Omega-Models of Finite Set Theory
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Biequivalence Vector Spaces in the Alternative Set Theory
Comment.Math.Univ.Carolin. 32,3 (1991)517–544 517 Biequivalence vector spaces in the alternative set theory Miroslav Smˇ ´ıd, Pavol Zlatoˇs Abstract. As a counterpart to classical topological vector spaces in the alternative set the- ory, biequivalence vector spaces (over the field Q of all rational numbers) are introduced and their basic properties are listed. A methodological consequence opening a new view to- wards the relationship between the algebraic and topological dual is quoted. The existence of various types of valuations on a biequivalence vector space inducing its biequivalence is proved. Normability is characterized in terms of total convexity of the monad and/or of the galaxy of 0. Finally, the existence of a rather strong type of basis for a fairly extensive area of biequivalence vector spaces, containing all the most important particular cases, is established. Keywords: alternative set theory, biequivalence, vector space, monad, galaxy, symmetric Sd-closure, dual, valuation, norm, convex, basis Classification: Primary 46Q05, 46A06, 46A35; Secondary 03E70, 03H05, 46A09 Contents: 0. Introduction 1. Notation and preliminaries 2. Symmetric Sd-closures 3. Vector spaces over Q 4. Biequivalence vector spaces 5. Duals 6. Valuations on vector spaces 7. The envelope operation 8. Bases in biequivalence vector spaces 0. Introduction. The aim of this paper is to lay a foundation to the investigation of topological (or perhaps also bornological) vector spaces within the framework of the alternative set theory (AST), which could enable a rather elementary exposition of some topics of functional analysis reducing them to the study of formally finite dimensional vector spaces equipped with some additional “nonsharp” or “hazy” first order structure representing the topology. -
Calibrating Determinacy Strength in Levels of the Borel Hierarchy
CALIBRATING DETERMINACY STRENGTH IN LEVELS OF THE BOREL HIERARCHY SHERWOOD J. HACHTMAN Abstract. We analyze the set-theoretic strength of determinacy for levels of the Borel 0 hierarchy of the form Σ1+α+3, for α < !1. Well-known results of H. Friedman and D.A. Martin have shown this determinacy to require α+1 iterations of the Power Set Axiom, but we ask what additional ambient set theory is strictly necessary. To this end, we isolate a family of Π1-reflection principles, Π1-RAPα, whose consistency strength corresponds 0 CK exactly to that of Σ1+α+3-Determinacy, for α < !1 . This yields a characterization of the levels of L by or at which winning strategies in these games must be constructed. When α = 0, we have the following concise result: the least θ so that all winning strategies 0 in Σ4 games belong to Lθ+1 is the least so that Lθ j= \P(!) exists + all wellfounded trees are ranked". x1. Introduction. Given a set A ⊆ !! of sequences of natural numbers, consider a game, G(A), where two players, I and II, take turns picking elements of a sequence hx0; x1; x2;::: i of naturals. Player I wins the game if the sequence obtained belongs to A; otherwise, II wins. For a collection Γ of subsets of !!, Γ determinacy, which we abbreviate Γ-DET, is the statement that for every A 2 Γ, one of the players has a winning strategy in G(A). It is a much-studied phenomenon that Γ -DET has mathematical strength: the bigger the pointclass Γ, the stronger the theory required to prove Γ -DET. -
COMPSCI 501: Formal Language Theory Insights on Computability Turing Machines Are a Model of Computation Two (No Longer) Surpris
Insights on Computability Turing machines are a model of computation COMPSCI 501: Formal Language Theory Lecture 11: Turing Machines Two (no longer) surprising facts: Marius Minea Although simple, can describe everything [email protected] a (real) computer can do. University of Massachusetts Amherst Although computers are powerful, not everything is computable! Plus: “play” / program with Turing machines! 13 February 2019 Why should we formally define computation? Must indeed an algorithm exist? Back to 1900: David Hilbert’s 23 open problems Increasingly a realization that sometimes this may not be the case. Tenth problem: “Occasionally it happens that we seek the solution under insufficient Given a Diophantine equation with any number of un- hypotheses or in an incorrect sense, and for this reason do not succeed. known quantities and with rational integral numerical The problem then arises: to show the impossibility of the solution under coefficients: To devise a process according to which the given hypotheses or in the sense contemplated.” it can be determined in a finite number of operations Hilbert, 1900 whether the equation is solvable in rational integers. This asks, in effect, for an algorithm. Hilbert’s Entscheidungsproblem (1928): Is there an algorithm that And “to devise” suggests there should be one. decides whether a statement in first-order logic is valid? Church and Turing A Turing machine, informally Church and Turing both showed in 1936 that a solution to the Entscheidungsproblem is impossible for the theory of arithmetic. control To make and prove such a statement, one needs to define computability. In a recent paper Alonzo Church has introduced an idea of “effective calculability”, read/write head which is equivalent to my “computability”, but is very differently defined. -
Order Types and Structure of Orders
ORDER TYPES AND STRUCTURE OF ORDERS BY ANDRE GLEYZALp) 1. Introduction. This paper is concerned with operations on order types or order properties a and the construction of order types related to a. The reference throughout is to simply or linearly ordered sets, and we shall speak of a as either property or type. Let a and ß be any two order types. An order A will be said to be of type aß if it is the sum of /3-orders (orders of type ß) over an a-order; i.e., if A permits of decomposition into nonoverlapping seg- ments each of order type ß, the segments themselves forming an order of type a. We have thus associated with every pair of order types a and ß the product order type aß. The definition of product for order types automatically associates with every order type a the order types aa = a2, aa2 = a3, ■ ■ ■ . We may further- more define, for all ordinals X, a Xth power of a, a\ and finally a limit order type a1. This order type has certain interesting properties. It has closure with respect to the product operation, for the sum of ar-orders over an a7-order is an a'-order, i.e., a'al = aI. For this reason we call a1 iterative. In general, we term an order type ß having the property that ßß = ß iterative, a1 has the following postulational identification: 1. a7 is a supertype of a; that is to say, all a-orders are a7-orders. 2. a1 is iterative. -
Handout from Today's Lecture
MA532 Lecture Timothy Kohl Boston University April 23, 2020 Timothy Kohl (Boston University) MA532 Lecture April 23, 2020 1 / 26 Cardinal Arithmetic Recall that one may define addition and multiplication of ordinals α = ot(A, A) β = ot(B, B ) α + β and α · β by constructing order relations on A ∪ B and B × A. For cardinal numbers the foundations are somewhat similar, but also somewhat simpler since one need not refer to orderings. Definition For sets A, B where |A| = α and |B| = β then α + β = |(A × {0}) ∪ (B × {1})|. Timothy Kohl (Boston University) MA532 Lecture April 23, 2020 2 / 26 The curious part of the definition is the two sets A × {0} and B × {1} which can be viewed as subsets of the direct product (A ∪ B) × {0, 1} which basically allows us to add |A| and |B|, in particular since, in the usual formula for the size of the union of two sets |A ∪ B| = |A| + |B| − |A ∩ B| which in this case is bypassed since, by construction, (A × {0}) ∩ (B × {1})= ∅ regardless of the nature of A ∩ B. Timothy Kohl (Boston University) MA532 Lecture April 23, 2020 3 / 26 Definition For sets A, B where |A| = α and |B| = β then α · β = |A × B|. One immediate consequence of these definitions is the following. Proposition If m, n are finite ordinals, then as cardinals one has |m| + |n| = |m + n|, (where the addition on the right is ordinal addition in ω) meaning that ordinal addition and cardinal addition agree. Proof. The simplest proof of this is to define a bijection f : (m × {0}) ∪ (n × {1}) → m + n by f (hr, 0i)= r for r ∈ m and f (hs, 1i)= m + s for s ∈ n. -
Axiomatic Set Teory P.D.Welch
Axiomatic Set Teory P.D.Welch. August 16, 2020 Contents Page 1 Axioms and Formal Systems 1 1.1 Introduction 1 1.2 Preliminaries: axioms and formal systems. 3 1.2.1 The formal language of ZF set theory; terms 4 1.2.2 The Zermelo-Fraenkel Axioms 7 1.3 Transfinite Recursion 9 1.4 Relativisation of terms and formulae 11 2 Initial segments of the Universe 17 2.1 Singular ordinals: cofinality 17 2.1.1 Cofinality 17 2.1.2 Normal Functions and closed and unbounded classes 19 2.1.3 Stationary Sets 22 2.2 Some further cardinal arithmetic 24 2.3 Transitive Models 25 2.4 The H sets 27 2.4.1 H - the hereditarily finite sets 28 2.4.2 H - the hereditarily countable sets 29 2.5 The Montague-Levy Reflection theorem 30 2.5.1 Absoluteness 30 2.5.2 Reflection Theorems 32 2.6 Inaccessible Cardinals 34 2.6.1 Inaccessible cardinals 35 2.6.2 A menagerie of other large cardinals 36 3 Formalising semantics within ZF 39 3.1 Definite terms and formulae 39 3.1.1 The non-finite axiomatisability of ZF 44 3.2 Formalising syntax 45 3.3 Formalising the satisfaction relation 46 3.4 Formalising definability: the function Def. 47 3.5 More on correctness and consistency 48 ii iii 3.5.1 Incompleteness and Consistency Arguments 50 4 The Constructible Hierarchy 53 4.1 The L -hierarchy 53 4.2 The Axiom of Choice in L 56 4.3 The Axiom of Constructibility 57 4.4 The Generalised Continuum Hypothesis in L. -
Algorithms, Turing Machines and Algorithmic Undecidability
U.U.D.M. Project Report 2021:7 Algorithms, Turing machines and algorithmic undecidability Agnes Davidsdottir Examensarbete i matematik, 15 hp Handledare: Vera Koponen Examinator: Martin Herschend April 2021 Department of Mathematics Uppsala University Contents 1 Introduction 1 1.1 Algorithms . .1 1.2 Formalisation of the concept of algorithms . .1 2 Turing machines 3 2.1 Coding of machines . .4 2.2 Unbounded and bounded machines . .6 2.3 Binary sequences representing real numbers . .6 2.4 Examples of Turing machines . .7 3 Undecidability 9 i 1 Introduction This paper is about Alan Turing's paper On Computable Numbers, with an Application to the Entscheidungsproblem, which was published in 1936. In his paper, he introduced what later has been called Turing machines as well as a few examples of undecidable problems. A few of these will be brought up here along with Turing's arguments in the proofs but using a more modern terminology. To begin with, there will be some background on the history of why this breakthrough happened at that given time. 1.1 Algorithms The concept of an algorithm has always existed within the world of mathematics. It refers to a process meant to solve a problem in a certain number of steps. It is often repetitive, with only a few rules to follow. In more recent years, the term also has been used to refer to the rules a computer follows to operate in a certain way. Thereby, an algorithm can be used in a plethora of circumstances. The word might describe anything from the process of solving a Rubik's cube to how search engines like Google work [4]. -
Equivalents to the Axiom of Choice and Their Uses A
EQUIVALENTS TO THE AXIOM OF CHOICE AND THEIR USES A Thesis Presented to The Faculty of the Department of Mathematics California State University, Los Angeles In Partial Fulfillment of the Requirements for the Degree Master of Science in Mathematics By James Szufu Yang c 2015 James Szufu Yang ALL RIGHTS RESERVED ii The thesis of James Szufu Yang is approved. Mike Krebs, Ph.D. Kristin Webster, Ph.D. Michael Hoffman, Ph.D., Committee Chair Grant Fraser, Ph.D., Department Chair California State University, Los Angeles June 2015 iii ABSTRACT Equivalents to the Axiom of Choice and Their Uses By James Szufu Yang In set theory, the Axiom of Choice (AC) was formulated in 1904 by Ernst Zermelo. It is an addition to the older Zermelo-Fraenkel (ZF) set theory. We call it Zermelo-Fraenkel set theory with the Axiom of Choice and abbreviate it as ZFC. This paper starts with an introduction to the foundations of ZFC set the- ory, which includes the Zermelo-Fraenkel axioms, partially ordered sets (posets), the Cartesian product, the Axiom of Choice, and their related proofs. It then intro- duces several equivalent forms of the Axiom of Choice and proves that they are all equivalent. In the end, equivalents to the Axiom of Choice are used to prove a few fundamental theorems in set theory, linear analysis, and abstract algebra. This paper is concluded by a brief review of the work in it, followed by a few points of interest for further study in mathematics and/or set theory. iv ACKNOWLEDGMENTS Between the two department requirements to complete a master's degree in mathematics − the comprehensive exams and a thesis, I really wanted to experience doing a research and writing a serious academic paper. -
Types for Describing Coordinated Data Structures
Types for Describing Coordinated Data Structures Michael F. Ringenburg∗ Dan Grossman [email protected] [email protected] Dept. of Computer Science & Engineering University of Washington, Seattle, WA 98195 ABSTRACT the n-th function). This section motivates why such invari- Coordinated data structures are sets of (perhaps unbounded) ants are important, explores why the scope of type variables data structures where the nodes of each structure may share makes the problem appear daunting, and previews the rest abstract types with the corresponding nodes of the other of the paper. structures. For example, consider a list of arguments, and 1.1 Low-Level Type Systems a separate list of functions, where the n-th function of the Recent years have witnessed substantial work on powerful second list should be applied only to the n-th argument of type systems for safe, low-level languages. Standard moti- the first list. We can guarantee that this invariant is obeyed vation for such systems includes compiler debugging (gener- by coordinating the two lists, such that the type of the n-th ated code that does not type check implies a compiler error), argument is existentially quantified and identical to the ar- proof-carrying code (the type system encodes a safety prop- gument type of the n-th function. In this paper, we describe erty that the type-checker verifies), automated optimization a minimal set of features sufficient for a type system to sup- (an optimizer can exploit the type information), and manual port coordinated data structures. We also demonstrate that optimization (humans can use idioms unavailable in higher- two known type systems (Crary and Weirich’s LX [6] and level languages without sacrificing safety). -
Self-Similarity in the Foundations
Self-similarity in the Foundations Paul K. Gorbow Thesis submitted for the degree of Ph.D. in Logic, defended on June 14, 2018. Supervisors: Ali Enayat (primary) Peter LeFanu Lumsdaine (secondary) Zachiri McKenzie (secondary) University of Gothenburg Department of Philosophy, Linguistics, and Theory of Science Box 200, 405 30 GOTEBORG,¨ Sweden arXiv:1806.11310v1 [math.LO] 29 Jun 2018 2 Contents 1 Introduction 5 1.1 Introductiontoageneralaudience . ..... 5 1.2 Introduction for logicians . .. 7 2 Tour of the theories considered 11 2.1 PowerKripke-Plateksettheory . .... 11 2.2 Stratifiedsettheory ................................ .. 13 2.3 Categorical semantics and algebraic set theory . ....... 17 3 Motivation 19 3.1 Motivation behind research on embeddings between models of set theory. 19 3.2 Motivation behind stratified algebraic set theory . ...... 20 4 Logic, set theory and non-standard models 23 4.1 Basiclogicandmodeltheory ............................ 23 4.2 Ordertheoryandcategorytheory. ...... 26 4.3 PowerKripke-Plateksettheory . .... 28 4.4 First-order logic and partial satisfaction relations internal to KPP ........ 32 4.5 Zermelo-Fraenkel set theory and G¨odel-Bernays class theory............ 36 4.6 Non-standardmodelsofsettheory . ..... 38 5 Embeddings between models of set theory 47 5.1 Iterated ultrapowers with special self-embeddings . ......... 47 5.2 Embeddingsbetweenmodelsofsettheory . ..... 57 5.3 Characterizations.................................. .. 66 6 Stratified set theory and categorical semantics 73 6.1 Stratifiedsettheoryandclasstheory . ...... 73 6.2 Categoricalsemantics ............................... .. 77 7 Stratified algebraic set theory 85 7.1 Stratifiedcategoriesofclasses . ..... 85 7.2 Interpretation of the Set-theories in the Cat-theories ................ 90 7.3 ThesubtoposofstronglyCantorianobjects . ....... 99 8 Where to go from here? 103 8.1 Category theoretic approach to embeddings between models of settheory . -
MAGIC Set Theory Lecture 6
MAGIC Set theory lecture 6 David Aspero´ University of East Anglia 15 November 2018 Recall: We defined (V : ↵ Ord) by recursion on Ord: ↵ 2 V = • 0 ; V = (V ) • ↵+1 P ↵ V = V : β<δ if δ is a limit ordinal. • δ { β } S (V : ↵ Ord) is called the cumulative hierarchy. ↵ 2 We saw: Proposition V↵ is transitive for every ordinal ↵. Proposition For all ↵<β, V V . ↵ ✓ β Definition For every x V , let rank(x) be the first ↵ such that 2 ↵ Ord ↵ x V . 2 2 ↵+1 S Definition For every set x, the transitive closure of x, denoted by TC(x), is X : n <! where { n } X = x S • 0 X = X • n+1 n So TC(x)=Sx x x x ... [ [ [ [ S SS SSS [Exercise: TC(x) is the –least transitive set y such that x y. ✓ ✓ In other words, TC(x)= y : y transitive, x y .] { ✓ } T Definition V denotes the class of all sets; that is, V = x : x = x . { } Definition WF = V : ↵ Ord : The class of all x such that x V for { ↵ 2 } 2 ↵ some ordinal ↵. S Note: WF is a transitive class: y x V implies y V since 2 2 ↵ 2 ↵ V↵ is transitive. Proposition If x WF, then there is some ↵ Ord such that x V . ✓ 2 ✓ ↵ Proof. Define the function F(y)=min γ y V . By the assumption { | 2 γ} on x, F is a well-defined function there. By Replacement γ y x : γ = F(y) is a set, and by Union it has a { |9 2 } supremum ↵. -
Undecidable Problems: a Sampler (.Pdf)
UNDECIDABLE PROBLEMS: A SAMPLER BJORN POONEN Abstract. After discussing two senses in which the notion of undecidability is used, we present a survey of undecidable decision problems arising in various branches of mathemat- ics. 1. Introduction The goal of this survey article is to demonstrate that undecidable decision problems arise naturally in many branches of mathematics. The criterion for selection of a problem in this survey is simply that the author finds it entertaining! We do not pretend that our list of undecidable problems is complete in any sense. And some of the problems we consider turn out to be decidable or to have unknown decidability status. For another survey of undecidable problems, see [Dav77]. 2. Two notions of undecidability There are two common settings in which one speaks of undecidability: 1. Independence from axioms: A single statement is called undecidable if neither it nor its negation can be deduced using the rules of logic from the set of axioms being used. (Example: The continuum hypothesis, that there is no cardinal number @0 strictly between @0 and 2 , is undecidable in the ZFC axiom system, assuming that ZFC itself is consistent [G¨od40,Coh63, Coh64].) The first examples of statements independent of a \natural" axiom system were constructed by K. G¨odel[G¨od31]. 2. Decision problem: A family of problems with YES/NO answers is called unde- cidable if there is no algorithm that terminates with the correct answer for every problem in the family. (Example: Hilbert's tenth problem, to decide whether a mul- tivariable polynomial equation with integer coefficients has a solution in integers, is undecidable [Mat70].) Remark 2.1.