Recent Debates About the a Priori Hartry Field
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												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. - 
												
												Machine Learning in Scientometrics
DEPARTAMENTO DE INTELIGENCIA ARTIFICIAL Escuela Tecnica´ Superior de Ingenieros Informaticos´ Universidad Politecnica´ de Madrid PhD THESIS Machine Learning in Scientometrics Author Alfonso Iba´nez˜ MS Computer Science MS Artificial Intelligence PhD supervisors Concha Bielza PhD Computer Science Pedro Larranaga˜ PhD Computer Science 2015 Thesis Committee President: C´esarHerv´as Member: Jos´eRam´onDorronsoro Member: Enrique Herrera Member: Irene Rodr´ıguez Secretary: Florian Leitner There are no secrets to success. It is the result of preparation, hard work, and learning from failure. Acknowledgements Ph.D. research often appears a solitary undertaking. However, it is impossible to maintain the degree of focus and dedication required for its completion without the help and support of many people. It has been a difficult long journey to finish my Ph.D. research and it is of justice to cite here all of them. First and foremost, I would like to thank Concha Bielza and Pedro Larra~nagafor being my supervisors and mentors. Without your unfailing support, recommendations and patient, this thesis would not have been the same. You have been role models who not only guided my research but also demonstrated your enthusiastic research attitudes. I owe you so much. Whatever research path I do take, I will be prepared because of you. I would also like to express my thanks to all my friends and colleagues at the Computa- tional Intelligence Group who provided me with not only an excellent working atmosphere and stimulating discussions but also friendships, care and assistance when I needed. My special thank-you goes to Rub´enArma~nanzas,Roberto Santana, Diego Vidaurre, Hanen Borchani, Pedro L. - 
												
												Application of Fuzzy Query Based on Relation Database Dongmei Wei Liangzhong Yi Zheng Pei
Application of Fuzzy Query Based on Relation Database Dongmei Wei Liangzhong Yi Zheng Pei School of Mathematics & Computer Engineering, Xihua University, Chengdu 610039, China Abstract SELECT <list of ¯elds> FROM <list of tables> ; The traditional query in relation database is unable WHERE <attribute> in <multi-valued attribute> to satisfy the needs for dealing with fuzzy linguis- tic values. In this paper, a new data query tech- SELECT <list of ¯elds> FROM <list of tables>; nique combined fuzzy theory and SQL is provided, WHERE NOT <condition> and the query can be implemented for fuzzy linguis- tic values query via a interface to Microsoft Visual SELECT <list of ¯elds> FROM <list of tables>; Foxpro. Here, we applied it to an realism instance, WHERE <subcondition> AND <subcondition> questions could be expressed by fuzzy linguistic val- ues such as young, high salary, etc, in Employee SELECT <list of ¯elds> FROM <list of tables>; relation database. This could be widely used to WHERE <subcondition> OR <subcondition> realize the other fuzzy query based on database. However, the Complexity is limited in precise data processing and is unable to directly express Keywords: Fuzzy query, Relation database, Fuzzy fuzzy concepts of natural language. For instance, theory, SQL, Microsoft visual foxpro in employees relation database, to deal with a query statement like "younger, well quali¯ed or better 1. Introduction performance ", it is di±cult to construct SQL be- cause the query words are fuzzy expressions. In Database management systems(DBMS) are ex- order to obtain query results, there are two basic tremely useful software products which have been methods of research in the use of SQL Combined used in many kinds of systems [1]-[3]. - 
												
												On Fuzzy and Rough Sets Approaches to Vagueness Modeling⋆ Extended Abstract
From Free Will Debate to Embodiment of Fuzzy Logic into Washing Machines: On Fuzzy and Rough Sets Approaches to Vagueness Modeling⋆ Extended Abstract Piotr Wasilewski Faculty of Mathematics, Informatics and Mechanics University of Warsaw Banacha 2, 02-097 Warsaw, Poland [email protected] Deep scientific ideas have at least one distinctive property: they can be applied both by philosophers in abstract fundamental debates and by engineers in concrete practi- cal applications. Mathematical approaches to modeling of vagueness also possess this property. Problems connected with vagueness have been discussed at the beginning of XXth century by philosophers, logicians and mathematicians in developing foundations of mathematics leading to clarification of logical semantics and establishing of math- ematical logic and set theory. Those investigations led also to big step in the history of logic: introduction of three-valued logic. In the second half of XXth century some mathematical theories based on vagueness idea and suitable for modeling vague con- cepts were introduced, including fuzzy set theory proposed by Lotfi Zadeh in 1965 [16] and rough set theory proposed by Zdzis¸saw Pawlak in 1982 [4] having many practical applications in various areas from engineering and computer science such as control theory, data mining, machine learning, knowledge discovery, artificial intelligence. Concepts in classical philosophy and in mathematics are not vague. Classical the- ory of concepts requires that definition of concept C hast to provide exact rules of the following form: if object x belongs to concept C, then x possess properties P1,P2,...,Pn; if object x possess properties P1,P2,...,Pn, then x belongs to concept C. - 
												
												Research Hotspots and Frontiers of Product R&D Management
applied sciences Article Research Hotspots and Frontiers of Product R&D Management under the Background of the Digital Intelligence Era—Bibliometrics Based on Citespace and Histcite Hongda Liu 1,2, Yuxi Luo 2, Jiejun Geng 3 and Pinbo Yao 2,* 1 School of Economics & Management, Tongji University, Shanghai 200092, China; [email protected] or [email protected] 2 School of Management, Shanghai University, Shanghai 200444, China; [email protected] 3 School of Management, Shanghai International Studies University, Shanghai 200333, China; [email protected] * Correspondence: [email protected] Abstract: The rise of “cloud-computing, mobile-Internet, Internet of things, big-data, and smart-data” digital technology has brought a subversive revolution to enterprises and consumers’ traditional patterns. Product research and development has become the main battlefield of enterprise compe- Citation: Liu, H.; Luo, Y.; Geng, J.; tition, facing an environment where challenges and opportunities coexist. Regarding the concepts Yao, P. Research Hotspots and and methods of product R&D projects, the domestic start was later than the international ones, and Frontiers of Product R&D many domestic companies have also used successful foreign cases as benchmarks to innovate their Management under the Background management methods in practice. “Workers must first sharpen their tools if they want to do their of the Digital Intelligence jobs well”. This article will start from the relevant concepts of product R&D projects and summarize Era—Bibliometrics Based on current R&D management ideas and methods. We combined the bibliometric analysis software Citespace and Histcite. Appl. Sci. Histcite and Citespace to sort out the content of domestic and foreign literature and explore the 2021, 11, 6759. - 
												
												Download (Accessed on 30 July 2016)
data Article Earth Observation for Citizen Science Validation, or Citizen Science for Earth Observation Validation? The Role of Quality Assurance of Volunteered Observations Didier G. Leibovici 1,* ID , Jamie Williams 2 ID , Julian F. Rosser 1, Crona Hodges 3, Colin Chapman 4, Chris Higgins 5 and Mike J. Jackson 1 1 Nottingham Geospatial Science, University of Nottingham, Nottingham NG7 2TU, UK; [email protected] (J.F.R.); [email protected] (M.J.J.) 2 Environment Systems Ltd., Aberystwyth SY23 3AH, UK; [email protected] 3 Earth Observation Group, Aberystwyth University Penglais, Aberystwyth SY23 3JG, UK; [email protected] 4 Welsh Government, Aberystwyth SY23 3UR, UK; [email protected] 5 EDINA, University of Edinburgh, Edinburgh EH3 9DR, UK; [email protected] * Correspondence: [email protected] Received: 28 August 2017; Accepted: 19 October 2017; Published: 23 October 2017 Abstract: Environmental policy involving citizen science (CS) is of growing interest. In support of this open data stream of information, validation or quality assessment of the CS geo-located data to their appropriate usage for evidence-based policy making needs a flexible and easily adaptable data curation process ensuring transparency. Addressing these needs, this paper describes an approach for automatic quality assurance as proposed by the Citizen OBservatory WEB (COBWEB) FP7 project. This approach is based upon a workflow composition that combines different quality controls, each belonging to seven categories or “pillars”. Each pillar focuses on a specific dimension in the types of reasoning algorithms for CS data qualification. - 
												
												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. - 
												
												A Framework for Software Modelling in Social Science Re- Search
A FRAMEWORK FOR SOFTWARE MODELLING IN SOCIAL SCIENCE RESEARCH by Piper J. Jackson B.Sc., Simon Fraser University, 2005 B.A. (Hons.), McGill University, 1996 a Thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the School of Computing Science Faculty of Applied Sciences c Piper J. Jackson 2013 SIMON FRASER UNIVERSITY Summer 2013 All rights reserved. However, in accordance with the Copyright Act of Canada, this work may be reproduced without authorization under the conditions for \Fair Dealing." Therefore, limited reproduction of this work for the purposes of private study, research, criticism, review and news reporting is likely to be in accordance with the law, particularly if cited appropriately. APPROVAL Name: Piper J. Jackson Degree: Doctor of Philosophy Title of Thesis: A Framework for Software Modelling in Social Science Re- search Examining Committee: Dr. Steven Pearce Chair Dr. Uwe Gl¨asser Senior Supervisor Professor Dr. Vahid Dabbaghian Supervisor Adjunct Professor, Mathematics Associate Member, Computing Science Dr. Lou Hafer Internal Examiner Associate Professor Dr. Nathaniel Osgood External Examiner Associate Professor, University of Saskatchewan Date Approved: April 29, 2013 ii Partial Copyright Licence iii Abstract Social science is critical to decision making at the policy level. Software modelling and sim- ulation are innovative computational methods that provide alternative means of developing and testing theory relevant to policy decisions. Software modelling is capable of dealing with obstacles often encountered in traditional social science research, such as the difficulty of performing real-world experimentation. As a relatively new science, computational research in the social sciences faces significant challenges, both in terms of methodology and accep- tance. - 
												
												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................. - 
												
												Adopted from Pdflib Image Sample
ON NUMBERS by LINDA ELIZABETH WETZEL B.A. City College of New York (1975) Submitted to the Department of Linguistics and Philosophy in Partial Fulfillment of the Requirements of the Degree of DOCTOR OF PHILOSOPHY at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY February 1984 @ Linda E. Wetzel The author hereby grants to M.I.T. permission to reproduce and to distribute copies of this thesis document in whole or in part, Signature of Author: Department of Linguistics and Philosophy January 13, 1984 Certified by : .-_ - Richard L, Cartwright Thesis Supervisor Accepted by: v Richard L. Cartwright Chairman, Departmental Graduate Committee ON NUMBERS Linda E, Wetzel Submitted to the Department of Linguistics and Philosophy of October 21, 1983 in partial fulfillment of the requirements for the degree of Doctor of Philosophy ABSTRACT We talk as though there are numbers. The view I defend, the "popularn view, has it that there -are numbers. However, since they clearly are not physical objects, we reason that they must be abstract ones. This suggests a realm of non-spatial non-temporal objects standing in numerical relations; arithmetic knowledge is then knowledge of this realm. But how do spatio~temporal creatures like ourselves come to have knowledge of this realm? The problem ("Benacerrafls probleni") can be avoided by arguing that there are no numbers. In "What Numbers Could Not Bew Benacerraf himself took such a route. In chapter one, I discuss three of Benacerrafls arguments, showing that the first is circular, that the second involves a consideration that can be explained by less drastic means than supposing there are no numbers, and that the third would, if successful, show that neither sets nor expressions exist either. - 
												
												Intrinsic Explanation and Field's Dispensabilist Strategy Sydney-Tilburg Conference on Reduction and the Special Sciences Russ
Intrinsic Explanation and Field’s Dispensabilist Strategy Sydney-Tilburg Conference on Reduction and the Special Sciences Russell Marcus Department of Philosophy, Hamilton College 198 College Hill Road Clinton NY 13323 [email protected] (315) 859-4056 (office) (315) 381-3125 (home) September 2007 ~2880 words Abstract: Philosophy of mathematics for the last half-century has been dominated in one way or another by Quine’s indispensability argument. The argument alleges that our best scientific theory quantifies over, and thus commits us to, mathematical objects. In this paper, I present new considerations which undermine the most serious challenge to Quine’s argument, Hartry Field’s reformulation of Newtonian Gravitational Theory. Intrinsic Explanation, Page 1 §1: Introduction Quine argued that we are committed to the existence mathematical objects because of their indispensable uses in scientific theory. In this paper, I defend Quine’s argument against the most popular objection to it, that we can reformulate science without reference to mathematical objects. I interpret Quine’s argument as follows:1 (QIA) QIA.1: We should believe the theory which best accounts for our empirical experience. QIA.2: If we believe a theory, we must believe in its ontic commitments. QIA.3: The ontic commitments of any theory are the objects over which that theory first-order quantifies. QIA.4: The theory which best accounts for our empirical experience quantifies over mathematical objects. QIA.C: We should believe that mathematical objects exist. An instrumentalist may deny either QIA.1 or QIA.2, or both. Regarding QIA.1, there is some debate over whether we should believe our best theories. - 
												
												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 .