Fuzzy Logic & Vagueness
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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. -
Learning to Spot Common Fallacies
LEARNING TO SPOT COMMON FALLACIES We intend this article to be a resource that you will return to when the fallacies discussed in it come up throughout the course. Do not feel that you need to read or master the entire article now. We’ve discussed some of the deep-seated psychological obstacles to effective logical and critical thinking in the videos. This article sets out some more common ways in which arguments can go awry. The defects or fallacies presented here tend to be more straightforward than psychological obstacles posed by reasoning heuristics and biases. They should, therefore, be easier to spot and combat. You will see though, that they are very common: keep an eye out for them in your local paper, online, or in arguments or discussions with friends or colleagues. One reason they’re common is that they can be quite effective! But if we offer or are convinced by a fallacious argument we will not be acting as good logical and critical thinkers. Species of Fallacious Arguments The common fallacies are usefully divided into three categories: Fallacies of Relevance, Fallacies of Unacceptable Premises, and Formal Fallacies. Fallacies of Relevance Fallacies of relevance offer reasons to believe a claim or conclusion that, on examination, turn out to not in fact to be reasons to do any such thing. 1. The ‘Who are you to talk?’, or ‘You Too’, or Tu Quoque Fallacy1 Description: Rejecting an argument because the person advancing it fails to practice what he or she preaches. Example: Doctor: You should quit smoking. It’s a serious health risk. -
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. -
The Fallacy of Composition and Meta-Argumentation"
CORE Metadata, citation and similar papers at core.ac.uk Provided by Scholarship at UWindsor University of Windsor Scholarship at UWindsor OSSA Conference Archive OSSA 10 May 22nd, 9:00 AM - May 25th, 5:00 PM Commentary on: Maurice Finocchiaro's "The fallacy of composition and meta-argumentation" Michel Dufour Sorbonne-Nouvelle, Institut de la Communication et des Médias Follow this and additional works at: https://scholar.uwindsor.ca/ossaarchive Part of the Philosophy Commons Dufour, Michel, "Commentary on: Maurice Finocchiaro's "The fallacy of composition and meta- argumentation"" (2013). OSSA Conference Archive. 49. https://scholar.uwindsor.ca/ossaarchive/OSSA10/papersandcommentaries/49 This Commentary is brought to you for free and open access by the Conferences and Conference Proceedings at Scholarship at UWindsor. It has been accepted for inclusion in OSSA Conference Archive by an authorized conference organizer of Scholarship at UWindsor. For more information, please contact [email protected]. Commentary on: Maurice Finocchiaro’s “The fallacy of composition and meta-argumentation” MICHEL DUFOUR Department «Institut de la Communication et des Médias» Sorbonne-Nouvelle 13 rue Santeuil 75231 Paris Cedex 05 France [email protected] 1. INTRODUCTION In his paper on the fallacy of composition, Maurice Finocchiaro puts forward several important theses about this fallacy. He also uses it to illustrate his view that fallacies should be studied in light of the notion of meta-argumentation at the core of his recent book (Finocchiaro, 2013). First, he expresses his puzzlement. Some authors have claimed that this fallacy is quite common (this is the ubiquity thesis) but it seems to have been neglected by scholars. -
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. -
Fallacies in Reasoning
FALLACIES IN REASONING FALLACIES IN REASONING OR WHAT SHOULD I AVOID? The strength of your arguments is determined by the use of reliable evidence, sound reasoning and adaptation to the audience. In the process of argumentation, mistakes sometimes occur. Some are deliberate in order to deceive the audience. That brings us to fallacies. I. Definition: errors in reasoning, appeal, or language use that renders a conclusion invalid. II. Fallacies In Reasoning: A. Hasty Generalization-jumping to conclusions based on too few instances or on atypical instances of particular phenomena. This happens by trying to squeeze too much from an argument than is actually warranted. B. Transfer- extend reasoning beyond what is logically possible. There are three different types of transfer: 1.) Fallacy of composition- occur when a claim asserts that what is true of a part is true of the whole. 2.) Fallacy of division- error from arguing that what is true of the whole will be true of the parts. 3.) Fallacy of refutation- also known as the Straw Man. It occurs when an arguer attempts to direct attention to the successful refutation of an argument that was never raised or to restate a strong argument in a way that makes it appear weaker. Called a Straw Man because it focuses on an issue that is easy to overturn. A form of deception. C. Irrelevant Arguments- (Non Sequiturs) an argument that is irrelevant to the issue or in which the claim does not follow from the proof offered. It does not follow. D. Circular Reasoning- (Begging the Question) supports claims with reasons identical to the claims themselves. -
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. -
Leveraging the Power of Place in Citizen Science for Effective Conservation Decision Making
BIOC-06887; No of Pages 10 Biological Conservation xxx (2016) xxx–xxx Contents lists available at ScienceDirect Biological Conservation journal homepage: www.elsevier.com/locate/bioc Leveraging the power of place in citizen science for effective conservation decision making G. Newman a,⁎,1,2, M. Chandler b,1,2,M.Clydec,1,2,B.McGreavyd,1,M.Haklaye,H.Ballardf, S. Gray g, R. Scarpino a,2, R. Hauptfeld a,2,D.Mellorh,J.Galloi,1 a Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA b Earthwatch Institute, Allston, MA 02134, USA c University of New Hampshire, Cooperative Extension, Durham, NH 03824-2500, USA d University of Maine, Department of Communication and Journalism, Orono, ME 04469, USA e University College London, WC1E 6BT London, UK f UC Davis, School of Education, Davis, CA 95616, USA g Department of Community Sustainability, Michigan State University, East Lansing, MI 48823, USA h Center for Open Science, Charlottesville, VA 22903, USA i Conservation Biology Institute, Corvallis, OR 97333, USA article info abstract Article history: Many citizen science projects are place-based - built on in-person participation and motivated by local conserva- Received 16 October 2015 tion. When done thoughtfully, this approach to citizen science can transform humans and their environment. De- Received in revised form 19 May 2016 spite such possibilities, many projects struggle to meet decision-maker needs, generate useful data to inform Accepted 17 July 2016 decisions, and improve social-ecological resilience. Here, we define leveraging the ‘power of place’ in citizen sci- Available online xxxx ence, and posit that doing this improves conservation decision making, increases participation, and improves community resilience. -
334 CHAPTER 7 INFORMAL FALLACIES a Deductive Fallacy Is
CHAPTER 7 INFORMAL FALLACIES A deductive fallacy is committed whenever it is suggested that the truth of the conclusion of an argument necessarily follows from the truth of the premises given, when in fact that conclusion does not necessarily follow from those premises. An inductive fallacy is committed whenever it is suggested that the truth of the conclusion of an argument is made more probable by its relationship with the premises of the argument, when in fact it is not. We will cover two kinds of fallacies: formal fallacies and informal fallacies. An argument commits a formal fallacy if it has an invalid argument form. An argument commits an informal fallacy when it has a valid argument form but derives from unacceptable premises. A. Fallacies with Invalid Argument Forms Consider the following arguments: (1) All Europeans are racist because most Europeans believe that Africans are inferior to Europeans and all people who believe that Africans are inferior to Europeans are racist. (2) Since no dogs are cats and no cats are rats, it follows that no dogs are rats. (3) If today is Thursday, then I'm a monkey's uncle. But, today is not Thursday. Therefore, I'm not a monkey's uncle. (4) Some rich people are not elitist because some elitists are not rich. 334 These arguments have the following argument forms: (1) Some X are Y All Y are Z All X are Z. (2) No X are Y No Y are Z No X are Z (3) If P then Q not-P not-Q (4) Some E are not R Some R are not E Each of these argument forms is deductively invalid, and any actual argument with such a form would be fallacious.