and hence set theory, and hence mathematics, to consist of generalisations about all singleton functions. We need only assume, to avoid vacuity, that there exists at least one singleton function. But we need not assume even that. For it now turns out that if the size of Reality is right, there must exist a singleton function. All we need as a foundation for mathematics, apart from the framework of megethology, are some hypotheses about the size of Reality. First published in this form in Philosophia Mathematila 3 (1993), 3--23. This paper consists in part of near-verbatim excerpts from David Lewis, Parts of Classes (Oxford, Blackwell, 1991). -
A Taste of Set Theory for Philosophers
Journal of the Indian Council of Philosophical Research, Vol. XXVII, No. 2. A Special Issue on "Logic and Philosophy Today", 143-163, 2010. Reprinted in "Logic and Philosophy Today" (edited by A. Gupta ans J.v.Benthem), College Publications vol 29, 141-162, 2011. A taste of set theory for philosophers Jouko Va¨an¨ anen¨ ∗ Department of Mathematics and Statistics University of Helsinki and Institute for Logic, Language and Computation University of Amsterdam November 17, 2010 Contents 1 Introduction 1 2 Elementary set theory 2 3 Cardinal and ordinal numbers 3 3.1 Equipollence . 4 3.2 Countable sets . 6 3.3 Ordinals . 7 3.4 Cardinals . 8 4 Axiomatic set theory 9 5 Axiom of Choice 12 6 Independence results 13 7 Some recent work 14 7.1 Descriptive Set Theory . 14 7.2 Non well-founded set theory . 14 7.3 Constructive set theory . 15 8 Historical Remarks and Further Reading 15 ∗Research partially supported by grant 40734 of the Academy of Finland and by the EUROCORES LogICCC LINT programme. I Journal of the Indian Council of Philosophical Research, Vol. XXVII, No. 2. A Special Issue on "Logic and Philosophy Today", 143-163, 2010. Reprinted in "Logic and Philosophy Today" (edited by A. Gupta ans J.v.Benthem), College Publications vol 29, 141-162, 2011. 1 Introduction Originally set theory was a theory of infinity, an attempt to understand infinity in ex- act terms. Later it became a universal language for mathematics and an attempt to give a foundation for all of mathematics, and thereby to all sciences that are based on mathematics. -
Families of Sets and Extended Operations Families of Sets
Families of Sets and Extended Operations Families of Sets When dealing with sets whose elements are themselves sets it is fairly common practice to refer to them as families of sets, however this is not a definition. In fact, technically, a family of sets need not be a set, because we allow repeated elements, so a family is a multiset. However, we do require that when repeated elements appear they are distinguishable. F = {A , A , A , A } with A = {a,b,c}, A ={a}, A = {a,d} and 1 2 3 4 1 2 3 A = {a} is a family of sets. 4 Extended Union and Intersection Let F be a family of sets. Then we define: The union over F by: ∪ A={x :∃ A∈F x∈A}= {x :∃ A A∈F∧x∈A} A∈F and the intersection over F by: ∩ A = {x :∀ A∈F x∈A}= {x :∀ A A∈F ⇒ x∈A}. A∈F For example, with F = {A , A , A , A } where A = {a,b,c}, 1 2 3 4 1 A ={a}, A = {a,d} and A = {a} we have: 2 3 4 ∪ A = {a ,b , c , d } and ∩ A = {a}. A∈F A∈F Theorem 2.8 For each set B in a family F of sets, a) ∩ A ⊆ B A∈F b) B ⊆ ∪ A. A∈F Pf: a) Suppose x ∈ ∩ A, then ∀A ∈ F, x ∈ A. Since B ∈ F, we have x ∈ B. Thus, ∩ A ⊆ B. b) Now suppose y ∈ B. Since B ∈ F, y ∈ ∪ A. Thus, B ⊆ ∪ A. Caveat Care must be taken with the empty family F, i.e., the family containing no sets. -
Learning from Streams
Learning from Streams Sanjay Jain?1, Frank Stephan??2 and Nan Ye3 1 Department of Computer Science, National University of Singapore, Singapore 117417, Republic of Singapore. [email protected] 2 Department of Computer Science and Department of Mathematics, National University of Singapore, Singapore 117417, Republic of Singapore. [email protected] 3 Department of Computer Science, National University of Singapore, Singapore 117417, Republic of Singapore. [email protected] Abstract. Learning from streams is a process in which a group of learn- ers separately obtain information about the target to be learned, but they can communicate with each other in order to learn the target. We are interested in machine models for learning from streams and study its learning power (as measured by the collection of learnable classes). We study how the power of learning from streams depends on the two parameters m and n, where n is the number of learners which track a single stream of input each and m is the number of learners (among the n learners) which have to find, in the limit, the right description of the target. We study for which combinations m, n and m0, n0 the following inclusion holds: Every class learnable from streams with parameters m, n is also learnable from streams with parameters m0, n0. For the learning of uniformly recursive classes, we get a full characterization which depends m only on the ratio n ; but for general classes the picture is more compli- cated. Most of the noninclusions in team learning carry over to nonin- clusions with the same parameters in the case of learning from streams; but only few inclusions are preserved and some additional noninclusions hold. -
Finding an Interpretation Under Which Axiom 2 of Aristotelian Syllogistic Is
Finding an interpretation under which Axiom 2 of Aristotelian syllogistic is False and the other axioms True (ignoring Axiom 3, which is derivable from the other axioms). This will require a domain of at least two objects. In the domain {0,1} there are four ordered pairs: <0,0>, <0,1>, <1,0>, and <1,1>. Note first that Axiom 6 demands that any member of the domain not in the set assigned to E must be in the set assigned to I. Thus if the set {<0,0>} is assigned to E, then the set {<0,1>, <1,0>, <1,1>} must be assigned to I. Similarly for Axiom 7. Note secondly that Axiom 3 demands that <m,n> be a member of the set assigned to E if <n,m> is. Thus if <1,0> is a member of the set assigned to E, than <0,1> must also be a member. Similarly Axiom 5 demands that <m,n> be a member of the set assigned to I if <n,m> is a member of the set assigned to A (but not conversely). Thus if <1,0> is a member of the set assigned to A, than <0,1> must be a member of the set assigned to I. The problem now is to make Axiom 2 False without falsifying Axiom 1 as well. The solution requires some experimentation. Here is one interpretation (call it I) under which all the Axioms except Axiom 2 are True: Domain: {0,1} A: {<1,0>} E: {<0,0>, <1,1>} I: {<0,1>, <1,0>} O: {<0,0>, <0,1>, <1,1>} It’s easy to see that Axioms 4 through 7 are True under I. -
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. -
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.