CT3340/CD5540 Research Methodology for Computer Science and Engineering Theory of Science LOGIC AND CRITICAL THINKING

Gordana Dodig-Crnkovic Department of Computer Science and Engineering Mälardalen University 2004 1 Theory of Science Lectures

1. SCIENCE, KNOWLEDGE, TRUTH 2. SCIENCE, RESEARCH, TECHNOLOGY AND SOCIETY 3. LOGIC AND CRITICAL THINKING 4. FORMAL LOGICAL SYSTEMS LIMITATIONS, LANGUAGE AND COMMUNICATION, BRIEF RETROSPECTIVE OF SCIENTIFIC THEORY 5. PROFESSIONAL ETHICS

2 2 LOGIC AND CRITICAL THINKING

• LOGICAL ARGUMENT • DEDUCTION • INDUCTION – Empirical Induction – Mathematical Induction – Induction vs. Deduction, Hypothetico-deductive Method • REPETITIONS, PATTERNS, IDENTITY • CAUSALITY AND DETERMINISM • FALLACIES

3 LOGIC

Logic is the science of reasoning, proof, thinking, or inference. Logic allows us to analyze a piece of reasoning and determine whether it is correct or not. To use the technical terms, we determine whether the reasoning is valid or invalid.

4 LOGIC

This lecture deals only with simple Boolean logic. Other sorts of mathematical logic, such as fuzzy logic, obey different rules. When people talk of logical arguments, though, they generally mean the type being described here.

5 What is an Argument?

"An argument is a connected series of statements intended to establish a proposition".

(Michael Palin i Monty Python’s Argument Clinic.)

See Monty Python's Argument Clinic under: http://www.duke.edu/~pms5/humor/argument.html

6 What is an Argument?

There are three stages to an argument: – premises – inference and – conclusion.

7 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.

8 What is an Argument?

An argument is thus a statement logically inferred from premises. Neither an opinion nor a belief can qualify as an argument!

Two sorts of arguments: – deductive – inductive

9 What is an Argument?

The building blocks of a logical argument are propositions, also called statements. A proposition is a statement which is either true or false; for example:

"The first programmable computer was built in Cambridge." "Dogs cannot see color."

10 1. Premises

One or more propositions are necessary for the argument to continue. They must be stated explicitly. They are called the premises of the argument. They are the evidence (or reasons) for accepting the argument and its conclusions.

11 2. Inference

The premises of the argument are used to obtain further propositions. This process is known as inference. In inference, we start with one or more propositions which have been accepted. We then derive a new proposition. There are various forms of valid inference. The propositions arrived at by inference may then be used in further inference. Inference is often denoted by phrases such as implies that or therefore.

12 3. Conclusion

Finally, we arrive at the conclusion of the argument, another proposition. The conclusion is often stated as the final stage of inference. It is affirmed on the basis the original premises, and the inference from them. Conclusions are often indicated by phrases such as therefore, it follows that, we conclude and so on.

13 Deductive inferences: general → particular

Inductive inferences: particular → general

14 DEDUCTION

A deductive argument is defined as: • constructed according to valid rules of inference • the conclusion necessarily follows from the premises.

15 Modus Ponens

All humans are mortal. (premise) Kevin is human. (premise) Thus, Kevin is mortal. (conclusion)

16 Modus Tollens

All birds have wings. (premise) Kevin has no wings. (premise) Kevin is not a bird. (conclusion)

17 Disjunctive Syllogism

The baby can either be a boy or a girl. (premise) The baby is not a girl. (premise) The baby is a boy. (conclusion)

18 Hypothetical Syllogism

If Karro is a terrier, Karro is a dog. (premise) If Karro is a dog, Karro is a mammal. (premise) If Karro is a terrier, Karro is a mammal. (conclusion)

19 NON-STANDARD LOGICS

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

20 NON-STANDARD LOGICS

http://www.earlham.edu/~peters/courses/logsys/nonstbib.htm http://www.math.vanderbilt.edu/~schectex/logics/ 21 INDUCTION

• Empirical Induction • Mathematical Induction

22 EMPIRICAL INDUCTION

The generic form of an inductive argument:

• Every A we have observed is a B. • Therefore, every A is a B.

23 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 specific conditions. ( In our example we could add ”in an open vessel” as well. )

24 An inductive argument have no way to logically (with certainty) 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 supports working theories based on the collected evidence.

25 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.

26 MATHEMATICAL INDUCTION

In the empirical induction we try 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 principle for the natural numbers.

27 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.

28 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.

29 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.

30 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.

31 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.

32 • 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.

33 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!

34 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.

35 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.

36 INDUCTION VS DEDUCTION, HYPOTHETICO-DEDUCTIVE METHOD

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) • Make predictions about the hypothesis (deduction). • Test the hypothesis (induction).

37 INDUCTION & DEDUCTION: AN ETERNAL GOLDEN BRAID

• Deduction, if applied correctly, leads to true conclusions. But deduction itself is based on the fact that we know something for sure. 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. 38 INDUCTION & DEDUCTION: AN ETERNAL GOLDEN BRAID

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.

39 INDUCTION & DEDUCTION: Traditional View

40 INDUCTION & DEDUCTION: GENERAL AN ETERNAL GOLDEN BRAID

PARTICULAR Problem domain41 INDUCTION & DEDUCTION: AN ETERNAL GOLDEN BRAID

“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.

42 INDUCTION & DEDUCTION: AN ETERNAL GOLDEN BRAID

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

43 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.

44 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.

45 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.)

46 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.)

47 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

48 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 49 SOME NOT ENTIRELY UNCOMMON “PROOF TECHNIQUES”

• Proof by vigorous handwaving Works well in a classroom or seminar setting. • Proof by cumbersome notation Best done with access to at least four alphabets and special symbols. • Proof by exhaustion Proof around until nobody knows if the proof is over or not…

READ THE REST ON PAGE 42!

50 REPETITIONS, PATTERNS, IDENTITY

Empirical method relies on observations and experiments, which lead to a collection of data describing phenomena.

In order to establish a pattern or regularity of behavior, we have to analyze (compare) the results (data) searching for similarities (repetitions) and differences.

All repetitions are approximate: the repetition B of an event A is not identical with A, or indistinguishable from A, but only similar to A.

51 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?

52 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.

53 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).

54 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.

55 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.

56 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?

57 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!

58 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.

59 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.

60 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.

61 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.

62 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.]

63 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.

64 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.

73-79 according to: Causes and correlations, Lars-Göran Johansson http://www.filosofi.uu.se/utbildning/Externt/slu/slultextcc.htm

65 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.

66 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.

67 REPETITION, SIMILARITY

As repetition is based upon similarity, it must be relative. Two things that are similar are always similar in certain respects.

68 REPETITION, SIMILARITY

Searching for similarity and differences leads to classifications i.e. the division of objects or events in different groups/classes. The simplest tool by for classification is the binary opposition or dichotomy (dualism). When we use dichotomy, we only decide if an object is of kind A or of kind ∼A. Examples of frequent dichotomies are yes/no, true/false, before/after, more/less, above/below, etc.

69 REPETITION, SIMILARITY

Whilst there are no opposites in 'nature', the binary oppositions we employ in our cultural practices have developed historically as they help to generate order out of the dynamic complexity of experience.

At the most basic level of individual survival humans share with other animals the need to distinguish between own species and other, safe and dangerous, edible and inedible, dominance and submission, etc.

70 IDENTITY

• The basic feature of experimental method is its reproducibility: It must be possible to establish essentially the same experimental situation in order to obtain the same results. This means that the experimental arrangement can be made with essentially equivalent parts. • What we call “essentially equivalent” (or we can call it “essentially the same”) depends on situation. Even here the principle of information hiding helps us to get a practical “level of resolution” which means information hiding for all objects below that level.

71 IDENTITY

So declaring two systems/particles/states as identical is entirely the matter of focus. For example if we focus on question of how many people in this country are vegetarians, we just treat all people as equal units. If we want to know how many women in this country are vegetarian, we discriminate between men and women in our analysis of people.

72 IDENTITY

We can e.g. also assume that bacteria of particular sort are interchangeable (indistinguishable) in certain context. That enables us to make repeated experiments with different agents and to treat all bacteria of the same type as equal. It does not mean that they are identical in the absolute sense. It only means that for our purpose the existing difference does not have any significance.

73 IDENTITY

• Example of ancient atomic theory. The problem of showing that one single physical body- say piece of iron is composed of atoms is at least as difficult as of showing that all swans are white. Our assertions go in both cases beyond all observational experience.

• The difficulty with these structural theories is not only to establish the universality of the law from repeated instances as to establish that the law holds even for one single instance.

74 IDENTITY

• A singular statement like “This swan here is white” may be said to be based on observation. Yet it goes beyond experience- not only because of the word “white”, but because of the word “swan”.

• For by calling something a “swan”, we attribute to it properties which go far beyond mere observation. So even the most ordinary singular statements are always the interpretations of the facts in the light of theories!

75 IDENTITY - SAMENESS

• Sameness is therefore a notion deserving of some attention. • If we define sameness to be interchangeability or intersubstitutability of objects that makes sameness dependent on use.

76 LIKENESS

– The state, quality, or fact of being like; resemblance. – Synonyms: similarity, similitude, resemblance, analogy, affinity. These nouns denote agreement or conformity. Likeness implies close agreement. – Similarity and similitude suggest agreement only in some respects or to some degree – Resemblance refers to similarity in external or superficial details – Analogy is similarity, as of properties or functions, between things that are otherwise not comparable.

77 ANALOGY vs. HOMOLOGY ANALOGY

1: similarity in some respect between things that are otherwise dissimilar: "the operation of a computer presents an interesting analogy to the working of the brain" 2: (logic) inference that if things agree in some respects they probably agree in others 3: drawing a comparison in order to show a similarity in some respect; "the models show by analogy how matter is built up" 4: a theoretical account based on a similarity between the model and the phenomena that are to be explained; "it was a computer simulation of problem solving" [syn: simulation]

78 HOMOLOGY

1 : a similarity often attributable to common origin

2 a : likeness in structure between parts of different organisms due to evolutionary differentiation from the same or a corresponding part of a remote ancestor b : correspondence in structure between different parts of the same individual

3 : a branch of the theory of topology.

79 CLASSIFICATION (1)

– A relation is an equivalence relation if it is reflexive, symmetric and transitive.

– An example of such is equality on a set.

80 EQUIVALENCE CLASS

An equivalence class is defined as a subset of the form {x ∈ X : xRa}, where a is an element of X and the notation "xRy" is used to mean that there is an equivalence relation between x and y.

It can be shown that any two equivalence classes are either equal or disjoint, hence the collection of equivalence classes forms a partition of X. For all a, b ∈ X , we have aRb iff a and b belong to the same equivalence class.

81 CLASSIFICATION (2)

CLASSES SHOULD BE DISJUNCT ...

class 1: positive effect class2 class1 class 2: negative effect class3 class 3: no effect

82 CLASSIFICATION (3)

... AND CHOSEN ACCORDING TO SAME CRITERIA ...

Universe here is a class2 group of patients class1 who test a new medicine. class3

83 CLASSIFICATION (4) (INCONGRUITY)

Here is the example of how not to classify.

Jorge Luis Borges in "The language of John Wilkins" Other Inquisitions 1937-52, explores the incongruity of classification. He invents a Chinese classification titled The Celestial Emporium of Benevolent Recognitions.

84 CLASSIFICATION (5) Jorge Luis Borges, "The Analytical Language of John Wilkins"

Borges's fictive encyclopedia divides animals into:

(a) those that belong to the (h) those that tremble as if they Emperor, were mad, (i) those that resemble flies from (b) embalmed ones, a distance (c) those that are trained, (j) those drawn with a very fine (d) suckling pigs, camel's hair brush, (e) mermaids, (k) innumerable ones, (l) others, (f) fabulous ones, (m) those that have just broken a (g) stray dogs, flower vase 85 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

86 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.

87 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.

88 PSEUDOSCIENCE (4)

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

89 PSEUDOSCIENCE (5) http://skepdic.com/ The Skeptic's Dictionary, Skeptical Inquirer http://www.physto.se/~vetfolk/Folkvett/199534pseudo.html The 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)

90 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.

91 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. Karl Popper attacked logical positivism and introduced his own criterion for demarcation, falsifiability. Thomas Kuhn and Imre Lakatos proposed his own criteria that distinguished between progressive and degenerative research programs. http://www.free-definition.com/Pseudoscience.html#The_Problem_of_Demarcation

92 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.

93 CRITICAL THINKING (2)

How Do We Critically Think? 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."

94 CRITICAL THINKING (3)

• C. Test 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"

95 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?

96 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 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"?

97 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?

98 CRITICAL THINKING (7)

Critical Thinking http://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

99 ASSIGNMENT 2 ANALYSIS OF DOWSING, CRITICAL THINKING

• USE THE TEMPLATE GIVEN ON THE COURSE HOME PAGE

• LEAVE THE TEMPLATE UNCHANGED, WRITE DOWN YOUR ANSWER AFTER EACH QUESTION. • THINK CRITICALLY • SEND YOUR ASSIGNMENTS BY MAIL TO ME. • IN YOUR MAIL WRITE FOLLOWING SUBJECT: CT3340 ASSIGNMENT 2

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