Due on Fri Dec 1.
All of these problems are adapted from Titelbaum.
B
and C
. These are logically independent, and suppose at t0 you assign equal credence to each of the four possible assignments of truth-value to each of them. Between t0 and t1 you perform a Jeffrey Conditionalization on the B:¬B
partition, that has the end result of setting your posterior credence New1(B) = 2/3
. Between t1 and t2, you perform a Jeffrey Conditionalization on the C:¬C
partition, that has the end result of setting your posterior credence New2(C) = 3/4
.New1(.)
and New2(.)
.B
change between t1 and t2? Does your credence in C
change between t0 and t1?C:¬C
partition, that has the same C:¬C
Bayes Factor as when you moved from New1(.)
to New2(.)
. What is the credence distribution you end up with in this case?C
after the update described in (d) the same or different from your credence New2(C)
at t2? Does this always happen if you do an update with the same Bayes Factor but starting with a different distribution? If so, why? If not, why did it happen in this case?Suppose an agent is indifferent between two gambles with the following utility outcomes:
| P | ¬P
---------|---|---
Gamble1 | x | y
Gamble2 | y | x
where x
and y
are utilties where x ≠ y
. Assuming this agent maximizes utilities in Savage’s way, what can you infer about the agent’s credence in P
?
Suppose the agent is also indifferent between these two gambles:
| Q | ¬Q
---------|---|---
Gamble3 | d | -d
Gamble4 | m | m
where the agent’s credence in Q
is 1/2. What can you infer about m
?
Finally, suppose the agent is indifferent between these two gambles:
| R | ¬R
---------|-----|---
Gamble5 | 100 | 20
Gamble6 | 80 | 80
What can you infer about the agent’s credence in R
?
Ash’s credences don’t satisfy the probability axioms. For some mutually exclusive P
and Q
, he assigns cr(P) = cr(Q) = 0.3
, but cr(P ∨ Q) = 0.8
. Construct a Dutch Book against Ash. Describe what bets compose your package, why Ash should find each one acceptable, and why your package of bets guarantee Ash a loss in every possible world.
Dot’s credence distribution includes these particular values for propositions H
and G
(don’t suppose that these propositions are mutually exclusive):
cred(H ∧ G) = 0.5
cred(H) = 0.1
cred(G) = 0.5
cred(H ∨ G) = 0.8
Suppose you measure inaccuracy using the Brier Score.
X
and Y
, and these credences are between 0
and 1
(inclusive). Draw a box diagram, like those in Titelbaum’s Figures 10.2, 10.3, and 10.4, illustrating possible distrubutions Alex may have over these two propositions. Then shade in the part of the box where cr(Y) ≤ cr(X)
.Y
logically entails X
. Use your diagram from (a) to show that if Alex’s credences violate the rule that cr(Y) ≤ cr(X)
, then there will exist a different distribution that is more accurate than Alex’s in every logically possible world. (Hint: when Y
entails X
, one of the three corners of the box diagram in (a) is no longer logically possible.)H
and G
that is more accurate than Dot’s in every logically possible world. (Hint: let H ∧ G
and H
play the roles of Y
and X
in result (b) of this Problem.) To show that you’ve succeeded, calculate Dot’s inaccuracy and your alternative’s inaccuracy in each of the three available possible worlds.