1. Metalogic

2. TWO CONCEPTIONS OF LOGICAL CONSEQUENCE

3. Deductive Validity We say that an argument is deductively valid when it has the following property: If the premises of the argument are true, then the conclusion of the argument must be true. A valid argument is “truth-preserving”: the truth of the premises gets passed on to the conclusion.

4. Soundness A sound argument is one that (i) is valid and (ii) has true premises. Every sound argument is valid (by definition), but the reverse is not true. Some valid arguments are not sound.

5. Example Consider the following argument: Premise 1: All dogs have eight legs. Premise 2: I am a dog. Therefore, Conclusion: I have eight legs.

6. Example This argument is valid. If the premises are true, then the conclusion must also be true. If all dogs truly have eight legs, and I am truly a dog, then it is true that I have eight legs! However, the argument is not sound. The premises are false (and so is the conclusion).

7. Comprehension Questions Can arguments that are deductively valid have false conclusions? False premises? Can arguments that are not deductively valid have true conclusions? True premises? Can deductively sound arguments have false conclusions? Can arguments that are not deductively sound still be deductively valid?

8. Deductive Logic Deductive logic (often just called ‘logic’) is the study of deductively valid argument forms.

9. Argument Forms Example Premise 1: If horses had wings, they could fly. Premise 2: Horses cannot fly. Therefore, Conclusion: Horses don’t have wings.

10. Argument Forms Example Premise 1: If the butler committed the murder, then the murder weapon is the candlestick. Premise 2: The murder weapon is not the candlestick. Therefore, Conclusion: The butler did not commit the murder.

11. Argument Forms Example Premise 1: If Sally is free this evening, George will take her to dinner. Premise 2: George did not take Sally to dinner. Therefore, Conclusion: Sally was not free this evening.

12. Argument Forms Example All of these arguments share a deductively valid argument form: Premise 1: if A, then B. Premise 2: not B. Conclusion: not A. Any argument with this form is valid, no matter what ‘A’ and ‘B’ are.

13. Argument Forms Example Premise: Everyone is happy. Conclusion: There is not someone who is not happy. Premise: Everyone is F. Conclusion: There is not someone who is not F.

14. Argument Forms Example Premise: You can’t be happy and successful. Conclusion: If you’re happy, you’re not successful. Premise: not (A and B). Conclusion: if A then not B.

15. Argument Forms Example Premise: Either Fred took the train or he took the ferry. Premise 2: Fred did not take the ferry. Conclusion: Fred took the train. Premise: Either A or B. Premise: not B Conclusion: A

16. Deductive Logic The goal of deductive logic is to identify deductively valid argument forms. We can use these as a formal test for validity: if an argument has a certain form, then that argument is deductively valid.

17. Sentential Logic Sentential Logic (SL, also known as Propositional Logic, or the Propositional Calculus) is a formal logical system that represents logical relations among sentences (or propositions).

18. Logical Consequence x2 There are two ways of thinking about one formula ‘logically following’ from another: Syntactic Criteria: formula 1 is provable (given the system’s rules) from formula 2. Semantic Criteria: formula 1 evaluates as true whenever formula 2 does.

19. SYNTAX: WELL-FORMEDNESS

20. Syntax Every formal logical system has a syntax (or grammar) that defines which sequences of symbols are well-formed expressions (called “well-formed formulas” or WFFs) and which are ill-formed.

21. Sentential Variables In SL, simple sentences are represented by capital letters A, B, C, D, etc. These are sometimes called “sentence letters” or “sentential variables”– they’re variables, because A can represent the sentence “snow is white” or the sentence “snow is green” or the sentence “turtles love noodles,” or whatever.

22. Sentential Variables By convention, we prefer the letters P, Q, R, S… for sentential variables.

23. Connectives In language, various words allow you to make new, complex sentences out of other sentences. For example, consider the sentences “snow is green” and “turtles love noodles.” The word “and” lets us make the complex sentence “snow is green and turtles love noodles” out of these two simple sentences.

24. Examples of Connectives Snow is white. Grass is green. It is false that snow is white. Michael believes that snow is white. It is possible that snow is white. Snow is white and grass is green. If snow is white, then grass is green.

25. SL Connectives Each truth-functional English connective is given an SL counterpart: “not…”: ~ (called “tilde”) “…and…”: & (called “ampersand”) “…or…”: v (called “wedge”) “if…then…”: → (called “arrow”) “…if, and only if,…”: ↔ (called “double- arrow”)

26. Definition of WFF i.All sentence letters are WFFs. ii.If φ is a WFF, then ~φ is a WFF. iii.If φ and ψ are WFFs, then (φ & ψ), (φ v ψ), (φ → ψ), (φ ↔ ψ) are also WFFs. iv.Nothing else is a WFF. Question: why do we use φ and ψ here instead of, for example, P and Q?

27. Demonstration Using the definition we can show that certain sequences of symbols are WFFs. For example (P↔(Q&~R)) is a WFF.

28. Demonstration By (i), P, Q, and R are all WFFs. By (ii) ~R is a WFF. By (iii), since Q and ~R are WFFs, (Q&~R) is a WFF By (iii), since P and (Q&~R) are WFFs, (P↔(Q&~R)) is a WFF.

29. Ill-Formed Formulas Some sequences of symbols are not well- formed. Some examples include: ))PQ((( (~P) P & Q (P and Q)

30. Scope Every occurrence of a connective in a WFF has a scope. The scope of that occurrence is the smallest WFF that contains it. For example The scope of “&” in “(~(~P&Q)→P)” is “(~P&Q)” “(~(~P&Q)→P)” is not a WFF. “(~(~P&Q)→P)” is a WFF, but is bigger than “(~P&Q)”

31. Occurrences Notice that the same symbol can occur different times in the same formula, and that its different occurrences can have different scopes. ~((~P&Q)&(R↔Q))

32. Main Connective The main connective in a formula is the occurrence of the connective whose scope is widest (the scope of the occurrence of the connective = the formula). A formula is a conditional if its main connective is →, a conjunction if it’s &, a negation if it’s ~, etc.

33. SYNTAX: SYNTACTIC CONSEQUENCE (PROOF)

34. The Parts of a Derivation 11. (A & D)A 22. (A → (B & C))A 13. A1 &E 1,24. (B & C)2,3 →E 1,25. C4 &E

35. Derived WFFs 11. (A & D)A 22. (A → (B & C))A 13. A1 &E 1,24. (B & C)2,3 →E 1,25. C4 &E

36. Derived WFFs A derivation proceeds in stages. Each stage involves writing a new WFF that we have proven. We number each of the stages (1, 2, …).

37. Rules of Inference 11. (A & D)A 22. (A → (B & C))A 13. A1 &E 1,24. (B & C)2,3 →E 1,25. C4 &E

38. Rules of Inference Whenever we write a new WFF that we have proven, we write the justification out to the right of the WFF. This justification includes: the name of the logical rule we used to prove the WFF, and the previous stages of the derivation that were needed by the rule (if any).

39. Dependencies 11. (A & D)A 22. (A → (B & C))A 13. A1 &E 1,24. (B & C)2,3 →E 1,25. C4 &E

40. Dependencies To the left of each stage we write the “dependencies.” So for example, 1,24. (B & C)2,3 →E This line says that in stage #4 we proved the WFF “(B & C),” using the rule →E on lines 2 and 3. But we have only proved (B & C) assuming that 1 and 2 are true.

41. The Rule of Assumption: A Assumption is the easiest rule to learn. It says at any stage in the derivation, we may write down any WFF we want to. That WFF only depends on itself. For example, on line 57 we might write: 5757. (((P ↔ (A & B)) → ~~~R)A

42. Turnstile The proper way to read this is “line 57 is provable from line 57” or “On the assumption that (((P ↔ (A & B)) → ~~~R) it can be proved that (((P ↔ (A & B)) → ~~~R).” We can re-write any line to state preciesely what we have proved. Line 57 would be: (((P↔(A & B))→~~~R) Ⱶ (((P↔(A & B))→~~~R)

43. &-Elimination: &E &E is also a very easy-to-learn rule. If we have proved (φ & ψ) on some line, then on any future line we may write down φ, and on any future line we may write down ψ. The result depends on everything (φ & ψ) depended on.

44. Example 1,24. (B & C)2,3 →E 1,25. C4 &E On Line 4 we have proved “(B & C)”

45. Example 1,24. (B & C)2,3 →E 1,25. C4 &E So on line 5 (or any future line) we can write down C.

46. Example 1,24. (B & C)2,3 →E 1,25. C4 &E Since the rule we used in arriving at 5 was &E applied to line 4, we write down “4 &E” as our justification.

47. Example 1,24. (B & C)2,3 →E 1,25. C4 &E Finally, the rule &E says the result (line 5) depends on everything the original conjunction depended on. Since line 4 depends on 1 and 2, we write “1,2” to the left of line 5.

48. Arrow Elimination: →E The →E rule says that if on one line we have a conditional (φ → ψ) and on another line we have the antecedent of the conditional φ then on any future line, we may write down the consequent of the conditional ψ depending on everything (φ → ψ) and φ depended on.

49. Example 22. (A → (B & C))A 13. A1 &E 1,24. (B & C)2,3 →E On line 2 we have a conditional “(A → (B & C)),” and on line 3 we have its antecedent, “A.”

50. Example 22. (A → (B & C))A 13. A1 &E 1,24. (B & C)2,3 →E So on line 4 we can write its consequent “(B&C).”

51. Example 22. (A → (B & C))A 13. A1 &E 1,24. (B & C)2,3 →E Since the rule we used on line 4 was →E applied to lines 2 and 3, we write down “2,3 →E” to the right of 4.

52. Example 22. (A → (B & C))A 13. A1 &E 1,24. (B & C)2,3 →E Finally, since line 2 depends on line 2, and line 3 depends on line 1, line 4 depends on lines 1 and 2. We copy those numbers to the left of 4.

53. SEMANTICS: TRUTH-FUNCTIONS

54. Truth-functional Connectives A connective is truth-functional if the truth of sentences involving the connective depends only on the truth of the sentences they have as parts. For example “and” is truth-functional. Whether a sentence like “P and Q” is true depends only on whether P is true and whether Q is true.

55. Truth-Functional Connectives So the sentence “snow is white and grass is green” is true, because “snow is white” is true and “grass is green” is true. Replace “snow is white” with any other true sentence, and the result is still true.

56. Truth-Functional Connectives If all you know about “P and Q” is that P is true and Q is true, then you know that “P and Q” is also true. Or if you know that Q is false, you know that “P and Q” is false. You don’t have to know what P and Q are.

57. Non-Truth-Functional Connectives Some connectives are not truth-functional. You can replace true sentences in them with other true sentences, and this can change whether they are true. Or you can replace false sentences in them with other false sentences, and this can change whether they are true.

58. Example 1.CY Leung believes that illegal structures at his house are OK. 2.It’s false that illegal structures at CY Leung’s house are OK. 3.It’s false that dogs have 17 legs. 4.CY Leung believes that dogs have 17 legs.

59. Example Even if (1) is true, substituting another false sentence (3) for its false part (2) results in a false sentence.

60. Truth-Functional Connectives Sentential logic only contains truth-functional connectives, not non-truth-functional connectives like “CY Leung believes that___” or “It’s possible that____.”

61. SL Connectives Each truth-functional English connective is given an SL counterpart: “not…”: ~ (called “tilde”) “…and…”: & (called “ampersand”) “…or…”: v (called “wedge”) “if…then…”: → (called “arrow”) “…if, and only if,…”: ↔ (called “double- arrow”)

62. Since SL connectives are truth-functional, we can summarize the truth-functions they stand for in a truth-table.

63. Negation φ~φ~φ TF FT

64. Conjunction φψ(φ & ψ) TTT TFF FTF FFF

65. Disjunction φψ(φ v ψ) TTT TFT FTT FFF

66. Biconditional φψ(φ ↔ ψ) TTT TFF FTF FFT

67. The Material Conditional φψ(φ → ψ) TTT TFF FTT FFT

68. SEMANTICS: EVALUATIONS

69. Evaluations An evaluation is an assignment of truth-values to sentence letters. For example: A = T B = T C = F D = T E = F...

70. Evaluating WFFs To evaluate a WFF is to determine whether it is true or false according to an evaluation. Let’s consider ((Q & ~P) → R) Here’s our evaluation: Q = T, P = T, R = F.

71. Evaluation: Stage 1 PQR Write down sentence letters.

72. Evaluation: Stage 1 PQR TTF Insert truth-values from evaluation.

73. Evaluation: Stage 2 PQR((Q&~P)→R) TTF Copy down the formula to evaluate.

74. Evaluation: Stage 3 PQR((Q&~P)→R) TTFT Copy the truth-values of each variable.

75. Evaluation: Stage 3 PQR((Q&~P)→R) TTFTT Copy the truth-values of each variable.

76. Evaluation: Stage 3 PQR((Q&~P)→R) TTFTTF Copy the truth-values of each variable.

77. Evaluation: Stage 4 PQR((Q&~P)→R) TTFTTF Find a connective to evaluate.

78. Evaluation: Stage 4 PQR((Q&~P)→R) TTFTTF Need these truth values.

79. Evaluation: Stage 4 PQR((Q&~P)→R) TTFTTF Need these truth values.

80. Evaluation: Stage 4 PQR((Q&~P)→R) TTFTTF Need this truth value.

81. Evaluation: Stage 4 PQR((Q&~P)→R) TTFTFTF Need this truth value.

82. Evaluation: Stage 4 PQR((Q&~P)→R) TTFTFTF Need these truth values.

83. Evaluation: Stage 4 PQR((Q&~P)→R) TTFTFFTF Need these truth values.

84. Evaluation: Stage 4 PQR((Q&~P)→R) TTFTFFTF Need these truth values.

85. Evaluation: Stage 4 PQR((Q&~P)→R) TTFTFFTTF Need these truth values.

86. SEMANTICS: TRUTH-TABLES

87. Possibilities for One Sentence Letter φ…φ……φ… T F

88. Possibilities for Two Sentence Letters φψ…φ…ψ……φ…ψ… TT TF FT FF

89. Possibilities for Three Sentence Letters φψχ…φ…ψ…χ……φ…ψ…χ… TTT TTF TFT TFF FTT FTF FFT FFF

90. ~(~P & ~Q) PQ~(~P&~Q) TT TF FT FF

91. Copy Whole Column PQ~(~P&~Q) TTT TFT FTF FFF

92. Copy Whole Column PQ~(~P&~Q) TTTT TFTF FTFT FFFF

93. Evaluate Each Row PQ~(~P&~Q) TTFTT TFFTF FTTFT FFTFF

94. Evaluate Each Row PQ~(~P&~Q) TTFTFT TFFTTF FTTFFT FFTFTF

95. ~(~P & ~Q) PQ~(~P&~Q) TTFTFFT TFFTFTF FTTFFFT FFTFTTF

96. ~(~P & ~Q) PQ~(~P&~Q) TTTFTFFT TFTFTFTF FTTTFFFT FFFTFTTF

97. (~(~P & ~Q) ↔ (P v Q)) So “~(~P & ~Q)” has the same truth-table as “(P v Q).” Why is that? Suppose I say: “you didn’t do your homework and you didn’t come to class on time.” When is this statement false? When either you did your homework or you came to class on time.

98. SEMANTICS: TRUTH-TABLE TEST

99. The Truth-Table Test for Validity We know that an argument is deductively valid when we know that if it is true, then its conclusion must be true. We can use truth-tables to show that certain arguments are valid.

100. The Test Suppose we want to show that the following argument is valid: (P → Q) ~Q Therefore, ~P We begin by writing down all the possible truth- values for the sentence letters in the argument.

101. Write Down All the Possibilities PQ TT TF FT FF

102. Write Truth-Table for Premises PQ(P → Q)~Q TTTF TFFT FTTF FFTT

103. Write Truth-Table for Conclusion PQ(P → Q)~Q~P TTTFF TFFTF FTTFT FFTTT

104. Look at Lines Where Premises are True PQ(P → Q)~Q~P TTT*FF TF TF FTT T FFTTT

105. NEXT TIME: SOUNDNESS & COMPLETENESS

106. With two understandings of “logical consequence” we can ask two questions: 1. Is it true that every time q is a syntactic consequence of (is provable from) p, that q is a semantic consequence of p? [Soundness]

107. With two understandings of “logical consequence” we can ask two questions: 1. Is it true that every time q is a semantic consequence of p, that q is a syntactic consequence of (is provable from) p? [Completeness]