Truth, Rationality, and the Growth of Scientific Knowledge
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
The Demarcation Problem
Part I The Demarcation Problem 25 Chapter 1 Popper’s Falsifiability Criterion 1.1 Popper’s Falsifiability Popper’s Problem : To distinguish between science and pseudo-science (astronomy vs astrology) - Important distinction: truth is not the issue – some theories are sci- entific and false, and some may be unscientific but true. - Traditional but unsatisfactory answers: empirical method - Popper’s targets: Marx, Freud, Adler Popper’s thesis : Falsifiability – the theory contains claims which could be proved to be false. Characteristics of Pseudo-Science : unfalsifiable - Any phenomenon can be interpreted in terms of the pseudo-scientific theory “Whatever happened always confirmed it” (5) - Example: man drowning vs saving a child Characteristics of Science : falsifiability - A scientific theory is always takes risks concerning the empirical ob- servations. It contains the possibility of being falsified. There is con- firmation only when there is failure to refute. 27 28 CHAPTER 1. POPPER’S FALSIFIABILITY CRITERION “The theory is incompatible with certain possible results of observation” (6) - Example: Einstein 1919 1.2 Kuhn’s criticism of Popper Kuhn’s Criticism of Popper : Popper’s falsifiability criterion fails to char- acterize science as it is actually practiced. His criticism at best applies to revolutionary periods of the history of science. Another criterion must be given for normal science. Kuhn’s argument : - Kuhn’s distinction between normal science and revolutionary science - A lesson from the history of science: most science is normal science. Accordingly, philosophy of science should focus on normal science. And any satisfactory demarcation criterion must apply to normal science. - Popper’s falsifiability criterion at best only applies to revolutionary science, not to normal science. -
Study Questions Philosophy of Science Spring 2011 Exam 1
Study Questions Philosophy of Science Spring 2011 Exam 1 1. Explain the affinities and contrasts between science and philosophy and between science and metaphysics. 2. What is the principle of verification? How was the logical positivist‟s view of verification influenced by Hume‟s fork? Explain. 3. Explain Hume‟s distinction between impressions and ideas. What is the relevance of this distinction for the empiricist‟s view of how knowledge is acquired? 4. Why was Hume skeptical about “metaphysical knowledge”? Does the logical positivist, such as A. J. Ayer, agree or disagree? Why or why not? 5. Explain Ayer‟s distinction between weak and strong verification. Is this distinction tenable? 6. How did Rudolf Carnap attempt to separate science from metaphysics? Explain the role of what Carnap called „C-Rules‟ for this attempt. Was he successful? 7. Explain one criticism of the logical positivist‟s principle of verification? Do you agree or disagree? Why or why not? 8. Explain the affinities and contrasts between A. J. Ayer‟s logical positivism and Karl Popper‟s criterion of falsifiability. Why does Popper think that scientific progress depends on falsifiability rather than verifiability? What are Popper‟s arguments against verifiability? Do you think Popper is correct? 9. Kuhn argued that Popper neglected what Kuhn calls “normal science.” Does Popper have a reply to Kuhn in his distinction between science and ideology? Explain. 10. Is Popper‟s criterion of falsifiability a solution to the problem of demarcation? Why or why not? Why would Gierre or Kuhn disagree? Explain 11. Kuhn, in his “Logic of Discovery or Psychology of Research?” disagrees with Popper‟s criterion of falsifiability as defining the essence of science. -
Corkett a Note on the Aristotelian Origin
1 A NOTE ON THE ARISTOTELIAN ORIGIN OF POPPER’S DEMARCATION CRITERION TOGETHER WITH ITS APPLICATION TO ATLANTIC CANADA’S FISHERIES Christopher J. Corkett* *Christopher is a retired Instructor from the Biology Department of Dalhousie University. He is currently applying Karl Popper’s non-inductive theory of method to the management of the world’s commercial fisheries. Abstract It has not always been realised that Karl Popper’s demarcation criterion, the criterion he uses to distinguish an empirical science from its ‘metaphysical’ complement involves an interpretation of the classical theory of terms. From the beginning Popper’s criterion never was an attempt to distinguish some subject matter called ‘science’ from some subject matter called ‘metaphysics’. His criterion of falsifiability always was an attempt to distinguish the logical strength of a universal law from the logical weakness of its complement, a complement that can bear no fruit. For example: if the falsifiability criterion is applied to the management of the fisheries of Atlantic Canada we can distinguish the bold and sound management of Atlantic lobster from the weak and unsound management of Atlantic groundfish. In the early 1990s Newfoundland’s fishery for Atlantic cod suffered a major collapse that has become one of the world’s most prominent case studies of failure in fisheries management. Under Popper’s analytic theory of demarcation a weak management with no problem solving potentiality is to be held responsible for the collapse of Newfoundland’s Atlantic cod fishery. 2 1. Introduction Logic is one of the most ancient of all disciplines. It was founded by the Greek scientist and philosopher Aristotle (384-322 B.C.) before even the Hellenistic development of mathematics. -
Contrastive Empiricism
Elliott Sober Contrastive Empiricism I Despite what Hegel may have said, syntheses have not been very successful in philosophical theorizing. Typically, what happens when you combine a thesis and an antithesis is that you get a mishmash, or maybe just a contradiction. For example, in the philosophy of mathematics, formalism says that mathematical truths are true in virtue of the way we manipulate symbols. Mathematical Platonism, on the other hand, holds that mathematical statements are made true by abstract objects that exist outside of space and time. What would a synthesis of these positions look like? Marks on paper are one thing, Platonic forms an other. Compromise may be a good idea in politics, but it looks like a bad one in philosophy. With some trepidation, I propose in this paper to go against this sound advice. Realism and empiricism have always been contradictory tendencies in the philos ophy of science. The view I will sketch is a synthesis, which I call Contrastive Empiricism. Realism and empiricism are incompatible, so a synthesis that merely conjoined them would be a contradiction. Rather, I propose to isolate important elements in each and show that they combine harmoniously. I will leave behind what I regard as confusions and excesses. The result, I hope, will be neither con tradiction nor mishmash. II Empiricism is fundamentally a thesis about experience. It has two parts. First, there is the idea that experience is necessary. Second, there is the thesis that ex perience suffices. Necessary and sufficient for what? Usually this blank is filled in with something like: knowledge of the world outside the mind. -
Understanding the Progress of Science
Understanding the Progress of Science C. D. McCoy 30 May 2018 The central problem of epistemology has always been and still is the problem of the growth of knowledge. And the growth of knowledge can be studied best by studying the growth of scientific knowledge. —(Popper, 2002b, xix) Merely fact-minded sciences make merely fact-minded people. —(Husserl, 1970, 6) The growth of human knowledge is realized most dramatically in the growth of scientific knowledge. What we have come to know through science about the workings of our human bodies and the workings of heavenly bodies, among much else, astounds, especially in comparison to our accumulating knowledge of the commonplace and everyday. Impressed by the evident epistemic progress made by science, many philosophers during the 20th century thought that studying how scientific knowledge increases is a crucial task of epistemological inquiry, and, indeed, the progress of science has been a topic of considerable interest. Yet, eventually, late in the century, attention waned and for the most part shifted to other tasks. Recently, however, a provocative article by Bird has re-ignited philosophical interest in the topic of scientific progress (Bird, 2007). Bird criticizes what he takes to have been the most prominent accounts advanced in the 20th century and presses for what he views as a venerable but lately overlooked one. He claims that realist philosophers of science have often presumed that truth is the goal of science and progress to be properly characterized by an accu- mulation of truth or truths, and notes that anti-realists have often rejected truth as a goal of science and characterized progress in terms of, among other things, “problem-solving effectiveness.”1 He criticizes the former for overlooking the relevance of justification to progress and the latter for giving up on truth. -
The Pragmatics of Explanation: Remarks on Van Fraassen's Theory of Why-Questions1
The Pragmatics of explanation: Remarks on van Fraassen’s theory of why-questions1 A Pragmática da explicação: Comentários sobre a teoria das questões-por-quê de van Fraassen Renato Cesar Cani Universidade Federal do Paraná – UFPR/CAPES – Brasil [email protected] Abstract: In this article, my aim is to analyze Bas van Fraassen’s pragmatic solution to two of the traditional problems concerning scientific explanation, namely, rejection and asymmetry. According to his view, an explanation is an answer to some request for information. The emergence of a question, as well as the evaluation of the explanations adduced, depends on considerations about contextual factors. In addition, I will evaluate the pertinence of objections raised by Philip Kitcher and Wesley Salmon against van Fraassen’s account. I will argue that their charge is not sound, for it actually misunderstands the role played by context in van Fraassen’s account. Although Salmon’s and Kitcher’s realist commitments motivate the point made by them, I will hold that a pragmatic account of explanation does not commit one to an anti-realist approach to science. Keywords: Pragmatics of Explanation. Bas van Fraassen. Why-questions. Asymmetry. Realism. Resumo: Neste artigo, meu objetivo é analisar a solução pragmática oferecida por Bas van Fraassen a dois dos tradicionais problemas da explicação científica, quais sejam, o da rejeição e o da assimetria. Em sua visão, uma explicação é uma resposta a alguma demanda por informação. O surgimento de uma questão, bem como a avaliação das possíveis explicações, depende de fatores contextuais. Além disso, avaliarei a pertinência das objeções de Philip Kitcher e Wesley Salmon contra a concepção de van Fraassen. -
An Argument Against the Unification Account of Explanation
An Argument Against the Unification Account of Explanation Michael Strevens Draft of December 2009 Abstract Unification accounts of explanation equate unifying power with explanatory power; you might expect, then, that an increase in the known unifying power of a theory would be accompanied by an increase the perceived quality of its explanations. This paper presents examples suggesting that there is no such relationship: a theory’s increased unifying power means that it explains many new things, but it does not explain the old things any better just because it now explains the new things. It seems that the relationship between unifying and explanatory power is rather complex—or non-existent. The negative implications for the unification account are investigated. 1 1. The Unication Approach to Explanation According to a unification account of explanation, to explain a phenomenon— an event, a sequence of events, a law, an ongoing “effect” such as the aurora borealis—is to show that it belongs to a set of phenomena which can be unified by a relatively simple theory (Friedman 1974; Kitcher 1981, 1989). A generic form of the unification account might, then, require that an explana- tion of a phenomenon e do three things: 1. Present a sufficiently simple theory T, 2. Present a sufficiently large and perhaps diverse set of phenomena E to which e belongs, and 3. Show that (the phenomena in) E can be derived in the right sort of way from T. To flesh out the view, three things must be provided: an account of what counts as deriving E from T in the “right sort of way”, an account of what counts as a “sufficiently large” (or sufficiently diverse) set of phenomena, and an account of what counts as a “sufficiently simple” theory. -
The Problem of Induction
The Problem of Induction Gilbert Harman Department of Philosophy, Princeton University Sanjeev R. Kulkarni Department of Electrical Engineering, Princeton University July 19, 2005 The Problem The problem of induction is sometimes motivated via a comparison between rules of induction and rules of deduction. Valid deductive rules are necessarily truth preserving, while inductive rules are not. So, for example, one valid deductive rule might be this: (D) From premises of the form “All F are G” and “a is F ,” the corresponding conclusion of the form “a is G” follows. The rule (D) is illustrated in the following depressing argument: (DA) All people are mortal. I am a person. So, I am mortal. The rule here is “valid” in the sense that there is no possible way in which premises satisfying the rule can be true without the corresponding conclusion also being true. A possible inductive rule might be this: (I) From premises of the form “Many many F s are known to be G,” “There are no known cases of F s that are not G,” and “a is F ,” the corresponding conclusion can be inferred of the form “a is G.” The rule (I) might be illustrated in the following “inductive argument.” (IA) Many many people are known to have been moral. There are no known cases of people who are not mortal. I am a person. So, I am mortal. 1 The rule (I) is not valid in the way that the deductive rule (D) is valid. The “premises” of the inductive inference (IA) could be true even though its “con- clusion” is not true. -
The P–T Probability Framework for Semantic Communication, Falsification, Confirmation, and Bayesian Reasoning
philosophies Article The P–T Probability Framework for Semantic Communication, Falsification, Confirmation, and Bayesian Reasoning Chenguang Lu School of Computer Engineering and Applied Mathematics, Changsha University, Changsha 410003, China; [email protected] Received: 8 July 2020; Accepted: 16 September 2020; Published: 2 October 2020 Abstract: Many researchers want to unify probability and logic by defining logical probability or probabilistic logic reasonably. This paper tries to unify statistics and logic so that we can use both statistical probability and logical probability at the same time. For this purpose, this paper proposes the P–T probability framework, which is assembled with Shannon’s statistical probability framework for communication, Kolmogorov’s probability axioms for logical probability, and Zadeh’s membership functions used as truth functions. Two kinds of probabilities are connected by an extended Bayes’ theorem, with which we can convert a likelihood function and a truth function from one to another. Hence, we can train truth functions (in logic) by sampling distributions (in statistics). This probability framework was developed in the author’s long-term studies on semantic information, statistical learning, and color vision. This paper first proposes the P–T probability framework and explains different probabilities in it by its applications to semantic information theory. Then, this framework and the semantic information methods are applied to statistical learning, statistical mechanics, hypothesis evaluation (including falsification), confirmation, and Bayesian reasoning. Theoretical applications illustrate the reasonability and practicability of this framework. This framework is helpful for interpretable AI. To interpret neural networks, we need further study. Keywords: statistical probability; logical probability; semantic information; rate-distortion; Boltzmann distribution; falsification; verisimilitude; confirmation measure; Raven Paradox; Bayesian reasoning 1. -
Explanatory Proofs and Beautiful Proofs
Journal of Humanistic Mathematics Volume 6 | Issue 1 January 2016 Explanatory Proofs and Beautiful Proofs Marc Lange University of North Carolina at Chapel Hill Follow this and additional works at: https://scholarship.claremont.edu/jhm Part of the Logic and Foundations of Mathematics Commons, Metaphysics Commons, and the Philosophy of Science Commons Recommended Citation Lange, M. "Explanatory Proofs and Beautiful Proofs," Journal of Humanistic Mathematics, Volume 6 Issue 1 (January 2016), pages 8-51. DOI: 10.5642/jhummath.201601.04 . Available at: https://scholarship.claremont.edu/jhm/vol6/iss1/4 ©2016 by the authors. This work is licensed under a Creative Commons License. JHM is an open access bi-annual journal sponsored by the Claremont Center for the Mathematical Sciences and published by the Claremont Colleges Library | ISSN 2159-8118 | http://scholarship.claremont.edu/jhm/ The editorial staff of JHM works hard to make sure the scholarship disseminated in JHM is accurate and upholds professional ethical guidelines. However the views and opinions expressed in each published manuscript belong exclusively to the individual contributor(s). The publisher and the editors do not endorse or accept responsibility for them. See https://scholarship.claremont.edu/jhm/policies.html for more information. Explanatory Proofs and Beautiful Proofs Cover Page Footnote My thanks to the organizers of and the participants at the Conference on Beauty and Explanation in Mathematics, University of Umea, Sweden, in March 2014, and to two referees for this journal. This work is available in Journal of Humanistic Mathematics: https://scholarship.claremont.edu/jhm/vol6/iss1/4 Explanatory Proofs and Beautiful Proofs1 Marc Lange Department of Philosophy, University of North Carolina at Chapel Hill, USA [email protected] Abstract This paper concerns the relation between a proof’s beauty and its explanatory power — that is, its capacity to go beyond proving a given theorem to explaining why that theorem holds. -
INDUCTION and EXPLANATORY DEFINITIONS in MATHEMATICS 1. Introduction the Question of Whether Mathematical Induction Is Explanato
INDUCTION AND EXPLANATORY DEFINITIONS IN MATHEMATICS ELLEN LEHET Abstract. In this paper, I argue that there are cases of explanatory induction in math- ematics. To do so, I first introduce the notion of explanatory definition in the context of mathematical explanation. A large part of the paper is dedicated to introducing and analyz- ing this notion of explanatory definition and the role it plays in mathematics. After doing so, I discuss a particular inductive definition in advanced mathematics | CW -complexes | and argue that it is explanatory. With this, we see that there are cases of explanatory induction. Philosophy of Mathematics and Explanation and Mathematical Induction and Mathemat- ical Definition and Mathematical Practice 1. Introduction The question of whether mathematical induction is explanatory has proven to be controver- sial. A lot of the discussion has relied on people's intuitions about proofs by induction. It is important to realize, however, that mathematical induction is not used solely for proofs, but is also used for mathematical definitions. These cases of inductive definition provide another way in which induction might be explanatory. With an account of explanatory definition, we are able to evaluate inductive definitions and will see that they are (at least in some cases) explanatory. The objective of this paper is to introduce the notion of explanatory definition, and to demonstrate that some inductive definitions are explanatory. In section 2, I will discuss the literature relating to explanation and mathematical induc- tion. I will focus on Marc Lange's argument that induction is not explanatory, and will discuss 1 2 ELLEN LEHET two established criticisms of his argument. -
Degrees of Riskiness, Falsifiability, and Truthlikeness. a Neo-Popperian
Degrees of riskiness, falsifiability, and truthlikeness. A neo-Popperian account applicable to probabilistic theories. Leander Vignero Sylvia Wenmackers [email protected] [email protected] KU Leuven, Institute of Philosophy, Centre for Logic and Philosophy of Science, Kardinaal Mercierplein 2 { bus 3200, 3000 Leuven, Belgium. July 7, 2021 (Forthcoming in Synthese.) Abstract In this paper, we take a fresh look at three Popperian concepts: riskiness, falsifiability, and truthlikeness (or verisimilitude) of scien- tific hypotheses or theories. First, we make explicit the dimensions that underlie the notion of riskiness. Secondly, we examine if and how degrees of falsifiability can be defined, and how they are related to var- ious dimensions of the concept of riskiness as well as the experimental context. Thirdly, we consider the relation of riskiness to (expected degrees of) truthlikeness. Throughout, we pay special attention to probabilistic theories and we offer a tentative, quantitative account of verisimilitude for probabilistic theories. \Modern logic, as I hope is now evident, has the effect of enlarging our abstract imagination, and providing an infinite number of possible hypotheses to be applied in the analysis of any complex fact." { Russell (1914, p. 58). A theory is falsifiable if it allows us to deduce predictions that can be compared to evidence, which according to our background knowledge could arXiv:2107.03772v1 [physics.hist-ph] 8 Jul 2021 show the prediction | and hence the theory | to be false. In his defense of falsifiability as a demarcation criterion, Popper has stressed the importance of bold, risky claims that can be tested, for they allow science to make progress.