Symbolic Logic II, Lecture 3 1 in Order to Apply the Axiomatic Method, We Need the Following
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Classifying Material Implications Over Minimal Logic
Classifying Material Implications over Minimal Logic Hannes Diener and Maarten McKubre-Jordens March 28, 2018 Abstract The so-called paradoxes of material implication have motivated the development of many non- classical logics over the years [2–5, 11]. In this note, we investigate some of these paradoxes and classify them, over minimal logic. We provide proofs of equivalence and semantic models separating the paradoxes where appropriate. A number of equivalent groups arise, all of which collapse with unrestricted use of double negation elimination. Interestingly, the principle ex falso quodlibet, and several weaker principles, turn out to be distinguishable, giving perhaps supporting motivation for adopting minimal logic as the ambient logic for reasoning in the possible presence of inconsistency. Keywords: reverse mathematics; minimal logic; ex falso quodlibet; implication; paraconsistent logic; Peirce’s principle. 1 Introduction The project of constructive reverse mathematics [6] has given rise to a wide literature where various the- orems of mathematics and principles of logic have been classified over intuitionistic logic. What is less well-known is that the subtle difference that arises when the principle of explosion, ex falso quodlibet, is dropped from intuitionistic logic (thus giving (Johansson’s) minimal logic) enables the distinction of many more principles. The focus of the present paper are a range of principles known collectively (but not exhaustively) as the paradoxes of material implication; paradoxes because they illustrate that the usual interpretation of formal statements of the form “. → . .” as informal statements of the form “if. then. ” produces counter-intuitive results. Some of these principles were hinted at in [9]. Here we present a carefully worked-out chart, classifying a number of such principles over minimal logic. -
Revisiting Gödel and the Mechanistic Thesis Alessandro Aldini, Vincenzo Fano, Pierluigi Graziani
Theory of Knowing Machines: Revisiting Gödel and the Mechanistic Thesis Alessandro Aldini, Vincenzo Fano, Pierluigi Graziani To cite this version: Alessandro Aldini, Vincenzo Fano, Pierluigi Graziani. Theory of Knowing Machines: Revisiting Gödel and the Mechanistic Thesis. 3rd International Conference on History and Philosophy of Computing (HaPoC), Oct 2015, Pisa, Italy. pp.57-70, 10.1007/978-3-319-47286-7_4. hal-01615307 HAL Id: hal-01615307 https://hal.inria.fr/hal-01615307 Submitted on 12 Oct 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License Theory of Knowing Machines: Revisiting G¨odeland the Mechanistic Thesis Alessandro Aldini1, Vincenzo Fano1, and Pierluigi Graziani2 1 University of Urbino \Carlo Bo", Urbino, Italy falessandro.aldini, [email protected] 2 University of Chieti-Pescara \G. D'Annunzio", Chieti, Italy [email protected] Abstract. Church-Turing Thesis, mechanistic project, and G¨odelian Arguments offer different perspectives of informal intuitions behind the relationship existing between the notion of intuitively provable and the definition of decidability by some Turing machine. One of the most for- mal lines of research in this setting is represented by the theory of know- ing machines, based on an extension of Peano Arithmetic, encompassing an epistemic notion of knowledge formalized through a modal operator denoting intuitive provability. -
An Un-Rigorous Introduction to the Incompleteness Theorems∗
An un-rigorous introduction to the incompleteness theorems∗ phil 43904 Jeff Speaks October 8, 2007 1 Soundness and completeness When discussing Russell's logical system and its relation to Peano's axioms of arithmetic, we distinguished between the axioms of Russell's system, and the theorems of that system. Roughly, the axioms of a theory are that theory's basic assumptions, and the theorems are the formulae provable from the axioms using the rules provided by the theory. Suppose we are talking about the theory A of arithmetic. Then, if we express the idea that a certain sentence p is a theorem of A | i.e., provable from A's axioms | as follows: `A p Now we can introduce the notion of the valid sentences of a theory. For our purposes, think of a valid sentence as a sentence in the language of the theory that can't be false. For example, consider a simple logical language which contains some predicates (written as upper-case letters), `not', `&', and some names, each of which is assigned some object or other in each interpretation. Now consider a sentence like F n In most cases, a sentence like this is going to be true on some interpretations, and false in others | it depends on which object is assigned to `n', and whether it is in the set of things assigned to `F '. However, if we consider a sentence like ∗The source for much of what follows is Michael Detlefsen's (much less un-rigorous) “G¨odel'stheorems", available at http://www.rep.routledge.com/article/Y005. -
Q1: Is Every Tautology a Theorem? Q2: Is Every Theorem a Tautology?
Section 3.2: Soundness Theorem; Consistency. Math 350: Logic Class08 Fri 8-Feb-2001 This section is mainly about two questions: Q1: Is every tautology a theorem? Q2: Is every theorem a tautology? What do these questions mean? Review de¯nitions, and discuss. We'll see that the answer to both questions is: Yes! Soundness Theorem 1. (The Soundness Theorem: Special case) Every theorem of Propositional Logic is a tautol- ogy; i.e., for every formula A, if A, then = A. ` j Sketch of proof: Q: Is every axiom a tautology? Yes. Why? Q: For each of the four rules of inference, check: if the antecedent is a tautology, does it follow that the conclusion is a tautology? Q: How does this prove the theorem? A: Suppose we have a proof, i.e., a list of formulas: A1; ; An. A1 is guaranteed to be a tautology. Why? So A2 is guaranteed to be a tautology. Why? S¢o¢ ¢A3 is guaranteed to be a tautology. Why? And so on. Q: What is a more rigorous way to prove this? Ans: Use induction. Review: What does S A mean? What does S = A mean? Theorem 2. (The Soun`dness Theorem: General cjase) Let S be any set of formulas. Then every theorem of S is a tautological consequence of S; i.e., for every formula A, if S A, then S = A. ` j Proof: This uses similar ideas as the proof of the special case. We skip the proof. Adequacy Theorem 3. (The Adequacy Theorem for the formal system of Propositional Logic: Special Case) Every tautology is a theorem of Propositional Logic; i.e., for every formula A, if = A, then A. -
On Basic Probability Logic Inequalities †
mathematics Article On Basic Probability Logic Inequalities † Marija Boriˇci´cJoksimovi´c Faculty of Organizational Sciences, University of Belgrade, Jove Ili´ca154, 11000 Belgrade, Serbia; [email protected] † The conclusions given in this paper were partially presented at the European Summer Meetings of the Association for Symbolic Logic, Logic Colloquium 2012, held in Manchester on 12–18 July 2012. Abstract: We give some simple examples of applying some of the well-known elementary probability theory inequalities and properties in the field of logical argumentation. A probabilistic version of the hypothetical syllogism inference rule is as follows: if propositions A, B, C, A ! B, and B ! C have probabilities a, b, c, r, and s, respectively, then for probability p of A ! C, we have f (a, b, c, r, s) ≤ p ≤ g(a, b, c, r, s), for some functions f and g of given parameters. In this paper, after a short overview of known rules related to conjunction and disjunction, we proposed some probabilized forms of the hypothetical syllogism inference rule, with the best possible bounds for the probability of conclusion, covering simultaneously the probabilistic versions of both modus ponens and modus tollens rules, as already considered by Suppes, Hailperin, and Wagner. Keywords: inequality; probability logic; inference rule MSC: 03B48; 03B05; 60E15; 26D20; 60A05 1. Introduction The main part of probabilization of logical inference rules is defining the correspond- Citation: Boriˇci´cJoksimovi´c,M. On ing best possible bounds for probabilities of propositions. Some of them, connected with Basic Probability Logic Inequalities. conjunction and disjunction, can be obtained immediately from the well-known Boole’s Mathematics 2021, 9, 1409. -
7.1 Rules of Implication I
Natural Deduction is a method for deriving the conclusion of valid arguments expressed in the symbolism of propositional logic. The method consists of using sets of Rules of Inference (valid argument forms) to derive either a conclusion or a series of intermediate conclusions that link the premises of an argument with the stated conclusion. The First Four Rules of Inference: ◦ Modus Ponens (MP): p q p q ◦ Modus Tollens (MT): p q ~q ~p ◦ Pure Hypothetical Syllogism (HS): p q q r p r ◦ Disjunctive Syllogism (DS): p v q ~p q Common strategies for constructing a proof involving the first four rules: ◦ Always begin by attempting to find the conclusion in the premises. If the conclusion is not present in its entirely in the premises, look at the main operator of the conclusion. This will provide a clue as to how the conclusion should be derived. ◦ If the conclusion contains a letter that appears in the consequent of a conditional statement in the premises, consider obtaining that letter via modus ponens. ◦ If the conclusion contains a negated letter and that letter appears in the antecedent of a conditional statement in the premises, consider obtaining the negated letter via modus tollens. ◦ If the conclusion is a conditional statement, consider obtaining it via pure hypothetical syllogism. ◦ If the conclusion contains a letter that appears in a disjunctive statement in the premises, consider obtaining that letter via disjunctive syllogism. Four Additional Rules of Inference: ◦ Constructive Dilemma (CD): (p q) • (r s) p v r q v s ◦ Simplification (Simp): p • q p ◦ Conjunction (Conj): p q p • q ◦ Addition (Add): p p v q Common Misapplications Common strategies involving the additional rules of inference: ◦ If the conclusion contains a letter that appears in a conjunctive statement in the premises, consider obtaining that letter via simplification. -
Predicate Logic. Formal and Informal Proofs
CS 441 Discrete Mathematics for CS Lecture 5 Predicate logic Milos Hauskrecht [email protected] 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Negation of quantifiers English statement: • Nothing is perfect. • Translation: ¬ x Perfect(x) Another way to express the same meaning: • Everything ... M. Hauskrecht 1 Negation of quantifiers English statement: • Nothing is perfect. • Translation: ¬ x Perfect(x) Another way to express the same meaning: • Everything is imperfect. • Translation: x ¬ Perfect(x) Conclusion: ¬ x P (x) is equivalent to x ¬ P(x) M. Hauskrecht Negation of quantifiers English statement: • It is not the case that all dogs are fleabags. • Translation: ¬ x Dog(x) Fleabag(x) Another way to express the same meaning: • There is a dog that … M. Hauskrecht 2 Negation of quantifiers English statement: • It is not the case that all dogs are fleabags. • Translation: ¬ x Dog(x) Fleabag(x) Another way to express the same meaning: • There is a dog that is not a fleabag. • Translation: x Dog(x) ¬ Fleabag(x) • Logically equivalent to: – x ¬ ( Dog(x) Fleabag(x) ) Conclusion: ¬ x P (x) is equivalent to x ¬ P(x) M. Hauskrecht Negation of quantified statements (aka DeMorgan Laws for quantifiers) Negation Equivalent ¬x P(x) x ¬P(x) ¬x P(x) x ¬P(x) M. Hauskrecht 3 Formal and informal proofs CS 441 Discrete mathematics for CS M. Hauskrecht Theorems and proofs • The truth value of some statement about the world is obvious and easy to assign • The truth of other statements may not be obvious, … …. But it may still follow (be derived) from known facts about the world To show the truth value of such a statement following from other statements we need to provide a correct supporting argument - a proof Important questions: – When is the argument correct? – How to construct a correct argument, what method to use? CS 441 Discrete mathematics for CS M. -
Proof of the Soundness Theorem for Sentential Logic
The Soundness Theorem for Sentential Logic These notes contain a proof of the Soundness Theorem for Sentential Logic. The Soundness Theorem says that our natural deduction proofs represent a sound (or correct) system of reasoning. Formally, the Soundness Theorem states that: If Γ├ φ then Γ╞ φ. This says that for any set of premises, if you can prove φ from that set, then φ is truth-functionally entailed by that set. In other words, if our rules let you prove that something follows from some premises, then it ‘really does’ follow from those premises. By definition Γ╞ φ means that there is no TVA that makes all of the members of Γ true and also makes φ false. Note that an equivalent definition would be “Γ╞ φ if any TVA that makes all the members of Γ true also makes φ true”. By contraposition, the soundness theorem is equivalent to “If it is not true that Γ╞ φ then it is not true that Γ├ φ.” Unpacking the definitions, this means “If there is a TVA that makes all the members of Γ true but makes φ false, then there is no way to prove φ from Γ.” We might call this “The Negative Criterion.” This is important since the standard way of showing that something is not provable is to give an invalidating TVA. This only works assuming that the soundness theorem is true. Let’s get started on the proof. We are trying to prove a conditional claim, so we assume its antecedent and try to prove its consequent. -
The Corcoran-Smiley Interpretation of Aristotle's Syllogistic As
November 11, 2013 15:31 History and Philosophy of Logic Aristotelian_syllogisms_HPL_house_style_color HISTORY AND PHILOSOPHY OF LOGIC, 00 (Month 200x), 1{27 Aristotle's Syllogistic and Core Logic by Neil Tennant Department of Philosophy The Ohio State University Columbus, Ohio 43210 email [email protected] Received 00 Month 200x; final version received 00 Month 200x I use the Corcoran-Smiley interpretation of Aristotle's syllogistic as my starting point for an examination of the syllogistic from the vantage point of modern proof theory. I aim to show that fresh logical insights are afforded by a proof-theoretically more systematic account of all four figures. First I regiment the syllogisms in the Gentzen{Prawitz system of natural deduction, using the universal and existential quantifiers of standard first- order logic, and the usual formalizations of Aristotle's sentence-forms. I explain how the syllogistic is a fragment of my (constructive and relevant) system of Core Logic. Then I introduce my main innovation: the use of binary quantifiers, governed by introduction and elimination rules. The syllogisms in all four figures are re-proved in the binary system, and are thereby revealed as all on a par with each other. I conclude with some comments and results about grammatical generativity, ecthesis, perfect validity, skeletal validity and Aristotle's chain principle. 1. Introduction: the Corcoran-Smiley interpretation of Aristotle's syllogistic as concerned with deductions Two influential articles, Corcoran 1972 and Smiley 1973, convincingly argued that Aristotle's syllogistic logic anticipated the twentieth century's systematizations of logic in terms of natural deductions. They also showed how Aristotle in effect advanced a completeness proof for his deductive system. -
Does Reductive Proof Theory Have a Viable Rationale?
DOES REDUCTIVE PROOF THEORY HAVE A VIABLE RATIONALE? Solomon Feferman Abstract The goals of reduction and reductionism in the natural sciences are mainly explanatory in character, while those in mathematics are primarily foundational. In contrast to global reductionist programs which aim to reduce all of mathematics to one supposedly “univer- sal” system or foundational scheme, reductive proof theory pursues local reductions of one formal system to another which is more jus- tified in some sense. In this direction, two specific rationales have been proposed as aims for reductive proof theory, the constructive consistency-proof rationale and the foundational reduction rationale. However, recent advances in proof theory force one to consider the viability of these rationales. Despite the genuine problems of foun- dational significance raised by that work, the paper concludes with a defense of reductive proof theory at a minimum as one of the principal means to lay out what rests on what in mathematics. In an extensive appendix to the paper, various reduction relations between systems are explained and compared, and arguments against proof-theoretic reduction as a “good” reducibility relation are taken up and rebutted. 1 1 Reduction and reductionism in the natural sciencesandinmathematics. The purposes of reduction in the natural sciences and in mathematics are quite different. In the natural sciences, one main purpose is to explain cer- tain phenomena in terms of more basic phenomena, such as the nature of the chemical bond in terms of quantum mechanics, and of macroscopic ge- netics in terms of molecular biology. In mathematics, the main purpose is foundational. This is not to be understood univocally; as I have argued in (Feferman 1984), there are a number of foundational ways that are pursued in practice. -
Notes on Proof Theory
Notes on Proof Theory Master 1 “Informatique”, Univ. Paris 13 Master 2 “Logique Mathématique et Fondements de l’Informatique”, Univ. Paris 7 Damiano Mazza November 2016 1Last edit: March 29, 2021 Contents 1 Propositional Classical Logic 5 1.1 Formulas and truth semantics . 5 1.2 Atomic negation . 8 2 Sequent Calculus 10 2.1 Two-sided formulation . 10 2.2 One-sided formulation . 13 3 First-order Quantification 16 3.1 Formulas and truth semantics . 16 3.2 Sequent calculus . 19 3.3 Ultrafilters . 21 4 Completeness 24 4.1 Exhaustive search . 25 4.2 The completeness proof . 30 5 Undecidability and Incompleteness 33 5.1 Informal computability . 33 5.2 Incompleteness: a road map . 35 5.3 Logical theories . 38 5.4 Arithmetical theories . 40 5.5 The incompleteness theorems . 44 6 Cut Elimination 47 7 Intuitionistic Logic 53 7.1 Sequent calculus . 55 7.2 The relationship between intuitionistic and classical logic . 60 7.3 Minimal logic . 65 8 Natural Deduction 67 8.1 Sequent presentation . 68 8.2 Natural deduction and sequent calculus . 70 8.3 Proof tree presentation . 73 8.3.1 Minimal natural deduction . 73 8.3.2 Intuitionistic natural deduction . 75 1 8.3.3 Classical natural deduction . 75 8.4 Normalization (cut-elimination in natural deduction) . 76 9 The Curry-Howard Correspondence 80 9.1 The simply typed l-calculus . 80 9.2 Product and sum types . 81 10 System F 83 10.1 Intuitionistic second-order propositional logic . 83 10.2 Polymorphic types . 84 10.3 Programming in system F ...................... 85 10.3.1 Free structures . -
Propositional Logic (PDF)
Mathematics for Computer Science Proving Validity 6.042J/18.062J Instead of truth tables, The Logic of can try to prove valid formulas symbolically using Propositions axioms and deduction rules Albert R Meyer February 14, 2014 propositional logic.1 Albert R Meyer February 14, 2014 propositional logic.2 Proving Validity Algebra for Equivalence The text describes a for example, bunch of algebraic rules to the distributive law prove that propositional P AND (Q OR R) ≡ formulas are equivalent (P AND Q) OR (P AND R) Albert R Meyer February 14, 2014 propositional logic.3 Albert R Meyer February 14, 2014 propositional logic.4 1 Algebra for Equivalence Algebra for Equivalence for example, The set of rules for ≡ in DeMorgan’s law the text are complete: ≡ NOT(P AND Q) ≡ if two formulas are , these rules can prove it. NOT(P) OR NOT(Q) Albert R Meyer February 14, 2014 propositional logic.5 Albert R Meyer February 14, 2014 propositional logic.6 A Proof System A Proof System Another approach is to Lukasiewicz’ proof system is a start with some valid particularly elegant example of this idea. formulas (axioms) and deduce more valid formulas using proof rules Albert R Meyer February 14, 2014 propositional logic.7 Albert R Meyer February 14, 2014 propositional logic.8 2 A Proof System Lukasiewicz’ Proof System Lukasiewicz’ proof system is a Axioms: particularly elegant example of 1) (¬P → P) → P this idea. It covers formulas 2) P → (¬P → Q) whose only logical operators are 3) (P → Q) → ((Q → R) → (P → R)) IMPLIES (→) and NOT. The only rule: modus ponens Albert R Meyer February 14, 2014 propositional logic.9 Albert R Meyer February 14, 2014 propositional logic.10 Lukasiewicz’ Proof System Lukasiewicz’ Proof System Prove formulas by starting with Prove formulas by starting with axioms and repeatedly applying axioms and repeatedly applying the inference rule.