1

CDT403 Research Methodology in Natural Sciences and Engineering

Theory of ScienceLANGUAGE AND COMMUNICATION,

CRITICAL THINKING AND PSEUDOSCIENCE

Gordana Dodig-Crnkovic

Department of Computer Science and ElectronicMälardalen University

2

Theory of Science

Lecture 1 SCIENCE, KNOWLEDGE, TRUTH, MEANING. FORMAL LOGICAL SYSTEMS LIMITATIONS

Lecture 2 SCIENCE, RESEARCH, TECHNOLOGY, SOCIETAL ASPECTS. PROGRESS. HISTORY OF SCIENTIFIC THEORY. POSTMODERNISM AND CROSSDISCIPLINES

Lecture 3 LANGUAGE AND COMMUNICATION. CRITICAL THINKING. PSEUDOSCIENCE - DEMARCATION

Lecture 4 GOLEM LECTURE. ANALYSIS OF SCIENTIFIC CONFIRMATION: THEORY OF RELATIVITY, COLD FUSION, GRAVITATIONAL WAVES

Lecture 5 COMPUTING HISTORY OF IDEAS

Lecture 6 PROFESSIONAL & RESEARCH ETHICS

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.

– Sign is any 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

SEMIOTICS (2)

syntactics

semantics

pragmatics

Three Levels of Semiotics (Theory of Signs)

6

SEMIOTICS (2A)

syntactics

semanticspragmatics

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 construction and 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

The sign consists of– signifier (a pointer)– signified (that what pointer points to)

 

CAT

(signifier)(signified)

SEMIOTICS (5)

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 would destroy 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

yes/nobefore/after

right/wrong

true/falseopen/closed

in/outup/down

win/losemind/bodyquestion/answerpositive/negativeart/scienceactive/passivetheory/practice

simple/complex

straight/curved

text/context

central/ peripheral

stability/change

quantity/quality

knowledge/ignorance

16

LANGUAGE -THOUGHT- WORLD Eskimo Terms for Snow

Snow ParticlesSnowflakeqanuk 'snowflake'qanir- 'to snow'qanunge- 'to snow' [NUN]qanugglir- 'to snow' [NUN]Frostkaneq 'frost'kaner- 'be frosty/frost sth.‘Fine snow/rain particleskanevvluk 'fine snow/rain particleskanevcir- to get fine snow/rain particlesDrifting particlesnatquik 'drifting snow/etc'natqu(v)igte- 'for snow/etc. to drift along ground'.'

Clinging particlesnevluk 'clinging debris/nevlugte- 'have clinging

debris/...'lint/snow/dirt...'Fallen SnowFallen snow on the groundaniu [NS] 'snow on ground'aniu- [NS] 'get snow on ground'apun [NS] 'snow on ground'qanikcaq 'snow on ground‘qanikcir- 'get snow on 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

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) …

20

AMBIGUITIES OF LANGUAGE (5)

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.

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.

25

USE OF LANGUAGE IN SCIENCE. LOGIC AND CRITICAL THINKING. PSEUDOSCIENCE

• LOGICAL ARGUMENT

• DEDUCTION

• INDUCTION

• REPETITIONS, PATTERNS, IDENTITY

• CAUSALITY AND DETERMINISM

• FALLACIES

• PSEUDOSCIENCE

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

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

30

NON-STANDARD LOGICS

http://www.earlham.edu/~peters/courses/logsys/nonstbib.htmhttp://www.math.vanderbilt.edu/~schectex/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

• 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.

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: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.

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 Mthen 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

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.

 

INDUCTION VS DEDUCTION, TWO SIDES OF THE SAME COIN

49

INDUCTION & DEDUCTION: Traditional View

50

GENERAL

PARTICULAR Problem domain

INDUCTION & DEDUCTION:

51

 “There is actually 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.

 

INDUCTION & DEDUCTION

52

 In short: deduction and induction are - like two sides of

a piece of paper - the inseparable parts of our recursive thinking process.

 

INDUCTION & DEDUCTION

53

FALLACIES

‘My brethren, I beseech you, in the name of common sense, to believe it possible that you may be mistaken.’—OLIVER CROMWELL.

What about not properly 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.

54

FORMAL FALLACIES “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.

55

FORMAL FALLACIES Incorrect deduction when using auxiliary hypotheses

 If H and A1, A2, …., An is true, then so is I.

But (As the evidence shows), I is not true.H and A1, A2, …., An are all false

(Comment: One can be certain that H is false, only if one is certain that all of A1, A2, …., An are all true.)

56

FORMAL FALLACIES “Affirming the consequent"

And now again the fallacy of affirming the consequent: 

If H is true, then so are A1, A2, …., An.

(As the evidence shows), A1, A2, …., An are all true.

H is true

(Comment: A1, A2, …., An can be a consequence of some other premise, and not H.)

 

57

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 Question

58

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://www.intrepidsoftware.com/fallacy/toc.htm

59

SOME NOT ENTIRELY UNCOMMON “PROOF TECHNIQUES”

Proof by vigorous handwavingWorks well in a classroom or seminar setting.

Proof by cumbersome notationBest done with access to at least four alphabets and special symbols.

Proof by exhaustionProof around until nobody knows if the proof is over or not…

READ THE REST ON PAGE 42 OF THE COMPENDIUM!

60

CAUSALITY AND DETERMINISM CAUSALITY

Causality refers to the way of knowing 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?

61

CAUSALITY

Early philosophers, as we mentioned before, concentrated on conceptual issues and questions (why?).

Later philosophers concentrated on more concrete issues and questions (how?).

The change in emphasis from conceptual to concrete coincides with the rise of empiricism.

62

CAUSALITY

Hume is probably the first philosopher to postulate a wholly 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 philosophers deny the existence of "cause" and

some 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).

63

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.

64

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.

65

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?

66

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, he could have predicted the collision!

67

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.

68

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.

69

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, and not a matter of fact.

70

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.

71

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.]

72

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.

73

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 that a correlation obtains.

74

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.

75

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.

 

76

MYTHOPOETIC THINKING

Mythopoetic (myth + poetry) truth is revealed through myths, stories and rituals.

Myths are stories about persons, where persons may be gods, heroes, or ordinary people.

77

MYTHOPOETIC THINKING

Myth allows for a multiplicity of explanations, where the explanations are not logically exclusive (can contradict each other) and are often humorous.

Indian ritual and ceremonies recorded on stone

78

MYTHOPOETIC THINKING

Mythic traditions are conservative. Innovation is slow, and radical departures from tradition rarely tolerated.

The Egyptian king Akhenaton and Queen Nefertiti making offerings to the Aton.

79

MYTHOPOETIC THINKING

Myths are self-justifying. The inspiration of the gods was enough to ensure their validity, and there was no other explanation for the creativity of poets, seers, and prophets than inspiration by the gods. Thus, myths are not argumentative.

80

MYTHOPOETIC THINKING

Myths are morally ambivalent. The gods and heroes do not always do what is right or admirable, and mythic stories do not often have edifying moral lessons to teach.

81

THE MYTHO-POETIC UNIVERSE

In ancient Egypt the dome of the sky was represented by the goddess Nut, She was the night sky, and the sun, the god Ra, was born from her every morning.

82

The Medieval Universe with Earth in the Centre

From Aristotle Libri de caelo (1519).

83

The Clockwork Universe

The mechanicistic paradigm which systematically revealed physical structure in analogy with the artificial. The self-functioning automaton - basis and canon of the form of the Universe. Newton Principia, 1687

84

THE UNIVERSE AS A COMPUTER

We are all living inside a gigantic computer. No, not The Matrix: the Universe.

Every process, every change that takes place in the Universe, may be considered as a kind of computation.

E Fredkin, S Wolfram

http://www.nature.com/nsu/020527/020527-16.html

85

Islands of Knowledge

“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, arith-metic truths. An island here, the calculus. And these are different fields of mathemat-ics where all the ideas are interconnected in ways that mathematicians love; they fallinto nice, interconnected patterns.

But what I've discovered is all this sea around the islands.”Gregory Chaitin, an interview, September 2003

86

CRITICAL THINKING (1)

What is Critical Thinking?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.

87

CRITICAL THINKING (2)

 How Do We Think Critically?A.  QuestionFirst, 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."

88

CRITICAL THINKING (3)

C.  TestTesting 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"

89

CRITICAL THINKING (4)

1.  Criteria for truthCriteria 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?  

               

90

CRITICAL THINKING (5)

b.  Consistent with itself 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 languageIs 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"?

       

91

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 consequencesIs 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?

92

CRITICAL THINKING (7)

Critical Thinkinghttp://www.criticalreflections.com/critical_thinking.htm

What is truth? Not a simple question to answer, but this excellent page at the Internet Encyclopedia of Philosophy will help show you the way. http://www.utm.edu/research/iep/t/truth.htm

93

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

94

PSEUDOSCIENCE (2)

Motivations for the advocacy or promotion of pseudoscience range from simple naivety about the nature of science or of the scientific method, to deliberate deception for financial or other benefit. Some people consider some or all forms of pseudoscience to be harmless entertainment. Others, such as Richard Dawkins, consider all forms of pseudoscience to be harmful, whether or not they result in immediate harm to their followers.

95

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 possiblity of reproducible results;

or • violating Occam's Razor (the principle of choosing the simplest

explanation when multiple viable explanations are possible); the more egregious the violation, the more likely.

96

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

97

PSEUDOSCIENCE (5)

http://skepdic.com/ The Skeptic's Dictionary, Skeptical Inquirer

http://www.physto.se/~vetfolk/Folkvett/199534pseudo.htmlThe Swedish Skeptic movement (in Swedish)

Scientific Evidence For Evolution Scientific American, July 2002: 15 Answers to Creationist

Nonsense Human Genome, Nature 409, 860 - 921 (2001)

98

THE PROBLEM OF DEMARCATION (1)

After more than a century of active dialogue, the question of what marks the boundary of science remains fundamentally unsettled. As a consequence the issue of what constitutes pseudoscience continues to be controversial. Nonetheless, reasonable consensus exists on certain sub-issues.

99

THE PROBLEM OF DEMARCATION (2)

Criteria for demarcation have traditionally been coupled to one philosophy of science or another.

Logical positivism, for example, supported a theory of meaning which 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.

100

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.

http://www.freedefinition.com/Pseudoscience.html#The_Problem_of_Demarcation

101

Assignments 1, 2 and 3

• Assignment 1 (Scientific papers review) September 5• Assignment 2 (Demarcation) September 9• Assignment 3 (Golem) September 23