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CDT403 Research Methodology in Natural Sciences and Engineering Theory of Science PowerPoint Presentation
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CDT403 Research Methodology in Natural Sciences and Engineering Theory of Science

CDT403 Research Methodology in Natural Sciences and Engineering Theory of Science

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CDT403 Research Methodology in Natural Sciences and Engineering Theory of Science

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  1. CDT403 Research Methodology in Natural Sciences and Engineering Theory of Science LANGUAGE AND COMMUNICATION, CRITICAL THINKING AND PSEUDOSCIENCE Gordana Dodig-Crnkovic Department of Computer Science and ElectronicMälardalen University


  3. COMMUNICATION • Communication is imparting 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. • Signisany physical event used in communication. • Language is a vocabulary and the way of using it.

  4. 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.)

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

  6. pragmatics semantics syntactics SEMIOTICS (2A)

  7. 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 constructionand maintenance of reality.

  8. 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.

  9. SEMIOTICS (5) (signified) (signifier) CAT • The sign consists of • signifier (a pointer) • signified (that what pointer points to)

  10. This is Not a Pipe . . . by Rene Magritte. . . . Surrealism

  11. SEMIOTICS (6) • 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.

  12. LANGUAGE (1) Examples The sign said "fine for parking here", and since it was fine, I parked. Last night he caught a burglar in his pyjamas.

  13. LANGUAGE (2) The Oracle of Delphi told Croseus that if he pursued the war he woulddestroy a mighty kingdom. (What the Oracle did not mention was that the kingdom he would destroy would be his own. From: Heroditus, The Histories.) The first mate, seeking revenge on the captain, wrote in his journal, "The Captain was sober today." (He suggests, by his emphasis, that the Captain is usually drunk.

  14. LANGUAGE - THOUGHT - WORLD Two approaches: • Translation is possible (linguistic realism). • Translation is essentially impossible (linguistic relativism) - Sapir-Whorf hypothesis .

  15. LANGUAGE - THOUGHT- WORLDBASIC STRUCTURE: DICHOTOMY simple/complex straight/curved text/context central/ peripheral stability/change quantity/quality knowledge/ignorance win/lose mind/body question/answer positive/negative art/science active/passive theory/practice yes/no before/after right/wrong true/false open/closed in/out up/down

  16. LANGUAGE -THOUGHT- WORLD Eskimo Terms for Snow Clinging particles nevluk 'clinging debris/ nevlugte- 'have clinging debris/...'lint/snow/dirt...' Fallen Snow Fallen snow on the ground aniu [NS] 'snow on ground' aniu- [NS] 'get snow on ground' apun [NS] 'snow on ground' qanikcaq 'snow on ground‘ qanikcir- 'get snow on ground‘ …… Snow Particles Snowflake qanuk 'snowflake' qanir- 'to snow' qanunge- 'to snow' [NUN] qanugglir- 'to snow' [NUN] Frost kaneq 'frost' kaner- 'be frosty/frost sth.‘ Fine snow/rain particles kanevvluk 'fine snow/rain particles kanevcir- to get fine snow/rain particles Drifting particles natquik 'drifting snow/etc' natqu(v)igte- 'for snow/etc. to drift along ground'.'

  17. LANGUAGE AND THOUGHTEskimo Terms for Snow “Horse breeders have various names for breeds, sizes, and ages of horses; botanists have names for leaf shapes; interior decorators have names for shades of mauve; printers have many different names for different fonts (Caslon, Garamond, Helvetica, Times Roman, and so on), naturally enough. If these obvious truths of specialization are supposed to be interesting facts about language, thought and culture, then I’m sorry, but include me out.“ (…)

  18. HIERARCHICAL STRUCTURE OF LANGUAGE Object-language  Meta-language In dictionaries on SCIENCE THERE IS no definition of science! The definition of SCIENCE can be found in PHILOSOPHY dictionaries.

  19. AMBIGUITIES OF LANGUAGE (1) Lexical ambiguity Lexicalambiguity, 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) …

  20. AMBIGUITIES OF LANGUAGE (5) Syntacticambiguity like in “small dogs and cats” (are cats small?). Semanticambiguity 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.

  21. AMBIGUITIES OF LANGUAGE (6) 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 17th, and the hearer think that they are talking about 24th, then there is miscommunication.)

  22. AMBIGUITIES OF LANGUAGE (8) 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.

  23. AMBIGUITIES OF LANGUAGE (9) 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.

  24. AMBIGUITIES OF LANGUAGE (10) On the other hand, flexibility makes the use of language all but uncomplicated. Nevertheless, the languages, both formal and natural, are the main tools we have on our disposal in science and research.


  26. Logical Argument An argument is a statement logically inferred from premises. Neither an opinion nor a belief can qualify as an argument! Two sorts of arguments: • Deductive general  particular • Inductiveparticular  general

  27. Logical Argument There are three stages to a logical argument: • premises • inference and • conclusion

  28. But Everything Basically Depends on Judgement Now, the question, What is a judgement? is no small question, because the notion of judgement 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.

  29. 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 NON-STANDARD LOGICS


  31. INDUCTION • Empirical Induction • Mathematical Induction

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

  33. An Example of Inductive Inference • Every instance of water (at sea level) 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. )

  34. 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.

  35. 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.

  36. 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 well-ordering for the natural numbers.

  37. 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  n0 it is sufficient to show these two things: 1.      P(n0) is true. 2.      For any k  n0, if P(k) is true, then P(k+1) is true.

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

  39. 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  n0 it is sufficient to show these two things: 1.      P(n0) is true. 2.      For any k  n0, if P(n) is true for every n satisfying n0  n  k, then P(k+1) is true.

  40. 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  n0 and that all the statements P(n0), P(n0 +1), …, P(k) are true.

  41. 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.

  42. Statement to be shown in induction step: Ifk+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.

  43. 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!

  44. 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.

  45. 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.

  46. 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.

  47. INDUCTION VS DEDUCTION, TWO SIDES OF THE SAME COIN 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 governing our deduction? Again, there is no other method at hand but (empirical) induction.

  48. INDUCTION VS DEDUCTION, TWO SIDES OF THE SAME COIN In fact, the truth is that even induction implies steps following 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 or 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.

  49. INDUCTION & DEDUCTION: Traditional View