The following principles have something in common.

**Conditional Coordination Principle.**

A rational person's credence in a conditional A->B should equal the
ratio of her credence in the corresponding propositions B and A&B;
that is, Cr(A->B) = Cr(B/A) = Cr(B)/Cr(A&B).

**Normative Coordination Principle.**

On the supposition that A is what should be done, a rational agent
should be motivated to do A; that is, very roughly, Des(A/Ought(A))
> 0.5.

**Probability Coordination Principle.**

On the supposition that the chance of A is x, a rational agent
should assign credence x to A; that is, roughly, Cr(A/Ch(A)=x) = x.

**Nomic Coordination Principle.**

On the supposition that it is a law of nature that A, a rational agent
should assign credence 1 to A; that is, Cr(A/L(A)) = 1.

All these principles claim that an agent's attitudes towards a certain
kind of proposition rationally constrain their attitudes towards other
propositions.

Humeans about laws of nature hold that the laws are nothing over
and above the history of occurrent events in the world. Many
anti-Humeans, by contrast, hold that the laws somehow "produce" or
"govern" the occurrent events and thus must be metaphysically prior to
those events. On this picture, the regularities we find in the world
are explained by underlying facts about laws. A common argument
against Humeanism is that Humeans can't account for the explanatory
role of laws: if laws are just regularities, then then laws can't
really *explain* the regularities — so the charge —
since nothing can explain itself.

In discussions of the raven paradox,
it is generally assumed that the (relevant) information gathered from an
observation of a black raven can be regimented into a statement of the
form *Ra & Ba* ('*a* is a raven and *a* is
black'). This is in line with what a lot of "anti-individualist" or
"externalist" philosophers say about the information we acquire
through experience: when we see a black raven, they claim, what we
learn is not a descriptive or general proposition to the effect that
whatever object satisfies such-and-such conditions is a black raven,
but rather a "singular" proposition about a particular object --
we learn that *this very object* is black and a raven. It seems
to me that this singularist doctrine makes it hard to account for many
aspects of confirmation.

Take the usual language of first-order logic from introductory
textbooks, without identity and function symbols. The vast majority of
sentences in this language are satisfied in models with very few
individuals. You even have to make an effort to come up with a sentence
that requires three or four individuals. The task is harder if you
want to come up with a fairly short sentence. So I wonder, for any given number n, what is the shortest
sentences that requires n individuals?

It is widely agreed that conditionalization is not an adequate norm
for the dynamics of self-locating beliefs. There is no agreement on
what the right norms should look like. Many hold that there are no
dynamic norms on self-locating beliefs at all. On that view, an
agent's self-locating beliefs at any time are determined on the basis
of the agent's evidence at that time, irrespective of the earlier
self-locating belief. I want to talk about an alternative approach
that assumes a non-trivial dynamics for self-locating beliefs. The
rough idea is that as time goes by, a belief that it is Sunday should
somehow turn into a belief that it is Monday.

Let's assume that propositional attitudes are not metaphysically
fundamental: if someone has such-and-such beliefs and desires, that is
always due to other, more basic, and ultimately non-intentional
facts. In terms of supervenience: once all non-intentional facts are
settled, all intentional facts are settled as well.

Let's look at the third type of case in which credences can come apart from known chances. Consider the following variation of the Sleeping Beauty problem (a.k.a. "The Absentminded
Driver"):

Next, undermining. Suppose we are testing a model H according to
which the probability that a certain type of coin toss results in
heads is 1/2. On some accounts of physical probability, including
frequency accounts and "best system" accounts, the truth of H is
incompatible with the hypothesis that all tosses of the relevant type
in fact result in heads. So we get a counterexample to simple
formulations of the Principal Principle: on the assumption that H is
true, we know that the outcomes can't be all-heads, even though H
assigns positive probability to all-heads. In such a case, we say that
all-heads is *undermining* for H.

Suppose we are testing statistical models of some physical process
-- a certain type of coin toss, say. One of the models in question
holds that the probability of heads on each toss is 1/2; another holds
that the probability is 1/4. We set up a long run of trials and
observe about 50 percent heads. One would hope that this confirms the
model according to which the probability of heads is 1/2 over the
alternative.

Most programming languages have conditional operators that combine a
(boolean) condition and two singular terms into a singular term. For
example, in Python the expression

'hi' if 2 < 7 else 'hello'

is a singular term whose value is the string 'hi' (because 2 < 7). In
general, the expression