CDT 403 Research Methodology in Natural Sciences and

CDT 403 Research Methodology in Natural Sciences and Engineering Theory of Science SCIENCE VERSUS PSEUDOSCIENCE AS CRITICAL THINKING VERSUS WISHFUL THINKING Gordana Dodig-Crnkovic School of Innovation, Design and Engineering Mälardalen University 1

THEORY OF SCIENCE Lecture 1 INFORMATION, COMPUTATION, KNOWLEDGE AND SCIENCE Lecture 2 SCIENCE AND CRITICAL THINKING. PSEUDOSCIENCE AND WISHFUL THINKING - DEMARCATION Lecture 3 SCIENCE, RESEARCH, TECHNOLOGY, SOCIETAL ASPECTS. PROGRESS. HISTORY OF SCIENTIFIC THEORY. POSTMODERNISM AND CROSSDISCIPLINES Lecture 4 PROFESSIONAL & RESEARCH ETHICS 2

FROM THE FIRST LECTURE: HISTORICAL DEVELOPMENT OF THINKING ABOUT THE WORLD/NATURE/UNIVERSE 3

Three Major Paradigm Shifts Mytho-poetic, God-Centric Universe (Classical) Mechanic Universe Info-Computational Human-Centric Universe Dodig-Crnkovic G and Müller V, A Dialogue Concerning Two World Systems: Info-Computational vs. Mechanistic. In: INFORMATION AND COMPUTATION , World Scientific Publishing Co. Series in Information Studies. Editors: G Dodig-Crnkovic and M Burgin, 2011. http: //arxiv. org/abs/0910. 5001 2009

KNOWLEDGE 5

Knowledge & Hinders to Knowledge – what we believe we know: (what we have strong reasons to believe we understand hold for correct/true). Ignorance – white spots on the map of knowledge – what we know that we do not know. False believes that look-like knowledge – a part of our knowledge, what we believe we know, but actually we are wrong! p. 6

Things That We Don’t Know Well Enough • COMPLEX SYSTEMS AND EMERGENCY • PHYSICS AT VERY SMALL AND VERY LARGE DIMENSIONS • MECHANISMS OF LIFE & ORIGINS OF LIFE • HUMAN BRAIN (including knowledge generation) p. 7

What We Miss in the Present Scientific Picture of the World Specific questions: ● MECHANISTIC VS. COMPLEX ● REDUCTION VS. HOLISM (SYSTEM VIEW) ● OBSERVER DEPENDENCE VS. GOD EYE VIEW ● EMBODDIEDNESS OF ALL NATURAL PHENOMENA INCLUDING MIND p. 8

What is Knowledge? Plato´s Definition Plato believed that we learn in this life by remembering knowledge originally acquired in a previous life, and that the soul already has knowledge, and we learn by recollecting what in fact the soul already knows. [At present we know that we inherit some physical preconditions, structures and abilities already at birth. In a sense those structures of our brains and bodies may be seen as the result of evolution, so in a sense they encapsulate memories of the historical development of our species. ] 9

What is Knowledge? Plato´s Definition Plato offers three definitions of knowledge, [dialogues Theaetetus 201 and Meno 98] all of which Socrates rejects. Plato's third definition: “Knowledge is justified, true belief. " The problem with this concerns the word “justified”. All interpretations of “justified” are deemed inadequate. Edmund Gettier, in the paper called "Is Justified True Belief Knowledge? “ argues that knowledge is not the same as justified true belief. (Gettier Problem) 10

What is Knowledge? Descartes´ Definition “Deduction by which we understand all necessary inference from other facts that are known with certainty, “ leads to knowledge when recommended method is being followed. "Intuition is the undoubting conception of an unclouded and attentive mind, and springs from the light of reasons alone; it is more certain than deduction itself in that it is simpler. " 11

What is Knowledge? Descartes´ Definition "Intuitions provide the ultimate grounds for logical deductions. Ultimate first principles must be known through intuition while deduction logically derives conclusions from them. These two methods [intuition and deduction] are the most certain routes to knowledge, and the mind should admit no others. " 12

What is Knowledge? – Propositional knowledge: knowledge that such-and-such is the case. – Non-propositional knowledge (tacit knowledge): the knowing how to do something. 13

Sources of Knowledge – A priori knowledge (built in, developed by evolution and inheritance) (resides in the brain as structure) – Perception (“on-line input”, information acquisition) – Reasoning (information processing) – Testimony (network, communication) 14

Knowledge and Ignorance “Our knowledge is an island in the infinite ocean of the unknown. “ Knowledge and wonder: the natural world as man knows it, Victor F. Weisskopf (1962) "We live in an island of knowledge surrounded by a sea of ignorance. As our island of knowledge grows, so does the shore of our ignorance. “ John Wheeler 15

Greg Chaitin: A More Elaborate, Fractal Picture of Knowledge Mathematics is more like an archipelago consisting of islands of truths in an ocean of incomprehensible and uncompressible information. Greg Chaitin, in an interview in September 2003 says: “You see, you have all of mathematical truth, this ocean of mathematical truth. And this ocean has islands. An island here, algebraic truths. An island there, arithmetic truths. An island here, the calculus. And these are different fields of mathematics where all the ideas are interconnected in ways that mathematicians love; they fall into nice, interconnected patterns. But what I've discovered is all this sea around the islands. ” http: //www. youtube. com/watch? v=WAJE 35 w. X 1 n. Q&feature=related Mandelbrot http: //books. google. se/books? id=RUedy. Fup. PY 4 C&pg=PA 265&lpg=PA 265&dq=chaitin+knowledge+island&source=bl&ots=p 7 Aac. MKrm u&sig=1 Wzbvx. Kb. JF 16 GCTMgx. CJMj. Oo. Yhw&hl=sv#v=onepage&q=chaitin%20 knowledge%20 island&f=false 16

Physical basis of knowledge 17

Cell Processing Information http: //www. youtube. com/watch? v=NJxobgk. PEAo&feature=related From RNA to Protein Synthesis http: //www. youtube. com/watch? v=3 a. VT 2 DTbt. A 8&feature=related Replication, Transcription, and Translation http: //www. goldenswamp. com/page/2 18

Blurring the Boundary Between Perception and Memory http: //www. scientificamerican. com/article. cfm? id=perc eption-and-memory http: //www. sciencedaily. com 19

The Extended Mind Andy Clark and David Chalmers propose the idea of mind delegating cognitive* functions to the environment - in which objects within the environment function as a part of the mind http: //consc. net/papers/extended. html The term cognition (Latin: cognoscere, "to know", "to conceptualize" or "to recognize") refers to a faculty for the processing of information, applying knowledge, and changing preferences. Cognition, or cognitive processes, can be natural or artificial, conscious or unconscious. (Wikipedia) 20

Embodied Cognition – Knowledge as a Physical Phenomenon (Process/Structure) From: http: //parliamodisalutegianugoberti. blogspot. se/2011/11/brief-guideto-embodied-cognition-why. html http: //psych. wisc. edu/glenberglab/GLindex. html Ago Ergo Cogito - "I act, therefore I think“ – Certesian divide bridged. No separation mind-body 21

BOTTOM-UP (BODY->MIND) VS. TOP DOWN (MIND->BODY) VIEW OF SENSE-MAKING 22

Blue Brain (Human Brain) Project http: //online. wsj. com/article/SB 124751881557234725. html In Search for Intelligence, a Silicon Brain Twitches http: //bluebrain. epfl. ch/page-52741 -en. html 23

HBP - Computational Brain Processing Information • The project • Introduction • Goals • Neuroscience • The computing challenge • Towards understanding the brain • Research areas • Neuroinformatics • Neuroscience • Medicine • Cognition • Theory • Simulation • Supercomputing • Neurorobotics • Neuromorphic computing • Brain interfaces • Education • Ethical, legal and social issues • A european flagship • Animated map • Organisation • The FET flagship programme • Flagship call 24

The Human Brain Project: Science of 21 st Century The FET Flagship Program – a new initiative launched by the European Commission as part of its Future and Emerging Technologies (FET) initiative. http: //ist. ac. at/fileadmin/user_upload/pictures/IST_Lecture_Markram/H BP_presskit__austria. pdf • • • http: //www. youtube. com/watch? v=_r. PH 1 Abuu 9 M Henry Markram: Simulating the Brain — The Next Decisive Years [1/3] http: //www. youtube. com/watch? v=w. DY 4 c. FJauls Henry Markram: Simulating the Brain — The Next Decisive Years [2/3] http: //www. youtube. com/watch? v=h 06 lgy. ES 6 Oc Henry Markram: Simulating the Brain — The Next Decisive Years [3/3] • http: //www. youtube. com/watch? v=Hr. JQ_qkkx 4 E Five Tomorrows 25

Cognitive Computing IBM have been working on a cognitive computing project called Systems of Neuromorphic Adaptive Plastic Scalable Electronics (Sy. NAPSE). http: //www. ibm. com/smarterplanet/us/en/business_analytics/article/cognitive_computing. html http: //cacm. org/magazines/2011/8/114944 -cognitive-computing/fulltext Communications of the ACM , Vol. 54 No. 8, Pages 62 -71 26

What is Universe? What is Knowledge? What is Science? Based on an enormous boost of extended mind of humanity we witness a major paradigm shift in our understanding of the universe and our place in it. This big picture is important as it sets the framework for how we think. That is why not only theory of particular sciences or specific phenomena but even philosophy of nature makes. (And empirical data are as well known theory-laden, even by implicit theory)

Network Paradigm Many interacting pieces in one system forming a network Metabolic theory of ecology http: //online. kitp. ucsb. edu/online/pattern_i 03/west/oh/29. html http: //www. santafe. edu/about/people/profile/Geoffrey%20 West 28

http: //www. cs. cornell. edu/home/kleinber/networks-book Networks, Crowds, and Markets: Reasoning About a Highly Connected World High School Dating (Bearman, Moody, and Stovel, 2004) (Image by Mark Newman) Corporate E-Mail Communication (Adamic and Adar, 2005) Trails of Flickr Users in Manhattan (Crandall et al. 2009) 29

Science as a result of Scientific Community Map of Science http: //www. lanl. gov/news/albums/science/PLOS Map. Of. Science. jpg This "Map of Science" illustrates the online behavior of scientists accessing different scientific journals, publications, aggregators, etc. Colors represent the scientific discipline of each journal, based on disciplines http: //www. lanl. gov/news/index. php/fuseaction/nb. story/story_id/%2015965 30

Summary on Networks and why ”big picture” is necessary: ”http: //www. youtube. com/watch? v=n. Jm. Gr. Nd. J 5 Gw The Power of Networks 31

MAKING SENSE – CONSTRUCTION OF MEANING 32

Bottom-up View, Cognitive Agency-based Meaning (1) All meaning is determined by the method of analysis where the method of analysis sets the context and so the rules that are used to determine the “meaningful” from “meaningless”. C. J. Lofting 33

Meaning (2) At the fundamental level meaning is the result of process of • Differentiation and • Integration or identification of differences and similarities, recognition of patterns. 34

Meaning (3) Human brain is not tabula rasa (clean slate) on birth but rather contains (gene-based, evolutionary acquired): • morphological structures used for meaning production based on the distinctions of same/different, what/how, where/when etc. • behavioral patterns, etc. 35

Top-down View, Language-centered SEMIOTICS (1) Semiotics, the science of signs, looks at how humans search for and construct meaning. Semiotics: reality is a system of signs! (with an underlying system which establishes mutual relationships among those and defines identity and difference, i. e. enables the description of the dynamics. ) 36

SEMIOTICS (2) Three Levels of Semiotics (Theory of Signs) syntactics semantics pragmatics 37

SEMIOTICS (2 A) pragmatics semantics syntactics 38

SEMIOTICS (3) Reality is a construction. Information or meaning is not 'contained' in the (physical) world and 'transmitted' to us - we actively create meanings (“make sense”!) through a complex interplay of perceptions, and agency based on hard-wired behaviors and coding-decoding conventions. The study of signs is the study of the construction and maintenance of reality. 39

SEMIOTICS (4) 'A sign. . . is something which stands to somebody for something in some respect or capacity'. Sign takes a form of words, symbols, images, sounds, gestures, objects, etc. Anything can be a sign as long as someone interprets it as 'signifying' something - referring to or standing for something. 40

SEMIOTICS (5) (signified) (signifier) CAT The sign consists of – signifier (a pointer) – signified (that what pointer points to) 41

SEMIOTICS (7) – Reality is divided up into arbitrary categories by every language. [However this arbitrariness is essentially limited by our physical predispositions as human beings. Our cognitive capacities are defined to a high extent by our physical constitution. ] – The conceptual world with which each of us is familiar with, could have been divided up in a very different way. – The full meaning of a sign does not appear until it is placed in its context, and the context may serve an extremely subtle function. 42

REASONING • Use of reason, especially to form conclusions, inferences, or judgments. • Evidence or arguments used in thinking or argumentation. • The process of drawing conclusions from facts, evidence, etc. 43

LOGICAL ARGUMENT An argument is a statement logically inferred from premises. Two sorts of arguments: – Deductive general particular – Inductive particular general 44

LOGICAL ARGUMENT Constituents of a logical argument: – premises – inference and – conclusion 45

JUDGMENT It is important to notice that all reasoning basically depends on judgment (the ability to perceive and distinguish relationships; the capacity to form an opinion by distinguishing and evaluating) “Now, the question, What is a judgment? is no small question, because the notion of judgment is just about the first of all the notions of logic, the one that has to be explained before all the others, before even the notions of proposition and truth, for instance. ” Per Martin-Löf On the Meanings of the Logical Constants and the Justifications of the Logical Laws; Nordic Journal of Philosophical Logic, 1(1): 11 60, 1996. 46

LOGIC 47

INDUCTION • Empirical Induction • Mathematical Induction 48

EMPIRICAL INDUCTION The generic form of an inductive argument: • Every A we have observed is a B. • Therefore, every A is a B. 49

An Example of Inductive Inference • Every instance of water (at sea level) that we have observed has boiled at 100 C. • Therefore, all water (at sea level) boils at 100 C. Inductive argument will never offer 100% certainty! A typical problem with inductive argument is that it is formulated generally, while the observations are made under some particular, specific conditions. ( In our example we could add ”in an open vessel” as well. ) 50

Inductive Inference has Limited Validity An inductive argument have no way to logically (with certainty, with necessity) prove that: • the phenomenon studied do exist in general domain • that it continues to behave according to the same pattern According to Popper, inductive argument only supports working theories based on the collected evidence. 51

Counter-example Perhaps the most well known counter-example was the discovery of black swans in Australia. Prior to the point, it was assumed that all swans were white. With the discovery of the counter-example, the induction concerning the color of swans had to be re-modeled. 52

MATHEMATICAL INDUCTION The aim of the empirical induction is to establish the law. In the mathematical induction we have the law already formulated. We must prove that it holds generally. The basis for mathematical induction is the property of the wellordering for the natural numbers. 53

THE PRINCIPLE OF MATHEMATICAL INDUCTION Suppose P(n) is a statement involving an integer n. Than to prove that P(n) is true for every n n 0 it is sufficient to show these two things: 1. P(n 0) is true. (the basis step) 2. For any k n 0, if P(k) is true, then P(k+1) is true. (the induction step) 54

THE TWO PARTS OF INDUCTIVE PROOF • the basis step • the induction step. • In the induction step, we assume that statement is true in the case n = k, and we call this assumption the induction hypothesis. 55

THE STRONG PRINCIPLE OF MATHEMATICAL INDUCTION Suppose P(n) is a statement involving an integer n. In order to prove that P(n) is true for every n n 0 it is sufficient to show these two things: 1. P(n 0) is true. 2. For any k n 0, if P(n) is true for every n satisfying n 0 n k, then P(k+1) is true. 56

INDUCTION VS DEDUCTION, TWO SIDES OF THE SAME COIN Deduction and induction occur as a part of the common hypothetico-deductive method, which can be simplified in the following scheme: • Ask a question and formulate a hypothesis (educated guess) - induction • Derive predictions from the hypothesis - deduction • Test the hypothesis and its predictions - induction. 57

INDUCTION VS DEDUCTION, TWO SIDES OF THE SAME COIN (1) Deduction, if applied correctly, leads to true conclusions. But deduction itself is based on the fact that we know something for sure (premises must be true). For example we know the general law which can be used to deduce some particular case, such as “All humans are mortal. Socrates is human. Therefore is Socrates mortal. ” How do we know that all humans are mortal? How have we arrived to the general rule that our deduction is based on? Again, there is no other method at hand but (empirical) induction. 58

INDUCTION VS DEDUCTION, TWO SIDES OF THE SAME COIN (2) Even the process of induction implies use of deductive rules. On our way from specific (particular) up to universal (general) we use deductive reasoning. We collect the observations or experimental results and extract the common patterns or rules and regularities by deduction. For example, in order to infer by induction the fact that all planets orbit the Sun, we have to analyze astronomical data using deductive reasoning. 59

INDUCTION & DEDUCTION: Traditional View 60

Deduction-Induction Roller Coaster (A Loop) general deduction induction particular 61

GENERAL INDUCTION & DEDUCTION PARTICULAR Problem domain 62

INDUCTION & DEDUCTION “There is actually no such thing as a distinct process of induction” said Stanly Jevons; “all inductive reasoning is but the inverse application of deductive reasoning” – and this was what Whewell meant when he said that induction and deduction went upstairs and downstairs on the same staircase. ” …(“Popper, of course, is abandoning induction altogether”). Peter Medawar, Pluto’s Republic, p 177. 63

INDUCTION & DEDUCTION In short: deduction and induction are - like two sides of a piece of paper - the inseparable parts of our recursive thinking process. 64

UNDERSTANDING PHENOMENA IN NATURE: CAUSALITY AND DETERMINISM 65

CAUSALITY AND DETERMINISM Causality establishes that one thing causes another. Practical question (object-level): what was the cause (of an event)? Philosophical question (meta-level): what is the meaning of the concept of a cause? 66

CAUSALITY Early natural philosophers, concentrated on conceptual issues and questions (why? ). Later natural philosophers and scientists concentrated on more concrete issues and questions (how? ). The change in emphasis from conceptual to concrete coincides with the rise of empiricism. 67

ARISTOTLE’S CAUSALITY: The Four Causes The material cause - constituents, substratum or materials. This reduces the explanation of causes to the parts (factors, elements, constituents, ingredients). The formal cause - form, pattern, essence, whole, synthesis or archetype. The account of causes in terms of fundamental principles or general laws - the influence of the form (essence). The efficient cause - 'what makes what is made and what causes change of what is changed - agency, nonliving or living, acting as the sources of change. The final cause or telos is the purpose or end that something is supposed to serve. Omitted from present day causal explanations. 68

CAUSALITY David Hume was probably the first philosopher to postulate a entirely empirical definition of causality. Of course, both the definition of "cause" and the "way of knowing" whether X and Y are causally linked have changed significantly over time. Some natural philosophers deny the existence of "cause" and some natural philosophers who accept its existence, argue that it can never be known by empirical methods. Modern scientists, on the other hand, define causality in limited contexts (e. g. , in a controlled experiment). 69

DETERMINISM Determinism is the philosophical doctrine which regards everything that happens as solely and uniquely determined by what preceded it. From the information given by a complete description of the world at time t, a determinist believes that the state of the world at time t + 1 can be deduced; or, alternatively, a determinist believes that every event is an instance of the operation of the laws of Nature. 70

Critique of Usual Naïve Image of Scientific Method 71

Critique of Usual Naïve Image of Scientific Method (1) The naive inductivist idea of scientific inquiry sees scientific process as consisting of the following steps: 1. All facts are observed and recorded. 2. All observed facts are analyzed, compared and classified, without hypotheses or postulates other than those necessarily involved in the logic of thought. 3. Generalizations inductively made about the relations, structural or causal, between the facts. 4. Further research consists of inferences (deductions) from previously established generalizations. 72

Critique of Usual Naïve Image of Scientific Method (2) This narrow idea of scientific investigation is groundless for several reasons: 1. A scientific investigation could never get off the ground, for a collection of all facts would take infinite time, as there are infinite number of facts. The only possible way to do data collection is to take only relevant facts. But in order to decide what is relevant and what is not, we have to have a theory or at least a hypothesis about what is it we are observing. 73

Critique of Usual Naïve Image of Scientific Method (3) A hypothesis (preliminary theory) is needed to give the direction to a scientific investigation! 2. A set of empirical facts can be analyzed and classified in many different ways. Without hypothesis, analysis and classification are blind. 3. Induction is sometimes imagined as a method that leads, by mechanical application of rules, from observed facts to general principles. Unfortunately, such rules do not exist! 74

Why is it not (yet)* possible to derive theory directly (automatically) from the data? (1) – For example, theories about atoms contain terms like “atom”, “electron”, “proton”, etc; yet what one actually measures are spectra (wave lengths), traces in bubble chambers, calorimetric data, etc. – So theory is formulated on a completely different (and more abstract) level than the observable data! – The transition from data to theory requests creative imagination. * However, we cannot exclude the possibility of intelligent automated process of discovery! 75

Why is it not (yet) possible to derive theory directly (automatically) from the data? (2) – Scientific hypothesis is formulated based on “educated guesses” at the connections between the phenomena under study, at regularities and patterns that might underlie their occurrence. Scientific guesses are completely different from any process of systematic inference. * – The discovery of important mathematical theorems, like the discovery of important theories in empirical science, requires inventive ingenuity. *Here it is instructive to study Automated discovery methods in order to see how much theory must be used in order to extract meaning from the “raw data” 76

KNOWLEDGE AND JUSTIFICATION Knowledge and Objectivity: Observations are always interpreted in the context of an a priori knowledge. (Kuhn, Popper) “What a man sees depends both upon what he looks at and also upon what his previous visual-conceptual experience has taught him to see”. 77

KNOWLEDGE AND OBJECTIVITY Observations – All observation is potentially ”contaminated”, whether by our theories, our worldview or our past experiences. – It does not mean that science cannot ”objectively” [intersubjectivity] choose from among rival theories on the basis of empirical testing. – Although science cannot provide one with hundred percent certainty, yet it is the most, if not the only, objective (intersubjective) mode of pursuing knowledge. 78

Perception and “Direct Observation” 79

Perception and “Direct Observation” 80

Perception and “Direct Observation” 81

Perception and “Direct Observation” "Reality is merely an illusion, albeit a very persistent one. " - Einstein 82

83

Perception and “Direct Observation” Checker-shadow illusion http: //web. mit. edu/persci/people/adelson/checkershadow_illusion. html See even: http: //web. mit. edu/persci/gaz-teaching/index. html http: //persci. mit. edu/people/adelson/publications/gazzan. dir/gazzan. htm Lightness Perception and Lightness Illusions 84

Direct Observation? ! An atom interferometer, which splits an atom into separate wavelets, can allow the measurement of forces acting on the atom. Shown here is the laser system used to coherently divide, redirect, and recombine atomic wave packets (Yale University). 85

Direct Observation? ! Electronic signatures produced by collisions of protons and antiprotons in the Tevatron accelerator at Fermilab provided evidence that the elusive subatomic particle known as top quark has been found. 86

KNOWLEDGE JUSTIFICATION – Foundationalism (uses architectural metaphor to describe the structure of our belief systems. The superstructure of a belief system inherits justification from a certain subset of beliefs – all rests on basic beliefs. ) – Coherentism – Internalism (a person has “cognitive grasp”) and Externalism (external justification) 87

TRUTH (1) – The correspondence theory – The coherence theory – The deflationary theory 88

TRUTH (2) The Correspondence Theory A common intuition is that when I say something true, my statement corresponds to the facts. But: how do we recognize facts and what kind of relation is this correspondence? 89

TRUTH (3) The Coherence Theory Statements in theory are believed to be true because being compatible with other statements. The truth of a sentence just consists in its belonging to a system of coherent statements. The most well-known adherents to such a theory was Spinoza (1632 -77), Leibniz (1646 -1716) and Hegel (1770 -1831). Characteristically they all believed that truths about the world could be found by pure thinking, they were rationalists and idealists. Mathematics was the paradigm for a real science; it was thought that the axiomatic method in mathematics could be used in all sciences. 90

TRUTH (4) The Deflationary Theory The deflationary theory is belief that it is always logically unnecessary to claim that a proposition is true, since this claim adds nothing further to a simple affirmation of the proposition itself. "It is true that birds are warm-blooded" means the same thing as "birds are warm-blooded". For the deflationist, truth has no nature beyond what is captured in ordinary claims such as that ‘snow is white’ is true just in case snow is white. 91

TRUTH (5) The Deflationary Theory is also called the redundancy theory, the disappearance theory, the notruth theory, the disquotational theory, and the minimalist theory. See: Stanford Encyclopedia of Philosophy http: //plato. stanford. edu/entries/truth-deflationary/ 92

Truth and Reality Noumenon "Ding an sich" is distinguished from Phenomenon "Erscheinung", an observable event or physical manifestation, and the two words serve as interrelated technical terms in Kant's philosophy. 93

Whole vs. Parts • • • tusk spear tail rope trunk snake side wall leg tree The flaw in all their reasoning is that speculating on the WHOLE from too few FACTS can lead to VERY LARGE errors in judgment. 94

Science and Truth With respect to the truth content, there are different views of science: – Science as controversy (new science, frontiers) – Science as consensus (old, historically settled) – Science as knowledge about complex systems – Open systems with paraconsistent logic 95

PROOF The word proof can mean: • a test assessing the validity or quality of something. • a rigorous, compelling argument, including: – a logical argument or a mathematical proof – a large accumulation of scientific evidence – and alike • In mathematics, a proof is a demonstration that, given certain axioms, some statement of interest is necessarily true. 96

PROOF OF PYTHAGORAS THEOREM http: //www. youtube. com/watch? v=Em. Bjt 0 b 2 BKE&feature=related Einsteins Proof http: //demonstrations. wolfram. com/Einsteins. Most. Excellent. Proof/ http: //www. youtube. com/watch? NR=1&v=CAk. MUde. B 06 o Water proof http: //www. youtube. com/watch? v=e 4 Lu. X 48 r. D_k&feature=related Geometrical proof http: //www. youtube. com/watch? v=x. Lkf. Ddsnpu. Y&feature=related 97

Pressupositions and Limitations of Axiomatic Logical Systems Axiomatic theory is built on a set of few axioms/postulates (ideas which are considered so elementary and obvious that they do not need to be proven as any proof would introduce more complex ideas). All theorems (true statements) are derived logically from those axioms. Thus axiomatic theories are closed logical systems. For open axiomatics, see Unconventional Algorithms: Complementarity of Axiomatics and Construction, Dodig Crnkovic G. and Burgin M. http: //www. mrtc. mdh. se/~gdc/work/Entropy-Dodig. Crnkovic-Burgin 20120913. pdf When a system requires increasing number of axioms (as e. g. number theory does), doubts begin to arise. How many axioms are needed? How do we know that the axioms aren't mutually contradictory? Each new axiom can change the meaning of the previous system. 98

GÖDEL: TRUTH AND PROVABILITY (1) Kurt Gödel proved two extraordinary theorems. They have revolutionized mathematics, showing that mathematical truth is more than bare logic and computation. Gödel has been called the most important logician since Aristotle. His two theorems changed logic and mathematics as well as the way we look at truth and proof. 99

GÖDEL: TRUTH AND PROVABILITY (2) Gödels first theorem proved that any formal system strong enough to support number theory has at least one undecidable statement. Even if we know that the statement is true, the system cannot prove it. This means the system is incomplete. For this reason, Gödel's first proof is called "the incompleteness theorem". 100

GÖDEL: TRUTH AND PROVABILITY (3) Gödel's second theorem is closely related to the first. It says that no one can prove, from inside any complex formal system, that it is self-consistent. "Gödel showed that provability is a weaker notion than truth, no matter what axiomatic system is involved. In other words, we simply cannot prove some things in mathematics (from a given set of premises) which we nonetheless can know are true. “ (Hofstadter) 101

TRUTH VS. PROVABILITY ACCORDING TO GÖDEL After: Gödel, Escher, Bach - an Eternal Golden Braid by Douglas Hofstadter. 102

TRUTH VS. PROVABILITY ACCORDING TO GÖDEL Gödel theorem is built upon Aristotelian logic. So it is true within the paradigm of Aristotelian logic. However, nowadays it is not the only logic existing. 103

CRITICAL THINKING (1) Critical thinking is rationally deciding what to believe or do. To rationally decide something is to evaluate claims to see whether they make sense, whether they are coherent, and whether they are well-founded on evidence, through inquiry and the use of criteria developed for this purpose. Critical Thinking http: //en. wikipedia. org/wiki/Critical_thinking 104

CRITICAL THINKING (2) How do we think critically? A. Question First, we ask a question about the issue that we are wondering about. For example “What is going on? " B. Answer (hypothesis) Next, we propose an answer or hypothesis for the question raised. A hypothesis is a "tentative theory provisionally adopted to explain certain facts. " We suggest a possible hypothesis, or answer, to the question posed. For example “A phenomenon occurs under certain conditions. " 105

CRITICAL THINKING (3) C. Testing the hypothesis is the next step. With testing, we draw out the implications of the hypothesis by deducing its consequences (deduction). We then think of a case which contradicts the claims and implications of the hypothesis (inference). For example, “If phenomenon really exists it will systematically occur under certain conditions. “ Criteria for truth Criteria are used for testing the truth of a hypothesis such as selfconsistency, consistency with existing knowledge, empirical confirmation, etc. 106

PSEUDOSCIENCE (1) “A pseudoscience is set of ideas and activities resembling science but based on fallacious assumptions and supported by fallacious arguments. ” Martin Gardner: Fads and Fallacies in the Name of Science 107

PSEUDOSCIENCE (2) Motivations for promotion of pseudoscience range from simple lack of knowledge or no skills in the scientific method, to deliberate deception for winning a power, financial gain or other benefits. Some people consider some or all forms of pseudoscience to be harmless entertainment or sort of counseling. Others, such as Richard Dawkins, and James Randi consider all forms of pseudoscience to be harmful, whether or not they result in immediate harm to their followers. http: //richarddawkins. net/ Richard Dawkins, Professor of the Public Understanding of Science at Oxford University web page. 108

PSEUDOSCIENCE (3) • • Typically, pseudoscience fails to meet the criteria met by science generally (including the scientific method), and can be identified by one or more of the following rules of thumb: asserting claims without supporting experimental evidence; asserting claims which contradict experimentally established results; failing to provide an experimental possibility of reproducible results; or violating Occam's Razor (the principle of choosing the simplest explanation when multiple viable explanations are possible. 109

PSEUDOSCIENCE (4) • • • Astrology Dowsing Creationism ETs & UFOs Supernatural Parapsychology/Paranormal New Age Divination (fortune telling) Graphology Numerology • Velikovsky's, von Däniken's, and Sitchen's theories • Pseudohistory • Homeopathy • Healing • Alternative Medicine • Cryptozoology • Lysenkoism • Psychokinesis • Occult & occultism 110

PSEUDOSCIENCE (5) http: //www. youtube. com/watch? v=p. YGjtlg. Gt. Y 4&feature=related James Randi Exposes Telekinesis (1: 48) http: //www. youtube. com/watch? v=OZe. QGld 5 QBU&NR=1 James Randi Tests An Aura Reader (3: 24) http: //www. youtube. com/watch? v=3 Dp 2 Zqk 8 v. Hw James Randi on Astrology (1: 36) http: //www. youtube. com/watch? v=6 Rt. J 0 y. JL 4 tg&feature=related James Randi Tests a Dowser (5: 46) http: //www. youtube. com/watch? v=LSOD 77 cl. NZM&feature=related James Randi and Richard Dawkins (6: 23) http: //www. youtube. com/watch? v=_VAas. VXt. COI&feature=related Dawkins debunks dowsing (4: 57) http: //www. youtube. com/watch? v=IZLKKW 2 SQoc&feature=related Richard Dawkins on alternative medicine and the nature of science (1: 14) http: //www. youtube. com/watch? v=Lgy. NNt. BHOYc&feature=related Astrology Numerology and You-4 (3: 26) http: //www. youtube. com/watch? NR=1&v=Iunr 4 B 4 wf. DA Carl Sagan on Astrology (8: 35) http: //www. youtube. com/watch? v=TZi. Ls. Fa. Ezog Ben Goldacre on Homeopathy (3: 04) 111

PSEUDOSCIENCE AS WISHFUL THINKING • No science can predict human future with certainty – pseudosciences fulfill human wish to know their future. • No science can cure all diseases – but pseudosciences fulfill human wish to have cure for every disease. • No science can what pseudosciences claim to be able to! • While sciences support critical thinking, pseudosciences apply wishful thinking. 112

PSEUDOSCIENCE (6) http: //skepdic. com/ The Skeptic's Dictionary, http: //www. csicop. org/si/ Skeptical Inquirer http: //www. physto. se/~vetfolk/Folkvett/199534 pseudo. html The Swedish Skeptic movement (in Swedish) http: //www 8. nationalacademies. org/onpinews/newsitem. aspx? Record. ID= 11876 Scientific Evidence Supporting Evolution http: //www. scientificamerican. com/article. cfm? id=15 -answers-tocreationist&page=2 Scientific American, July 2002: 15 Answers to Creationist Nonsense Human Genome, Nature 409, 860 - 921 (2001) 113

THE PROBLEM OF DEMARCATION (1) After more than a century of active dialogue, the question of what marks the boundary of science remains formally unsettled. As a consequence the issue of what constitutes pseudoscience continues to be controversial. Nonetheless, reasonable consensus exists on a number of issues. 114

THE PROBLEM OF DEMARCATION (2) Criteria for demarcation have traditionally been coupled to philosophy of science. Logical positivism, for example, held that only statements about empirical observations are meaningful, effectively asserting that statements which are not derived in this manner (including all metaphysical statements) are meaningless. 115

THE PROBLEM OF DEMARCATION (3) Karl Popper attacked logical positivism and introduced his own criterion for demarcation, falsifiability. Thomas Kuhn and Imre Lakatos proposed criteria that distinguished between progressive and degenerative research programs. 116

THE PROBLEM OF DEMARCATION (4) Read a book by astrophysicist Carl Sagan against pseudoscience: http: //www. youtube. com/watch? v=Cs 3 S i. Km 2 i. MQ The Demon-Haunted World (13: 56) http: //www. youtube. com/watch? v=h. Gkf s 9 WU 98 s (2: 49: 35) The book explains the scientific method and encourage people to learn critical thinking. It explains methods to help distinguish between science, and pseudoscience by means of critical 117 thinking.

THEORY OF SCIENCE ASSIGNMENTS – Assignment 2: Demarcation of Science vs. Pseudoscience (in groups of two) – Discussion of Assignment 2 - compulsory – Assignment 2 -extra (For those who miss the discussion of the Assignment 2) – Assignment 3: GOLEM: Three Cases of Theory Confirmation (in groups of two) – Discussion of Assignment 3 - compulsory – Assignment 3 -extra (For those who miss the discussion of the Assignment 3) http: //www. idt. mdh. se/kurser/ct 3340/ht 12/deadlines. html Deadlines 118

Assignment 2: Demarcation: Pseudoscience vs. Science (done in groups of two) [1] Hansson, Sven Ove, "Science and Pseudo-Science", The Stanford Encyclopedia of Philosophy (Fall 2008 Edition), Edward N. Zalta (ed. ), http: //plato. stanford. edu/archives/fall 2008/entries/pseudo-science [2] http: //en. wikipedia. org/wiki/Pseudoscience [3] The Astrotest A tough match for astrologers (Rob Nanninga), http: //www. skepsis. nl/astrot. html Further reading Popper on Demarcation (Stanford Encyclopedia): http: //plato. stanford. edu/entries/popper/ Astrology Fact Sheet (North Texas Skeptics), http: //www. ntskeptics. org/factsheets/astrolog. htm 119

Assignment 2: Demarcation: Pseudoscience vs. Science (done in groups of two) • Use the template (Answer form) • Leave the template unchanged, write down your answer after each question. • Think critically! • You are expected to work in groups of two. • Your text should not be shorter than two A 4 pages written in usual text format. • Prepare for the discussion in the class! • Please write the file name in the following format: name 1_name 2_a 2. doc 120

APPENDIX For additional reading 121

SEMIOTICS (3) Reality is a construction. Information or meaning is not 'contained' in the (physical) world and 'transmitted' to us - we actively create meanings (“make sense”!) through a complex interplay of perceptions, and agency based on hard-wired behaviors and coding-decoding conventions. The study of signs is the study of the construction and maintenance of reality. 122

SEMIOTICS (4) 'A sign. . . is something which stands to somebody for something in some respect or capacity'. Sign takes a form of words, symbols, images, sounds, gestures, objects, etc. Anything can be a sign as long as someone interprets it as 'signifying' something - referring to or standing for something. 123

SEMIOTICS (5) (signified) (signifier) CAT The sign consists of – signifier (a pointer) – signified (that what pointer points to) 124

SEMIOTICS (6) This is Not a Pipe. . . by Rene Magritte. . Surrealism 125

COMMUNICATION – Communication is exchange of information, interaction through signs/messages. – Information is the meaning that a human gives to signs by applying the known conventions used in their representation. – Sign is any physical event used in communication. – Language is a vocabulary and the way of using it. 126

HIERARCHICAL STRUCTURE OF LANGUAGE Object-language Meta-language In dictionaries of SCIENCE there is no definition of science! The definition of SCIENCE can be found in PHILOSOPHY dictionaries. 127

AMBIGUITIES OF LANGUAGE (1) Lexical ambiguity, where a word have more than one meaning: meaning (sense, connotation, denotation, import, gist; significance, importance, implication, value, consequence, worth) – sense (intelligence, brains, intellect, wisdom, sagacity, logic, good judgment; feeling) – connotation (nuance, suggestion, implication, undertone, association, subtext, overtone) – denotation (sense, connotation, import, gist) … 128

AMBIGUITIES OF LANGUAGE (2) Syntactic ambiguity like in “small dogs and cats” (are cats small? ). Semantic ambiguity comes often as a consequence of syntactic ambiguity. “Coast road” can be a road that follows the coast, or a road that leads to the coast. 129

AMBIGUITIES OF LANGUAGE (3) Referential ambiguity is a sort of semantic ambiguity (“it” can refer to anything). Pragmatic ambiguity (If the speaker says “I’ll meet you next Friday”, thinking that they are talking about 17 th, and the hearer think that they are talking about 24 th. ) Vagueness is an important feature of natural languages. “It is warm outside” says something about temperature, but what does it mean? A warm winter day in Sweden is not the same thing as warm summer day in Kenya. 130

AMBIGUITIES OF LANGUAGE (4) Ambiguity of language results in its flexibility, that makes it possible for us to cover the whole infinite diversity of the world we live in with a limited means of vocabulary and a set of rules that language is made of. On the other hand, flexibility makes the use of language complex. Nevertheless, the languages, both formal and natural, are the main tools we have on our disposal in science and research. 131

USE OF LANGUAGE IN SCIENCE. LOGIC AND CRITICAL THINKING. PSEUDOSCIENCE • LOGICAL ARGUMENT • DEDUCTION • INDUCTION • REPETITIONS, PATTERNS, IDENTITY • CAUSALITY AND DETERMINISM • FALLACIES • PSEUDOSCIENCE 132

FALLACIES - ERRORS IN REASONING What about incorrectly built arguments? Let us make the following distinction: • A formal fallacy is a wrong formal construction of an argument. • An informal fallacy is a wrong inference or reasoning. 133

FORMAL FALLACIES (1) An example: “Affirming the consequent" "All fish swim. Kevin swims. Therefore Kevin is a fish", which appears to be a valid argument. It appears to be a modus ponens, but it is not! If H is true, then so is I. (As the evidence shows), I is true. H is true This form of reasoning, known as the fallacy of "affirming the consequent" is deductively invalid: its conclusion may be false even if premises are true. 134

FORMAL FALLACIES (2) Incorrect deduction from auxiliary hypotheses If H and A 1, A 2, …. , An is true, then so is I. But (As the evidence shows), I is not true. H and A 1, A 2, …. , An are all false (Comment: One can be certain that H is false, only if one is certain that all of A 1, A 2, …. , An are all true. ) 135

INFORMAL FALLACIES (1) An informal fallacy is a mistake in reasoning related to the content of an argument. Appeal to Authority Ad Hominem (personal attack) False Cause (synchronicity; unrelated facts that appear at the same time coupled) Leading Questions 136

INFORMAL FALLACIES (2) Appeal to Emotion Straw Man (attacking the different problem) Equivocation (not the common meaning of the word) Composition (parts = whole) Division (whole = parts) See more on: http: //en. wikipedia. org/wiki/List_of_fallacies 137

Two Examples of Axiomatic Systems Limitations and Developments 138

Pressupositions and Limitations of Formal Logical Systems Axiomatic System of Euclid: Shaking up Geometry Euclid built geometry on a set of few axioms/postulates (ideas which are considered so elementary and manifestly obvious that they do not need to be proven as any proof would introduce more complex ideas). When a system requires increasing number of axioms (as e. g. number theory does), doubts begin to arise. How many axioms are needed? How do we know that the axioms aren't mutually contradictory? 139

Pressupositions and Limitations of Formal Logical Systems Axiomatic System of Euclid: Shaking up Geometry Until the 19 th century no one was too concerned about axiomatization. Geometry has stood as conceived by Euclid for 2100 years. If Euclid's work had a weak point, it was his fifth axiom, the axiom about parallel lines. Euclid said that for a given straight line, one could draw only one other straight line parallel to it through a point somewhere outside it. 140

EUCLID'S AXIOMS (1) 1. Every two points lie on exactly one line. 2. Any line segment with given endpoints may be continued in either direction. 3. It is possible to construct a circle with any point as its center and with a radius of any length. (This implies that there is neither an upper nor lower limit to distance. In-other-words, any distance, no mater how large can always be increased, and any distance, no mater how small can always be divided. ) 141

EUCLID'S AXIOMS (2) 4. If two lines cross such that a pair of adjacent angles are congruent, then each of these angles are also congruent to any other angle formed in the same way. (Says that all right angles are equal to one another. ) 5. (Parallel Axiom): Given a line l and a point not on l, there is one and only one line which contains the point, and is parallel to l. 142

NON-EUCLIDEAN GEOMETRIES (1) Mid-1800 s: mathematicians began to experiment with different definitions for parallel lines. Lobachevsky, Bolyai, Riemann: new non-Euclidean geometries by assuming that there could be several parallel lines through the outside point or there could be no parallel lines. 143

NON-EUCLIDEAN GEOMETRIES (2) Two ways to negate the Euclidean Parallel Axiom: – 5 -S (Spherical Geometry Parallel Axiom): Given a line l and a point not on l, no lines exist that contain the point, and are parallel to l. – 5 -H (Hyperbolic Geometry Parallel Axiom): Given a line l and a point not on l, there at least two distinct lines which contains the point, and are parallel to l. 144

Reproducing the Euclidean World in a model of the Elliptical Non-Euclidean World. 145

Spherical/Elliptical Geometry In spherical geometry lines of latitude are not great circles (except for the equator), and lines of longitude are. Elliptical Geometry takes the spherical plan and removes one of two points directly opposite each other. The end result is that in spherical geometry, lines always intersect in exactly two points, whereas in elliptical geometry, lines always intersect in one point. 146

Properties of Elliptical/Spherical Geometry In Spherical Geometry, all lines intersect in 2 points. In elliptical geometry, lines intersect in 1 point. In addition, the angles of a triangle always add up to be greater than 180 degrees. In elliptical/spherical geometry, all of Euclid's postulates still do hold, with the exception of the fifth postulate. This type of geometry is especially useful in describing the Earth's surface. 147

Hyperbolic Cubes 148

DEFINITION: Parallel lines are infinite lines in the same plane that do not intersect. Hyperbolic Universe Flat Universe Spherical Universe Einstein incorporated Riemann's ideas into relativity theory to describe the curvature of space. 149

MORE PROBLEMS WITH AXIOMATIZATION… Not only had Riemann created a system of geometry which put commonsense notions on its head, but the philosophermathematician Bertrand Russell had found a serious paradox for set theory! He has shown that Frege’s attempt to reduce mathematics to logical reasoning starting with sets as basics leads to contradictions. 150

HILBERT’S PROGRAM Hilbert’s hope was that mathematics would be reducible to finding proofs (manipulating the strings of symbols) from a fixed system of axioms that everyone could agree were true. Can all of mathematics be made algorithmic, or will there always be new problems that outstrip any given algorithm, and so require creative mind to solve? 151

AXIOMATIC SYSTEM OF PRINCIPIA: PARADOX IN SET THEORY Mathematicians hoped that Hilbert's plan would work because axioms and definitions are based on logical commonsense intuitions, such as e. g. the idea of set. A set is any collection of items chosen for some characteristic common for all its elements. 152

RUSSELL'S PARADOX (1) There are two kinds of sets: – Normal sets, which do not contain themselves, and – Non-normal sets, which are sets that do contain themselves. The set of all apples is not an apple. Therefore it is a normal set. The set of all thinkable things is itself thinkable, so it is a non-normal set. 153

RUSSELL'S PARADOX (2) Let 'N' stand for the set of all normal sets. Is N a normal set? If it is a normal set, then by the definition of a normal set it cannot be a member of itself. That means that N is a non-normal set, one of those few sets which actually are members of themselves. 154

RUSSELL'S PARADOX (3) But on the other hand…N is the set of all normal sets; if we describe it as a non-normal set, it cannot be a member of itself, because its members are, by definition, normal. 155

RUSSELL'S PARADOX (4) Russell resolved the paradox by redefining the meaning of 'set' to exclude peculiar (self-referencing) sets, such as "the set of all normal sets“. Together with Whitehead in Principia Mathematica he founded mathematics on that new set definition. They hoped to get self-consistent and logically coherent system … 156

RUSSELL'S PARADOX (5) … However, even before the project was complete, Russell's expectations were dashed! The man who showed that Russell's aim was impossible was Kurt Gödel, in a paper titled "On Formally Undecidable Propositions of Principia Mathematica and Related Systems. " 157

LOGIC 158

LOGIC (1) The precision, clarity and beauty of mathematics are the consequence of the fact that the logical basis of classical mathematics possesses the features of parsimony and transparency. Classical logic owes its success in large part to the efforts of Aristotle and the philosophers who preceded him. In their endeavour to devise a concise theory of logic, and later mathematics, they formulated so-called "Laws of Thought". 159

LOGIC (2) One of these, the "Law of the Excluded Middle, " states that every proposition must either be True or False. When Parminedes proposed the first version of this law (around 400 B. C. ) there were strong and immediate objections. For example, Heraclitus proposed that things could be simultaneously True and not True. 160

NON-STANDARD LOGIC FUZZY LOGIC (1) Plato laid the foundation for fuzzy logic, indicating that there was a third region (beyond True and False). Some among more modern philosophers follow the same path, particularly Hegel. But it was Lukasiewicz who first proposed a systematic alternative to the bi-valued logic of Aristotle. 161

NON-STANDARD LOGIC FUZZY LOGIC (2) In the early 1900's, Lukasiewicz described a three-valued logic, along with the corresponding mathematics. The third value "possible, " assigned a numeric value between True and False. Eventually, he proposed an entire notation and axiomatic system from which he hoped to derive modern mathematics. 162

NON-STANDARD LOGICS • • • • Categorical logic Combinatory logic Conditional logic Constructive logic Cumulative logic Deontic logic Dynamic logic Epistemic logic Erotetic logic Free logic Fuzzy logic Higher-order logic Infinitary logic Intensional logic Intuitionistic logic Linear logic • • • • Many-sorted logic Many-valued logic Modal logic Non-monotonic logic Paraconsistent logic Partial logic Prohairetic logic Quantum logic Relevant logic Stoic logic Substance logic Substructural logic Temporal (tense) logic Other logics 163

MATHEMATICAL INDUCTION The aim of the empirical induction is to establish the law. In the mathematical induction we have the law already formulated. We must prove that it holds generally. The basis for mathematical induction is the property of the wellordering for the natural numbers. 164

THE PRINCIPLE OF MATHEMATICAL INDUCTION Suppose P(n) is a statement involving an integer n. Than to prove that P(n) is true for every n n 0 it is sufficient to show these two things: 1. P(n 0) is true. 2. For any k n 0, if P(k) is true, then P(k+1) is true. 165

THE TWO PARTS OF INDUCTIVE PROOF • the basis step • the induction step. • In the induction step, we assume that statement is true in the case n = k, and we call this assumption the induction hypothesis. 166

THE STRONG PRINCIPLE OF MATHEMATICAL INDUCTION (1) Suppose P(n) is a statement involving an integer n. In order to prove that P(n) is true for every n n 0 it is sufficient to show these two things: 1. P(n 0) is true. 2. For any k n 0, if P(n) is true for every n satisfying n 0 n k, then P(k+1) is true. 167

THE STRONG PRINCIPLE OF MATHEMATICAL INDUCTION (2) A proof by induction using this strong principle follows the same steps as the one using the common induction principle. The only difference is in the form of induction hypothesis. Here the induction hypothesis is that k is some integer k n 0 and that all the statements P(n 0), P(n 0 +1), …, P(k) are true. 168

Example. Proof by Strong Induction • P(n): n is either prime or product of two or more primes, for n 2. • Basic step. P(2) is true because 2 is prime. • Induction hypothesis. k 2, and for every n satisfying 2 n k, n is either prime or a product of two or more primes. 169

• Statement to be shown in induction step: If k+1 is prime, the statement P(k+1) is true. • Otherwise, by definition of prime, k+1 = r·s, for some positive integers r and s, neither of which is 1 or k+1. It follows that 2 r k and 2 s k. • By the induction hypothesis, both r and s are either prime or product of two or more primes. • Therefore, k+1 is the product of two or more primes, and P(k+1) is true. 170

The strong principle of induction is also referred to as the principle of complete induction, or course-of-values induction. It is as intuitively plausible as the ordinary induction principle; in fact, the two are equivalent. As to whether they are true, the answer may seem a little surprising. Neither can be proved using standard properties of natural numbers. Neither can be disproved either! 171

This means essentially that to be able to use the induction principle, we must adopt it as an axiom. A well-known set of axioms for the natural numbers, the Peano axioms, includes one similar to the induction principle. 172

PEANO'S AXIOMS 1. N is a set and 1 is an element of N. 2. Each element x of N has a unique successor in N denoted x'. 3. 1 is not the successor of any element of N. 4. If x' = y' then x = y. 5. (Axiom of Induction) If M is a subset of N satisfying both: 1 is in M x in M implies x' in M then M = N. 173

CAUSALITY What does the scientist mean when (s)he says that event b was caused by event a? Other expressions are: – bring about, bring forth – produce – create… …and similar metaphors of human activity. Strictly speaking it is not a thing but a process that causes an event. 174

CAUSALITY Analysis of causality, an example (Carnap): Search for the cause of a collision between two cars on a highway. • According to the traffic police, the cause of the accident was too high speed. • According to a road-building engineer, the accident was caused by the slippery highway (poor, low-quality surface) • According to the psychologist, the man was in a disturbed state of mind which caused the crash. 175

CAUSALITY • An automobile construction engineer may find a defect in a structure of a car. • A repair-garage man may point out that brake-lining of a car was worn-out. • A doctor may say that the driver had bad sight. Etc… Each person, looking at the total picture from certain point of view, will find a specific condition such that it is possible to say: if that condition had not existed, the accident might not have happened. But what was The cause of the accident? 176

CAUSALITY • It is quite obvious that there is no such thing as The cause! • No one could know all the facts and relevant laws. (Relevant laws include not only laws of physics and technology, but also psychological, physiological laws, etc. ) • But if someone had known, (s)he could have predicted the collision! 177

CAUSALITY The event called the cause, is a necessary part of a more complex web of circumstances. John Mackie, gives the following definition: A cause is an INSUFFICIENT BUT NECESSARY part of a complex of conditions which together are UNNECESSARY BUT SUFFICIENT for the effect. This definition has become famous and is usually referred to as the INUS-definition: a cause is an INUS-condition. 178

CAUSALITY The reason why we are so interested in causes is primarily that we want either to prevent the effect or else to promote it. In both cases we ask for the cause in order to obtain knowledge about what to do. Hence, in some cases we simply call that condition which is easiest to manipulate as the cause. 179

CAUSALITY Summarizing: Our concept of a cause has one objective and subjective component. The objective content of the concept of a cause is expressed by its being an INUS condition. The subjective part is that our choice of one factor as the cause among the necessary parts in the complex is a matter of interest. 180

CAUSE AND CORRELATION Instead of saying that the same cause always is followed by the same effect it is said that the occurrence of a particular cause increases the probability for the associated effect, i. e. , that the cause sometimes but not always are followed by the effect. Hence cause and effect are statistically correlated. 181

CAUSE AND CORRELATION X and Y are correlated if and only if: P(X/Y) > P(X) and P(Y/X) > P(Y) [The events X and Y are positively correlated if the conditional probability for X, if Y has happened, is higher than the unconditioned probability, and vice versa. ] 182

CAUSE AND CORRELATION Reichenbach's principle: If events of type A and type B are positively correlated, then one of the following possibilities must obtain: i) A is a cause of B, or ii) B is a cause of A, or iii) A and B have a common cause. 183

CAUSE AND CORRELATION The idea behind Reichenbach’s principle is: Every real correlation must have an explanation in terms of causes. It just can’t happen that as a matter of mere coincidence a correlation obtains. 184

CAUSE AND CORRELATION We and other animals notice what goes on around us. This helps us by suggesting what we might expect and even how to prevent it, and thus fosters survival. However, the expedient works only imperfectly. There are surprises, and they are unsettling. How can we tell when we are right? We are faced with the problem of error. W. V. Quine, 'From Stimulus To Science', Harvard University Press, Cambridge, MA, 1995. 185

The Classical (Ideal) Model of Science The Classical Model of Science is a system S of propositions and concepts satisfying the following conditions: • All propositions and all concepts (or terms) of S concern a specific set of objects or are about a certain domain of being(s). • There are in S a number of so-called fundamental concepts (or terms). • All other concepts (or terms) occurring in S are composed of (or are definable from) these fundamental concepts (or terms). 186

The Classical (Ideal) Model of Science • There are in S a number of so-called fundamental propositions. • All other propositions of S follow from or are grounded in (or are provable or demonstrable from) these fundamental propositions. • All propositions of S are true. • All propositions of S are universal and necessary in some sense or another. 187

The Classical (Ideal) Model of Science • All concepts or terms of S are adequately known. A nonfundamental concept is adequately known through its composition (or definition). • The Classical Model of Science is a reconstruction a posteriori and sums up the historical philosopher’s ideal of scientific explanation. • The fundamental is that “All propositions and all concepts (or terms) of S concern a specific set of objects or are about a certain domain of being(s). ” Betti A & De Jong W. R. , Guest Editors, The Classical Model of Science I: A Millennia. Old Model of Scientific Rationality, Forthcoming in Synthese, Special Issue 188

SCIENCE, KNOWLEDGE, TRUTH, MEANING. FORMAL LOGICAL SYSTEMS AND THEIR LIMITATIONS The science is not about the search for truth (“absolute truth”) but the search for meaning in the form of explanations/models/ simulations that work: “No such (scientific) model, however comprehensive, coherent or well entrenched it might be, can lay an automatic claim to objective truth, even though contextually it may provide a reliable and successful explanatory tool for making sense of what is going on around us. ” Edo Pivčević, The Reason Why: A Theory of Philosophical Explanation, Kru. Zak, 2007 Knowledge networks in communities of practice - Language 189

Classical Sciences in their Cultural Context – A Language Based Scheme Logic & Mathematics 1 Natural Sciences (Physics, Chemistry, Biology, …) 2 Social Sciences (Economics, Sociology, Anthropology, …) 3 Culture (Religion, Art, …) 5 The Humanities (Philosophy, History, Linguistics …) 4 190

CRITICAL THINKING (1) Critical thinking is rationally deciding what to believe or do. To rationally decide something is to evaluate claims to see whether they make sense, whether they are coherent, and whether they are well-founded on evidence, through inquiry and the use of criteria developed for this purpose. Critical Thinking http: //en. wikipedia. org/wiki/Critical_thinking 191

CRITICAL THINKING (2) How Do We Think Critically? A. Question First, we ask a question about the issue that we are wondering about. For example, "Is there right and wrong? " B. Answer (hypothesis) Next, we propose an answer or hypothesis for the question raised. A hypothesis is a "tentative theory provisionally adopted to explain certain facts. " We suggest a possible hypothesis, or answer, to the question posed. For example, "No, there is no right and wrong. " 192

CRITICAL THINKING (3) C. Testing the hypothesis is the next step. With testing, we draw out the implications of the hypothesis by deducing its consequences (deduction). We then think of a case which contradicts the claims and implications of the hypothesis (inference). For example, "So if there is no right or wrong, then everything has equal moral value (deduction); so would the actions of Hitler be of equal moral value to the actions of Mother Theresa (inference)? as Value nihilism ethics claims" 193

CRITICAL THINKING (4) 1. Criteria for truth Criteria are used for testing the truth of a hypothesis. The criteria may be used singly or in combination. a. Consistent with a precondition Is the hypothesis consistent with a precondition necessary for its own assertion? For example, is the assertion "there is no right or wrong" made possible only by assuming a concept of right or wrong - namely, that it is right that there is no right or wrong and that it is wrong that there is right or wrong? 194

CRITICAL THINKING (5) b. Consistent with itself (self-consistent) Is the hypothesis consistent with itself? For example, is the assertion that "there is no right or wrong" itself an assertion of right or wrong? c. Consistent with language Is the hypothesis consistent with the usage and meaning of ordinary language? For example, do we use the words "right" or "wrong" in our language and do the words refer to concepts and meanings which we consider "right" and "wrong"? 195

CRITICAL THINKING (6) d. Consistent with experience Is the hypothesis consistent with experience? For example, do people really live as if there is no right or wrong? e. Consistent with the consequences Is the hypothesis consistent with its own consequences, can it actually bear the burden of being lived? For example, what would the consequences be if everyone lived as if there was no right or wrong? 196

If you want to learn more here are reach sources of further reading… A Mathematical Analysis of The Scientific Method, The Axiomatic Method, and Darwin's Theory Of Evolution, G. J. Chaitin http: //www. umcs. maine. edu/~chaitin/ufrj. html http: //www. cs. auckland. ac. nz/~chaitin/ufrj. html 197

Chaitin’s work on Epistemology, Information Theory, and Metamathematics important for understanding of Formal Systems and their Relationship with Biology: http: //www. umcs. maine. edu/~chaitin/ecap. pdf Epistemology as Information Theory: From Leibniz to Ω http: //www. umcs. maine. edu/~chaitin/mjm. pdf The Halting Probability Omega: Irreducible Complexity in Pure Mathematics http: //www. umcs. maine. edu/~chaitin/unm. html Randomness in Arithmetic and the Decline & Fall of Reductionism in Pure Mathematics http: //www. umcs. maine. edu/~chaitin/hu. html The Search for the Perfect Language 198

Despite the fact that there can be no TOE (Theory Of Everything) for pure mathematics as Hilbert hoped, mathematicians remain enamored with formal proof. See the special issue on formal proof of the AMS Notices, December 2008 (From Chaitin’s lectures) http: //www. ams. org/notices/200811/index. html David Malone, Dangerous Knowledge, BBC TV, 90 minutes, Google video vividly illustrates the search for TOE in mathematics http: //video. google. com/videoplay? docid=-5122859998068380459# 199