\documentstyle [12pt]{article}

\title{Reasoning About the Future: Doom and Beauty}
\author{Dennis Dieks\\History and Foundations of Science, Utrecht
University\\P.O.Box 80.000, 3508 TA Utrecht}

\begin{document}

\maketitle

\begin{abstract}
According to the Doomsday Argument we have to rethink the
probabilities we assign to a soon or not so soon extinction of
mankind when we realize that we are living now, rather early in
the history of mankind. Sleeping Beauty finds herself in a similar
predicament: on learning the date of her first awakening, she is
asked to re-evaluate the probabilities of her two possible future
scenarios.

In connection with Doom, I argue that it is wrong to assume that
our ordinary probability judgements do not already reflect our
place in history: we justify the predictive use we make of the
probabilities yielded by science (or other sources of information)
by our knowledge of the fact that we live now, a certain time
before the possible occurrence of the events the probabilities
refer to. Our degrees of belief should change drastically when we
forget the date---importantly, this follows without invoking the
``Self Indication Assumption''. Subsequent conditionalization on
information about which year it is cancels this probability shift
again. The Doomsday Argument is about such probability
\textit{shifts}, but tells us nothing about the concrete values of
the probabilities---for these, experience provides the only basis.
Essentially the same analysis applies to the Sleeping Beauty
problem. I argue that Sleeping Beauty ``thirders'' should be
committed to thinking that the Doomsday Argument is ineffective;
whereas ``halfers'' should agree that doom is imminent---but they
are wrong.
\end{abstract}
\newpage

\section{The Doomsday Argument}
There are dangers threatening the future of mankind. Using
information about the political situation in the world, the risks
of nuclear technology, the possibility of epidemics, and so on, or
perhaps relying more on our intuition or even astrology, we may
attempt to become precise about how serious we think these dangers
are. We can do so by assigning a probability to the hypothesis
that mankind will become extinct soon, within the present century,
say. Let us assume that there is only one alternative hypothesis:
that the human civilization will flourish for a very long time,
with population figures that grow and grow as time goes on.
Perhaps the human race will in this case colonize other planets,
or even galaxies, to find room for all humans alive in the
future.\footnote{Assuming that there are only two scenarios is of
course unrealistic. A more natural assumption would be that there
are many hypotheses to consider, each one with its own prediction
for when mankind will die out. However, we could collect these
hypotheses in two groups: those predicting doom before a certain
date, and those predicting doom later. We would thus effectively
end up with two possibilities, and the argument in the text would
apply. Nothing essential therefore hinges on the restriction to
two rival scenarios.}





Suppose I have determined probabilities that measure my degrees of
belief in these two hypotheses. Then the following thought occurs
to me. In the evaluation of my probabilities I have not taken into
account that I live now, in the beginning of the third millennium
AD. Of course, I have used information about the present state of
the world; but I have not paid attention to the fact that
\textit{I myself} happen to live at this particular point in time.
This is, however, relevant additional information. For according
to the Doom Soon hypothesis, my place in history makes me a fairly
typical human being: a significant proportion of all humans ever
alive will live in this century if the Doom Soon scenario
corresponds to the actual fate of the world. By contrast, the
number of people living in the far future will be overwhelmingly
great if the Doom Late hypothesis is true. So on the latter
hypothesis the situation in which I actually find myself is a very
unlikely one. As a general methodological principle, I should
prefer the hypothesis that endows the available evidence with the
highest probability. Taking into account that I live now should
therefore move my odds appreciably in favor of the Doom Soon
scenario.

This is the Doomsday Argument, in the form defended by John
Leslie. It has been around in the literature for more than fifteen
years (\cite{leslie1,leslie2}) and continues to be discussed as a
controversial, but basically unrefuted line of reasoning
\cite{bostrom1,bostrom3,bostrom4,olum,bostrom5,monton,sober}. In
his book, {\em The End of the World: The Ethics and Science of
Human Extinction} (\cite{leslie3}), Leslie draws the conclusion
that we should do our best to counteract the just-described
probability shift by making the Earth a safer place.

The Doomsday Argument can be regarded as an example of Bayesian
reasoning. The proponent of the Argument asks us to consider two
hypotheses, $H_{1}$ and $H_{2}$, Doom Soon and Doom Late,
respectively. These are assigned prior probabilities $p_{1}$ and
$p_{2}$. When new evidence $E$ is taken into account, the
probabilities shift in accordance with Bayes's rule:
\begin{equation} P(H_{i}/E)= \frac{p_{i}. P(E/H_{i})}{p_{1}. P(E/H_{1}) +
p_{2}. P(E/H_{2})}, \;\; i=1,2. \end{equation} Here  $P(E/H_{i})$
is the probability that $E$ will occur if hypothesis $H_{i}$ is
true. $P(H_{i}/E)$ is the a posteriori probability that we should
assign to $H_{i}$ once we know that $E$ actually occurred.

To see the seriousness of the doomsayer's conclusions, suppose
that on the basis of our ordinary evidence we think that Doom Late
has a probability of 99$\%$, so $p_{1}=1/100$ and $p_{2}=99/100$.
Let us denote by $N_{1}$ the number of human beings ever alive
according to $H_{1}$; and let the corresponding number according
to $H_{2}$ be denoted by $N_{2}$. The proponent of the Argument
wants us to consider the case in which $N_{2}$ is very much larger
than $N_{1}$, $N_{2}=10^{6} N_{1}$, say. (The Argument's
conclusions become the more pessimistic the more optimistic the
scenario $H_{2}$ is.) Finally, denote by $n$ the number of people
alive in the present year. (We have to assume that both
hypotheses, $H_{1}$ and $H_{2}$, predict the same population
figures at least up to and including the present year---otherwise
we could falsify at least one of the hypotheses directly by
inspection of past or present population numbers.)

The doomsayer now reasons as follows. I am an arbitrary member of
mankind, and can consider myself as a random selection from all
people ever alive. Therefore, according to $H_{i}$ the probability
of actually finding myself in the present year is $n/N_{i}$. I can
treat the fact that I live now as new evidence $E$, because I did
not reflect on my place in history when I first estimated how
probable $H_{1}$ and $H_{2}$ were. So I can use Bayes's formula to
update my probability estimates, with $P(E/H_{i}) = n/N_{i}$. For
the posterior probability of Doom Soon, I thus find
\begin{eqnarray*}
 P(H_{1}/E) = \frac{(1/100). (n/N_{1})}{(1/100). (n/N_{1}) + (99/100).
\{n/(10^{6}.N_{1})\}} \\  = \frac{1/100}{1/100 + 99/10^{8}}=0.9999
\: .
\end{eqnarray*}
Therefore, it becomes virtually certain to me that the extinction
of mankind is imminent.

\section{The essential weakness in the Argument}
In (\cite{dieks1}) it was pointed out that the doomsayer should be
careful to formulate his hypotheses $H_{1}$ and $H_{2}$ in a
non-indexical way: if $H_{1}$ says ``Doom will strike
\textit{soon}'', that would presuppose my existence now, because
``soon'' means ``a short time from now''. But if the hypotheses
already contain information about my existence in the present
year, subsequent conditionalization on that information will not
lead to a shift in probabilities (because $P(E/H_{i})=1$ in that
case). So the Argument can get off the ground only if the
hypotheses have a form like: ``Doom will strike soon/late {\em
after 2005}'', say. Moreover, the Argument needs the supposition
that both hypotheses leave it completely open when in human
history I am living: I may turn out to be any human in the history
of mankind, and all these possibilities are equiprobable. This is
behind taking $n/N_{i}$ as the probability, on hypothesis $H_{i}$,
that I live in the year I actually live in.

It might initially be doubted whether it makes sense at all to
consider myself a random member of the human race: my existence
here and now may well be the outcome of a deterministic process.
But this is not the weak point of the Argument. What is at stake
are credences, probabilities that measure the strength of my
belief. A situation in which I am completely uncertain about my
place in human history is certainly logically possible.

The initial probabilities in the Argument should therefore refer
to a situation in which I consider two possible scenarios about
the fate of mankind after 2005, say, without having any clue about
the present date. When I subsequently add information about when I
live, this leads via Bayes's rule to a probability shift. Whether
the resulting posterior probabilities are different from my usual
ones depends, of course, on the concrete values of the prior
probabilities I started with. What the proponent of the Argument
has to assume in order to arrive at his alarming conclusion is
that we should use our usual probability values as priors, and
that the posterior probabilities that we end up with after the
Bayesian shift are the ones that really apply to our actual
situation. The doomsayer justifies this assumption about the
priors by pointing out that in making our actual probability
evaluations we never reflect explicitly on the fact that we live
now---it appears that we make our actual calculations while
leaving our place in history open.

As I will explain in detail in the following sections, this claim
about the situation to which our actual probabilities refer
constitutes the essential weakness in the Argument. To see the
basic difficulty already now, think a bit more about the situation
the prior probabilities in the Argument pertain to. It is a
situation in which we are asked to consider the hypothesis ``the
human race will become extinct soon after 2005'', and assign a
probability to it. We are allowed to use evidence available to
humans living in 2005 (the dangers of technological developments
in 2005, wars going on in that year, etc.), while we must also
assume that we could subsequently get the information that we
ourselves are cavemen living very, very long before 2005; or
humans drastically changed by evolution, living millions of years
later. This is a bizarre situation, very different from our actual
one. It is not immediately obvious that our probability
assignments in this strange predicament should be equal to our
usual ones. In particular, we have to take into account that we
could be living \textit{after} the date of Doom Soon; but in this
case our ordinary probability judgements (the ones we use now, in
our actual situation) are irrelevant for our credence. The Doom
Late scenario gets probability $1$ if we know that it is later
than Doom Soon. We should factor this in in our probability
considerations---as we will do below---with the foreseeable result
that the probabilities will become different from our actual ones.
The resulting difference will turn out to be exactly such as to
balance the Bayesian shift that ensues when we subsequently
conditionalize on our place in history. Consequently, the final
net results are the probabilities that we entertained before this
whole issue of the Doomsday Argument arose.

There is a line of argument in the literature (starting with
\cite{dieks1}, see \cite[p.\ 122]{bostrom4} for further
references) that similarly says that the prior probabilities in
the Doomsday Argument should be different from our actual ones, in
precisely such a way that we are led back to our usual
probabilities after conditionalization on information about the
date. This line of argument is based on what has become known as
the ``Self Indication Assumption'' (SIA): knowledge that I exist
favors hypotheses that predict populous human civilizations over
hypotheses according to which there will not be so many humans
\cite[p.\ 66]{bostrom4}. According to SIA the probabilities of the
various hypotheses should be taken as proportional to the numbers
of observers the hypotheses predict.

As Bostrom (\cite[pp.\ 122-126]{bostrom4}) has correctly observed,
however, SIA is untenable when interpreted as a general \textit{a
priori} principle that would apply even if there is no subsequent
balancing by a Doomsday reasoning: Such a principle would entail,
e.g., the unpalatable conclusion that armchair philosophizing
would suffice for deciding between cosmological models that
predict vastly different chances for the development of human
civilization. The infinity of the universe would become certain
\textit{a priori}.

\textit{The argument of this paper does not make use of such a
general Self Indication Assumption about priors.} Although it is
true that we will arrive at the same prior probabilities as SIA
would give us when applied to the Doomsday Argument, we will base
our conclusions exclusively on a consideration of the consequences
of \textit{a lack of information about the date}, together with an
analysis of the status of our ordinary probability estimates. The
objection that SIA can lead to absurd results when used as a
general independent rule does not touch our argument, as we will
discuss below.




\section{The relevance of our knowledge of the date}

That the lack of information about the date, characteristic of the
initial situation in the Doomsday Argument, is very relevant to
the prior probabilities to be assigned can easily be seen.

Suppose that I have been hypnotized and do not know when in human
history I am living. I firmly believe that I may turn out to be
any one of all individuals ever alive, be it Napoleon, a
prehistoric troglodyte, or someone living in 130000 AD (if human
civilization will last that long); I think that every possibility
is equally likely. Suppose further that I have been told that
physical theory predicts a probability of $1/2$ that the world
will end in early 2006, given the state of the world in 2005; and
an equally large probability that civilization will go on for a
very long time after 2005. It could be, for example, that a
quantum mechanical spin measurement is performed in the last
minutes of 2005, with possible outcomes $+1$ and $-1$. If the
outcome $+1$ is realized, a destructive device is automatically
activated that annihilates the world within one hour; if $-1$ is
the result, physical conditions then obtaining will make it
certain that the human race will prosper for many thousands years.
The best available physical theory, quantum mechanics, says that
for each of the two possibilities the chance of being realized is
$1/2$, given the details of the experimental set-up. I now want to
determine prior probabilities for my two hypotheses; probabilities
that I can update when information about who I am will come in
later. What probability should I assign to the possibility that
the world ends in 2006 (Doom Soon)?

Let us consider what happens if I decide to follow common-sense
strategy, and set my credence equal to the quantum
chance\footnote{Quantum mechanics is mentioned only for
illustrative purposes; the same argument would apply if the
probabilities were derived from the prediction of a fortune
teller. We shall return to the justification of equating
subjective probabilities to physical chances in section 5.}. I
thus take $1/2$ as my probability of Doom Soon. When I
subsequently learn that it is 2005 AD, and update my probability
with the help of Eqn.\ 1 in the way explained before, doom in 2006
becomes a virtual certainty to me.

So I appear to have justified the doomsayer's conclusion. However,
a problem occurs to me. I have been assuming that it may now be
any year in the history of mankind. But that implies that it may
be a year far after 2006, the date of Doom Soon. However, if the
actual date is some year {\em after} the date of Doom Soon, the
outcome of the chance experiment must have been $-1$. In this
case, the probability to be assigned to the outcome $+1$ would
certainly not be $1/2$; it would be $0$. I have to make a more
sophisticated calculation, taking the just-mentioned complication
into account.

I denote the probabilities whose values I am going to determine by
$p_{1}$ and $p_{2}$, pertaining to doom in 2006 and doom later,
respectively. There are two possibilities for me to consider.
First, I may be living before the date of Doom Soon, in which case
the experiment is still going to take place and I can use my
information about the physical chances to gauge my belief (if I
accept the aforementioned common-sense strategy). In this case I
will judge Doom Soon and Doom Late as equiprobable. Second, I may
be living after the date of Doom Soon, in which case the physical
chance is irrelevant for my belief in the truth of the hypotheses.
In the latter case the probability that I will assign to
hypothesis $H_{1}$ will be zero. My probability $p_{1}$ is
therefore equal to
\begin{eqnarray}
\lefteqn{P(H_{1} |\, \mbox{I live before Doom Soon}) . \,
P(\mbox{I live before Doom Soon})}
\nonumber\\
& & + P(H_{1}|\, \mbox{I live after Doom Soon}). \, P(\mbox{I live
after Doom Soon}). \nonumber
\end{eqnarray}
In other words:
\begin{eqnarray}
\lefteqn{ p_{1} = P(\mbox{I live before the date of Doom Soon}).\,
1/2}
\nonumber \\
& & +
 P(\mbox{I live after the date of Doom Soon}).\, 0.
\end{eqnarray}
The probability that I live before Doom Soon is itself the sum of
two probabilities, pertaining to two mutually exclusive cases:
either $H_{1}$ is true, in which case I will certainly live before
Doom Soon, or $H_{2}$ is true, in which case the probability that
I, a human randomly selected from my long-lasting civilization,
live before the date of Doom Soon is $N_{1}/N_{2}$.

Things are simple now. It follows from what has just been said
that the belief I should attach to the possibility of finding
myself in some year before the date of Doom Soon is represented by
$p_{1} + p_{2}. N_{1}/N_{2}$. Inserting this in Eq.\ 2, I obtain:
\[ p_{1}= ( p_{1} + p_{2}. N_{1}/N_{2}).1/2, \]
or, equivalently, $p_{1}=p_{2}.N_{1}/N_{2}$. Therefore, the
assumption that I lack all information about my place in history,
together with consistency, compel me to adopt a value of the a
priori probability of Doom Soon that is very much smaller than the
a priori probability of Doom Late!

Substituting the result in Bayes's formula, I find for the
\textit{a posteriori} probability of Doom Soon, after I have taken
the new information that I live in 2005 into account,
\[ P(H_{1}/E)= \frac{p_{1}. n/N_{1}}{p_{1}. n/N_{1} +
(N_{2}/N_{1}).p_{1}.(n/N_{2})}=1/2  .\] So when I finally have the
same information as I would have had without the intervention of
the mesmerist, I think that $H_{1}$ and $H_{2}$ are
equiprobable---just as I would have thought if I had not been
hypnotized in the first place.

Let me briefly rehearse the point for the general case. Consider
the situation in which I decide, in full awareness that it is now
2005, and on the basis of whatever further considerations (be they
scientific or not), to assign probabilities $q_{1}$ to $H_{1}$
(Doom Soon) and $q_{2}$ to $H_{2}$ (Doom Late), respectively. If
my memory is partly erased, so that I no longer know the date, or
if I decide to discard my information about the date, I have to
revise my probabilities. In the new situation $q_{1}$ and $q_{2}$
are still the probabilities I {\em would} assign to the possible
fates of the world if I knew it were 2005 now; but I do not know
what year it is. I might be living after the date of Doom Soon, in
which case the probability I have to assign to Doom Soon is not
$q_{1}$ but $0$.

Making a calculation in the same way as before, I find that the
probability I should assign to the Doom Soon hypothesis in the new
situation, $p_{1}$, should satisfy
\begin{eqnarray}
\lefteqn{ p_{1} = P(\mbox{I live before the date of Doom Soon}).\,
q_{1}}
\nonumber \\
& & +
 P(\mbox{I live after the date of Doom Soon}).\, 0.
\end{eqnarray}
Furthermore, $P(\mbox{I live before the date of Doom Soon}) =
p_{1} + p_{2}.N_{1}/N_{2}$. Using this in Eqn.\ 3 I find that my
new probabilities for the two hypotheses should be:
\begin{equation} p_{i}= \frac{q_{i}. N_{i}}{q_{1}. N_{1} + q_{2}.N_{2}} \, ,
\; i=1,\, 2. \end{equation} Subsequent conditionalization on the
evidence about which year it is now, by means of Bayes's rule with
$p_{1}$ and $p_{2}$ as priors, leads to $q_{1}$ and $q_{2}$ as the
posterior probabilities. These are the same probabilities I had
assigned directly, knowing it is 2005.

The main point is that it is inconsistent to assign the same
probabilities to hypotheses about what is going to happen after a
certain date \textit{both} in the situation in which I do not know
my place in history \textit{and} in my actual situation, in which
I know that the events in question have not yet occurred.
Probability theory specifies a definite relation between the
probabilities in the two cases. This relation is such that the
probabilities in my actual situation can be recovered from the
probabilities that apply if I do not know which year it is by
Bayesian updating with evidence about the present year. This point
is quite general, and does not depend on the basis of my
assignment of probability values---whether I use scientific
results, astrology, or intuition does not matter because what is
at stake is the consistency of my probabilistic reasoning rather
than the concrete values of my probabilities.

The Self Indication Assumption, in the general form discussed by
Bostrom \cite{bostrom4}, has not been used in the above. SIA says
that the probabilities of hypotheses should always be taken as
proportional to how many observers they say that exist (given the
fact that I myself am an observer). It is true that in our example
we found that $p_{2}/p_{1}=N_{2}/N_{1}$ in the initial situation
of the Doomsday argument; but this conclusion was not drawn on the
basis of SIA, but on the basis of a consideration of the relevance
of knowledge of the date. Our analysis does not lead to SIA as a
general principle, but only says that a hypothesis that leaves my
position in time entirely open, and which I consider without any
knowledge of the date, should be assigned a probability that
differs from my ordinary one by a factor that is proportional to
the population size the hypothesis predicts. If we consider, in
our actual situation, theories that predict different population
numbers, this result does not provide any reasons for assigning
probabilities that differ from our ordinary degrees of belief. The
objection that SIA would enable presumptuous philosophers to
decide about the credibility of cosmological models by just
looking at the numbers of humans they predict is therefore not
effective against our argument.






\section{The justification of our ordinary probability estimates}
The analysis of the previous sections emphasized the inconsistency
of using the same probability values both in our actual situation
and in the situation in which we lack information about when we
are living. If we start with our actual probability judgements,
correct them to obtain probabilities for the situation in which we
lack knowledge about the year we are living in, and finally apply
Bayes's rule to add the missing information again, we end up with
the probability we started with---exactly as it should be. Now,
the point remains that if we became convinced that our usual
probabilities do not really pertain to our actual circumstances
but rather to the situation in which we lack information about the
date, Bayesian upgrading would lead to a probability of Doom Soon
in our actual situation that is much larger than our usual one. So
the force of the Doomsday Argument depends essentially on the
question of whether we are justified in thinking that our usual
probability judgements pertain to our actual situation. The
doomsayer's basic claim is (or should be) that they do not; if he
is right about this, it is a matter of course that we should
urgently revise our probabilities.

We already heard one argument for the doomsayer's basic
claim---namely that in arriving at our usual probability
assignments we have made no use of our knowledge that we live
now---and showed that it is fallacious. We {\em do} use the
information that we live now. If we had thought that we could be
living anytime in human history, we should have taken into account
that we could be living {\em after} the date of Doom Soon. In the
latter case the scientific, astrological, etc., data (of the form:
if the situation at $t$ is this, then the situation at
$t+\triangle$ will be that) that we actually use  have no
relevance for our credence; in this case we should calculate
revised probability values, in the way explained. We do not make
such calculations, and this demonstrates that we make use of our
knowledge about the year we are living in.

Still, we might be mistaken in the use we make of the data and the
doomsayer might challenge us to provide a fundamental
justification of our ordinary probability estimates. After all, he
himself thinks it is rational to believe in a much higher
probability of Doom Soon than the value provided by science
\cite{bostrom3}. The first thing we have to observe in this
connection is that as long as purely \textit{subjective}
probabilities are at stake, there can be no question of disproving
the doomsayer's probability values. The doomsayer has the right to
fancy any subjective belief he likes. The dispute about the
justification of our actual probability values becomes interesting
only if the probabilities are not purely individual.

The obvious way of making our probability judgements objective is
by relating them to observed relative frequencies or to
probabilities predicted by science. The hypothesis that there is a
correlation between our own rank in series of events involving
humans and the total number of humans in such series can be tested
empirically; as argued by Sober, there are no indications that
available empirical evidence supports the existence of such
correlations \cite{sober}. The following additional example
illustrates the situation. Suppose that there is a country in
which families have either one child or two children; this has
been an empirical fact since times immemorial. I am my mother's
first child and have lost contact with my family soon after my
birth. I ask myself: what is the probability that I have a brother
or sister? I look at the birth statistics and find out that
exactly half of all families have one child, and half have two
children. I conclude that the probability I am looking for is
$1/2$. I feel quite confident about this, because of all families
I could be a member of $50\%$ have one child and $50\%$ have two.
But now someone asks me: ``Have you fully considered the special
status you have? You are a first-born, and this makes it more
probable that you are alone---the chance of being a first-borne is
only $1/2$ if you have a brother or sister, and $1$ if you are
alone.'' Swayed by this, I use Bayes's formula to update my belief
and conclude that the probability that I have a brother or sister
is only $1/3$. Now, I am entitled to believing whatever I want;
but if I am going to base predictions on this new probability I
will place myself in a bad position. If all first-borns use this
value, a radically wrong prediction of the total number of
children will be only one of the consequences. What went wrong is
that I overlooked that my original $p=1/2$ judgement already took
into account that I am a first-born child. Indeed, if I forget
about this piece of information my initial probability should be
different: on average, two out of every three children have a
brother or sister, so my probability of not being alone becomes
$2/3$. Needless to say, when I use Bayes to update this
probability with the evidence that I am a first-born child, I
return to my original $p=1/2$. The case closely parallels that of
the Doomsday Argument, and also that of the Sleeping Beauty
problem of the next section. The moral is that what the right
probability estimate is, in an objective probability dispute, is
determined by empirical data. The relative frequency of
first-borns who possess a brother or sister is $1/2$, and this
provides me with the justification of my credence.

Probability statements coming from scientific theories function in
the same way. Such scientific probability judgements are
relational: they state a relation between the state of the world
at a certain instant and the probability of an event at a later
time. The example described in section 3 illustrates the general
point. There it was supposed that our best fundamental physical
theory supplied a probability value of $1/2$ of Doom Soon (in
2006), given the state of the world in 2005. If we know that we
are living in or before 2005, we can use this value to guide our
credence. More generally, the scientific probability of something
happening in the year $t+\triangle$ given the situation in year
$t$, can be used for gauging our belief that the event in question
will take place if we know that we live in the year $t$.

The doomsayer objects to this procedure. What reasons does he
have? As we have seen, the putative fact that we have not used our
information about our position in time does not afford a valid
ground. But the doomsayer will be hard-pressed to supply another
reason. Acceptance of a probabilistic physical theory entails the
belief that the predicted probabilities will materialize as
relative frequencies in the long run. If we \textit{accept} a
physical theory that leads to probability statements, and are
intent on assigning probabilities in an objective way, without
making claims that would cost us money if we based bets on them,
it would therefore be inconsistent not to use the physical
probabilities as the values for our credence function. This is
Lewis's Principal Principle \cite{lewis}, about which more will be
said in the next section.

The upshot of all this is that there are good empirical reasons
for relying on the usual experimental and scientific probability
values. By contrast, all the doomsayer offers to convince us of
the errors of our ways is the incorrect idea that in our actual
probability assignments we have not fully used our information
about our own place in history, and the Doomsday reasoning, which
is irrelevant because it is only about the {\em relation} between
prior and posterior probabilities and does not touch upon the
question of what the concrete values of our probabilities should
be.

\section{Sleeping Beauty}

The most interesting aspect of the doomsday argument is that it
casts light on the role played by information about the date in
probabilistic reasoning about possible scenarios. It shares this
feature with the Sleeping Beauty problem, which has been
attracting much attention recently. Indeed, I will argue that the
two problems are structurally the same and that the analysis of
one can be carried over to the other.

In the Sleeping Beauty problem we are asked to assign
probabilities to the possible outcomes of a fair coin toss, in a
situation in which we do not know the date; subsequently,
information about the date is supplied and we are asked to update
our probabilities. The story is as follows. Sleeping Beauty is put
into deep sleep late Sunday night. She is awakened on Monday;
after some time she will be told that it is Monday (at first, when
she awakes, she has no clue about the date). After this she is put
into sleep again, and her memories of the awakening are erased.
Then, depending on the outcome of the toss of a fair coin, she
will \textit{either} be awakened again on Tuesday (when everything
looks to her the same as on Monday), put into sleep again and
sleep until Wednesday when the experiment ends (Tails),
\textit{or} will not be wakened a second time and sleep on until
the end of the experiment (Heads). Beauty knows about this
protocol before the experiment starts, on Sunday.

Immediately after waking up on Monday, what should be Beauty's
probabilities for the hypotheses that the coin lands heads or
tails (H or T), respectively? And what should these probabilities
become when she learns that it is Monday?

If Beauty decides to calibrate her credence on the basis of the
physical data available to her, she will assign probabilities
$1/2$ to H and T on Sunday. This can be regarded as an application
of the Principal Principle: Beauty has only admissible information
about the future toss of a fair coin. So when she falls asleep,
$p(H)=p(T)=1/2$. Now, one is perhaps inclined to argue that during
her sleep until her first awakening, and also in this first
awakening, Beauty does not receive any new information: she knew
for certain that she would be awakened all along. So upon waking
up on Monday her probabilities of H and T should still be $1/2$
(this position was defended by Lewis, \cite{lewis1}).

However, something \textit{did} change when Beauty fell asleep:
she lost information about the date, like the mesmerized
protagonist of the story in section 3. When Beauty wakes up, she
does not know whether it is Monday or Tuesday. When she
subsequently learns that it is in fact Monday, this constitutes
new and relevant information. Her odds should shift accordingly,
namely in favor of H: On hypothesis H, Monday is the only day on
which an awakening can take place, whereas on T the awakening
could have been on Tuesday as well. So H makes the fact that the
awakening is on Monday more ``typical'' than T. We can calculate
the associated probability shift by Bayesian updating---but
obviously, the values of the resulting probabilities depend on the
values of the priors we use. If we follow Lewis's approach, and
assume that Beauty's probabilities are still the same on Monday,
when she awakes, as what they were on Sunday---in other words, if
we take $p(H)=p(T)=1/2$ as priors---we find $p(H|M)=2/3$ and
$p(T|M)=1/3$, where $M$ stands for the evidence that it is Monday.
So Beauty is going to believe that the future toss of the fair
coin will more probably than not result in heads.

We may consider a variation on the Sleeping Beauty problem in
which the experiment takes many days and in which Beauty is
awakened day after day if the coin lands tails, whereas she
remains asleep after the awakening on Monday if the coin lands
heads. The necessary precautions are taken to ensure that Beauty
does not remember anything about previous awakenings. Let $n$ be
the number of days the experiment lasts. Adapting the above
argument to the new experiment, we conclude that it becomes
practically certain to Beauty that H is true when she learns that
it is the first Monday after the Sunday the experiment started.
This follows because according to T the probability that her
awakening is on this Monday becomes smaller and smaller when $n$
grows. Bayes's formula yields $p(H|M)=n/(n+1)$, if we take
$p(H)=p(T)=1/2$ as priors again; this probability approaches $1$
when $n$ increases.

The analogy to the Doomsday Argument is obvious. Saying that it
should not make a difference to Beauty's probabilities whether it
is Sunday or whether it is Monday immediately after her awakening
(when she does not know the date) is tantamount to saying that I
should use my usual probabilities (think of the $1/2$
probabilities in the quantum example) even when I do not know the
date in the initial situation of the Doomsday Argument. Just as
Beauty becomes virtually certain about Heads when she hears it is
Monday, I become sure of Doom Soon when I take the present date
into account. Defenders of the $1/2$ line in the Sleeping Beauty
problem (in the original version) should therefore also subscribe
to the doomsayer's conclusion that Doom Soon is imminent.

Repetition of the experiment, which can be done in the case of
Sleeping Beauty, unlike in the case of Doom, will result in
approximately $50\%$ of heads and $50\%$ of tails after Beauty,
having heard it is Monday, expresses her credence---the coin is
fair. This is also true, of course, in the variant of the
experiment with $n$ days. In the latter case Beauty will
practically never be right in her predictions if she adheres to
the policy explained above: she will expect tails in only $1$ out
of $n+1$ cases. Also in the original version of the experiment her
credences will systematically deviate from the actual relative
frequencies. So if the probabilities assigned by Beauty should
reflect what will happen in repetitions of the experiment,
something has gone seriously wrong.

Our preceding discussion of the Doomsday Argument contains the
diagnosis of the problem. Taking the probabilities as $1/2$ on
awakening on Monday means thinking that there was no relevant
change in information since Sunday afternoon. But this is wrong.
On Sunday Beauty knew that it was Sunday, and that a fair coin
would be tossed in the future. She was justified in applying the
Principal Principle then. By contrast, when she awakes on Monday
she no longer knows the date; it may be either Monday or Tuesday.
If it is Tuesday, however, the coin toss has already happened and
certainly had the result tails. In other words, if Beauty knew it
is Tuesday, she would possess \textit{inadmissible information}
and would not be allowed to use the Principal Principle; she
should assign probability $1$ to T. So her going to sleep and the
resulting loss of information about the day it is do involve a
significant change in her knowledge situation. Because it may be
Tuesday she is no longer allowed to use the Principal Principle to
determine the odds of T and H in her new situation; she has to
factor in her uncertainty about the date. Taking into account this
uncertainty in the way described in section 3, she finds $1/3$ for
her probability of H and $2/3$ for her probability of T. When she
subsequently learns that it is in fact Monday, and updates her
credences using that information, she returns to the probabilities
of H and T she entertained on Sunday, before the experiment
started.

On Sunday Beauty is already sure that she will be going to assign
new probabilities to H and T upon awakening on Monday. She also
sees very well that the situation on Monday will be drastically
different from her present one, in which she knows the date.
Although she knows that she will inevitably end up in that future
situation, there is consequently no reason for her to adopt the
new probabilities already now, on Sunday.

As pointed out by Elga \cite{elga}, the probabilities $1/3$ and
$2/3$ have counterparts in relative frequencies that are realized
in repetitions of the experiment. Of all awakenings, $1/3$ will be
awakenings that go together with heads, and $2/3$ that go together
with tails. The probabilities $1/2$ that result from updating on
the date will also be borne out by empirical data: of all coin
tosses after Beauty's predictions on Monday, roughly half will
result in heads. An investigation of which betting quotients will
not lead to a systematic loss of money, when confronted with the
empirical frequencies, not surprisingly also leads to the
conclusion that Beauty should take $1/3$ and $2/3$ as her
probabilities upon waking up on Monday
\cite{hitchcock}\footnote{Hitchcock objects to Elga's simple
argument for $1/3$, based on the relative frequency of Head
awakenings, because the two Tails awakenings are not statistically
independent. However, this seems irrelevant in the present
context: what counts is that in each trial Beauty cannot
distinguish between the three possible awakening instances and
considers her own awakening a random selection from all
possibilities---in analogy with the randomness assumption in the
Doomsday case.}.

\section{Conclusion}

Both in the Doomsday Argument and the Sleeping Beauty problem one
is asked to assign probabilities to two rival scenarios about the
history of the world, while being ignorant about one's own place
in history. In both cases one is provided with the missing
information and uses this to updates one's probabilities. The
question is whether the resulting probabilities are different from
the usual ones---the probabilities one uses directly, in full
awareness of the date. The answer depends, evidently, on the
values of the priors. The proponent of the Doomsday Argument
maintains that these priors should be equal to our usual ones;
similarly, in the context of the Sleeping Beauty problem the
``halfer'' contends that it should make no difference for her
probabilities that Beauty loses information about the date when
she falls asleep. Both claims are wrong, I have argued. Whoever
thinks that Beauty should believe that heads and tails are
equiprobable in a future toss of a fair coin after she has learnt
it is Monday, should not be impressed by the Doomsday Argument;
who thinks that at that point her correct degree of belief in
Heads is $2/3$ should also think Doom is near.


\section*{Acknowledgement}

My thanks go to Luc Bovens for suggesting the analogy between the
Doomsday Argument and the Sleeping Beauty problem, and to Andrea
Lubberdink for comments on earlier versions of this paper and
stimulating discussions.




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\end{document}
