Mathematical Induction and Induction in Mathematics
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There Is No Pure Empirical Reasoning
There Is No Pure Empirical Reasoning 1. Empiricism and the Question of Empirical Reasons Empiricism may be defined as the view there is no a priori justification for any synthetic claim. Critics object that empiricism cannot account for all the kinds of knowledge we seem to possess, such as moral knowledge, metaphysical knowledge, mathematical knowledge, and modal knowledge.1 In some cases, empiricists try to account for these types of knowledge; in other cases, they shrug off the objections, happily concluding, for example, that there is no moral knowledge, or that there is no metaphysical knowledge.2 But empiricism cannot shrug off just any type of knowledge; to be minimally plausible, empiricism must, for example, at least be able to account for paradigm instances of empirical knowledge, including especially scientific knowledge. Empirical knowledge can be divided into three categories: (a) knowledge by direct observation; (b) knowledge that is deductively inferred from observations; and (c) knowledge that is non-deductively inferred from observations, including knowledge arrived at by induction and inference to the best explanation. Category (c) includes all scientific knowledge. This category is of particular import to empiricists, many of whom take scientific knowledge as a sort of paradigm for knowledge in general; indeed, this forms a central source of motivation for empiricism.3 Thus, if there is any kind of knowledge that empiricists need to be able to account for, it is knowledge of type (c). I use the term “empirical reasoning” to refer to the reasoning involved in acquiring this type of knowledge – that is, to any instance of reasoning in which (i) the premises are justified directly by observation, (ii) the reasoning is non- deductive, and (iii) the reasoning provides adequate justification for the conclusion. -
Chapter 3 Induction and Recursion
Chapter 3 Induction and Recursion 3.1 Induction: An informal introduction This section is intended as a somewhat informal introduction to The Principle of Mathematical Induction (PMI): a theorem that establishes the validity of the proof method which goes by the same name. There is a particular format for writing the proofs which makes it clear that PMI is being used. We will not explicitly use this format when introducing the method, but will do so for the large number of different examples given later. Suppose you are given a large supply of L-shaped tiles as shown on the left of the figure below. The question you are asked to answer is whether these tiles can be used to exactly cover the squares of an 2n × 2n punctured grid { a 2n × 2n grid that has had one square cut out { say the 8 × 8 example shown in the right of the figure. 1 2 CHAPTER 3. INDUCTION AND RECURSION In order for this to be possible at all, the number of squares in the punctured grid has to be a multiple of three. It is. The number of squares is 2n2n − 1 = 22n − 1 = 4n − 1 ≡ 1n − 1 ≡ 0 (mod 3): But that does not mean we can tile the punctured grid. In order to get some traction on what to do, let's try some small examples. The tiling is easy to find if n = 1 because 2 × 2 punctured grid is exactly covered by one tile. Let's try n = 2, so that our punctured grid is 4 × 4. -
A Philosophical Treatise on the Connection of Scientific Reasoning
mathematics Review A Philosophical Treatise on the Connection of Scientific Reasoning with Fuzzy Logic Evangelos Athanassopoulos 1 and Michael Gr. Voskoglou 2,* 1 Independent Researcher, Giannakopoulou 39, 27300 Gastouni, Greece; [email protected] 2 Department of Applied Mathematics, Graduate Technological Educational Institute of Western Greece, 22334 Patras, Greece * Correspondence: [email protected] Received: 4 May 2020; Accepted: 19 May 2020; Published:1 June 2020 Abstract: The present article studies the connection of scientific reasoning with fuzzy logic. Induction and deduction are the two main types of human reasoning. Although deduction is the basis of the scientific method, almost all the scientific progress (with pure mathematics being probably the unique exception) has its roots to inductive reasoning. Fuzzy logic gives to the disdainful by the classical/bivalent logic induction its proper place and importance as a fundamental component of the scientific reasoning. The error of induction is transferred to deductive reasoning through its premises. Consequently, although deduction is always a valid process, it is not an infallible method. Thus, there is a need of quantifying the degree of truth not only of the inductive, but also of the deductive arguments. In the former case, probability and statistics and of course fuzzy logic in cases of imprecision are the tools available for this purpose. In the latter case, the Bayesian probabilities play a dominant role. As many specialists argue nowadays, the whole science could be viewed as a Bayesian process. A timely example, concerning the validity of the viruses’ tests, is presented, illustrating the importance of the Bayesian processes for scientific reasoning. -
Mathematical Induction
Mathematical Induction Lecture 10-11 Menu • Mathematical Induction • Strong Induction • Recursive Definitions • Structural Induction Climbing an Infinite Ladder Suppose we have an infinite ladder: 1. We can reach the first rung of the ladder. 2. If we can reach a particular rung of the ladder, then we can reach the next rung. From (1), we can reach the first rung. Then by applying (2), we can reach the second rung. Applying (2) again, the third rung. And so on. We can apply (2) any number of times to reach any particular rung, no matter how high up. This example motivates proof by mathematical induction. Principle of Mathematical Induction Principle of Mathematical Induction: To prove that P(n) is true for all positive integers n, we complete these steps: Basis Step: Show that P(1) is true. Inductive Step: Show that P(k) → P(k + 1) is true for all positive integers k. To complete the inductive step, assuming the inductive hypothesis that P(k) holds for an arbitrary integer k, show that must P(k + 1) be true. Climbing an Infinite Ladder Example: Basis Step: By (1), we can reach rung 1. Inductive Step: Assume the inductive hypothesis that we can reach rung k. Then by (2), we can reach rung k + 1. Hence, P(k) → P(k + 1) is true for all positive integers k. We can reach every rung on the ladder. Logic and Mathematical Induction • Mathematical induction can be expressed as the rule of inference (P(1) ∧ ∀k (P(k) → P(k + 1))) → ∀n P(n), where the domain is the set of positive integers. -
Ground and Explanation in Mathematics
volume 19, no. 33 ncreased attention has recently been paid to the fact that in math- august 2019 ematical practice, certain mathematical proofs but not others are I recognized as explaining why the theorems they prove obtain (Mancosu 2008; Lange 2010, 2015a, 2016; Pincock 2015). Such “math- ematical explanation” is presumably not a variety of causal explana- tion. In addition, the role of metaphysical grounding as underwriting a variety of explanations has also recently received increased attention Ground and Explanation (Correia and Schnieder 2012; Fine 2001, 2012; Rosen 2010; Schaffer 2016). Accordingly, it is natural to wonder whether mathematical ex- planation is a variety of grounding explanation. This paper will offer several arguments that it is not. in Mathematics One obstacle facing these arguments is that there is currently no widely accepted account of either mathematical explanation or grounding. In the case of mathematical explanation, I will try to avoid this obstacle by appealing to examples of proofs that mathematicians themselves have characterized as explanatory (or as non-explanatory). I will offer many examples to avoid making my argument too dependent on any single one of them. I will also try to motivate these characterizations of various proofs as (non-) explanatory by proposing an account of what makes a proof explanatory. In the case of grounding, I will try to stick with features of grounding that are relatively uncontroversial among grounding theorists. But I will also Marc Lange look briefly at how some of my arguments would fare under alternative views of grounding. I hope at least to reveal something about what University of North Carolina at Chapel Hill grounding would have to look like in order for a theorem’s grounds to mathematically explain why that theorem obtains. -
Analysis - Identify Assumptions, Reasons and Claims, and Examine How They Interact in the Formation of Arguments
Analysis - identify assumptions, reasons and claims, and examine how they interact in the formation of arguments. Individuals use analytics to gather information from charts, graphs, diagrams, spoken language and documents. People with strong analytical skills attend to patterns and to details. They identify the elements of a situation and determine how those parts interact. Strong interpretations skills can support high quality analysis by providing insights into the significance of what a person is saying or what something means. Inference - draw conclusions from reasons and evidence. Inference is used when someone offers thoughtful suggestions and hypothesis. Inference skills indicate the necessary or the very probable consequences of a given set of facts and conditions. Conclusions, hypotheses, recommendations or decisions that are based on faulty analysis, misinformation, bad data or biased evaluations can turn out to be mistaken, even if they have reached using excellent inference skills. Evaluative - assess the credibility of sources of information and the claims they make, and determine the strength and weakness or arguments. Applying evaluation skills can judge the quality of analysis, interpretations, explanations, inferences, options, opinions, beliefs, ideas, proposals, and decisions. Strong explanation skills can support high quality evaluation by providing evidence, reasons, methods, criteria, or assumptions behind the claims made and the conclusions reached. Deduction - decision making in precisely defined contexts where rules, operating conditions, core beliefs, values, policies, principles, procedures and terminology completely determine the outcome. Deductive reasoning moves with exacting precision from the assumed truth of a set of beliefs to a conclusion which cannot be false if those beliefs are untrue. Deductive validity is rigorously logical and clear-cut. -
Measure Theory and Probability
Measure theory and probability Alexander Grigoryan University of Bielefeld Lecture Notes, October 2007 - February 2008 Contents 1 Construction of measures 3 1.1Introductionandexamples........................... 3 1.2 σ-additive measures ............................... 5 1.3 An example of using probability theory . .................. 7 1.4Extensionofmeasurefromsemi-ringtoaring................ 8 1.5 Extension of measure to a σ-algebra...................... 11 1.5.1 σ-rings and σ-algebras......................... 11 1.5.2 Outermeasure............................. 13 1.5.3 Symmetric difference.......................... 14 1.5.4 Measurable sets . ............................ 16 1.6 σ-finitemeasures................................ 20 1.7Nullsets..................................... 23 1.8 Lebesgue measure in Rn ............................ 25 1.8.1 Productmeasure............................ 25 1.8.2 Construction of measure in Rn. .................... 26 1.9 Probability spaces ................................ 28 1.10 Independence . ................................. 29 2 Integration 38 2.1 Measurable functions.............................. 38 2.2Sequencesofmeasurablefunctions....................... 42 2.3 The Lebesgue integral for finitemeasures................... 47 2.3.1 Simplefunctions............................ 47 2.3.2 Positivemeasurablefunctions..................... 49 2.3.3 Integrablefunctions........................... 52 2.4Integrationoversubsets............................ 56 2.5 The Lebesgue integral for σ-finitemeasure................. -
The Development of Mathematical Logic from Russell to Tarski: 1900–1935
The Development of Mathematical Logic from Russell to Tarski: 1900–1935 Paolo Mancosu Richard Zach Calixto Badesa The Development of Mathematical Logic from Russell to Tarski: 1900–1935 Paolo Mancosu (University of California, Berkeley) Richard Zach (University of Calgary) Calixto Badesa (Universitat de Barcelona) Final Draft—May 2004 To appear in: Leila Haaparanta, ed., The Development of Modern Logic. New York and Oxford: Oxford University Press, 2004 Contents Contents i Introduction 1 1 Itinerary I: Metatheoretical Properties of Axiomatic Systems 3 1.1 Introduction . 3 1.2 Peano’s school on the logical structure of theories . 4 1.3 Hilbert on axiomatization . 8 1.4 Completeness and categoricity in the work of Veblen and Huntington . 10 1.5 Truth in a structure . 12 2 Itinerary II: Bertrand Russell’s Mathematical Logic 15 2.1 From the Paris congress to the Principles of Mathematics 1900–1903 . 15 2.2 Russell and Poincar´e on predicativity . 19 2.3 On Denoting . 21 2.4 Russell’s ramified type theory . 22 2.5 The logic of Principia ......................... 25 2.6 Further developments . 26 3 Itinerary III: Zermelo’s Axiomatization of Set Theory and Re- lated Foundational Issues 29 3.1 The debate on the axiom of choice . 29 3.2 Zermelo’s axiomatization of set theory . 32 3.3 The discussion on the notion of “definit” . 35 3.4 Metatheoretical studies of Zermelo’s axiomatization . 38 4 Itinerary IV: The Theory of Relatives and Lowenheim’s¨ Theorem 41 4.1 Theory of relatives and model theory . 41 4.2 The logic of relatives . -
Plato on the Foundations of Modern Theorem Provers
The Mathematics Enthusiast Volume 13 Number 3 Number 3 Article 8 8-2016 Plato on the foundations of Modern Theorem Provers Ines Hipolito Follow this and additional works at: https://scholarworks.umt.edu/tme Part of the Mathematics Commons Let us know how access to this document benefits ou.y Recommended Citation Hipolito, Ines (2016) "Plato on the foundations of Modern Theorem Provers," The Mathematics Enthusiast: Vol. 13 : No. 3 , Article 8. Available at: https://scholarworks.umt.edu/tme/vol13/iss3/8 This Article is brought to you for free and open access by ScholarWorks at University of Montana. It has been accepted for inclusion in The Mathematics Enthusiast by an authorized editor of ScholarWorks at University of Montana. For more information, please contact [email protected]. TME, vol. 13, no.3, p.303 Plato on the foundations of Modern Theorem Provers Inês Hipolito1 Nova University of Lisbon Abstract: Is it possible to achieve such a proof that is independent of both acts and dispositions of the human mind? Plato is one of the great contributors to the foundations of mathematics. He discussed, 2400 years ago, the importance of clear and precise definitions as fundamental entities in mathematics, independent of the human mind. In the seventh book of his masterpiece, The Republic, Plato states “arithmetic has a very great and elevating effect, compelling the soul to reason about abstract number, and rebelling against the introduction of visible or tangible objects into the argument” (525c). In the light of this thought, I will discuss the status of mathematical entities in the twentieth first century, an era when it is already possible to demonstrate theorems and construct formal axiomatic derivations of remarkable complexity with artificial intelligent agents the modern theorem provers. -
Relations Between Inductive Reasoning and Deductive Reasoning
Journal of Experimental Psychology: © 2010 American Psychological Association Learning, Memory, and Cognition 0278-7393/10/$12.00 DOI: 10.1037/a0018784 2010, Vol. 36, No. 3, 805–812 Relations Between Inductive Reasoning and Deductive Reasoning Evan Heit Caren M. Rotello University of California, Merced University of Massachusetts Amherst One of the most important open questions in reasoning research is how inductive reasoning and deductive reasoning are related. In an effort to address this question, we applied methods and concepts from memory research. We used 2 experiments to examine the effects of logical validity and premise– conclusion similarity on evaluation of arguments. Experiment 1 showed 2 dissociations: For a common set of arguments, deduction judgments were more affected by validity, and induction judgments were more affected by similarity. Moreover, Experiment 2 showed that fast deduction judgments were like induction judgments—in terms of being more influenced by similarity and less influenced by validity, compared with slow deduction judgments. These novel results pose challenges for a 1-process account of reasoning and are interpreted in terms of a 2-process account of reasoning, which was implemented as a multidimensional signal detection model and applied to receiver operating characteristic data. Keywords: reasoning, similarity, mathematical modeling An important open question in reasoning research concerns the arguments (Rips, 2001). This technique can highlight similarities relation between induction and deduction. Typically, individual or differences between induction and deduction that are not con- studies of reasoning have focused on only one task, rather than founded by the use of different materials (Heit, 2007). It also examining how the two are connected (Heit, 2007). -
Chapter 4 Induction and Recursion
Chapter 4 Induction and Recursion 4.1 Induction: An informal introduction This section is intended as a somewhat informal introduction to The Principle of Mathematical Induction (PMI): a theorem that establishes the validity of the proof method which goes by the same name. There is a particular format for writing the proofs which makes it clear that PMI is being used. We will not explicitly use this format when introducing the method, but will do so for the large number of different examples given later. Suppose you are given a large supply of L-shaped tiles as shown on the left of the figure below. The question you are asked to answer is whether these tiles can be used to exactly cover the squares of an 2n × 2n punctured grid { a 2n × 2n grid that has had one square cut out { say the 8 × 8 example shown in the right of the figure. 1 2 CHAPTER 4. INDUCTION AND RECURSION In order for this to be possible at all, the number of squares in the punctured grid has to be a multiple of three. By direct calculation we can see that it is true when n = 1; 2 or 3, and these are the cases we're interested in here. It turns out to be true in general; this is easy to show using congruences, which we will study later, and also can be shown using methods in this chapter. But that does not mean we can tile the punctured grid. In order to get some traction on what to do, let's try some small examples. -
Introduction to Deductive Logic
Introduction to Deductive Logic Part I: Logic & Language There is no royal road to logic, and really valuable ideas can only be had at the price of close attention. Charles Saunders Peirce INTRODUCTION TO DEDUCTIVE LOGIC PART I: LOGIC AND LANGUAGE I. INTRODUCTION What is Logic? . 4 What is an argument? . 5 Three Criteria for Evaluating Arguments . 6 II. ARGUMENT ANALYSIS – PART 1 Preliminary Issues . 9 Statements and Truth Value Premises and Conclusions . 9 Statements vs Sentences 10 Simple vs Compound Statements 12 Recognizing premises and conclusions . 13 Indicator Language. 15 TWO TYPES OF ARGUMENTS Inductive Reasoning/Argument . 18 Deductive Reasoning/Argument . 19 III. ANALYSIS – PART II FORMALIZING ARGUMENT Preliminary Issue: Formal vs. Informal Argument . 24 Simple and Compound Statements . 25 Types of Compound Statements . 26 Formation Rules for Sentential . 26 DEFINING LOGICAL OPERATORS . 29 Negation . 29 Conjunction . 30 Disjunction . 31 Conditional/ Material Implication . 32 Bi-conditional . 33 APPLICATION: TRANSLATING USING LOGICAL OPERATORS . 35 TRUTH TABLE RULES/SUMMARY OF LOGICAL OPERATORS . 38 TRUTH TABLE APPLICATIONS . 39 Application I: Substitute and Calculate Method . 39 Application II: Calculating with Unknown Values . 41 Application III: Full Truth Table Method . 42 A Short Cut Technique for statements. 44 Truth Table Test for Logical Equivalence . 45 Application IV: Using Truth Tables to Facilitate Reasoning . 47 THE NEED FOR PREDICATE . 50 Using Predicate to Represent Categorical Statements . 52 Preliminary Issues– Grammar . 53 Formation Rules for Predicate . 55 Expressing Quantity . 56 APPLICATION: Symbolizing Categorical Statements using Predicate . 56 Negating Standard Form Categorical Statements. 57 Summary Chart for Standard Form Categorical Statements . 59 Extending Predicate . 61 A Universe of Discourse .