Confirmation

Question 1

basic idea bayesianism

Answer 1

S beliefs that p with degree g between 0 & 1 equal to some probability function

Question 2

Three pillars of Bayesianism

Answer 2

1. quantitative conception of belief
2. rational beliefs at any fixed given moment in time
3. how beliefs at different moments in time ought to be related

Question 3

credence g=0

Answer 3

total disbelief, contradictions (absurdum)

Question 4

credence g=1

Answer 4

certain belief, tautology (veritas)

Question 5

probability function of g

Answer 5

probability function is always in between absurdum & veritas, so between 0 & 1

Question 6

incompatibility of p & q

Answer 6

Pr(p or q) = pr (p) + pr (q)
or Pr (p and q) = 0

Question 7

bayesian explanation of propositional knowledge

Answer 7

p is true
s believes that p to a degree of 0.90 or larger
s has reached 0.90 by bayesian updating

Question 8

confirmation

Answer 8

confirmation (e,h) iff e is empirical evidence & f (e,h) > 1

Question 9

Infirmation

Answer 9

infirmation (e,h) iff e is empirical evidence & f (e,h) < 1

Question 10

Irrelevance

Answer 10

irrelevant (e,h) iff e is empirical evidence and f (e,h) = 1

Question 11

verification

Answer 11

maximal confirmation
Pr1(h) = Pr0 (h|e) = 1

Question 12

Falsification

Answer 12

maximal infirmation
Pr1 (h) = Pr0 (h|e) = 0

Question 13

mu (e,h)

Answer 13

quantify the degree of confirmation or disconfirmation that evidence e provides for a hypothesis. 10 for verification, -10 for falsification, irrelevance = 0.

Question 14

Diachronic coherence

Answer 14

change of beliefs over time, quantified by belief-change factor f(e,h)

Question 15

Hempel’s raven paradox

Answer 15

things that confirm should not confirm and things that do not confirm should confirm. Therefore, background knowledge is needed.
Example: seeing colored things that are not ravens confirms hypothesis that all ravens are black.

Question 16

Goodman’s new riddle of induction

Answer 16

even if we are able to see all emeralds in the world are green, we can still not promote hgreen to knowledge, since our observations also are able to confirm ‘magical’ hypotheses, like all emeralds are grue.
‘solution’: magical hypothesis will most likely not reach the epistemic level of >0.90 by bayesian updating