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\centerline{\bigbold Resource Material for Promoting the Bayesian View of
Everything}

\medskip

\centerline{Prepared by Carlton M. Caves}

\medskip

{\narrower\sl\noindent
Notes mainly for the use of CMC, Christopher A.~Fuchs, and R\"udiger
Schack, although anyone is welcome to make use of the notes and even
buy in, in whole or in part, if he/she finds the notes convincing.
Not everything here is endorsed by Fuchs and Schack, but it is all
part of a joint program aimed at producing a Bayesian synthesis.
Fuchs, in particular, probably disagrees with every statement about
Hamiltonians being the objective part of quantum theory.
Nonetheless, nothing should be attributed to me alone until Fuchs and
Schack have disavowed~it.
\medskip}

\centerline{Version 2.2: 2001 August~15 (plain \TeX\ files of V1, V2,
and V2.1 available on request)}

\bigskip

\topic{Introduction.}  Science involves investigating the properties
of the real world and processing and using what we know about the
world.  To make progress, it is crucial to separate what's really out
there (ontology) from what we know about what's really out there
(epistemology).  The language for dealing with our (necessarily
incomplete) knowledge of the world is Bayesian probability theory,
which holds that probabilities are subjective, based on what we know.
Here I contrast the Bayesian view of probabilities with other
interpretations and consider two natural applications of Bayesian
probabilities in physics: statistical physics and quantum theory.

\paraone
Because physicists believe their science is the most fundamental,
they have an ingrained tendency to attribute ontological status to
the mathematical objects in their theories, including probabilities.
Statistical physics provides a cautionary example of the hazards of
this tendency: it leads to the notion that thermodynamic entropy is
an objective quantity and thus to fruitless efforts to derive the
Second Law of Thermodynamics from the time-symmetric laws of physics.
It is now well established---though still not accepted by many
practitioners---that entropy is a subjective quantity, based on what
one knows.  This leads to effortless derivations of the Second Law
and also to a deep understanding of the operation of intelligent
agents (Maxwell demons) whose objective is to circumvent the Second
Law.  Quantum theory is tougher: the cut between ontology and
epistemology is notoriously hard to identify, because of the
intrinsic indeterminism of quantum mechanics.  For this reason, we
believe that it is in quantum theory that a consistent application of
Bayesian probabilities has the most to offer.

\topic{Interpretations of probabilities.}

\subtopic{a.}{Empirical (actual) frequentism}, i.e., defining
probability as the frequency of occurrence in an {\it actual\/}
sequence of trials or {\it real\/} ensemble.  There are immediate
problems with this approach. How does one deal with probabilities for
single cases, e.g., Laplace's determination of the mass of Saturn or
the openness vs.\ closedness of the Universe, where a real ensemble
doesn't exist?  Even in cases where a real ensemble can be
identified, why should the probability for an individual member
depend on what other members of the ensemble do?  Moreover, there are
serious problems with finite ensembles: What about irrational
probabilities?  How big an ensemble does one need to get {\it the\/}
probability?  These problems force one to infinite ensembles, but
they present insuperable problems: there being no real infinite
ensembles, this approach floats free from its initially attractive
empiricism, and the frequency is ultimately undefinable (ordering
problem).

\subtopic{b.}{Mathematical frequentism (law of large numbers)}, i.e.,
defining probability as the limiting occurrence frequency in an
imaginary (hypothetical) sequence (an ensemble).  This is the
mathematical way of trying to do frequentism; the emphasis here is
entirely on mathematical rigor, with no reference to empiricism
(i.e., actual ensembles).  Despite its rigor---more precisely,
because of its rigor---this approach has fundamental flaws.  Because
the infinite ensemble is purely hypothetical, one must introduce
additional {\it mathematical\/} structure to characterize the
ensemble.  What is introduced, right from the start, is the notion of
probability.  One uses the notion of probability for each member of
the ensemble and the notion of the statistical independence of
different members of the ensemble to provide the mathematical
structure of the i.i.d. (distribution that {\it i\/}ndependent and {\it
i\/}dentically {\it d\/}istributed).  Thus this approach is circular
since it relies on the notion of probability to define the limiting
frequency, which is supposed to define probability.

\paratwo
Moreover, what is proved is that the ``right frequency'' occurs with
probability-one in the infinite ensemble.  This result cannot be
interpreted without reference to probabilities.  The frequentist
hopefully asserts that probability-one means certainty, which would
allow him to escape reference to probabilities in interpreting the
result.  Yet this identification can't be justified. Though
probability-one does mean certainty for finite sets, this strict
connection can't be maintained in the infinite limit.  To see this,
imagine deleting a set of measure zero from the right-frequency set;
the modified set still has probability-one, so are we to think that
the deleted sequences are certain not to occur?  This is not very
reasonable, since any particular sequence with the ``right
frequency'' could be deleted in this way, so we would be forced to
conclude that all sequences are certain not to occur.  (Another way
of saying the same thing is that each infinite sequence has
probability zero; should one then conclude that each sequence is
certain not to occur?)  What this demonstrates is that the only way
to {\it interpret\/} what probability-one means in the infinite limit
is to have already at hand a notion of the meaning of probability and
to apply that notion to a limit as the sequence becomes infinitely
long.

\paratwo
Finally, suppose one could define probability as a limiting frequency
in a hypothetical ensemble.  One would still be left without the
ability to make any quantitative statements about finite (real)
ensembles.  One would not be able to say, for example, that in 1,000
tosses of a fair coin, the number of heads will be near 500 with high
probability, for this probability would mean nothing without
referring it to yet another hypothetical infinite ensemble, each
member of which is a sequence of 1,000 tosses.

\paraone
One lesson taught by the above two approaches is that it is very
important in any discussion of probabilities to determine whether one
is discussing real or hypothetical ensembles, because the problems
with the two approaches are different.  The problem with real
ensembles is that usually we don't have them and we certainly don't
want to be required to have them.  The problem with hypothetical
ensembles as fundamental, rather than derived objects is that one
must use the probability measure provided by the i.i.d.\  to give
structure to the ensemble, so one can hardly be using the ensemble to
define probabilities.

\paraone
A more important lesson is that trying to define probabilities in
terms of frequencies is hopeless.   One ends up referring
probabilities to an unempirical limit that is undefinable and
uninterpretable unless one already has the structure of probability
theory in hand.  One is trying to define probabilities in terms of
concepts derived from probabilities.  To make progress, one must get
serious about defining probability for a single trial, since we
evidently use probabilities in that situation.  The leading
objectivist candidate is propensity (objective chance).

\subtopic{c.}{Propensity or objective chance}, i.e., a probability
that is asserted to be an objective property of a physical system or
situation. Yet how can propensity be an objective property of a coin
when the chance of heads clearly depends on the method of tossing?
The Bayesian has no trouble admitting that the probability assignment
depends on what one knows.  This could include what one knows about
the physical properties of the coin, the method of tossing it, the
properties of the medium through which the coin passes, the
properties of the surface on which it lands, and any other relevant
factors, or it could mean knowing nothing about any of these or
anything else, except that the coin has two faces.  Any rational
observer, asked to make a bet on the toss of a coin, can see that the
probability assignment on which he bases his bet ought to depend on
what he knows about all these factors.  The propensitist is thus
backed into a corner: which of the Bayesian probabilities deserves to
be the propensity, and what should one do in those situations where
the propensity is not the best probability assignment?

\paratwo
From a Bayesian perspective, the question of what propensitists are
talking about is easy to answer and depends on what one knows about a
sequence of trials.  A propensity is a single-trial probability that
arises from a robust prior on probabilities, or in other words, a
situation where the probabilities on the multi-trial hypothesis space
are well approximated by an i.i.d.\ for a very large number of
trials, or in yet other words, where one's probabilistic predictions
for future trials change from the original i.i.d.\ predictions based
on the propensity only after one has gathered data from a very large
number of trials.

\subtopic{d.}{Principal principle}, i.e., use ignorance probabilities
till objective ones are identified.  This is a desperate retreat in
the face of the logic of Bayesianism, hoping against hope that some
basis for objective probabilities will eventually emerge even as one
admits that most probabilities are Bayesian.

\paratwo
In the example of the coin, if one knows everything about the coin
and its environment, then given that a classical, deterministic
description is sufficient, there is no chance at all; the outcome of
the toss can be predicted with certainty using the laws of classical
mechanics.  This conclusion is quite general; as Giere (1973a) notes,
there are no objective probabilities, except 0 or 1, in a
realistic/deterministic world.  All probabilities in a
realistic/deterministic world are Bayesian probabilities, reflecting
ignorance of the exact conditions that hold in such a world.

\paratwo
Despite the inescapability of this conclusion, cunning attempts are
made to get around it.  Such attempts should always be examined by
asking what happens when one has complete information. For example,
it is asserted that for two successive coin flips the probability for
head-tail is {\it objectively\/} the  same as the probability for
tail-head.  These equal probabilities are then used to build up
``objective'' probabilities of any magnitude.  There has to be
something wrong with the conclusion that head-tail and tail-head
probabilities are objectively the same, independent of what one
knows, for if one knows everything about the two coin tosses, then
one can say with certainty what the outcomes of the tosses will be;
the head-tail and tail-head probabilities are the same only if both
are zero.  So what gives?  The equality of the head-tail and
tail-head probabilities follows from assuming the exchangeability of
the two tosses, a condition that is violated in the case of complete,
but opposite knowledge for the two tosses.  Exchangeability is
clearly a part of the state of knowledge on which a Bayesian bases a
probability assignment; it is not an objective property of the world.
The equal ``objective'' probabilities are thus an example of
assigning Bayesian probabilities based on what one knows, in this
case, that one's state of knowledge is symmetric under exchange of
successive tosses.

\subtopic{e.}{The Bayesian view}, i.e., that probabilities are not a
state of the world, but are based on one's state of knowledge. A
simple, but compelling argument that probabilities are not objective
properties is that one cannot determine them by addressing the
alternatives (no multiple trials here: we've seen that that's a dead
end, because it requires another probability assignment on the bigger
multi-trial hypothesis space); to find out what probabilities have
been assigned, you have to ask the assigner.

\paratwo
The mathematical foundations for the Bayesian view come from (i)~the
Dutch-book argument, which shows that if probabilities are regarded
as betting odds that determine one's willingness to place bets,
consistency in placing the bets yields the probability rules;
(ii)~the Cox analysis, which shows that if probabilities are measures
of credible belief, then consistency with deductive logic gives the
probability rules; (iii)~decision theory, which is the application of
Bayesian, single-case probabilities to rational decision making (a
generalization of betting odds); and (iv)~the de Finetti
representation theorem (Caves, Fuchs, and Schack 2001a), which shows
that probabilities on probabilities are in one-to-one correspondence
with exchangeable probability assignments on infinite sequences.  The
de Finetti theorem banishes the concept of an unknown probability in
favor of a primary probability assignment on infinite sequences.

\topic{Application of the Bayesian view to statistical physics.}

\subtopic{a.}{Entropy is subjective}, defined relative to a
probability assignment that is based on what one knows about a
system.  The subjectivity of entropy lies in the fact that it is the
``missing information'' required to specify a system's microstate.
The relation to physics is that entropy---more precisely, free
energy---quantifies available work: each bit of missing information
reduces the available work by $k_BT\ln2$.  The subjectivity of
entropy is natural in this context, for different observers, knowing
different things about a system and thus assigning different
entropies, will devise different procedures that extract different
amounts of work from the system.  Anyone who thinks entropy is
objective should be asked how to extract work from a Szilard engine
before observing which side the molecule occupies; there is no chance
of success in such an endeavor, of course, because success would
violate the Second Law.

\subtopic{b.}{How does entropy change?} Since entropy represents
the amount of information missing toward a maximal description of a
system, it changes only when information about the system is obtained
or discarded.  Entropy does not change under Hamiltonian dynamical
evolution, because Hamiltonian evolution is the rule for updating
maximal information.

\paratwo
Thermodynamics is the science of macrostates, i.e., states specified
by system probability distributions defined by consistently
discarding all information about a system---most importantly,
information about a system's past---except information about a few
macroscopic parameters that determine the system's macroscopic
behavior.  Jaynes's (1957a,1957b) derivation of the Second Law is
based on discarding everything but the values of a few macroscopic
parameters and then maximizing the entropy relative to the
constraints imposed by the macroscopic parameters.  Indeed, like
Jaynes's derivation, {\it all\/} derivations of an increase in
entropy proceed by discarding information about the system: (i)
Boltzmann's H-theorem is based on discarding information about
two-particle correlations; (ii) projection-operator techniques throw
away information about the microscopic probability distribution,
replacing it with a coarse-grained distribution; (iii) master
equations are based on discarding information about the correlation
between the system and a heat bath or environment.

\subtopic{c.}{Entropy decreases when one observes a system.}  Though
this increases the available work, the increase is offset by the
energy required to erase the record of the observation.  This {\it
Landauer erasure cost}, amounting to $k_BT\ln2$ per bit, is required,
from the inside view, for consistency with the Second Law and is,
from the outside view, simply the free energy that must be dissipated
to reduce the entropy of a demon/memory as it is returned to a
standard state.

\paratwo
The Landauer erasure cost leads to the notion of {\it total entropy},
the sum of the system entropy and the information required to specify
the system state.  When a demon/memory observes the system and stores
a record of its observation, the total entropy does not change;
likewise, if the demon/memory discards its record, the total entropy
again remains constant.  Thus the process of observing and discarding
information can be done at no thermodynamic cost.  Since the erasure
requires the Landauer cost, however, there is no overall
thermodynamic cost only if the demon/memory extracts the work made
available by its observation before discarding its record.

\paratwo
Under Hamiltonian evolution the total entropy is nearly constant: the
system entropy remains exactly constant, but the information needed
to specify the system state increases by the amount of information,
$\log t$, needed to give the time $t$ in some appropriate units.
Another way of saying this is that the evolved state does not become
much more complex than the initial state as a system evolves under
Hamiltonian evolution, because the information needed to specify the
evolved state consists of the initial state and Hamiltonian---these
are background information---and the time.  The near constancy of the
total entropy means that one does not lose available work as a system
evolves.  One way to extract the work is to time reverse the
system---this is usually difficult to do physically (but note spin
echo), but is easy to describe and thus is simple
algorithmically---and then use the $\log t$ bits of information to
tell one how much time~$t$ to wait before extracting the work from
the initial state.

\subtopic{d.}{Why does one discard any information at all}, since it
always has a cost in available work? In particular, why does one
discard information about a system's past? The standard explanations
fall into three classes: (i)~{\sl Coarse graining.}  One discards
fine-grained information because it is irrelevant to the future
behavior of the coarse-grained macroscopic parameters of interest;
(ii)~{\sl Complexity of evolved state.}  The evolved state is so
complex that it requires an increasing amount of information about
the initial conditions keep track of it; (iii)~{\sl Leakage of
information to an environment.} The information about the system's
initial state leaks into an environment, where it is lost.  All of
these putative justifications deserve discussion.

\paratwo
(i)~{\sl Coarse graining.}  This is a powerfully good explanation,
which underlies all methods for justifying the increase of entropy in
isolated systems.  It leads to an entropy increase in the following
way: divide phase space into coarse-grained cells $j$, with
phase-space volume $\Gammaj$; at each coarse-grained time $t_n\equiv
n\tau$, where $\tau$ is a macroscopic coarse-graining interval,
replace the evolved phase-space distribution from the previous step
with a coarse-grained one, smeared out to be uniform on each
coarse-grained cell, with probabilities $p_j(n)$; the resulting
coarse-grained (Gibbs) entropy at the $n$th time step~is
$$
S(n)=k_B\!\left(-\sum_jp_j(n)\log p_j(n)+\sum_jp_j(n)\log\Gammaj\right)\;,
$$
where the first term is the information needed to specify which cell
the system is in and the second term is the average over cells of the
information needed to specify a point (microstate) within the cell.
This entropy increases monotonically as information about the
fine-grained distribution is discarded and, for a mixing system,
approaches the microcanonical entropy on the accessible region of
phase space.  The chief question to be investigated in this approach
is whether the behavior of the macroscopic parameters of interest is
indeed insensitive to the coarse graining.  Though this is a very
reasonable approach, it begs the deeper question of why entropy
increases, because it discards information for convenience in
describing the behavior of macroscopic parameters without addressing
why one is willing to accept the corresponding decrease in available
work.

\paratwo
{}(ii)~{\sl Complexity of evolved state.}  Even though this
explanation is the most widely accepted one for isolated systems
undergoing Hamiltonian evolution, it is wholly misguided.  These days
it is usually phrased in terms of chaotic dynamics: to predict the
trajectory of a chaotic system requires an exponentially increasing
amount of information about the system's initial conditions.  Where
this explanation goes wrong is in thinking that trajectories are the
relevant concept for entropy.  Entropy has to do with phase-space
distributions, made up of many trajectories, not with individual
trajectories.  It does not require an exponentially increasing amount
of information to predict the evolved phase-space distribution.
Indeed, as we have already seen, the complexity of phase-space
distributions increases hardly at all under Hamiltonian evolution.
Another way of saying this is that phase-space distributions evolve
according to the Liouville equation, which is linear and preserves
overlaps between distributions.  A small error in the initial
distribution remains a small error in the evolved distribution.

\paratwo
{}(iii)~{\sl Leakage of information to an environment.} This is the
best explanation for the increase of entropy, because {\it no\/}
system that we have access to is completely isolated from its
environment. Nonetheless, it also begs the deepest question, for it
simply posits, with no ultimate justification, that the environment
is somehow so complicated that information which leaks into it is
unrecoverable.  The ultimate question is avoided: why does one
discard information that leaks into the environment?  This leads to
the Schack-Caves hypersensitivity-to-perturbation program (Caves
1994a, Caves and Schack 1997a): a system is {\it hypersensitive to
perturbation\/} when the environmental information required to reduce
the system entropy far exceeds the entropy reduction; when a system
is hypersensitive to perturbation, the discarding of information in
the environment can be justified on strict grounds of relative
thermodynamic cost.  Schack and Caves have shown that classical
chaotic (mixing) systems have an {\it exponential\/} hypersensitivity
to perturbation, in which the environmental information required to
purchase a system entropy reduction increases exponentially with
time, and they have accumulated numerical evidence that quantum
versions of classically chaotic systems display a similar exponential
hypersensitivity to perturbation.  Perhaps the most succinct way to
describe the Schack-Caves program provides a way to rescue
explanation~(ii): the evolved phase-space distribution (or quantum
state) is not itself algorithmically complex, but for chaotic
(mixing) systems it lies close to highly complex distributions into
which a perturbing environment can easily push it.

\subtopic{e.}{Ergodicity.}  Though ergodicity has nothing to do with
the approach to thermodynamic equilibrium, since the exploration of
all of accessible phase space occurs on too long a time scale to be
relevant for the approach to equilibrium, it nonetheless plays a
central role in the application of Bayesian probabilities to
dynamical systems.  If one knows only the energy of a system and that
it is constrained by certain external parameters, then one should
assign a time-invariant distribution since one's state of knowledge
is time-invariant. Ergodicity and the conservation of phase-space
volume under Hamiltonian evolution imply that the only time-invariant
distribution is the microcanonical distribution.

\subtopic{f.}{Why does the canonical distribution lead to predictions
of frequencies}, such as the angular distribution of effusion of gas
through a small hole, when the Bayesian view does not assert any
necessary connection between probabilities and frequencies?  Imagine
drawing sequences from a probability distribution ${\bf p}$.  In $N$
trials the number of sequences whose probability exceeds some
threshold is proportional to $e^{NH({\bf p})}$. Thus, of all the
probability distributions consistent with the mean-value constraints,
the canonical (MAXENT) distribution is the one that generates the
most high-probability sequences; indeed, for many trials, the
high-probability sequences generated by the MAXENT distribution are
essentially {\it all\/} the sequences that are consistent with the
constraints.  If the constraints that went into the MAXENT
distribution are truly the only thing you know about the system, then
an i.i.d.\ based on the MAXENT distribution is your most unprejudiced
way of predicting frequencies.

\paratwo
This idea can be used to put MAXENT in a broader context, where
instead of assigning the MAXENT distribution, one weights the
probability on probabilities by the number of high-probability
sequences in some number of trials that one contemplates doing; i.e.,
one assigns an exchangeable multi-system distribution whose
probability on probabilities is chosen to be proportional to
$e^{NH({\bf p})}$ times delta functions that enforce the mean-value
constraints.  In this formulation, the parameter $N$ characterizes
one's confidence in the MAXENT predictions, which can be overridden
by conflicting data in a sufficiently large number of trials.

\subtopic{g.}{The Lebowitz program} (Lebowitz 1999a).  Lebowitz
explains the increase of entropy using what he characterizes as a
modern presentation of Boltzmann's approach.  He divides phase space
into coarse-grained cells $j$, with volume $\Gammaj$. The cells are
defined by the values of some macroscopic parameters and are not of
equal size, there being one cell that dominates, the one that has
equilibrium values for the macroscopic parameters.  He associates
with each microstate (point) within cell $j$ a ``Boltzmann entropy''
$S_B=\log\Gammaj$.  The dominant cell has a Boltzmann entropy that
approximates the thermodynamic equilibrium entropy.  His argument is
that if the system is initially confined to a cell $M$, defined by
some macroscopic constraints, then ``typical'' initial conditions
within that cell end up after a short while in the dominant cell,
thus leading to the entropy given by thermodynamic equilibrium.

\paratwo
Probabilities are introduced to characterize ``typical'' initial
conditions---i.e., almost all initial conditions selected randomly
from an initial probability distribution that is uniform on $M$ with
respect to the standard phase-space measure.  Even though it is
recognized that probabilities are absolutely essential for this
purpose---``any meaningful statement about probable or improbable
behavior of a physical system has to refer to some agreed upon
measure (probability distribution)''---they are introduced
apologetically, with a defensive tone, because the system is at all
times actually in a particular microstate, or as Boltzmann is quoted
as putting it, ``The applicability of probability theory to a
particular case cannot of course be proved rigorously.''  Indeed, the
probabilities are never spelled out precisely---they are `` a measure
which assigns (at least approximately) equal weights to the different
microstates consistent with the `initial' macrostate $M$''---and are
never given a symbol, for to do so would give too much legitimacy to
what are obviously Bayesian probabilities based on one's lack of
knowledge of the exact initial conditions within $M$.

\paratwo
A Bayesian sees through this smokescreen immediately.  The initial
uniform probability distribution does apply to an individual system
and is justified on the grounds stated above.  The Lebowitz program
is a species of coarse graining [(i) above], the problem being that
the refusal to use Bayesian probabilities renders the coarse graining
unnecessarily mysterious and the explanation of what is going on
nearly nonsensical.  The Boltzmann entropy of a microstate has no
physical meaning, in contrast to the Gibbs entropy of a distribution,
which quantifies the amount of available work. Indeed, the Boltzmann
entropy is not a property of the microstate at all, but a property of
the coarse graining.  For a particular trajectory, the Boltzmann
entropy bobs up and down like a cork as the trajectory moves from one
coarse-grained cell to another, but this bobbing up and down has no
physical meaning whatsoever.  To take advantage of a decrease in the
Boltzmann entropy, say to extract additional work, you would need to
know what trajectory the system is on, but you don't know that and if
you did know it, you wouldn't be messing around with the coarse
graining, because you would know exactly what the system is doing at
all times.  The Lebowitz program is a perfect example of the
contortions that come from insisting that physics is only about
what's really out there, when it is evident here that what one can do
depends on what you know about what's really happening.

\paratwo
A Bayesian, asked to fix the Lebowitz program, might do so in three
steps.  First, he would point out that since one doesn't know which
trajectory the system is on, if the program is to make any sense at
all, it must deal with the {\it average\/} Boltzmann entropy
$$
\bar S_B\equiv\sum_j p_j(t)\log\Gammaj\;,
$$
where $p_j(t)$ is the probability to be in cell~$j$ at time $t$,
given an initial uniform distribution on $M$.  Lebowitz's refusal to
use the average Boltzmann entropy and his insistence on using the
Boltzmann entropy for individual trajectories are probably based on
his distrust of probabilities for a single system, which after all
does have a particular trajectory.  Second, since the average
Boltzmann entropy is only part of the missing information, the
Bayesian would replace it with the closely related Gibbs entropy of
the coarse-grained distribution, which quantifies the total missing
information and is directly related to the available work:
$$
S(t)=k_B\!\left(-\sum_jp_j(t)\log p_j(t)+\sum_jp_j(t)\log\Gammaj\right)\;.
$$
Third, the Bayesian would notice that the procedure used to get
$p_j(t)$ and, hence, $S(t)$ does not consistently discard
information: $p_j(t)$ comes from coarse graining at each time $t$ the
exact distribution that evolves from the initial distribution,
instead of coarse graining the distribution that comes from the
coarse graining at the preceding time step.  As a result, $S(t)$
occasionally decreases.  Although the bobbing up and down of $S(t)$ is
very much suppressed relative to the bobbing up and down of the
Boltzmann entropy for a particular trajectory, a decrease of $S(t)$,
no matter how small, describes a decrease in the missing information,
even though one has not acquired any new information about the
system.  This doesn't make any sense, so a Bayesian would replace
$p_j(t)$ with the probability $p_j(n)$ that comes from consistently
discarding fine-grained information. The resulting coarse-grained
Gibbs entropy,
$$
S(n)=k_B\!\left(-\sum_jp_j(n)\log p_j(n)+\sum_jp_j(n)\log\Gammaj\right)\;,
$$
is the one introduced earlier to describe coarse graining.  It
increases monotonically and has a direct physical interpretation in
terms of available work.

\paratwo
The result of a fix of the Lebowitz program is to disenthrone the
Boltzmann entropy and to replace it with the coarse-grained Gibbs
entropy that lies at the heart of any coarse-graining strategy.  What
an irony it is that Lebowitz heaps scorn on the Gibbs entropy, saying
that it can't be the right entropy for nonequilibrium situations,
because unlike the Boltzmann entropy, it ``does not change in time
even for time-dependent ensembles describing (isolated) systems not
in equilibrium.''

\paratwo
How do people fool themselves that the Lebowitz program is sensible
when its underlying principles are so fundamentally flawed? There are
two reasons.  First, as we have seen, the Lebowitz program can be
placed on a sensible Bayesian foundation simply by reinterpreting it
as a standard coarse-graining procedure that uses the coarse-grained
probabilities $p_j(n)$ and the corresponding Gibbs entropy $S(n)$.
Second, for the coarse graining used by Lebowitz, the first term in
the coarse-grained Gibbs entropy, which is the missing information
about which coarse-grained cell the system occupies, is negligible
compared to the second term, and after a short time, the second term
is dominated by a single cell, which has nearly unity probability and
nearly all the phase-space volume. After a short time, the
coarse-grained Gibbs entropy is given approximately by the Boltzmann
entropy for the dominant cell, thus making the Lebowitz program a
very good approximation to a well founded Bayesian coarse graining,
even though its justification doesn't make sense.

\paratwo
This means that almost all of what is done in the Lebowitz program
can be given a sensible Bayesian reinterpretation.  For example,
Lebowitz notes, ``The microstates in $\Gamma_{M_b}$, which have come
from $\Gamma_{M_a}$ through the time evolution during the time
interval from $t_a$ to $t_b$, make up only a very small fraction of
the volume of $\Gamma_{M_b}$, call it $\Gamma_{ab}$.  Thus we have to
show that the overwhelming majority of points in $\Gamma_{ab}$ (with
respect to the Liouville measure on $\Gamma_{ab}$, which is the same
as the Liouville measure on $\Gamma_{M_a}$) have {\it future\/}
macrostates like those typical of $\Gamma_b$---while still being very
special and unrepresentative of $\Gamma_{M_b}$ as far as their {\it
past\/} macrostates are concerned.'' When reinterpreted, this is
simply the statement that one desires that the future behavior of
macroscopic variables be insensitive to the coarse graining.

\paratwo
Lebowitz says that the ``big question'' is why are the initial
conditions so special and concludes, along with many others, that one
must posit that the Universe was originally in a much more ordered
state than it is now.  We have seen above that this conclusion simply
cannot be supported, but that it can be replaced by the conclusion
that the evolved state, though not complex itself, is close to very
complex states.

\topic{Bayesian or information-based interpretation of quantum
mechanics.}  Much of the material in Secs.~4.a--c is contained in
Caves, Fuchs and Schack (2001a, 2001b).

\paraone
Let's begin with motivation provided by E.~T. Jaynes (1990a), the
great physicist and Bayesian:

{\twonarrow
 Let me stress our motivation: if quantum theory were not
successful pragmatically, we would have no interest in its
interpretation.  It is precisely {\eightit because\/} of the enormous
success of the QM mathematical formalism that it becomes crucially
important to learn what that mathematics means.  To find a rational
physical interpretation of the QM formalism ought to be considered
the top priority research problem of theoretical physics; until this
is accomplished, all other theoretical results can only be
provisional and temporary.

This conviction has affected the whole course of my career.  I had
intended originally to specialize in Quantum Electrodynamics, but
this proved to be impossible.  Whenever I look at any
quantum-mechanical calculation, the basic craziness of what we are
doing rises in my gorge and I have to try to find some different way
of looking at the problem, that makes physical sense.  Gradually, I
came to see that the foundations of probability theory and the role
of human information have to be brought in, and so I have spent many
years trying to understand them in the greatest generality.

\dots

Our present QM formalism is a peculiar mixture describing in part
laws of Nature, in part incomplete human information about
Nature---all scrambled up together by Bohr into an omelette that
nobody has seen how to unscramble.  Yet we think the unscrambling is
a prerequisite for any further advance in basic physical theory, and
we want to speculate on the proper tools to do this.
\vskip0pt}

\paraone\noindent
The information-based or Bayesian interpretation of quantum mechanics
is founded on the notion that quantum states, both pure and mixed,
represent states of knowledge and that all the probabilities they
predict are Bayesian probabilities.

\paraone
This point of view, particularly as it applies to the probabilities
that arise from pure states, seems crazy at first. The probabilities
that come from a pure state are intrinsic and unavoidable.  How can
they not be objective properties of a quantum system when they are
prescribed by physical law?  How can they be ignorance probabilities
when one knows everything possible about the quantum system?  Indeed,
as Giere (1973a) notes, if one is to find objective probabilities,
one must look outside the determinism of the classical world, and
quantum mechanics, with its intrinsic indeterminism, seems to be just
the place to look.  Many physicists, even Bayesian ones, have assumed
instinctively that quantum probabilities are different from the
ignorance probabilities of a realistic/deterministic world.
Nonetheless, our view is that {\it all\/} probabilities---even
quantum probabilities---are Bayesian, i.e., based on what one knows,
the Bayesian view being the only consistent way to think about
probabilities.  The probabilities of quantum mechanics---even those
that arise from a pure state---are based on what the describer knows.
Let's give E.~T. Jaynes (1990a) another hearing:

{\twonarrow
For some sixty years it has appeared to many physicists that
probability plays a fundamentally different role in quantum theory
than it does in statistical mechanics and analysis of measurement
errors. It is a commonly heard statement that probabilities
calculated within a pure state have a different character than the
probabilities with which different pure states appear in a mixture,
or density matrix.  As Pauli put it, the former represents
``\thinspace\dots\thinspace eine prinzipielle {\eightit
Unbestimmtheit}, nicht nur {\eightit Unbekanntheit}''.  But this
viewpoint leads to so many paradoxes and mysteries that we explore
the consequences of the unified view, that all probability signifies
only human information.
\vskip0pt}

\subtopic{a.}{Why and how probabilities?  Kochen-Specker and
Gleason.} We adopt the Hilbert-space structure of quantum questions,
which in its finest-grained form deals with questions described by
orthogonal one-dimensional projectors.

\paratwo
The Kochen-Specker theorem says that there is no way (in three or
more dimensions) to assign truth or falsity to every one-dimensional
projector (only finite sets of projectors are required for the proof)
in such a way that every quantum question has a definite, predictable
answer.  As a consequence, quantum mechanics cannot be reduced to
certainties, but rather must deal with probabilities.  The crucial
assumption in the Kochen-Specker theorem, called {\it
noncontextuality}, is that the truth or falsity of a projector does
not depend on which orthogonal set it is a member of. This
assumption, unreasonable for a hidden variable theory, which ought to
be able to snub its nose at the Hilbert-space structure of quantum
questions, is a reasonable, even necessary assumption for our purpose
of demonstrating that quantum mechanics must deal with probabilities.
Noncontextual truth assignments ignore the Hilbert-space structure;
that being the only input from quantum mechanics, if one ignores it,
one can't hope to find out anything about quantum mechanics.

\paratwo
The rule for assigning probabilities comes from Gleason's Theorem.  A
frame function assigns to each one-dimensional projector
$\Pi=|\psi\rangle\langle\psi|$ a number between 0 and 1 inclusive,
with the property that the function sums to 1 on orthogonal
projectors.  A frame function makes a noncontextual probability
assignment to all quantum questions (noncontextual because the
probability assigned to a projector does not depend on which
orthogonal set it is a member of).  Gleason's Theorem shows that (in
three or more dimensions) any frame function can be derived from a
density operator $\rho$ according to the standard quantum rule, ${\rm
tr}(\rho\Pi)=\langle\psi|\rho|\psi\rangle$.  Thus in one stroke,
Gleason's theorem establishes that density operators provide the
state-space structure of quantum mechanics and gives the rule for
calculating probabilities from states.  For the same reason as above,
the assumption of a noncontextual probability assignment is perfectly
reasonable here (although perhaps less convincing because probability
assignments can be tweaked slightly, whereas truth assignments
cannot), where we are trying to determine the consequences of quantum
mechanics for probability assignments.  From a Bayesian perspective,
what Gleason's theorem says is that the only way for someone to
assign probabilities to quantum questions in a way that doesn't
correspond to a density operator is to make a contextual assignment.

\paratwo
In a realistic/deterministic world, maximal information corresponds
to knowing which of a set of mutually exclusive, exhaustive
alternatives is the true one.  It provides a definite, predictable
answer for all questions, including the finest-grained ones, i.e.,
those that ask which alternative is true.  The slogan is that in a
realistic/deterministic world, ``maximal information is complete,''
providing certainty for all questions.  Quantum mechanics is
different, since no density operator (quantum state) gives certainty
for all questions.  Mixed states cannot provide certainty for any
fine-grained question.  Only pure states---themselves one-dimensional
projectors---can provide certainty for some fine-grained questions.
Thus they are the states of maximal information.  The quantum slogan
is that ``maximal information is not complete and cannot be
completed,'' thus giving rise to Bayesian probabilities even when one
knows as much as possible about a quantum system.

\paratwo
States of maximal information correspond to well defined preparation
procedures both in a realistic/deterministic world and in quantum
mechanics, the procedure being the one that renders certain the
answers to the appropriate questions.   Other states of knowledge do
not correspond to well defined procedures.  There are many different
kinds of situations where one assigns probabilities in a
realistic/deterministic world or a mixed state in quantum mechanics,
but one such situation is where the describer knows that the system
has been prepared by a well defined procedure, but does not know
which procedure.

\paratwo
A complete theory must have a rule for assigning probabilities in the
case of maximal information: if one has maximal information, there is
no other information that can be brought to bear on the probability
assignment; thus, if the theory itself is complete, it must supply a
rule.  In a realistic/deterministic world, maximal information
corresponds to certainty, and the Dutch book argument requires all
probabilities to be 0 or 1.  In quantum mechanics, where maximal
information is not complete, the Dutch book argument only prescribes
probabilities for those questions whose outcome is certain.
Fortunately, Gleason's theorem comes to the rescue, providing the
unique rule for assigning probabilities that is consistent with the
Hilbert-space structure of questions.  For this essential service,
{\it Gleason's theorem can be regarded as the greatest triumph of
Bayesian reasoning}.

\paratwo
Perhaps the most compelling argument for the subjectivity of quantum
probabilities comes from the multiplicity of ensemble decompositions
of a density operator.  The ensemble probabilities are clearly
Bayesian, reflecting ignorance of which pure state in the ensemble,
for all the reasons cited for classical probabilities.  The
probabilities derived from the pure states in the ensemble are
natural candidates for ``objective probabilities.''  The problem with
this idea is that the multiplicity of ensemble decompositions means
that is impossible to separate cleanly the subjective and objective
probabilities.  Here's how Jaynes (1957b) put it long ago in one of
his pioneering articles on the foundations of statistical physics:

{\threenarrow
A density matrix represents a fusion of two different statistical
aspects; those inherent in a pure state and those representing our
uncertainty as to which pure state is present.  If the former probabilities
are interpreted in the objective sense, while the latter are clearly
subjective, we have a very puzzling situation. Many different arrays,
representing different combinations of subjective and objective aspects,
all lead to the same density matrix, and thus to the same predictions.
However, if the statement, ``only certain specific aspects of the
probabilities are objective,'' is to have any operational meaning, we
must demand that some experiment be produced which will distinguish
between these arrays.
\vskip0pt}

\paratwo\noindent
The multiplicity of decompositions implies that all the probabilities
must be given the same interpretation.  Since some of the
probabilities are clearly subjective, one is forced to acknowledge
that all quantum probabilities are Bayesian.  The mixed-state
decomposition problem in quantum mechanics tell us that in quantum
mechanics, even more than in a realistic/deterministic world, you
have to have a consistent view of probabilities and stick with it,
and the only consistent view of probabilities is the Bayesian view.
Anyone who promotes an interpretation of quantum mechanics without
first declaring his interpretation of probabilities should be sent
back to the starting gate.

\subtopic{b.}{Quantum states as states of knowledge.} The simplest
argument for why quantum states are states of knowledge is the same
as the argument for probabilities: If you want to know the state of a
quantum system, you cannot ask the system; it doesn't know its state.
If you want to know the state, you must ask the describer.

\paratwo
Given maximal information, the Dutch book argument requires
assignment of a unique pure state, so once you obtain the maximal
information of the describer, you must assign the same pure state.
If the describer tells you that the state is mixed, then you are free
to assign a different mixed state based on the same information or to
acquire privileged information that permits you to assign a different
state, pure or mixed (the Dutch-book argument does require your mixed
state to have support the lies within the support of the describer's
mixed state).

\paratwo
The notion that a pure state corresponds to maximal information
already says that the properties whose statistics are given by
quantum probabilities cannot be objective properties of a quantum
system.  If they were, then the purportedly maximal information would
not be maximal and should be completed by determining the values of
all these properties.

\paratwo
A curious feature of this simple argument is that it doesn't have to
change at all to accommodate hidden-variable theories.  Indeed,
Bayesian probabilities are the natural way to think about quantum
states in a hidden-variable theory.  In a hidden-variable theory, all
the properties of the system are objective, having actual values, but
values that are determined by ``hidden variables'' that are for the
present inaccessible.  The quantum probabilities are naturally
regarded as Bayesian probabilities, but now reflecting ignorance of
the hidden variables, and the quantum state, as a summary of those
probabilities, is a state of knowledge.  As long as the hidden
variables remain inaccessible, it is still not possible to determine
the state by asking the system.  The only difference is that in a
hidden variable theory, the purportedly maximal information
corresponding to a pure state isn't maximal at all, even though it
might be impossible in principle to complete it.  Thus the slogan for
hidden-variable theories is, ``Apparently maximal information is not
maximal, but might or might not be completable.''

\paratwo
The virtue of Bell inequalities is that they show that the hidden
variables must be nonlocal, if they are to duplicate the statistical
predictions of quantum mechanics, and that they provide experimental
tests that distinguish quantum mechanics from local hidden variable
theories.  For this reason, variants of the ``you can't ask the
system'' argument for entangled states are perhaps more convincing.
In particular, there is Chris's favorite: by making an appropriate
measurement on one member (Alice's) of an entangled pair, you can
make the pure state of the other member (Bob's) be a state chosen
randomly from any orthonormal basis.  This is accomplished without in
any way interacting with Bob's particle.  Different von Neumann
measurements on Alice's particle will leave Bob's particle in a state
chosen randomly from incompatible bases.  This is a cogent argument
for pure states being states of knowledge instead of states of the
world.  Nonetheless, the argument can be put in the context of {\it
nonlocal\/} hidden-variable theories, where there would be a real
nonlocal (potentially acausal) influence of Alice's choice of
measurement on Bob's particle.  Thus the only real advantage of this
argument over the simple you-can't-ask-the-system argument---perhaps
this is a considerable advantage---is that it forces the hidden
variables to be nonlocal if they are to be capable of providing an
ontology underneath the quantum state.

\paratwo
We can summarize our position as follows: A pure state described by a
state vector corresponds to a state of maximal information, for which
there is a well defined, repeatable preparation procedure.  That is
the reason one assigns the product state to many copies of a system
identically prepared, for one then has maximal information about the
composite system and must assign a state that gives certainty for
repeated yes/no questions corresponding to the state.  On the other
hand, one shouldn't fall into the trap of regarding the state vector
as real.  It corresponds to a state of knowledge.  The proof is in
the fact that even though one can prepare a pure state reliably,
using the maximal information, one can't determine it, which one
ought to be able to do if it is real. Someone else cannot determine
the state of a system, because it is not out there, but in the mind
of the describer.  If you want to know the state I assign, who do you
ask? The system or me?  The state vector can change as a consequence
of my obtaining information, and this also argues strongly for its
subjective status.  A pure state, rather than being objective, is
{\it intersubjective}, because of the reproducibility of maximal
information and the Dutch-book-enforced agreement on assigning a pure
state.

\subtopic{c.}{Principle of quantum indeterminism and the quantum de
Finetti theorem.} A guiding principle that we use in assigning
probabilities is that one should never make a probability assignment
that prohibits learning from data, by which we mean using data to
update probabilistic predictions for situations from which data has
not yet been gathered, {\it unless\/} one already has maximal
information, in which case there is nothing further to learn. If you
do not have maximal information, there is always room for hypotheses
about things you do not know, so you should allow for these
hypotheses in your probability assignments.  When you do have maximal
information, your probability assignment should not allow learning
from data.

\paratwo
In a realistic/deterministic world, maximal information means
certainty, so all probabilities are 0 or 1.  In contrast, in a
quantum world, ``maximal information is not complete and cannot be
completed'' and thus gives rise to Bayesian probabilities even when
one knows as much as possible about a quantum system.

\paratwo
The classical and quantum de Finetti theorems (Caves, Fuchs, and
Schack 2001a), which deal with exchangeable probabilities or density
operators, provide a setting for applying this guiding principle.
For exchangeable sequences, i.i.d.\ probability assignments are the
unique ones that do not allow any learning.  The guiding principle
implies that one should never assign the i.i.d., except when one has
maximal information.  In a realistic/deterministic world, where
maximal information is complete, this means that one should assign
the i.i.d. only in the trivial case of certainty.  This refusal to
assign the i.i.d.\ leaves open the possibility of using frequency
data from initial trials to update probabilistic predictions for
further trials.  Things are different in a quantum-mechanical world.
Since maximal information is not complete, one can assign
(nontrivial) i.i.d.'s to exchangeable trials when all the systems are
described by the same pure state.  Notice that the data gathered from
such trials comes from ``the bottomless quantum well of information''
and is useless for updating the state assignment for additional
systems.

\paratwo
Since it is easy to fail to appreciate the content of our guiding
principle in the classical case, it is worth discussing that
situation in some detail.  Should someone not see the point of the
guiding principle, ask if he would assign equal probabilities to
further tosses of an allegedly fair coin after getting 100
consecutive heads.  If he continues to accept even bets, you've found
a gold mine.  If he doesn't, then point out that any time one doesn't
continue to assign probabilities based on the initial single-trial
probability, it means one didn't assign the i.i.d.\ to the
multi-trial hypothesis space.  This is because the data from initial
trials cannot be used to update via Bayes's rule the probabilities
for further trials when the multi-trial assignment is an i.i.d.

\paratwo
It is an interesting aside to note that many people react in
contradictory ways---you might want to test your own reaction---when
presented with the same problem in slightly different guises.  Handed
what is said to be fair coin, they will assert that one ought to
stick with 50-50 predictions for future tosses even after many
consecutive tosses give heads.  On the other hand, given a coin about
which they are told nothing is known, they will assert that the the
probability for heads in the $(N+1)$th toss is the observed frequency
of heads, $n/N$, in the first $N$ tosses, and they will heap scorn on
Laplace's Rule of Succession, which says to use head probability
$(n+1)/(N+2)$ for the $(N+1)$th toss.  From a Bayesian perspective,
these different attitudes reflect different probabilities on
probabilities or, using the de Finetti representation, different
exchangeable multi-trial probabilities. The desire to stick with an
initial 50-50 probability comes from a probability on probabilities
that is highly concentrated near 50-50---the robust 50-50 odds are
then what was called a propensity above---the consequence being an
extreme reluctance to let the data change the initial 50-50
probabilistic predictions.  The use of observed frequency to predict
future tosses reflects just the opposite prejudice, i.e., a prejudice
for letting the data dictate predictions for further trials.
Laplace's Rule of Succession lies in between, but much closer to
learning from the data than to a stubborn insistence on an initial
single-trial probability.  An excellent discussion of these questions
and their relation to probabilities on single-trial probabilities can
be found in Jaynes (1986b).

\paratwo
Hypothesis testing and parameter estimation (continuous variable
hypothesis testing) are real-life situations where one uses
probabilities on probabilities.  Each hypothesis leads to different
probabilistic predictions for data that will be used to decide among
the hypotheses, and thus each hypothesis represents some state of
knowledge about the data.  The state of knowledge might include
knowledge of the actual value of some objective property, but it
cannot include complete knowledge, for then the data could be
predicted with certainty.  Thus the prior probabilities on the
hypotheses are probabilities on probabilities, which in a Bayesian
perspective should be banished in favor of primary probability
assignments on the data.  The de Finetti representation theorem shows
how to do this in the case of data that is exchangeable.  Notice,
however, that if the only difference between hypotheses lies in
different, but unknown values of an objective property, then the goal
of collecting data is to make the best possible determination of that
objective value.

\paratwo
Before returning to quantum mechanics, we have to deal with a couple
of objections to our statement that one should never assign the
i.i.d. in a realistic/deterministic world.  For {\it any\/}
exchangeable probability assignment for binary variables, if one
selects the successive occurrences of head-tail and tail-head, these
occurrences have equal probabilities and thus are governed by the
50-50 i.i.d. Recall that this scenario, which is the simplest of many
such scenarios, raised its ugly head earlier in the context of an
attempt to find objective classical probabilities; easily disposed of
in that context, it is more insidious now because within a Bayesian
context, it challenges our guiding principle that one should never
assign the i.i.d.\ classically except when there is certainty.  The
challenge is easily met, however, because the essence of our guiding
principle is that one should never assign probabilities that prohibit
learning from data except in the case of maximal information.  We did
not intend, for it is not true, that such a probability assignment
prohibit selecting subsets of the data from which nothing can be
learned, and that's what happens in the head-tail vs.\ tail-head
scenario.  We don't need to change our statement that one should
never assign probabilities that forbid learning, except in the case
of maximal information; in the case of i.i.d.'s, however, when being
utterly precise, we should say that one should never assign the
i.i.d.\ to the base hypothesis space, except in the case of maximal
information.

\paratwo
There is another, more troubling objection.  In the case of
exchangeable multi-trial probabilities, collecting frequency data
from many trials allows one to update the probabilities for further
trials.  In the limit of infinitely many trials, the probability
assignment for further trials converges to an i.i.d.\ whose
single-trial probabilities are given by the measured frequencies.  So
what happens to our statement that you shouldn't assign the i.i.d.\
except in the case of maximal information?

\paratwo
There are two good answers to this question.  The first, easier
answer is that you don't converge to the i.i.d.\ except in the
unattainable infinite limit.  For finite numbers of trials, your
single-trial probabilities will become increasingly robust, but there
will always be some remaining doubt.  Since we never questioned the
idea that one might make arbitrarily robust single-trial probability
assignments, there is no contradiction with our guiding principle.

\paratwo
The second, probably better answer takes one outside the arena of
exchangeable probabilities.  If successive trials do not all yield
the same result, then in a realistic/deterministic world, there are
undiscovered details about the trials that if known, would give
certainty for each trial.  A random-number generator or a calculation
of the digits of $\pi$ provides a good example of the kind of
underlying realistic mechanism might be at work in generating
successive trials.  Knowing all the details or some part of them
necessarily takes one outside the province of exchangeable
probabilities unless all the trials yield the same result.  Learning
about these details from data requires more than frequency data---it
would involve information about correlations between trials---and
updating probabilities based on this nonfrequency data requires a
nonexchangeable probability assignment.  Thus the guiding principle
actually says that one should never make a strictly exchangeable
probability assignment in a realistic/deterministic world, except in
the case of maximal information, and this neatly avoids the
possibility of assigning an i.i.d.\ or converging to an i.i.d.

\paratwo
In quantum mechanics we replace the objectivist's obsession with
objective probabilities with a Bayesian attention to the conditions
under which one should assign the i.i.d.  We argue that one assigns
the i.i.d.\ only in a situation corresponding to maximal information,
which can be reproduced reliably from trial to trial. In a
realistic/deterministic world this gives the i.i.d.\ only in the case
of certainty, but in a quantum world it gives the i.i.d.\ for pure
states, i.e., maximal information.

\paratwo
One can argue that one never actually has maximal information about a
system, either classical or quantum mechanical, and that this means
that one never does assign the i.i.d.\ even in quantum mechanics.
Though it is perhaps true that maximal information is an unattainable
limit of more and more refined information, the crucial point is that
this limit corresponds to certainty in a realistic/deterministic
world, whereas it corresponds to a pure state---and the consequent
i.i.d.'s---in quantum mechanics.

\paratwo
From the Bayesian perspective, there is no necessary connection
between probabilities and frequencies in a realistic/deterministic
world.  That is the content of our guiding principle when applied to
exchangeable sequences.  Nonetheless, what we have learned is that in
quantum mechanics, there can be a strict connection between
single-trial probabilities and observed frequencies.  The reason is
that the well defined procedure for preparing a pure state allows one
to prepare many copies of a system, all in the same state, and this
leads to the i.i.d.\ for repeated measurements of the same quantity
on the successive copies.  This is R\"udiger's argument, and he has
dubbed the fundamental new quantum-based connection between
probabilities the {\it principle of quantum indeterminism}.  It is
the Bayesian answer to why the probabilities that come from pure
states can be used to predict frequencies.

\paratwo
Notice the similarity of our argument to Giere's (Giere 1973a) view of
objective chance.  Giere admits that in a deterministic world the
only objective chance is certainty, but maintains that in a quantum
world the quantum probabilities correspond to objective chance.  The
difference is that Giere has the probabilities change character, from
subjective to objective in the limit of maximal information, whereas
we regard all probabilities as Bayesian.  Even in the limit of
maximal information, we think of the probabilities as Bayesian, i.e.,
based on the maximal information, but we say that the maximal
information can be reproduced reliably from trial to trial.

\paratwo
A potential weakness of our argument is that there is no operational
difference between our view---assign Bayesian i.i.d.'s only in the
case of maximal information---and Giere's view---the only objective
probabilities, which give the i.i.d., occur in the situations we call
maximal information.  Indeed, I think one of the responses to our
view will be that maximal information defines those situations where
probabilities are objective properties.  The difficulty is that we
want to retain in quantum mechanics some, but not all of the features
of classical maximal information, and there will certainly be
disagreement over the features we choose.  States of classical
maximal information can be prepared and verified reliably; they
provide the ontology of a realistic/deterministic world.  States of
maximal information in quantum mechanics can be prepared reliably,
but they cannot be verified (you can't ask the system); because they
cannot be verified, we do not accord them objective reality, and we
regard the probabilities they generate as Bayesian probabilities.

\subtopic{d.}{Ontology.}  In the Bayesian interpretation, the states
of quantum systems do not have objective reality---they are states of
knowledge, not states of the world---and the values of the properties
of microscopic systems do not have objective reality.  The apparatus
of quantum states and associated probabilities is an elaborate theory
of inference---a law of thought, in Chris's phrase---in which we put
in what we know and get out statistical predictions for things we can
observe.  In my version of the Bayesian interpretation, the objective
parts of the theory---the ontology---lie in the other part of quantum
mechanics, i.e., in the physical laws that govern the structure and
dynamics of physical systems.  These physical laws are encoded in
Hamiltonians and Lagrangians.

\paratwo
What is the evidence that Hamiltonians have ontological status.  The
most compelling argument is the following: {\it whereas you can't ask
a quantum system for its state, you can ask it for its Hamiltonian.}
By careful experimentation, you can deduce the Hamiltonian of a
quantum system---that's what physics is all about.

\paratwo
A second argument comes from dynamics.  It is possible to argue that
the only kind of dynamics consistent with the convex structure of
density operators is given by positive superoperators (linear maps on
operators).  Among these positive superoperators, a special place is
occupied by those maps that preserve maximal information, and it is
possible to show that the only positive maps that preserve maximal
information and are connected continuously to the identity are
generated by Hamiltonians.  If you have maximal information about a
quantum system and you want to retain such maximal information, you
must know the system's Hamiltonian.  Where does that Hamiltonian come
from?  I think it must be an objective property of the system.

\paratwo
What does it mean to say that Hamiltonians are objective?  Does it
mean that the formula written on a page is real?  That's silly.  Does
it mean that the positions and momenta and spins in it are real?
Certainly their values don't have objective status, but their
appearance in the Hamiltonian does determine the kind of Hilbert
space that applies to the system and thus dictates the structure
and---this is important---to some extent the meaning of the quantum
questions that began our discussion of quantum probabilities.
Moreover, the form of the Hamiltonian in terms of positions, momenta,
spin, and so forth together with the parameters in the
Hamiltonian---masses, charges, coupling constants---determines the
physical properties of systems.  If microscopic systems are to have
any real properties, it is these physical properties that are the
best candidates, and their objective status is equivalent to the
objective status of at least some part of the Hamiltonians governing
microscopic systems.

\paratwo
Again we run up against the question of which aspects of maximal
information are to be promoted to objective status.  In a
realistic/deterministic world, where maximal information leads to
certainty, we regard as objective the alternatives that correspond to
maximal information.  In quantum mechanics, we do not grant objective
status to the pure states that correspond to maximal information.
Fundamentally, this is because maximal information not being
complete, pure states lead to probabilities, and we know that the
only consistent way to interpret probabilities is the subjective,
Bayesian interpretation.  How could the pure state be objective if
the probabilities it predicts aren't?  When we come to Hamiltonians,
which provide the rule for updating maximal information, we face a
choice.  Classically they can naturally be regarded as objective, but
what should we do in quantum mechanics?  The choice is by no means
clear, but the choice made in this document is to say that they are
objective properties of the system being updated.  Fundamentally,
this choice comes down to the fact that Hamiltonians aren't directly
associated with probabilities, so we are free of the prejudice to
declare them subjective.  The slogan is that {\it quantum systems
know what they're doing, but we often don't}.  The hope is that this
point of view can be an ingredient in constructing the effective
reality that emerges from quantum mechanics.

\paratwo
Quantum computation provides some support for the notion that
Hamiltonians are objective.  The standard description of an ideal
quantum computation is something like the following.  The computer is
prepared initially in a pure state in the computational basis; this
input state might encode input information.  The computer then runs
through a prescribed set of unitary steps that leave it in a
computational basis state that stores the result of the computation.
A measurement reveals the result.  The user doesn't know the output
state---otherwise he wouldn't need to do the computation---so he
assigns a density operator to the output.  In a Grover-type search
algorithm, the user doesn't know the output state because he is
trying to discover the actions of an ``oracle,'' which prepared the
output state.  In a Shor-type algorithm, however, the user doesn't
know the output state even if he knows the prescribed sequence of
unitaries, because the consequences of the unitary steps are too
complex too be simulated.  In this case the output tells the user
something objective about the input---the factors in Shor's
algorithm---and the unitary steps act as a sort of guarantee that the
answer can be trusted.  For a quantum computer, even if one knows the
Hamiltonian, one cannot retain maximal information, but nonetheless
trusts that the computer will output the right answer.  This is truly
an example where ``the system knows what it is doing, even though the
user doesn't.''

\subtopic{e.}{Emergent effective reality.}  The
realistic/deterministic reality of everyday experience is an emergent
property in the Bayesian interpretation.  It arises in sufficiently
complicated, ``macroscopic'' systems in which one is able to observe
only ``macroscopic'' variables.  The mechanism for its emergence is
Zurek-style (1998a) decoherence that comes from not having access to
the microscopic variables (formally, one traces over the microscopic
variables).  The result is hoped to be the ``effective reality'' of
our everyday experience.  An important question is the extent to
which the properties of the effective reality are dictated by the
laws of physics---Hamiltonians and Lagrangians---as opposed to
depending on the quantum state of the microscopic variables.  It
would be nice if the character of the effective reality came mainly
from the microscopic ontology, i.e., Hamiltonians, with only minimal
dependence on the subjective quantum state.

\paratwo
There is some reason to think this is true.  One argument is that
since the physical laws involve local interactions, natural selection
would favor a local reality, not nonlocal superpositions, which would
be difficult to follow because of decoherence unless one had the
ability to monitor the details of the environment.  This, or some
other argument, perhaps not involving natural selection, gives the
separation between the ontology and the epistemology in the Bayesian
interpretation: the Hamiltonians are the ontology, giving rise to the
effective reality, and the structure of quantum states provides rules
of inference for making predictions in view of what we know.  Even
without the whole Bayesian apparatus of quantum inference, active
agents can take advantage of the predictability within the almost
realist/almost deterministic effective reality.

\paratwo
We are now in a position to be more specific about quantum states as
states of knowledge---knowledge about what?---and quantum
probabilities as ignorance pro\-ba\-bi\-li\-ties---ignorance of what?
The probabilities of quantum mechanics---even those that arise from a
pure state---are based on what the describer knows.  They do not
reflect ignorance of some underlying reality, as in a hidden-variable
theory, but rather ignorance of results of possible observations.
They are ultimately probabilities for ``macroscopic'' alternatives in
the emergent effective reality of the ``macroscopic'' world.  They
are ignorance probabilities because the describer cannot predict
which of the macroscopic alternatives is realized.  They are based on
what the describer knows about ``microscopic'' systems that intervene
between himself and the ultimate alternatives, microscopic systems
that cannot be described within the approximately realistic,
approximately deterministic effective reality, but must be described
by quantum mechanics, thereby introducing uncertainty into the
ultimate predictions.

\paratwo
This kind of effective reality is the best we can hope for.  Einstein
emphasized that there are not two presentations of reality: one out
there and one constructed from our theories that are tested against
what's out there.  Rather there is a single reality constructed from
our perceptions and our theories about them.  This is just what the
effective reality provides.  It is the reality that is relevant for
avoiding a predator, catching prey, or playing baseball {\it and\/}
for making statistical predictions about the behavior of microscopic
systems whose behavior lies outside the
almost-realistic/almost-deterministic world of everyday experience.

\paratwo
The notion of an effective reality is clearly a program that is
mainly not even started, but it builds on the considerable work
already done on decoherence and decoherent histories.  There is an
important consistency issue in constructing the effective reality
from physical law applied to quantum systems and then using that same
effective reality as the backdrop for our investigations of quantum
systems.

\paratwo
Two further comments on Hamiltonians: (i)~Non-Hamiltonian (i.e.,
nonunitary) evolutions inevitably involve a state assignment to
another system that interacts with the primary system, and thus they
include a subjective component.  Evidence for the subjectivity of
nonunitary evolutions thus has no bearing on the reality of
Hamiltonians. (ii)~Most Hamiltonians are emergent in some sense.  The
Hamiltonians of classical mechanics, for example, take as given
parameters that ultimately come from a quantum description of atomic
systems; these Hamiltonians are thus a part of the effective reality,
not an ontological input to it.  As another example, the Hamiltonians
of atomic physics and condensed-matter physics take as input the
masses and charges of electrons and protons, which come as input from
a more fundamental description like QCD.

\paratwo
A final point before moving on.  The Bayesian interpretation takes as
given that particular events happen in the effective reality.  The
almost-realistic/almost-deterministic flow of events that occur
wholly within the effective reality can only be pushed so far into
the microscopic level; pushed farther, it fails.  Even though we
operate wholly within the macroscopic level, our description of what
happens there sometimes requires us to dip into the quantum world and
thus to introduce uncertainty into the consequences within the
effective reality; under these circumstances, the responsibility of
the theory is to provide probabilities for events that cannot be
predicted with certainty.  Quantum theory gives us a reliable method
for predicting the probabilities of these uncertain events, and with
that accomplished, its job is finished.  In particular, a detailed
explanation of how a particular event is actualized out of the
several possibilities is, we contend, outside the provenance of
quantum theory.

\subtopic{f.}{Other issues.}

\paratwo
We have a Dutch-book argument for why two observers, sharing the same
maximal information, cannot assign different pure states.  This
argument ought to be tested in a variety of circumstances where there
seems to be no pure state or there seems to be more than one pure
state: (i) two-time situations where there is no consistent
state-vector assignment (measure $x$ spin and later $z$ spin; in
between, measurements of $x$ and $z$ spin are both determined, when
conditioned on pre- and post-measurements; no state vector has these
statistics); (ii) relativistic situations where two observers assign
different state vectors (not really a problem, because they never run
into any betting contradictions); (ii) pure-state assignments in the
situation where one observer is part of the state vector of another
super-observer (one can get contradictory wave function assignments,
although the super-observer will be unable to assign a wave function
to the system being considered by the subobserver).

\paratwo
What is a measurement?  It is the acquisition of information that is
used to update the quantum state (collapse of the wave function, to
use less neutral terms).  Can dogs collapse the wave function?  This
is a dumb question, since dogs don't use wave functions.  What they
do use is the emergent reality, in which they and other agents gather
and process information and make decisions based on the results. What
we are now doing in quantum information science is getting beyond
information processing that occurs entirely within the effective
reality of our everyday experience, even though it uses the
structures of quantum systems; instead we are doing rudimentary
information processing part of which occurs in the quantum world,
where the realism of everyday experience doesn't apply.  The power of
quantum information lies in the fact that it allows us to escape the
constraints of realistic classical information.  This is something a
dog can't do, so it might be said that just as we are beginning to
understand the complex operations of our own genome, we are also
leaving dogs behind for the first time.

\paratwo
There is a misconception about applying the conclusions of quantum
mech\-an\-ics---that the properties of microscopic systems do not
have objectively real values, loosely stated as there being no
objectivity at the microscopic level---to the wider world around us.
The founders of quantum mechanics---Bohr, Heisenberg, and
Pauli---sometimes hinted that their thinking about quantum mechanics
could be applied to cultural, political, historical, and social
questions.  Postmodernists can make the same mistake in a different
way: to the extent that they pay any attention to quantum mechanics,
they might look at the subjective features of quantum mechanics and
say that cultural, political, historical, and social questions
inherit the same subjectivity.  This is nonsense.  It is, ironically,
the basic mistake of reductionism---thinking that what occurs at
lower levels in the hierarchy of our description of the world is
happening at higher levels, when in fact the microscopic, quantum
level provides the effective, realistic structure in which the higher
levels operate.  The objectivity of events is an emergent property,
which applies in the effective reality, but this doesn't make them
any less objective.  The theory provides the stage of an emergent
effective reality, and culture, politics, history, and sociology act
on that stage.

\topic{The four questions.} There are four questions that should be
addressed in thinking about any interpretation of quantum mechanics.

{\parindent=2\basicindent
\item{$\bullet$}{\bf Probabilities.}  What is the nature of
probabilities (ignorance or Bayesian probabilities vs.\ frequentist
or ensemble probabilities)?  In particular, what is the nature of
quantum probabilities?

\item{$\bullet$}{\bf Ontology vs.\ epistemology.}  Does the
interpretation posit an ontology (underlying objective reality), or
is it wholly epistemological?  If there is an ontology, what is it?

\item{$\bullet$}{\bf Which basis and which alternative?}  How does
the interpretation explain which basis (i.e., which observable) is to
be used and how a particular alternative is actualized within that
basis?

\item{$\bullet$}{\bf Classical reality.} How does the interpretation
explain the world we observe around us?  Does its ontology aid in
this explanation?
\vskip0pt}

\paraone
For theories that go beyond quantum mechanics, we should include a
fifth question:

{\parindent=2\basicindent
\item{$\bullet$}{\bf Different predictions.} How do the the
statistical predictions of the new theory differ from the predictions
of quantum mechanics?  At V\"axj\"o in 2001 June, Lucien Hardy
suggested to me that this fifth question should be expanded to
something like:  How much can the theory be tweaked so as to provide
a range of alternatives to quantum mechanics? Such theories are
extremely valuable even if they turn out to be wrong, because they
provide a way of getting at which features are special to quantum
mechanics and which are only incidental.
\vskip0pt}

\paraone
For the Bayesian interpretation outlined in Sec.~3, the answers to
these questions---or at least the hope for an answer---should be
clear. It is worth emphasizing that the Bayesian interpretation
places actualization outside its provenance.  The reason for
emphasizing this point is that much pointless wrangling over
interpretations comes from repeated accusations that an
interpretation doesn't deal with actualization.  It is best to
explicitly eschew actualization if your interpretation doesn't deal
with it.  Then, though it is a perhaps devastating criticism that your
interpretation doesn't deal with actualization, it is a criticism
that only needs to be made once.

\topic{Other interpretations.}

\subtopic{a.}{Copenhagen interpretations.}  The Copenhagen
interpretation has acquired so much baggage---classical apparatuses,
classical language, quantum/classical cuts, necessity of a classical
world, complementarity, uncertainty-principle limits, forbidden
questions, Bohrian obscurity---that it is next to impossible to sort
out what it is.  At this stage in history, it is probably best just
to wipe the slate clean and give the Copenhagen interpretation a new,
more informative name that highlights its essential feature. The {\it
information-based interpretation}, with its insistence that quantum
states are states of knowledge, {\it is\/} the new Copenhagen
interpretation.

\subtopic{b.}{Ensemble interpretations.}  These aren't really
interpretations.  They are misconceptions about probabilities that
are taken directly over into quantum mechanics because quantum
mechanics necessarily deals with probabilities.  If you believe in an
ensemble interpretation, you need first to get your notion of
probabilities straight before proceeding to quantum mechanics.  Again
quoting E.~T. Jaynes (1986a):

{\threenarrow
We think it unlikely that the role of probability in quantum theory
will be understood until it is generally understood in classical theory
and in applications outside of physics.  Indeed, our fifty-year-old
bemusement over the notion of state reduction in the quantum-mechanical
theory of measurement need not surprise us when we note that today,
in all applications of probability theory, basically the same controversy
rages over whether our probabilities represent real situations, or only
incomplete human knowledge.
\vskip0pt}

\paratwo
The traditional ensemble interpretation, mainly encountered in old
quantum mechanics texts, means the idea that the wave function must
be interpreted as describing a {\it real\/} ensemble of identical
systems.  It has fallen out of favor because we apply the wave
function (more generally, the density operator) routinely (in quantum
optics, for example) to individual systems subjected to repeated
measurements.  But the real problem is that it takes a misconception
about probabilities---that they can't be applied to single cases and
thus must be referred to a real ensemble---and infects quantum
mechanics with the same misconception.  The right approach, as Jaynes
says, is to get the interpretation of probabilities straight before
proceeding to quantum mechanics.

\paratwo
What about hypothetical ensembles?  Here the main candidate is the
Hartle (1968a) approach to getting quantum probabilities as limiting
frequencies.  Hartle argues from the perspective of a (mathematical)
frequentist who believes that probabilistic statements acquire
meaning as limiting frequencies in an infinite ensemble.  He attempts
to show, by taking limits as the number of systems goes to $\infty$,
that an infinite product of a given state is an eigenstate of the
frequency operator, with eigenvalue given by the usual quantum
probability.  He uses the eigenstate hypothesis to conclude that a
measurement of the frequency operator must yield the quantum
probability, thus establishing the quantum probability law as a
consequence of the weaker eigenstate hypothesis.  In this quantum
derivation, one does have an advantage over trying to get classical
probabilities as limiting frequencies, because the inner-product
structure of Hilbert space provides a measure.  Nonetheless, one
still has the probability-one problem: even though an
infinite-product state is an eigenstate of the frequency operator,
this doesn't mean that the limiting frequency occurs with certainty
(i.e., the eigenstate hypothesis fails for continuously infinite
sets).  Thus one can't escape the need to have a pre{\"e}xisting
notion of probability to interpret that being an eigenstate of the
frequency operator means that the eigenvalue occurs with
probability-one.  Furthermore, one still has the problem of making
quantitative statements about finite (actual) ensembles, since each
finite ensemble will require yet another infinite hypothetical
ensemble.

\paratwo
Note that Zurek's (1998a) objection to the Hartle argument misses the
mark.  Zurek contends that the frequency operator is a collective
observable whose measurement has nothing to do with frequencies of
``events'' for the individual systems (I believe this was Chris's
original objection).  Zurek's conclusion applies to a direct
measurement of the frequency operator on the joint system. The result
of such a measurement is a particular frequency, and the measurement
projects the joint system into the {\it subspace\/} corresponding to
that frequency.  In such a measurement there are no measurement
results for the individual systems: it isn't known how the individual
systems are ordered so as to give rise to the observed frequency.
This is not, however, the only way to measure the frequency operator.
Suppose one measures the individual systems in the relevant basis; in
this case, if the state of the system is a frequency eigenstate, but
not necessarily a product state in the relevant basis, then the
frequency constructed from the measurement results will definitely be
the frequency eigenvalue.  Measurements on the individual systems
provide more information than a direct measurement of the frequency
operator---they give an ordered sequence of results, not just the
frequency---but this should not obscure the fact that the frequency
operator has a direct connection to frequencies of ``events'' on the
individual systems. Formally, what is going on is that the frequency
operator commutes with the product observable for the joint system;
measuring the product observable removes the degeneracies in the
frequency operator, thus giving more information than a direct
measurement of the frequency operator, but certainly providing a
measurement of the frequency operator.

\subtopic{c.}{Consistent-histories and decoherent-histories
interpretations.}  Consistent histories (Om\-n\`es 1992a) and decoherent
histories generalize the third question from being about which basis
and which alternative in a basis to being about about which set of
histories and which history within the set.  As such, they are not
interpretations so much as useful and instructive generalizations of
the framework in which interpretations are considered.  They
generalize the incompatible bases that go with different sets of
commuting observables to the different, incompatible sets of
consistent histories.

\paratwo
Consistent- and decoherent-histories interpretations don't provide
compelling answers to any of the four questions, particularly the
third, which is just generalized from bases to histories. The
consistent historians, Griffiths and Omn\`es, seem to grant
simultaneous existence to all the incompatible sets of consistent
histories and to hold that one history within each of the sets is
realized.  They explicitly eschew the need to explain actualization,
and they don't seem to care whether all the sets of consistent
histories correspond to worlds like our classical experience, being
content to know that some set of consistent histories corresponds to
a world like ours.  The decoherent historians, Gell-Mann and Hartle,
originally thought that decoherence would be sufficient to restrict
decoherent histories to those that match our experience.  When it
didn't, they were left a bit out at sea, with no clear answer to any
of the questions.

\subtopic{d.}{Realistic, deterministic, causal interpretations. The
Bohm interpretation.}  In Bohmian mechanics, the phase of the wave
function defines trajectories for particle position; these
trajectories obey a Hamilton-Jacobi equation that is modified from
the classical equation by the addition of a ``quantum potential''
that is determined by the magnitude of the wave function.  This is
just a re-write of wave mechanics.   The Bohm {\it interpretation\/}
promotes particle position to ontological status as the (hidden)
reality underlying quantum mechanics, with the absolute square of the
wave function giving probabilities for particle position.

\paratwo
The interpretation of probabilities presents a problem for the Bohm
interpretation.  As discussed above, the natural probability
interpretation for hidden-variable theories is the Bayesian
interpretation, but then you have to explain how probabilities that
are states of mind can push particles around via the quantum
potential.  A frequentist (ensemble) interpretation has the same
problem: how is it that other members of the ensemble affect the
motion of a particle through the action of the quantum potential?
Notice that there are problems whether the ensemble is thought to be
real or a theoretical construct, although they are different in the
two cases. These problems have led Bohmians to speculate about how
the ``actual'' probabilities might ``relax'' to the quantum
probabilities, much as in relaxation to thermodynamic equilibrium,
but these efforts have not been very convincing and, from a Bayesian
perspective, are wholly misguided anyway.  The best strategy for a
Bohmian might be to adopt the idea that the probabilities are
objective propensities; then they could push particles around.

\paratwo
Though the ontology of the Bohm interpretation is superficially
attractive, the realistic particle trajectories in the case of many
interacting particles are highly nonlocal (they must be nonlocal for
Bohmian mechanics to agree with the predictions of quantum mechanics
for entangled states).  The nonlocal influences will be along
spacelike separations in a relativistic version of the theory and
thus acausal. The whole world is thus connected together in an
acausal web.  This is Eastern reality stretched to nightmarish
proportions---a reality that is completely disconnected from the
reality of our everyday perceptions. Though this picture is inherent
in the Bohmian reality, it remained an abstraction whose impact was
not fully appreciated till the work of Englert, Scully, S\"ussmann,
and Walther (1992a) [for me the most accessible version of these
ideas has been given by Aharonov and Vaidman (1996a)]. They showed
conclusively, for a simple example of two interacting systems, that
the nonlocality of Bohmian mechanics means that the Bohmian
trajectories have nothing whatsoever to do with the reality of
everyday life.

\paratwo
Now the Bohmian has a problem.  The initially attractive ontology
turns out to be useless for understanding everyday experience, so he
will have to do as much hard work as anybody else---probably more to
give an actual Bohmian account---to construct an emergent reality for
the macroscopic world.  In fact, he must carefully exclude the
nonlocal, acausal aspects of the ontology from the emergent reality.
Far from being an aid, the underlying trajectories are a serious
nuisance, for he has to wipe out any trace of them and substitute in
their place something that looks like the macroscopic world.  In the
Bayesian interpretation the problem is how to get an effective
reality to emerge from a microscopic theory that doesn't have the
objective properties of our everyday experience, whereas in the Bohm
interpretation the problem is how to get a local, causal reality to
emerge from the nonlocal web of Bohmian trajectories.

\paratwo
The Bohm interpretation accords position a special ontological status
because our perceptions are so closely connected to location, but
once one realizes that the Bohmian trajectories are irrelevant to our
perceptions, the choice of position begins to smell like an arbitrary
choice.  Bohm-type theories can be constructed in other bases, e.g.,
the momentum representation (Brown and Hiley 2001a).  As a result,
Bohmian mechanics is far from unique as a foundation for a realistic
interpretation, and the Bohm interpretation becomes just one
possibility out of many.

\paratwo
The Bohmian trajectories are an example of what I call a {\it
gratuitous reality\/}: they are pasted onto the theory because of an
ingrained need for an ontology and the resulting habit of
na{\"\i}vely assigning reality to mathematical objects in the theory,
but they are irrelevant to constructing and understanding the reality
of our everyday experience.  The defenses of Bohmian mechanics against
the attack of Englert {\it et al.}\ (Englert 1992a) have focused on
pointing out that the statistical prediction of Bohmian mechanics
agree with those of quantum theory, but this misses the point.  The
point is that Englert {\it et al.}\ demonstrate that the ontology
of Bohmian trajectories is a gratuitous reality that helps not at all
in constructing the emergent effective reality of everyday experience.

\subtopic{e.}{Many-worlds interpretations} (Vaidman 1999a, Wallace
2001a).  Find wave-function collapse distasteful, so banish it.  Make
the most na{\"\i}ve realistic assumption: declare the wave function
to be objectively real, and then---damn the torpedos!---plow straight
ahead, undaunted by the mind-boggling consequences (well, actually,
revel in them a bit; they make good press and great science fiction).
That's the spirit of many-worlds interpretations.  If you want to be
perceived as a deep thinker without actually having to do any
thinking, this is your interpretation.

\paratwo
The many-worlds interpretation posits a single, objective wave
function for the entire Universe.  Superpositions within some
(arbitrarily chosen) basis correspond to branchings into different
worlds, all of which are actualized.  Fundamentally there are no
probabilities in the theory, since all possibilities are actualized,
but for subsystems where quantum probabilities are used to make
predictions, there are attempts to derive those probabilities
objectively from the Universal wave function.  The world of everyday
experience is the way it is because that's the way it is (thanks,
Walter) on the branch we're on.

\paratwo
Talk about a gratuitous reality! This is the granddaddy of them all.
To avoid a physical wave-function collapse, the many-worlds
interpretation pastes onto quantum theory an unobserved and
unobservable infinity of worlds that explains nothing about---it
simply posits---the world we actually live in.  As far as I can see,
many-worlds interpretations provide no insight into any of the four
questions, particularly the third, because the branching occurs in a
basis chosen for no other reason than to give worlds that mirror our
our macroscopic experience (Vaidman 1998a, Steane 2000a). Many-worlds
interpretations, far from providing deep insights into the nature of
quantum reality, are really founded on an inability to imagine
anything except a na{\"\i}ve realistic world view, which is to apply
in each branch.

\paratwo
Let's not be be too negative.  There are deep thinkers who work on
the many-worlds interpretation, and it has led to important insights,
notably David Deutsch's idea of the quantum parallelism that is
thought to provide the power behind quantum
computers.\footnote*{\baselineskip=10.5pt\eightrm To be fair, one
should note that quantum parallelism might be a misleading way to
think about quantum computation (Steane 2000a) and also that the
other great advances in quantum computation---the Shor factoring
algorithm, the Cirac-Zoller proposal for a semirealistic quantum
computing system involving trapped ions, and the Shor-Steane
realization that quantum error correction is possible---don't seem to
have been motivated by a many-worlds perspective.\vfil}  Still, I think
that much of the current popularity of many-worlds is an example of
what Richard Dawkins calls an ``argument from personal incredulity'':
``I thought about it for a little while and couldn't figure out how
to reconcile wave-function collapse with the Schr\"odinger equation,
so I signed up with the many-worlders.''

\paratwo
The attitude of many physicists might be summarized in the following
way.  Schr\"o\-dinger formulated his equation, which is the essence
of quantum mechanics; Born introduced the probability rule, and then
von Neumann tacked on his ugly ``collapse of the wave function,''
which interrupts the beautiful flow of Schr\"odinger evolution.  We
are taught that there are two kinds of evolution in quantum
mechanics: the pristine evolution governed by Schr\"odinger's
differential equation, and the {\it ad hoc\/} and unjustified
collapse introduced by von Neumann.  Phrased in these ways, our job
as physicists is clear: find a way to get rid of the collapse.  The
Bayesian interpretation offers a useful corrective.  From the
Bayesian view the apparatus of quantum probabilities, including the
updating that results from observation and that goes under the name
of collapse, lies at the very heart of quantum mechanics.  It's not
the ugly part; it's the main part of the theory and certainly a
beautiful part.  Schr\"odinger's equation tells us how to update
maximal information when a system is isolated from the rest of the
world.  That's important, too, as the place where physical laws find
expression and thus perhaps as the objective part of the theory, but
certainly not more important than the extraordinarily successfully
quantum prescription for reasoning when ``maximal information is not
complete.''

\subtopic{f.}{Other interpretations.}  Comments on other
interpretations will be added as I learn enough about them to make
the comments sensible.

\topic{Actualization and indeterminism vs.~determinism.}  Which is
better?  An indeterministic world is certainly more interesting than
a deterministic one, whose history is just a progressive revelation
of the initial conditions.  Moreover, if the world is intrinsically
indeterministic, it means that the problem of actualization must lie
outside the province of our theories. Omn\`es (1992a) provides a
powerful and poetic account of this:

{\twonarrow
Perhaps the best way to see what it is all about is to consider what
would happen if a theory were able to offer a detailed mechanism for
actualization.  This is, after all, what the advocates of hidden
variables are asking for.  It would mean that everything is deeply
determined.  The evolution of the universe would be nothing but a
long reading of its initial state.  Moreover, nothing would
distinguish reality from theory, the latter being an exact copy of
the former.  More properly, nothing would distinguish reality from
logos, the time-changing from the timeless.  Time itself would be an
illusion, just a convenient ordering index in the theory. \dots\
Physics is not a complete explanation of reality, which would be its
insane reduction to pure unchanging mathematics. It is a {\eightit
representation\/} of reality that does not cross the threshold of
actuality. \dots\ It is wonderful how quantum mechanics succeeds in
giving such a precise and, as of now, such an encompassing
description of reality, while avoiding the risk of an
overdeterministic insanity.  It does it because it is probabilistic
in an essential way.  This is not an accident, nor a blemish to be
cured, since probability was found to be an intrinsic building block
of logic long before reappearing as an expression of ignorance, as
empirical probabilities.  Moreover, and this is peculiar to quantum
mechanics, theory ceases to be identical with reality at their
ultimate encounter, precisely when potentiality becomes actuality.
This is why one may legitimately consider that the inability of
quantum mechanics to account for actuality is not a problem nor a
flaw, but the best mark of its unprecedented success.
\vskip0pt}

\topic{A final note.} Why even a statistical order in an
indeterministic world?  The Bayesian version of this question might
be the following: Why should an intrinsically indeterministic, but
supposedly complete theory---i.e., one in which maximal information
is not complete---supply a unique rule for assigning probabilities in
the case of maximal information?  One can argue that it would be very
unsatisfactory to have an indeterministic, but complete theory that
failed to supply such a probability rule in the case of maximal
information, there being no place outside the theory---it's
complete!---to look for the rule.  Unsatisfactory though it might be,
however, it is hard to see why all theories would have this property.

\paraone
Quantum mechanics, of course, obliges with the standard quantum
probability rule, which follows just from applying Dutch-book
consistency to probabilities that are faithful (i.e., noncontextual)
to the Hilbert-space structure of quantum questions.  The fact that
the Hilbert-space structure so tightly constrains quantum
probabilities that it gives a unique rule in the case of maximal
information is certainly trying to tell us something very basic.
Perhaps this tight constraint is the key feature of quantum
mechanics, indeed the key to unlocking the ontology of quantum
mechanics.  In the Bayesian interpretation, there is a quantum
reality that we are describing, in the best way possible, using the
rules of quantum mechanics, but that reality is more subtle than the
realist's direct, one-to-one correspondence between the theory (our
model) and reality (what's out there).  Perhaps the surprisingly
constrained quantum probability rule is the first element of this
Bayesian reality.  The emergent effective reality would then be the
second aspect.  Demonstrating the emergence and consistency of the
effective reality is a long-term goal of the Bayesian~program.

\bigskip
\bigskip

\leftline{{\bf REFERENCES} (completely incomplete)}

\medskip
\refno=0
\ref
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do not show the Bohmian particle position,'' in |Bohmian Mechanics
and Quantum Theory: An Appraisal||, edited by J.~T. Cushing, A.~Fine,
and S.~Goldstein (Kluwer Academic, Dordrecht, The Netherlands),
pp.~141--154 (Proceedings of a Conference on Quantum Theory Without
Observers, Bielefeld, Germany, July 24--28, 1995).|
\ref
M.~R. Brown and B.~J. Hiley, ``Schr\"odinger revisited: An algebraic
approach'' |||({\tt arXiv.org e-print quant-ph/0005026}).|
\ref
C.~M. Caves 1994a, ``Information, entropy, and chaos,'' in |Physical Origins
of Time Asymmetry,|| edited by J.~J. Halliwell, J.~P\'erez-Mercader, and
W.~H. Zurek (Cambridge University Press, Cambridge, England), pages~47--89
(Proceedings of the NATO Advanced Research Workshop on the Physical
Origins of Time Asymmetry, Mazag{\'o}n, Huelva, Spain,
September~29--October~4, 1991).|
\ref
C.~M. Caves and R.~Schack 1997a, ``Unpredictability, information, and chaos,''
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\ref
C.~M. Caves, C.~A. Fuchs, and R.~Schack 2001a, ``Unknown quantum states:
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Journal of Physics|| ({\tt arXiv.org e-print quant-ph/0104088}).|
\ref
C.~M. Caves, C.~A. Fuchs, and R.~Schack 2001b, ``Making good sense
of quantum probabilities''||| ({\tt arXiv.org e-print quant-ph/0106133}).|
\ref
B.-G.~Englert, M.~O. Scully, G.~S{\"u}ssmann, and H.~Walther 1992a,
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|47a|, 1175--1186.|
\ref
R.~N. Giere 1973a, ``Objective single-case probabilities and the foundations
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Amsterdam), pp.~467--483.|
\ref
J.~B. Hartle 1968a, ``Quantum mechanics of individual systems,''
|American Journal of Physics |36|, 704--712.|
\ref
E.~T. Jaynes 1957a, ``Information Theory and Statistical Mechanics,''
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\ref
E.~T. Jaynes 1957b, ``Information Theory and Statistical Mechanics. II,''
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\ref
E.~T. Jaynes 1986a, ``Predictive statistical mechanics,'' |Frontiers of
Nonequilibrium Statistical Physics||, edited by G.~T. Moore and M.~O.
Scully (Plenum Press, New York), pp.~33--55 (Proceedings of a NATO
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Physics, Santa Fe, New Mexico, June 3--16, 1984).|
\ref
E.~T. Jaynes 1986b, ``Monkeys, kangaroos, and $N$,'' in |Maximum
Entropy and Bayesian Methods in Applied Statistics||, edited by J.~H.
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pages~26--58 (Proceedings of the Fourth Maximum Entropy Workshop,
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\ref
E.~T. Jaynes 1990a, ``Probability in quantum theory,'' in |Complexity,
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J.~L. Lebowitz 1999a, ``Statistical mechanics: A selective review of two
central issues,'' |Reviews of Modern Physics\/ |71|, S346--S357.|
\ref
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|Reviews of Modern Physics\/ |64|, 339--382.|
\ref
A.~M. Steane 2000a, ``A quantum computer only needs one universe,''
||| unpublished ({\tt arXiv.org e-print quant-ph/0003084}).|
\ref
L.~Vaidman 1999a, ``On schizophrenic experiences of the neutron or why
we should believe in the many-worlds interpretation of quantum theory,''
|International Studies in the Philosophy of Science\/ |12|, 245--261.|
\ref
D.~Wallace 2001a, ``Worlds in the {E}verett interpretation''|||
({\tt arXiv.org e-print quant-ph/0103092}).|
\ref
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\vskip 36pt plus 12pt minus 12pt
\line{\hglue 1truein\hrulefill\hglue1truein}
\vskip 36pt plus 12pt minus 12pt

My two heroes of Bayesian probabilities are Bruno de Finetti and,
especially, Edwin T.~Jaynes, the former a fascist and the latter a
very conservative Republican.  I certainly don't equate the two, but
this does give me pause, though it probably shouldn't.  It just
confirms that a person's politics are a very poor guide to a his/her
value as a scientist or even as a person.  To those concerned, Fuchs,
Schack, and I offer Bayesian role models from other parts of the
political spectrum.

\bye
