In English, quantifiers are words like “all”, “one”, or “some” that indicate how broadly true the quantified clause is. Formal logic has adopted symbols for such words, namely “all”, “exactly one”, and “some” (in the sense of “at least one”). Probability theory offers us a reason to incorporate another symbol, with a meaning along the lines of “some, and I have a sense of how to find them”.

I want to present the notation, justify it as a coherent and uniform extension of classic logical practice, and use it to explain something I understood much less clearly before: the nature of the distinction between frequentist and Bayesian statistics, specifically on the example of comparing confidence intervals to credibility intervals.

## Contents

## Formulae

*From* Logic *to* Games

Logic can be embedded into game theory. A (closed) logical formula with quantifiers (in prenex form) can be taken to be a two-player zero-sum game where I choose a value for every exists-quantified variable and my adversary chooses a value for every forall-quantified variable. I win if the quantifier-free part ends up being true, and the adversary wins if the quantifier-free part ends up being false. The order of making choices and the information available to each player has to follow quantifier scope. In this embedding, we call a quantified formula “True” if I win this game under perfect play, and “False” if the adversary does.^{1}

For example, the formula \[ \forall k \in \Z. \exists n \in \Z. n > k \] is the game

- Adversary chooses an integer \(k\) (not knowing \(n\))
- I choose an integer \(n\) (knowing \(k\))
- I win if \(n > k\), adversary wins if not \(n > k\).

Since we are playing over the integers, this game is one I can always win, which is the same as saying that this formula is true.

The order of quantification, or in other words the order of choices in the game,^{2} matters—the formula \[ \exists n \in \Z. \forall k \in \Z. n > k \] is the game

- I choose an integer \(n\) (not knowing \(k\))
- Adversary chooses an integer \(k\) (knowing \(n\))
- I win if \(n > k\), adversary wins if not \(n > k\),

which under correct play the adversary can always win. (To wit, the formula is false). I will not bore you with the argument that this embedding is exact (it proceeds by induction on the number of quantifiers in the formula).

*The* Random Player

So far, so good. What does this have to do with probability, you ask? Well, game theorists have noticed that it can be useful to allow a different kind of player in their games—one that behaves randomly instead of trying to optimize some payoff like the other players. This random player is often called Nature (exercise for the reader: why does game theory never need more than one random player?) and the probability distribution(s) governing Nature’s behavior are taken to be part of the definition of the game (just like the legal move sets and objective functions governing the behavior of the normal players).

For instance, the card game Bridge fits into this framework: at the beginning, Nature makes a move dealing the cards (which is traditionally taken to be a uniformly random choice among all possible deals), then each of the four players observes a portion of the deal (to wit, their own hand), and they start making moves according to the (now deterministic) rules of Bridge. After the bidding, three of the players observe some more of the deal (the dummy’s hand), and then continue making strategic moves.

### Back *to* Logic

We can bring the Nature player back to logic. I propose using the symbol \(\st\) (a backwards letter ‘S’, for Stochastic) as a quantifier for randomly chosen variables.^{3} Just like you have to say what set an \(\exists\) or a \(\forall\) are drawn from, you have to say what probability distribution an \(\st\) is drawn from. The usual quantifier order and scope rules apply.

As to semantics, let us say that a (probabilistic) formula is “true with probability \(\geq p\)” if I win the corresponding two-and-a-half player zero-sum game (the random player is counted as half) with probability \(\geq p\) under optimal play.

For example, calling a flip of a fair coin looks like \[ \exists k \in \{\textrm{H},\textrm{T}\}. \st x \sim \textrm{uniform}\{\textrm{H},\textrm{T}\}. x = k, \] which is the game

- I choose \(\textrm{H}\) or \(\textrm{T}\)
- Nature flips a (fair) coin
- I win if I chose what Nature flipped.

This formula can reasonably be taken as “true with probability 50%”.

In the case of fair coins, it doesn’t matter who calls it. The formula \[ \forall k \in \{\textrm{H},\textrm{T}\}. \st x \sim \textrm{uniform}\{\textrm{H},\textrm{T}\}. x = k, \] which is the game

- The adversary chooses \(\textrm{H}\) or \(\textrm{T}\)
- Nature flips a (fair) coin
- I win if the adversary chose what Nature flipped,

is also “true with probability 50%”.

Observe that each of these is a very different game from the ones where the chooser gets to see the result of Nature’s flip before making their choice: \[ \st x \sim \textrm{uniform}\{\textrm{H},\textrm{T}\}. \exists k \in \{\textrm{H},\textrm{T}\}. x = k \] is the game

- Nature flips a coin
- I choose \(\textrm{H}\) or \(\textrm{T}\) (knowing the result)
- I win if I chose what Nature flipped.

I can always win this game, so this formula is true (with probability 100%).

Conversely, \[ \st x \sim \textrm{uniform}\{\textrm{H},\textrm{T}\}. \forall k \in \{\textrm{H},\textrm{T}\}. x = k \] is the game

- Nature flips a coin
- The adversary chooses \(\textrm{H}\) or \(\textrm{T}\) (knowing the result)
- I win if the adversary chose what Nature flipped.

The adversary can always make me lose this game, so this formula is false (i.e., true with probability 0%).

I hope this example has convinced you that when probability occurs in logic, one must take the same care about scoping quantifiers as one does when mixing \(\exists\) with \(\forall\).

### Summary

- \(\forall x \in X\) means the adversary chooses \(x\) from the set \(X\).
- \(\exists x \in X\) means I choose \(x\) from the set \(X\).
- \(\st x \sim P\) means an impartial player chooses \(x\) according to the probability distribution \(P\).

## Statistics

Now what does this have to do with Bayesian and frequentist statistics? Bayesian statistics always reasons about probability distributions, but frequentist statistics makes definitions with foralls in them. In other words, Bayesians play solitaire against Nature, whereas frequentists take on strategic adversaries. This means that frequentism is both harder and more pessimistic than Bayesianism.^{4}

I will illustrate by comparing confidence intervals and credible intervals, the textbook frequentist and Bayesian, respectively, approaches to the interval estimation problem. Interval estimates are the sort of statistics one sees in the news: there is some number of interest, such as the proportion of voters that lean towards one or another political party in an upcoming election; some evidence about this number is gathered, such as polling a random fraction of those voters; some computation is done; and an interval is announced, that is purported to contain the number of interest with some degree of confidence. As we shall soon see, the frequentist and Bayesian notions of “confidence” are actually quite different; but that is the basic set up.

### Frequentist

The standard frequentist tool for inverval estimation is the confidence interval. More generally, one can define confidence regions for set estimation of parameters that have other structure than single numbers. To give the construction, we start with a little notation:

- Suppose the item we are interested in is drawn from a
*parameter space*\(A\).

In a simple rendition of the political example, this would be \([0,1]\), representing all possible fractions of voters preferring one particular party over the other.

- Suppose the experiment we conduct produces results in some
*observation space*\(B\).

In the example, the experiment could be a poll, and \(B\) could be the space of possible results.

- We model the influence the parameter exerts on the observations as a function from \(A\) to \(B\), which we take to be random because we assume the observations are also affected by other influences. This function is traditionally called the
*likelihood*^{5}, and can also be seen as a deterministic function from \(A\) to probability distributions over \(B\): \[\textrm{likelihood}: A \to \Pr(B).\]

In the polling example, the randomness of the \(\textrm{likelihood}\) would include, for instance, our choice of whom to poll.

The definition of the likelihood function is the place where our qualitative modeling assumptions turn into analyzable objects that we can do mathematics with. Now,

- a
*95% confidence interval*is a (deterministic) procedure for going from an observation to a set of possible parameters \[\textrm{conf_int}: B \to \mathcal P(A)\] such that \[ \forall a \in A. \st b \sim \textrm{likelihood}(a). a \in \textrm{conf_int}(b) \] with probability \(\geq\) 95%, where the probability is taken over the randomness of the likelihood.

In words, this formula means that for any \(a \in A\) (chosen to be as difficult as possible), at least 95% of the \(b\) drawn according to \(\textrm{likelihood}(a)\) are such that the original \(a\) is inside the confidence interval computed from the given \(b\). In the political example, this translates to the following requirement on the confidence interval procedure: whatever the true leanings of the population may be, at least 95% of possible polls conducted according to our design must lead, via our \(\textrm{conf_int}\), to intervals that contain those true leanings.

The thing to remember is that \(\textrm{conf_int}\) does not depend on \(a\). As a game, finding confidence intervals for a given problem looks like

- I choose a function \(\textrm{conf_int}: B \to \mathcal P(A)\)
- The adversary chooses an \(a\) (knowing \(\textrm{conf_int}\))
- Nature chooses a \(b\), given the adversary’s \(a\), according to the \(\textrm{likelihood}\)
- I win if \(a \in \textrm{conf_int}(b)\).

The design task when choosing \(\textrm{conf_int}\) is usually to minimize the cardinality of the sets that it returns, subject to the above game being won with probability at least 95%.

### Bayesian

The standard Bayesian tool for inverval estimation is the credible interval. In general, a *95% credible interval* for some probability distribution \(\pi\) on some set \(A\) is a set of \(S \subset A\) such that \(\st a \sim \pi. a \in S\) with probability \(\geq\) 95%, where the probability is taken over the given distribution. The design task is usually to minimize the cardinality of the set.

This applies to the interval estimation setting as follows:

- Start with a parameter space \(A\), an observation space \(B\), and a \(\textrm{likelihood}: A \to \Pr(B)\) as before.

In fact, the choice of \(\textrm{likelihood}\) function for any given problem is often common between frequent and Bayesian analyses.

- We model our existing, pre-experiment knowledge about our problem as a probability distribution \(\pi\) on \(A\), which is called the
*prior*.

In the political example, the prior might be the uniform distribution on the interval \([0,1]\) if we modeled the problem assuming relatively little knowledge about politics; or it might be a Gaussian distribution with mean 50% and standard deviation 1 percentage point, if we modeled the problem assuming pretty strong external evidence that the election was going to be close. Choice of prior is important—different priors mathematically encode different problems, so yield different answers.

- Given a prior and a likelihood, Bayes’ rule gives the procedure for finding
*posteriors*conditioned on possible observations \(b \in B\), which are also probability distributions on \(A\). The application of Bayes’ rule can be viewed as a function, \[\textrm{posterior}: B \to \Pr(A),\] which updates our prior distribution to reflect learning the information \(b\).

In the political example, the posterior distribution describes the state of our knowledge about the coming election after digesting the poll results. It’s called the posterior (as opposed to the prior) because it is the distribution post-experiment.

- To get an interval estimation procedure, we can compose computing posteriors with choosing 95% credible intervals to get \[\textrm{cred_int}: B \to \mathcal P(A).\] This \(\textrm{cred_int}\) then has the property that \[\st a \sim \pi. \st b \sim \textrm{likelihood}(a). a \in \textrm{cred_int}(b)\] with probability \(\geq\) 95%, where the probability is taken over
*the prior and the likelihood*.

In words, this formula means that at least 95% of the time, when \(a\) is drawn according to \(\pi\) and \(b\) is drawn according to \(\textrm{likelihood}(a)\), it turns out that \(a\) is inside the credible interval computed from \(b\). The translation to the political example is direct: in 95% of leaning-poll pairs, where the population leanings are drawn according to the prior and the poll results are drawn according to the likelihood, the credible interval computed from the poll result will contain the true leaning of the population.

One way to render selection of credible intervals as a game is

- I choose a function \(\textrm{cred_int}: B \to \mathcal P(A)\)
- Nature chooses \(a\) according to the prior
- Nature chooses \(b\) given \(a\) according to the likelihood
- I win if \(a \in \textrm{cred_int}(b)\).

The design task when choosing \(\textrm{cred_int}\) is usually to minimize the cardinality of the sets that it returns, subject to the above game being won with probability at least 95%.

### Comparison

Look at these formulae side by side. A 95% confidence interval for a given parameter space \(A\) and a given \(\textrm{likelihood}: A \to \Pr(B)\) is a function from \(B\) to \(\mathcal P(A)\) such that \[ \forall a \in A. \st b \sim \textrm{likelihood}(a). a \in \textrm{conf_int}_{A,\textrm{likelihood}}(b) \] with probability \(\geq\) 95%.

A 95% credibility interval for a given prior \(\pi\) over \(A\) and a given \(\textrm{likelihood}: A \to \Pr(B)\) is a function from \(B\) to \(\mathcal P(A)\) such that \[\st a \sim \pi. \st b \sim \textrm{likelihood}(a). a \in \textrm{cred_int}_{\pi,\textrm{likelihood}}(b)\] with probability \(\geq\) 95%.

The difference between these two formulations is that the frequentist formula has a \(\forall a \in A\) where the Bayesian one has a \(\st a \sim \pi\). In other words, where the frequentist analysis assumes an adversary, the Bayesian one postulates a fixed (probabilistic) behavior. This has several consequences:

Frequentist statistics answer a different kind of question from Bayesian ones.

The frequentist question should be

*askable*in situations where the Bayesian one is not, namely where the information available about possible \(a\)s cannot be captured as a probability distribution.^{6}The Bayesian question should be

*answerable*in situations where the frequentist one is not, because having more than one kind of quantifier always causes trouble. Here I mean mathematically answerable; empirically there are circumstances where a forall is computationally more tractable than a probability distribution.When a situation is modelable in both styles, one would expect the answer to the Bayesian style of question to be more optimistic than the frequentist, because the adversary is assumed to always choose the worst possible \(a\). Sometimes, when the prior encodes more information than we are actually justified in assuming, optimism can lead to incorrect conclusions. Other times, when “all” possibilities admit arbitrarily extraordinary coincidences, pessimism can lead to conclusions so weak as to be paralyzing.

What question, exactly, “the frequentist question” actually is depends very strongly on the details of the game design: where the foralls/adversaries actually go, and in what order the choices are made. For example, it is important that the confidence interval function is to be chosen before the adversary chooses the \(a\) at which to test it.

In practice, the choice of how to encode a given complex statistical situation as a frequentist adversarial game can be just as contentious as the choice of prior in a Bayesian analysis.

## Reflection

A quantifier indicates the “quantity” of things about which something is true. In this sense, all three of the symbols that appear in this essay are quantifiers—\(\exists\) is “at least one”, \(\forall\) is “all”, and \(\st\) is “several”. The game theoretic view, however, exposes a distinction between \(\exists\) and \(\forall\) as opposed to \(\st\). The former two are optimization quantifiers: they define the value they bind by specifying a set of possibilities and an objective function, and assuming the optimal value for that objective is somehow found. \(\exists\) and \(\forall\) differ only in the objective (to make the subsequent formula true or false, respectively), so even in the game theoretic sense they are very similar creatures.

The random choice quantifier \(\st x \sim P\) is different. The \(x\) is not an extremum of anything; it is chosen at random. And yet \(\st\) is also the same as the classical quantifiers, in that it also hides the details of how \(x\) is chosen—this time behind the probability distribution \(P\) (which could be very complicated, and very difficult to actually select a value from computationally).^{7}

I wonder, then, what other complex processes would it be worth making symbols for? One thought is that \(\exists\) takes on a slightly different meaning in intuitionistic rather than classical logic. I’m a bit fuzzy on the details, but I guess it corresponds to computable optimization (rather than the absolute optimization of classical \(\exists\)). Does it make sense to ask for computationally limited optimization? Something like a game where the player is permitted only a polynomial amount of computation after seeing the last move before having to make theirs?

## Notes

The embedding can be generalized to formulae that are not in prenex normal form. Just treat each quantifier as a move by a player whose goal is to make the expression in that quantifier’s scope come out true (for \(\exists\)) or false (for \(\forall\)). The truth is dependent upon the values already chosen by all quantifiers in scope at that point (which values the player knows). I will, however, stick with prenex formulae in the main text, because they are easier to think about.↩

When we say “order of choices”, what we are actually talking about is the information available to a decision maker about the results of other decisions in the game. Chronology is a potent metaphor for capturing one pattern of information flow, namely complete knowledge about choices made “in the past” and complete absence of knowledge about choices that remain to be made “in the future”. Non-chronological information structures are possible, however. For example, in a three-player game, A might make some move, then B might make some move knowing what A did, but then C might have to move knowing what B did but not knowing what A did (except to the extent that it can be inferred from B’s activities).

Logic generally does not try to encode such patterns, perhaps because they tend to make the games even more difficult to solve. One pattern has been recognized by some authors, however: a “branch quantifier” is when the adversary and I are to make “simultaneous” choices, each not knowing what the other has chosen.↩

Colophon: The glyph \(\st\) is the capital reversed S. That letter is called

`LATIN CAPITAL LETTER TONE TWO`

in Unicode (code point 423), and has`Ƨ`

for a numeric HTML entity reference.↩The reason for the name “frequentist” is that this is the kind of statistics one is forced into if one subscribes to the frequentist justification for probability theory. In a nutshell, the frequentist philosophical view is that probability theory legitimately describes only situations that correspond in a reasonable way to repeatable experiments with variable outcomes, where the probabilities are the frequencies (hence the name) of observed results. Probability therefore cannot, on this view, be applied to unique situations such as the true value of some parameter of interest. Given that the parameter is nonetheless unknown, one resorts to reasoning about what one can say for all possible values of the parameter.

Bayesian statistics, in contrast, relies on the more permissive view (now associated with the 18th-century philosopher Thomas Bayes, hence the name) that probability theory is an extension of logic to propositions whose truth is not known with certainty. Under this view, there is nothing wrong with treating things like fixed but unknown parameters probabilistically, so no foralls are necessary.↩

This function is actually a probability distribution over \(B\) conditioned on a value from \(A\). The reason it’s called a “likelihood” and not a “probability” is because in this use case we are interested in its behavior over the space \(A\), with a value in \(B\) held fixed. Holding \(b\) fixed, it measures how good—“likely”—various \(a\) look, but it does not give a probability distribution over \(A\).↩

I stress that this situation is rarer than one might think, because many collections of information are capturable as probability distributions, without requiring appeal to repeated experiments with known mechanisms. In particular, the Bayesian statistics community has derived priors for many problems with the goal to “let the data speak of themselves”—to wit, encode no additional information at all, beyond the modeling assumptions encoded in the \(\textrm{likelihood}\) function. I encourage the reader to look up “uninformative priors” or “reference priors”.↩

Perhaps it should not be surprising that students get confused by frequentist statistics. That field treads the relatively unexplored ground of mixing different kinds of quantifiers in a single theory. Besides game theory, it is the only theory I know about that does so; and since both of them are pretty new, perhaps we haven’t worked out good ways to think about such mixtures, or to teach people to think about such mixtures.↩