Empiricism, Probability, and Knowledge of Arithmetic: a Preliminary Defense
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Creating Modern Probability. Its Mathematics, Physics and Philosophy in Historical Perspective
HM 23 REVIEWS 203 The reviewer hopes this book will be widely read and enjoyed, and that it will be followed by other volumes telling even more of the fascinating story of Soviet mathematics. It should also be followed in a few years by an update, so that we can know if this great accumulation of talent will have survived the economic and political crisis that is just now robbing it of many of its most brilliant stars (see the article, ``To guard the future of Soviet mathematics,'' by A. M. Vershik, O. Ya. Viro, and L. A. Bokut' in Vol. 14 (1992) of The Mathematical Intelligencer). Creating Modern Probability. Its Mathematics, Physics and Philosophy in Historical Perspective. By Jan von Plato. Cambridge/New York/Melbourne (Cambridge Univ. Press). 1994. 323 pp. View metadata, citation and similar papers at core.ac.uk brought to you by CORE Reviewed by THOMAS HOCHKIRCHEN* provided by Elsevier - Publisher Connector Fachbereich Mathematik, Bergische UniversitaÈt Wuppertal, 42097 Wuppertal, Germany Aside from the role probabilistic concepts play in modern science, the history of the axiomatic foundation of probability theory is interesting from at least two more points of view. Probability as it is understood nowadays, probability in the sense of Kolmogorov (see [3]), is not easy to grasp, since the de®nition of probability as a normalized measure on a s-algebra of ``events'' is not a very obvious one. Furthermore, the discussion of different concepts of probability might help in under- standing the philosophy and role of ``applied mathematics.'' So the exploration of the creation of axiomatic probability should be interesting not only for historians of science but also for people concerned with didactics of mathematics and for those concerned with philosophical questions. -
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. -
The Interpretation of Probability: Still an Open Issue? 1
philosophies Article The Interpretation of Probability: Still an Open Issue? 1 Maria Carla Galavotti Department of Philosophy and Communication, University of Bologna, Via Zamboni 38, 40126 Bologna, Italy; [email protected] Received: 19 July 2017; Accepted: 19 August 2017; Published: 29 August 2017 Abstract: Probability as understood today, namely as a quantitative notion expressible by means of a function ranging in the interval between 0–1, took shape in the mid-17th century, and presents both a mathematical and a philosophical aspect. Of these two sides, the second is by far the most controversial, and fuels a heated debate, still ongoing. After a short historical sketch of the birth and developments of probability, its major interpretations are outlined, by referring to the work of their most prominent representatives. The final section addresses the question of whether any of such interpretations can presently be considered predominant, which is answered in the negative. Keywords: probability; classical theory; frequentism; logicism; subjectivism; propensity 1. A Long Story Made Short Probability, taken as a quantitative notion whose value ranges in the interval between 0 and 1, emerged around the middle of the 17th century thanks to the work of two leading French mathematicians: Blaise Pascal and Pierre Fermat. According to a well-known anecdote: “a problem about games of chance proposed to an austere Jansenist by a man of the world was the origin of the calculus of probabilities”2. The ‘man of the world’ was the French gentleman Chevalier de Méré, a conspicuous figure at the court of Louis XIV, who asked Pascal—the ‘austere Jansenist’—the solution to some questions regarding gambling, such as how many dice tosses are needed to have a fair chance to obtain a double-six, or how the players should divide the stakes if a game is interrupted. -
1 Stochastic Processes and Their Classification
1 1 STOCHASTIC PROCESSES AND THEIR CLASSIFICATION 1.1 DEFINITION AND EXAMPLES Definition 1. Stochastic process or random process is a collection of random variables ordered by an index set. ☛ Example 1. Random variables X0;X1;X2;::: form a stochastic process ordered by the discrete index set f0; 1; 2;::: g: Notation: fXn : n = 0; 1; 2;::: g: ☛ Example 2. Stochastic process fYt : t ¸ 0g: with continuous index set ft : t ¸ 0g: The indices n and t are often referred to as "time", so that Xn is a descrete-time process and Yt is a continuous-time process. Convention: the index set of a stochastic process is always infinite. The range (possible values) of the random variables in a stochastic process is called the state space of the process. We consider both discrete-state and continuous-state processes. Further examples: ☛ Example 3. fXn : n = 0; 1; 2;::: g; where the state space of Xn is f0; 1; 2; 3; 4g representing which of four types of transactions a person submits to an on-line data- base service, and time n corresponds to the number of transactions submitted. ☛ Example 4. fXn : n = 0; 1; 2;::: g; where the state space of Xn is f1; 2g re- presenting whether an electronic component is acceptable or defective, and time n corresponds to the number of components produced. ☛ Example 5. fYt : t ¸ 0g; where the state space of Yt is f0; 1; 2;::: g representing the number of accidents that have occurred at an intersection, and time t corresponds to weeks. ☛ Example 6. fYt : t ¸ 0g; where the state space of Yt is f0; 1; 2; : : : ; sg representing the number of copies of a software product in inventory, and time t corresponds to days. -
Infinite Time Computable Model Theory
INFINITE TIME COMPUTABLE MODEL THEORY JOEL DAVID HAMKINS, RUSSELL MILLER, DANIEL SEABOLD, AND STEVE WARNER Abstract. We introduce infinite time computable model theory, the com- putable model theory arising with infinite time Turing machines, which provide infinitary notions of computability for structures built on the reals R. Much of the finite time theory generalizes to the infinite time context, but several fundamental questions, including the infinite time computable analogue of the Completeness Theorem, turn out to be independent of ZFC. 1. Introduction Computable model theory is model theory with a view to the computability of the structures and theories that arise (for a standard reference, see [EGNR98]). Infinite time computable model theory, which we introduce here, carries out this program with the infinitary notions of computability provided by infinite time Turing ma- chines. The motivation for a broader context is that, while finite time computable model theory is necessarily limited to countable models and theories, the infinitary context naturally allows for uncountable models and theories, while retaining the computational nature of the undertaking. Many constructions generalize from finite time computable model theory, with structures built on N, to the infinitary theory, with structures built on R. In this article, we introduce the basic theory and con- sider the infinitary analogues of the completeness theorem, the L¨owenheim-Skolem Theorem, Myhill’s theorem and others. It turns out that, when stated in their fully general infinitary forms, several of these fundamental questions are independent of ZFC. The analysis makes use of techniques both from computability theory and set theory. This article follows up [Ham05]. -
Probability and Logic
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Journal of Applied Logic 1 (2003) 151–165 www.elsevier.com/locate/jal Probability and logic Colin Howson London School of Economics, Houghton Street, London WC2A 2AE, UK Abstract The paper is an attempt to show that the formalism of subjective probability has a logical in- terpretation of the sort proposed by Frank Ramsey: as a complete set of constraints for consistent distributions of partial belief. Though Ramsey proposed this view, he did not actually establish it in a way that showed an authentically logical character for the probability axioms (he started the current fashion for generating probabilities from suitably constrained preferences over uncertain options). Other people have also sought to provide the probability calculus with a logical character, though also unsuccessfully. The present paper gives a completeness and soundness theorem supporting a logical interpretation: the syntax is the probability axioms, and the semantics is that of fairness (for bets). 2003 Elsevier B.V. All rights reserved. Keywords: Probability; Logic; Fair betting quotients; Soundness; Completeness 1. Anticipations The connection between deductive logic and probability, at any rate epistemic probabil- ity, has been the subject of exploration and controversy for a long time: over three centuries, in fact. Both disciplines specify rules of valid non-domain-specific reasoning, and it would seem therefore a reasonable question why one should be distinguished as logic and the other not. I will argue that there is no good reason for this difference, and that both are indeed equally logic. -
Notes on Incompleteness Theorems
The Incompleteness Theorems ● Here are some fundamental philosophical questions with mathematical answers: ○ (1) Is there a (recursive) algorithm for deciding whether an arbitrary sentence in the language of first-order arithmetic is true? ○ (2) Is there an algorithm for deciding whether an arbitrary sentence in the language of first-order arithmetic is a theorem of Peano or Robinson Arithmetic? ○ (3) Is there an algorithm for deciding whether an arbitrary sentence in the language of first-order arithmetic is a theorem of pure (first-order) logic? ○ (4) Is there a complete (even if not recursive) recursively axiomatizable theory in the language of first-order arithmetic? ○ (5) Is there a recursively axiomatizable sub-theory of Peano Arithmetic that proves the consistency of Peano Arithmetic (even if it leaves other questions undecided)? ○ (6) Is there a formula of arithmetic that defines arithmetic truth in the standard model, N (even if it does not represent it)? ○ (7) Is the (non-recursively enumerable) set of truths in the language of first-order arithmetic categorical? If not, is it ω-categorical (i.e., categorical in models of cardinality ω)? ● Questions (1) -- (7) turn out to be linked. Their philosophical interest depends partly on the following philosophical thesis, of which we will make frequent, but inessential, use. ○ Church-Turing Thesis:A function is (intuitively) computable if/f it is recursive. ■ Church: “[T]he notion of an effectively calculable function of positive integers should be identified with that of recursive function (quoted in Epstein & Carnielli, 223).” ○ Note: Since a function is recursive if/f it is Turing computable, the Church-Turing Thesis also implies that a function is computable if/f it is Turing computable. -
A FIRST COURSE in PROBABILITY This Page Intentionally Left Blank a FIRST COURSE in PROBABILITY
A FIRST COURSE IN PROBABILITY This page intentionally left blank A FIRST COURSE IN PROBABILITY Eighth Edition Sheldon Ross University of Southern California Upper Saddle River, New Jersey 07458 Library of Congress Cataloging-in-Publication Data Ross, Sheldon M. A first course in probability / Sheldon Ross. — 8th ed. p. cm. Includes bibliographical references and index. ISBN-13: 978-0-13-603313-4 ISBN-10: 0-13-603313-X 1. Probabilities—Textbooks. I. Title. QA273.R83 2010 519.2—dc22 2008033720 Editor in Chief, Mathematics and Statistics: Deirdre Lynch Senior Project Editor: Rachel S. Reeve Assistant Editor: Christina Lepre Editorial Assistant: Dana Jones Project Manager: Robert S. Merenoff Associate Managing Editor: Bayani Mendoza de Leon Senior Managing Editor: Linda Mihatov Behrens Senior Operations Supervisor: Diane Peirano Marketing Assistant: Kathleen DeChavez Creative Director: Jayne Conte Art Director/Designer: Bruce Kenselaar AV Project Manager: Thomas Benfatti Compositor: Integra Software Services Pvt. Ltd, Pondicherry, India Cover Image Credit: Getty Images, Inc. © 2010, 2006, 2002, 1998, 1994, 1988, 1984, 1976 by Pearson Education, Inc., Pearson Prentice Hall Pearson Education, Inc. Upper Saddle River, NJ 07458 All rights reserved. No part of this book may be reproduced, in any form or by any means, without permission in writing from the publisher. Pearson Prentice Hall™ is a trademark of Pearson Education, Inc. Printed in the United States of America 10987654321 ISBN-13: 978-0-13-603313-4 ISBN-10: 0-13-603313-X Pearson Education, Ltd., London Pearson Education Australia PTY. Limited, Sydney Pearson Education Singapore, Pte. Ltd Pearson Education North Asia Ltd, Hong Kong Pearson Education Canada, Ltd., Toronto Pearson Educacion´ de Mexico, S.A. -
History of Probability (Part 4) - Inverse Probability and the Determination of Causes of Observed Events
History of Probability (Part 4) - Inverse probability and the determination of causes of observed events. Thomas Bayes (c1702-1761) . By the early 1700s the work of Pascal, Fermat, and Huygens was well known, mainly couched in terms of odds and fair bets in gambling. Jacob Bernoulli and Abraham DeMoivre had made efforts to broaden the scope and interpretation of probability. Bernoulli had put probability measures on a scale between zero and one and DeMoivre had defined probability as a fraction of chances. But then there was a 50 year lull in further development in probability theory. This is surprising, but the “brains” of the day were busy applying the newly invented calculus to problems of physics – especially astronomy. At the beginning of the 18 th century a probability exercise like the following one was not too difficult. Suppose there are two boxes with white and black balls. Box A has 4 white and 1 black, box B has 2 white and 3 black. You pick one box at random, with probability 1/3 it will be Box A and 2/3 it will be B. Then you randomly pick one ball from the box. What is the probability you will end up with a white ball? We can model this scenario by a tree diagram. We also have the advantage of efficient symbolism. Expressions like = had not been developed by 1700. The idea of the probability of a specific event was just becoming useful in addition to the older emphasis on odds of one outcome versus another. Using the multiplication rule for independent events together with the addition rule, we find that the probability of picking a white ball is 8/15. -
LOGIC I 1. the Completeness Theorem 1.1. on Consequences
LOGIC I VICTORIA GITMAN 1. The Completeness Theorem The Completeness Theorem was proved by Kurt G¨odelin 1929. To state the theorem we must formally define the notion of proof. This is not because it is good to give formal proofs, but rather so that we can prove mathematical theorems about the concept of proof. {Arnold Miller 1.1. On consequences and proofs. Suppose that T is some first-order theory. What are the consequences of T ? The obvious answer is that they are statements provable from T (supposing for a second that we know what that means). But there is another possibility. The consequences of T could mean statements that hold true in every model of T . Do the proof theoretic and the model theoretic notions of consequence coincide? Once, we formally define proofs, it will be obvious, by the definition of truth, that a statement that is provable from T must hold in every model of T . Does the converse hold? The question was posed in the 1920's by David Hilbert (of the 23 problems fame). The answer is that remarkably, yes, it does! This result, known as the Completeness Theorem for first-order logic, was proved by Kurt G¨odel in 1929. According to the Completeness Theorem provability and semantic truth are indeed two very different aspects of the same phenomena. In order to prove the Completeness Theorem, we first need a formal notion of proof. As mathematicians, we all know that a proof is a series of deductions, where each statement proceeds by logical reasoning from the previous ones. -
Unifying Logic and Probability
review articles DOI:10.1145/2699411 those arguments: so one can write rules Open-universe probability models show merit about On(p, c, x, y, t) (piece p of color c is on square x, y at move t) without filling in unifying efforts. in each specific value for c, p, x, y, and t. Modern AI research has addressed BY STUART RUSSELL another important property of the real world—pervasive uncertainty about both its state and its dynamics—using probability theory. A key step was Pearl’s devel opment of Bayesian networks, Unifying which provided the beginnings of a formal language for probability mod- els and enabled rapid progress in rea- soning, learning, vision, and language Logic and understanding. The expressive power of Bayes nets is, however, limited. They assume a fixed set of variables, each taking a value from a fixed range; Probability thus, they are a propositional formal- ism, like Boolean circuits. The rules of chess and of many other domains are beyond them. What happened next, of course, is that classical AI researchers noticed the perva- sive uncertainty, while modern AI research- ers noticed, or remembered, that the world has things in it. Both traditions arrived at the same place: the world is uncertain and it has things in it. To deal with this, we have to unify logic and probability. But how? Even the meaning of such a goal is unclear. Early attempts by Leibniz, Bernoulli, De Morgan, Boole, Peirce, Keynes, and Carnap (surveyed PERHAPS THE MOST enduring idea from the early by Hailperin12 and Howson14) involved days of AI is that of a declarative system reasoning attaching probabilities to logical sen- over explicitly represented knowledge with a general tences. -
Arxiv:1902.05902V2 [Math.LO] 5 Aug 2020 Ee Olnrfrsnigm I Rfso 2,2]Pirt P to Prior 28] [27, of by Drafts Comments
GODEL’S¨ INCOMPLETENESS THEOREM AND THE ANTI-MECHANIST ARGUMENT: REVISITED YONG CHENG Abstract. This is a paper for a special issue of the journal “Studia Semiotyczne” devoted to Stanislaw Krajewski’s paper [30]. This pa- per gives some supplementary notes to Krajewski’s [30] on the Anti- Mechanist Arguments based on G¨odel’s incompleteness theorem. In Section 3, we give some additional explanations to Section 4-6 in Kra- jewski’s [30] and classify some misunderstandings of G¨odel’s incomplete- ness theorem related to Anti-Mechanist Arguments. In Section 4 and 5, we give a more detailed discussion of G¨odel’s Disjunctive Thesis, G¨odel’s Undemonstrability of Consistency Thesis and the definability of natu- ral numbers as in Section 7-8 in Krajewski’s [30], describing how recent advances bear on these issues. 1. Introduction G¨odel’s incompleteness theorem is one of the most remarkable and pro- found discoveries in the 20th century, an important milestone in the history of modern logic. G¨odel’s incompleteness theorem has wide and profound influence on the development of logic, philosophy, mathematics, computer science and other fields, substantially shaping mathematical logic as well as foundations and philosophy of mathematics from 1931 onward. The im- pact of G¨odel’s incompleteness theorem is not confined to the community of mathematicians and logicians, and it has been very popular and widely used outside mathematics. G¨odel’s incompleteness theorem raises a number of philosophical ques- tions concerning the nature of mind and machine, the difference between human intelligence and machine intelligence, and the limit of machine intel- ligence.