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The Difference between Knowledge and Understanding Sherri Roush Department of Philosophy Group in Logic and the Methodology of Science U.C. Berkeley

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Page 1: The Difference between Knowledge and Understandingphilosophy.berkeley.edu/file/860/Gettier_talk_UBC... · 2014-03-24 · Gettier cases, relevance matching, and understanding . p =

The Difference between Knowledge and Understanding

Sherri Roush Department of Philosophy

Group in Logic and the Methodology of Science U.C. Berkeley

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1962 4

Smith Jones

Brown

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Gettier case

Smith believes from experience

q … Jones owns a Ford.

and also believes ⇓

p … Someone in the office owns a Ford.

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Gettier case

q … Jones owns a Ford

⇓ p … Someone in the office owns a Ford.

↓ justified belief in p

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Gettier case

q = Jones owns a Ford. false

p = Someone in the office owns a Ford.

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Gettier case

q = Jones owns a Ford. false

p = Someone in the office owns a Ford. true

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Gettier case

q = Jones owns a Ford. false

⇓ p = Someone in the office owns a Ford. true

r = Brown owns a Ford. true

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Gettier case

q = Jones owns a Ford. false

⇓ p = Someone in the office owns a Ford. true

r = Brown owns a Ford. true

… oops

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Gettier case

q = Jones owns a Ford. false ⇓ p = Someone in the office owns a Ford. true r = Brown owns a Ford. true

… justified, true belief in p but not knowledge

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Plan

1. Added value of knowledge over true belief follows from the tracking conditions.

2. Tracking improves relevance matching, hence Gettierization avoidance (w/o ad hoc additions).

3. Don’t need to presuppose value of knowledge to see value of gettierization avoidance.

4. Understanding ≈ relevance matching.

5. Understanding is simulation.

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The True Belief Game – Approx.

You → World ↓ p

- p

b(p) - b(p)

Payoff assumptions: p true → (believe > not believe), p false → (not believe > believe)

(0,10)

(0,-20)

(0,-7)

(0,5)

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“Mere” good and bad states

Good belief states: p true S believes p true belief p false S does not believe p good lack of belief

Bad belief states: p true S does not believe p bad lack of belief

p false S believes p false belief

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“Mere” good and bad states

Good belief states: p true S believes p true belief p false S does not believe p good lack of belief

Bad belief states: p true S does not believe p bad lack of belief

p false S believes p false belief

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Belief state vs. Strategy

Belief state: p true, S doesn’t believe p Strategy: In response to p, don’t believe p In response to –p, don’t believe p

(disposition, regularity)

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The True Belief Game – Approx.

You → World ↓ p

- p

b(p) - b(p)

Payoff assumptions: p true → (believe > not believe), p false → (not believe > believe)

(0,10)

(0,-20)

(0,-7)

(0,5)

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Belief state vs. Strategy

Belief state: p true, S doesn’t believe p Strategy: In response to p, don’t believe p In response to –p, don’t believe p

disposition, rule for responding to all possible plays of opponent.

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Belief state vs. Strategy

Belief state: p true, S doesn’t believe p p, -b(p)

Strategy: disposition, regularity for responding to all possible plays of opponent.

e.g. Tracking is a strategy:

1) P(-b(p)/-p) > s 2) P(b(p)/p) > t

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Knowledge = Tracking

Tracking is a strategy:

1) P(-b(p)/-p) > s 2) P(b(p)/p) > t

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Variation (Sensitivity) Adherence

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The True Belief Game – Approx.

You → World ↓ p

- p

b(p) - b(p)

Payoff assumptions: p true → (believe > not believe), p false → (not believe > believe)

(0,10)

(0,-20)

(0,-7)

(0,5)

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The subject who is a tracker of p has an

Evolutionarily Stable Strategy (ESS)

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Tracker is evolutionarily stable Tracking type (R) strictly dominates any type following any

other conditions beyond true belief (-R), in the struggle for survival and utiles.

Once this strategy is achieved by some level of majority of the population, no small population with an alternative strategy can “invade” and drive it out.

These properties hold independently of the dynamics of interaction.

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If we think intuitively that knowledge can be of evolutionary or utilitarian value, then this is a unique explanatory advantage of the tracking theory.

This shows (tracking) knowledge is more valuable than mere true belief, without ad hoc tinkering.

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Larissa

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p = Route A will get me to Larissa by 12.

Suppose: p is true S, S’ believe p

S uses a paper map. S’ uses real-time GPS.

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p = Route A will get me to Larissa by 12.

p is true S, S’ believe p S’ has a strong disposition to believe p when it’s true and not believe p when it’s false. S uses a paper map. S’ uses real-time GPS. S has a true belief. S’ has a true belief and is tracking.

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p = Route A will get me to Larissa by 12.

p is true S, S’ believe p S’ has a strong disposition to believe p when it’s true and not believe p when it’s false.

S uses a paper map. S’ uses real-time GPS. S has a true belief. S’ has a true belief and a contingency detector.

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“The Value of Knowledge and the Pursuit of Survival,” Metaphilosophy (2010)

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The Gettier Problem

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Gettier cases and relevance

p = Someone in the office owns a Ford. true q = Jones owns a Ford. false r = Brown owns a Ford. true

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Gettier cases and relevance

p = Someone in the office owns a Ford. true q = Jones owns a Ford. false r = Brown owns a Ford. true P(b(p)/-q.r) = P(b(p)/-q.-r) but P(p/-q.r) ≠ P(p/-q.-r)

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q is (positively) relevant to your believing p.

P(b(p)/q) >> P(b(p)/-q) Or: P(b(p)/q)/P(b(p)/-q) >> 1

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q is (positively) relevant to p

P(p/q) >> P(p/-q) Or: P(p/q)/P(p/-q) >> 1

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Relevance matching on q for p:

P(b(p)/q)/P(b(p)/-q) = P(p/q)/P(p/-q)

The difference q’s truth value makes to your belief in p is the same as the difference q’s truth value makes to p’s truth value.

Relevance mismatch on q for p

P(b(p)/q)/P(b(p)/-q) ≠ P(p/q)/P(p/-q)

q’s truth value makes more of a difference, or less of a difference, to your belief in p than it does to p’s truth value.

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Gettier case

p = Someone in the office owns a Ford. true q = Jones owns a Ford. false r = Brown owns a Ford. true

P(b(p)/q) >> P(b(p)/-q) but P(p/q) > P(p/-q)

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Relevance matching on q for p: P(b(p)/q)/P(b(p)/-q) = P(p/q)/P(p/-q)

Relevance mismatch on q for p P(b(p)/q)/P(b(p)/-q) ≠ P(p/q)/P(p/-q) Gettierization relevance mismatch for p on some q for which P(b(p)/q) >> P(b(p)/-q) or …

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Relevance matching on q for p: P(b(p)/q)/P(b(p)/-q) = P(p/q)/P(p/-q)

Relevance mismatch on q for p P(b(p)/q)/P(b(p)/-q) ≠ P(p/q)/P(p/-q) Gettierization relevance mismatch for p on some r for which P(p/r) >> P(p/-r)

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Gettierized belief in p

Depends on: 1) basing belief in p on q (the helper) when q is false 2) having a relevance mismatch on q for 1) to exploit 3) p is true

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Relation of Relevance Matching for p and Tracking p

P(b(p)/q) = P(b(p)/p)P(q/b(p).p)P(p/q) + P(q/p)

P(b(p)/-p)P(q/b(p).-p)P(-p/q) P(q/-p)

P(b(p)/-q) = P(b(p)/p)P(-q/b(p).p)P(p/-q) + P(-q/p)

P(b(p)/-p)P(-q/b(p).-p)P(-p/-q) P(-q/-p)

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Relevance Matching

P(b(p)/q) P(p/q) =

P(b(p)/-q) P(p/-q)

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Relation of Relevance Matching for p and Tracking p

P(b(p)/q) = P(b(p)/p)P(q/b(p).p)P(p/q) + P(q/p)

P(b(p)/-p)P(q/b(p).-p)P(-p/q) P(q/-p)

P(b(p)/-q) = P(b(p)/p)P(-q/b(p).p)P(p/-q) + P(-q/p)

P(b(p)/-p)P(-q/b(p).-p)P(-p/-q) P(-q/-p)

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Perfect Sensitivity to p

P(b(p)/q) = P(b(p)/p)P(q/b(p).p)P(p/q) P(q/p)

P(b(p)/-q) = P(b(p)/p)P(-q/b(p).p)P(p/-q) P(-q/p)

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Relation of Tracking p to Relevance Matching for p on q

P(b(p)/q) = α P(p/q) P(b(p)/-q) = α P(p/-q)

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Relation of Tracking p to Relevance Matching for p

P(b(p)/q) P(p/q) = P(b(p)/-q) P(p/-q)

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Relation of Tracking p to Relevance Matching for p

P(b(p)/q) P(p/q) = P(b(p)/-q) P(p/-q)

1. Perfect tracking of p ⇒ Perfect relevance matching for p on q

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Relation of Tracking p to Relevance Matching for p

P(b(p)/q) P(p/q) =

P(b(p)/-q) P(p/-q)

1. Perfect tracking of p ⇒ Perfect relevance matching for p on q, for all q I.e., perfect tracking ⇒ No possibility of gettierization (on any q)

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Relation of Tracking p to Relevance Matching for p

P(b(p)/q) P(p/q) =

P(b(p)/-q) P(p/-q)

1. Perfect tracking of p ⇒ Perfect relevance matching for p on q, for all q

2. Increased tracking ⇒ Increased relevance matching for p on every q

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Relation of Tracking p to Relevance Matching for p

P(b(p)/q) P(p/q) =

P(b(p)/-q) P(p/-q)

1. Perfect tracking of p ⇔ Perfect relevance matching for p on all q

2. Increased tracking of p ⇒ Increased relevance matching for p on all q

3. Increased relevance matching for p on a given q ⇒ Increased tracking of p

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p b(p) q

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Perfect tracking

Perfect relevance matching

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p b(p) q

p b(p) q

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p b(p) q

p b(p) q

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p b(p) q p b(p) q3

q2

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q1

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p b(p) q p b(p) q3

q2

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q1

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Gettier cases, relevance matching, and understanding

p = Someone in the office owns a Ford. q = Jones owns a Ford. r = Brown owns a Ford. Have: P(p/q) = 1, P(b(p)/q) = 1 But: P(p/-q) ≠ P(b(p)/-q) Other ways than q of making p true are more relevant to p than S’s belief dispositions reflect.

S doesn’t understand why p is true.

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Definition – first pass

If S believes p and p is true, then S’s understanding of why p is true improves iff there is an increase in relevance matching for p on some q and no outweighing decrease in relevance matching for other q.

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Recall

Increasing your tracking of p will increase your relevance matching for p on every q.

Tracking brings relevance matching, G-avoidance, and understanding.

Increasing your relevance matching on a given q doesn’t necessarily increase your tracking of p.

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Knowledge and Understanding

Increasing your tracking of p will increase your relevance matching for p on every q.

Knowledge brings relevance matching, G-avoidance, and understanding.

Increasing your relevance matching on a given q doesn’t necessarily increase your tracking of p.

But improved understanding of p always improves level of tracking (knowledge) of p.

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Understanding and Explanation

Fact: Relevance matching your belief in p to the web of q’s relevant to p does not require you to be able to cite the factors probabilistically relevant to p. Opinions: 1. If we add a citation requirement, then we get a definition of ability to give an explanation. (= Salmon statistical relevance view)

2. Not all understanding brings ability to give explanations.

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Prediction of human behavior

S: I know what she’ll do. A: How do you know? S: I understand her. We do this without being able to list all the factors. (Challenge for the higher-order view of understanding other minds.)

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p’s web of relevance

q1

q4

q5 q6

p

q2 q3

q7

q8

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Mere true belief in p

q1

q4

q5 q6

p

q2 q3

q7

q8

b(p)

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Relevance Matching, Understanding?

q1

q4

q5 q6

p

q2 q3

q7

q8

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b(p)

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Hyperbolic intellectualism

q1

q4

q5 q6

p

q2 q3

q7

q8

75

b(q1)

b(q4)

b(q5) b(q6)

b(p)

b(q2) b(q3)

b(q7)

b(q8)

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Understanding

Understanding why you should believe p Understanding why p is true

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Understanding p = Jefferson is dead Understanding why you should believe p q1 = lack of pulse Understanding why p q2 = gunshot wound q3 = political disputes indicators of p vs. what makes p true

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Awkward

You track p via a great indicator ⇒ You relevance match on all q. ⇒ You understand why Jefferson is dead.

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Awkward

Your believing p (hurricane tomorrow) co-varies with output of a great computer simulation programmed by someone else.

⇒ You track p. ⇒ You relevance match on all q. ⇒ You understand why p is true.

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Hyperbolic Intellectualism

q1

q4

q5 q6

p

q2 q3

q7

q8

80

b(q1)

b(q4)

b(q5) b(q6)

b(p)

b(q2) b(q3)

b(q7)

b(q8)

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Understanding?

q1

q4

q5 q6

p

q2 q3

q7

q8

81

b(p)

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Owning the relevance matching

q1

q4

q5 q6

p

q2 q3

q7

q8

82

m(q1)

m(q4)

m(q5) m(q6)

b(p)

m(q2) m(q3)

m(q7)

m(q8)

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Prediction of human behavior

S: I know what she’ll do. A: How do you know? S: I understand her. We do this without being able to list all the factors. (Challenge for the higher-order view of understanding other minds.)

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Understanding as simulation

q1

q4

q5 q6

p

q2 q3

q7

q8

84

m(q1)

m(q4)

m(q5) m(q6)

b(p)

m(q2) m(q3)

m(q7)

m(q8)

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Summary 1. Knowledge (tracking) is more valuable than mere true

belief; it is an ESS.

2. What explains that value (tracking) also directly opposes gettierization.

3. Gettierization avoidance for p has a value – contributing to understanding p – even if we don’t assume knowledge of p has value.

4. Understanding ∼ relevance matching ∼ simulation

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p b(p)

87

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p b(p) q3

88

q1

q2

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p b(p) q3

89

q1

q2

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p q3

90

q1

q2

m1

m1

m1

b(p)

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p q3

91

q1

q2

m1

m1

m1

b(p)

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p q3

92

q1

q2

m1

m1

m1

b(p)

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p q3

93

q1

q2

m1

m1

m1

b(p)

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q3

94

q1

q2

m1

m1

m1

b(p) p

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q3

95

q1

q2

m1

m1

m1

b(p) p

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96

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97