Good’s Theorem basically says that a utility-maximizing agent can
expect to make decisions that are at least as good if they get more
information. (And under some additional conditions, one can expect the
decisions to be better.)
Now consider this case:
- You will be offered a chance to make a bet at certain odds on the
result of a coin toss, where as far as you can tell it’s equally likely
that the coin is fair and that it is double-headed. Someone offers to
tell you how the previous toss of the coin went.
Good’s Theorem says your decision whether to make the bet will be at
least as good given the information about the previous three tosses as
without that information. Hence, if the information is being announced,
you don’t need to cover your ears. This is, of course, very intuitive.
But now consider a slightly different case:
- Things are set up just as in (1), except now instead of information
about the previous toss, you are offered a chance to have the
following experiment get performed before your decision: the coin will
be tossed an extra time and the result will be announced to you.
The difference is that in (2) you are not simply being offered
additional information about how things are. For whether you go for the
experiment or not, either way, you have full information about the
experiment and its results. If you don’t go for the experiment, that
full information is that the coin was not tossed an extra time (and
hence did not land either heads or tails). If you do go for the
experiment, the full information is that the coin was tossed and it
landed heads, or else that it was tossed and it landed tails. In (2),
you are not just finding out information by going for the deal: you are
making something happen—an extra toss—and then finding out
something about that.
So you can’t apply Good’s Theorem directly to (2). It would be nice
to have a formulation of Good’s Theorem that works in cases where
instead of merely finding out information, you perform an
experiment.
I initially thought this would be easy. Maybe it is, but I don’t see
it. There are, after all, cases where performing a cost-free experiment
is not a good idea. Suppose, for instance, that you will be allowed to
bet tomorrow that a certain car has more than 10 gallons of gasoline.
The experiment is to start up the car and look at the gas gauge. But
starting the car reduces the amount of gasoline in it, and one can
easily rig the case so that benefits from the information gain are
outweighed by the fact that you have made that bet less favorable.
So, we want to rule out cases where there is dependence between
whether you perform the experiment and the payoffs of the wagers. If
F is the event of performing
the experiment, it may seems initially we should assume something
like:
- E(U|Wi∩F) = E(U|Wi∩Fc)
for all i,
where Wi is your
choosing wager i and U is the utility random variable. In
other words, the expected utility of each wager is unaffected by whether
the experiment has been performed. But no! Suppose a coin has been
tossed, and you are choosing between W1 where you get a dollar
on heads and W2
where you get a dollar on tails. But let F be the experiment of looking at
the coin. (This is a case for the original Good’s Theorem.) Then E(U|Wi∩Fc) = 0.50,
while E(U|Wi∩F)
is very close to 1.00 for the reason
that when you find out what the coin is like, you are close to certain
to bet on what you see, and hence you are close to certain to win your
bet.
If F1 is heads
and F2 is tails, we
solve the problem by replacing (3) with:
- E(U|Wi∩Fj∩F) = E(U|Wi∩Fj∩Fc)
for i and j.
Namely, the expected utility of wager Wi given
information Fj is
independent of whether you performed the experiment F. But that only works because it
makes sense to ask what the coin is showing if you aren’t looking: it
makes sense to conditionalize on Fj ∩ Fc.
But in the cases that interest me, there is no fact of the matter as to
the result of the experiment when the experiment is not performed, since
Molinism is false and we live in an indeterministic world. And in these
cases, Fj ∩ Fc
is the empty set: the Fj represent the
possible results of the experiment but the experiment has no result when
it is not performed.
I can get something by supposing a two-step procedure. You perform
the experiment, event F, and
you learn the result, event L.
Then we can assume:
E(U|Wi∩F∩Lc) = E(U|Wi∩Fc)
for all i
E(U|Wi∩Fj∩F∩L) = E(U|Wi∩Fj∩F∩Lc)
for all i and j
P(Fj|F∩L) = P(Fj|F∩Lc).
Assumption (5) says that it makes no difference to the expected
utility of a wager whether (3) the experiment is performed but its
result is not learned or (b) the experiment is not performed at all. In
other words, the experiment itself doesn’t affect things. Assumption (6)
says that given a specific experimental result, learning the result
makes no difference to the expected utility of each wager–result pair.
Assumption (7) says that the results of the experiment are unaffected by
whether you learn the result of the experiment.
Without (6) or (7), we wouldn’t expect to get the result we want. If
we don’t have (6), it might be that utilities are wildly affected by
whether you learn the result. (The simplest case is that the wagers all
have a big negative payoff on L.) If we don’t have (7), then
learning the result might have some evidential or retrocausal impact on
what the result is, and then again we shouldn’t expect that learning the
result is a good thing.
Given (5)–(7), I think we can now reason as follows. You are choosing
between:
- performing the experiment and learning the results
and
- not performing the experiment and (hence) not learning the
results.
By (5), a rational agent will decide the same way in (ii) as in:
- performing the experiment and not learning the results,
and the expected utilities of (ii) and (iii) will be the same for
this rational agent.
We now apply Good’s Theorem to the choice between (i) and (iii) (we
will use (6) and (7) here, and assume the case is non-Newcombian and
hence allows the use of Evidential Decision Theory) and get the result
that (i) is at least as good as (iii). Since we have indifference
between (ii) and (iii), it follows that (i) is at least as good as (ii).
(We can also analyze the cases of a strict expected utility
inequality.)
This is roundabout, but that’s not main main worry.
What I am really worried about is one technicality. To run the above
argument, I had to assume that there is a way of performing the
experiment without learning the result, namely that F ∩ Lc
is non-empty. In general, however, we cannot assume this. Suppose, for
instance, that we have a world with a quantum mechanics where
observation causes collapse. Then the experiment of collapsing a
wavefunction by means of observation cannot be done without observing
the result of the experiment. In such scenarios, I cannot simply
introduce a third option of performing the experiment and not learning
the results, since that third option may not be consistent with the laws
of physics. (And, of course, the utilities for breaking the laws of
physics could be wild.)
But without introducing that third option, namely F ∩ Lc,
I don’t know how to formulate the independence assumptions that are
needed. I also don’t know if the problem is “merely technical” or
“deep”. If I had to bet at even odds, I would bet on its being merely
technical. But it might be deep.