Just review and clarification this time, folks. Dirty hard work. But necessary given the confusion from last post. Time to pump some neurons! Next time we—finally!—get to parameters, models, and all that.
(All the stuff in this series is, in a fuller form, in my new upcoming book, which is tentatively called Logical Probability and Statistics—but I’ve only changed the name 342 times, so don’t count on this one sticking.)
- Probability is the measure of uncertainty; matters of certainty and uncertainty speak of our knowledge and how we know what we know or of what we are unsure.
- Given a fixed set of premises and conclusion, it follows that conditionally the conclusion (a proposition) is either true, false, or somewhere in between, i.e uncertain. A proposition can be true given one set of premises, false given another, or uncertain given a third.
- A proposition is necessarily true when it (a) validly follows a chain of true propositions back to a bedrock set of propositions which are accepted as true because (b) they are axiomatically true, i.e. just plain true. We cannot explain why or how these fundamental propositions are true: we accept they are becaue of their obviousness, they are revealed to us, (Socrates would say we remember them), i.e by faith.
- A proposition is contingently true when it validly follows from chain of propositions which are accepted as true. If these accepted propositions (and their sires, grandsires, etc. if present) are themselves true then the proposition at hand is necessarily true as above, and it is misleading to say it is contingently true. It can be that a proposition is accepted as contingently true because it is not known to be necessarily true.
- An objectivist takes an argument as it is and neither adds to or subtracts from it. The truth, falsity, or in-betweenness of the conclusion follow only from the evidence stipulated.
- An subjectivist takes an argument and adds to or subtracts from it; either from the premises or in modifying the conclusion. This is acceptable only if these modifications are manifest. They usually are not. Logic and probability do not guaurantee an absence of confusion.
- A frequentist often acts like a subjectivist unaware of her subjectivism; but even if not, she makes other errors. See the previous post for a sample—and only a sample—of these errors.
- Probability need not be a number, can be a range, or can be unique value. A probability of 1 implies truth, of 0 falsity. There is no such thing as probability; it is not a physical thing; neither are numbers.
- An easily seen result of probability is that adding a truth to a list of premises does not change the argument. If, for example, a conclusion follows (or doesn’t) from some set of premises, then saying, “Accepting these premises and this truth” is equivalent to saying, “Accepting these premises.”
Given all these, there was confusion last time exactly how evidence in premises allows us to deduce probabilities.
Suppose the proposition (conclusion) “A Q will emerge”. What does knowing only the premise, “I have no idea how things emerge from this process” do for us? That is, what is the objectivist probability the conclusion is true given this and only this premise?
The answer “I don’t know” floats to mind. After all, we admitted ignorance and ignorance says nothing about Q. The logical nothing means no thing, incidentally, and not just a little thing, nor mostly nothing, nor uniformity, nor anything else. The probability doesn’t exist if we know nothing about Q. But this is only so if the premise itself truly has nothing to say about Q. Is there anything that can be deduced from the premise which allows us to uncover occult evidence about Q?
Well, it might be argued that “I have no idea how things emerge from this process” taken in conjunction with the conclusion “A Q will emerge” implies that Q is possible. But this is to act like a subjectivist and to change the premise to “I really don’t know, but since the guy is asking about Q, it seems as if Q is at least possible.” This inference is false because it cheats. We cannot go from “I know nothing about Q” to “Q is possible.” At best, this argument is circular because it takes information in the conclusion and places it is the premise (subjectively). Thus “I don’t know” is the probability; i.e. the probability doesn’t exist.
Switch the premise to “A Q might emerge from this process.” The conclusion is still “A Q will emerge.” The argument is invalid, but we feel on firmer probabilistic ground. The answer which appears first might be, “Since a Q might emerge, but we have no other idea about Q, then the probability is some number between 0 and up to and including 1.”
Is there anything that can be deduced from the premise this time? Yes. Taken one way, from “A Q might emerge from this process” it follows that “A Q might not emerge from this process.” Written tersely, this is “Either a Q will emerge or it won’t,” which is evidently a tautology, a statement which is always true, i.e. it is a truth.
From above we know that adding a truth to a list of premises does not change the argument. And any truth in a list of premises may be swapped for another truth. For example, “It will rain July 4th, 2561 in New York City or it won’t.” Making this substitution and keeping the same fixed conclusion, it does not follow that its probability is the interval (0, 1]. Instead, it is admitting we know nothing about Q, and nothing cannot imply a probability.
But, given the fluidity of English, there is another way we can interpret the premise: “A Q is one of the possibilities of this process.” We can still derive the same tautology from this, but now there is ever-so-slightly more information about Q, and with that we can claim our original answer, which is some number between 0 and up to and including 1. Which still isn’t saying much. All we can infer from this premise is that Q is not impossible. That is why the interval is (0, 1] and not [0, 1]. Small comfort!
Incidentally, we could say that the probability is [0, 1] for the tautological or ignorance premises, but since this interval is everything there is—truth, falsity, and in-betweenness—it really says nothing, which is our answer.
Back to the statistical syllogism. Premise: “There are n states which could emerge from this process, just one of which is called Q, and just one must emerge.” Same conclusion. The statistical syllogism allows us to deduce the probability 1/n from this premise, an answer which confused some who insisted that no probability can be deduced.
If that is so—if no probability whatsoever can be derived from this new premise—then we necessarily are in the logically equivalent situation of the tautological or ignorance premises. We have seen it was only from these (or other logically equivalent propositions) that we can deduce the interval [0,1], which is to say no probability at all.
Is it true that “I know nothing about Q” is logically equivalent to “There are n states which could emerge from this process, just one of which is called Q, and just one must emerge”? Obviously not. We could, of course, deduce the tautology “Q emerges or not”, but that is because this tautology is always true even if there is no process in the universe which produces Qs.
Is “There are n states which could emerge from this process, just one of which is called Q, and just one must emerge” logically equivalent to “A Q is one of the possibilities of this process”? If so, then the answer is the interval (0,1], which says something but very little, and which may have been in the mind of some commenters. Well, at least from our new premise we can derive that “A Q is one of the possibilities of this process”, which was the old premise. But if there is no more information than that in the new premise, then we are done and the critics are right.
But what are we to make of the other words in the new premise? In the old premise, we do not know how many different possibilities exist: the number could be infinite. But in the new premise we know, in addition to that Q can be a state (the old premise), that there are n-1 other states besides Q and that one of these states (Q or another) must emerge. There are only these n-1 states and no other. We have certain evidence which says n different things can happen, that there is a distinction between them (somehow). It is from this other information the statistical syllogism works its magic. Let’s see why.
Notice that we have a “variable” in the premise, which we can replace with actual values. Try n = 1. It is doubtful a critic would object to the statistical syllogism in this case and claim we can say “nothing” about whether a Q will emerge.
Now let n be greater than 1 (and an integer). What of the other n-1 states we know are possibilities? These also have probabilities. Switch the conclusion to “A Q will not emerge.” The statistical syllogism would give (n-1)/n for the probability. Well, if that doesn’t seem true to you, then I can offer no proof, just as I can offer no proof that the probability for “A Q will emerge” is 1/n. Yes, the statistical syllogism is axiomatic (in part).
Technical Mumbo Jumbo
Some people—Jaynes, notably, Stove, Diaconis, others—have thought there was a proof of the statistical syllogism. All these attempts fail. The proofs rest on certain principles, like the “Principle of Indifference”, “Principle of Maximum Entropy”, or “Principle of Symmetry”, etc. etc. All of them reach a point where they make claims like “Pr(State i | Premise) = Pr(State j | Premise), i,j = 1,…,n” and where “Premise” is our new premise. Propositions like this are certainly true, and if you accept them (and have some mathematical training) you can easily see how acceptance leads to the statistical syllogism.
The difficulty comes in accepting “Pr(State i | Premise) = Pr(State j | Premise) etc.” Why should we? Where does this truth come from? Well, ( as I show in this paper) it must be axiomatic. Adding all the various principles of “indifference”, “symmetry” and so on only serve to make the arguments circular.
It is also to act subjectivity because we no longer have “There are n states which could emerge from this process, just one of which is called Q, and just one must emerge” as our premise, but “There are n states which could emerge from this process, just one of which is called Q, and just one must emerge and the principle of indifference” (or another principle). Now that and its brother principles just is to say “It is equally likely that each state should emerge.” Well, if we take as a premise that it is equally likely that each state should emerge, then it necessarily follows each state is equally likely. But the argument is circular. Instead, it is from the statistical syllogism we infer these principles.
We cannot say we have no reason to believe the probability of the conclusion. We have definite reasons, and these are the knowledge that there are n possible states of the process and that one of these is Q and that one of these (and none other) must show. This is a heckofa lot of information to have, and hence sufficient reason to believe the probability.
This is worth emphasizing because phrases like “no reason”, “ignorance”, and the like are often tossed around, especially when it comes to formal models. It is good to see up front what little epistemic value these have or how their use can be misleading.
End result? The objectivist must accept the statistical syllogism. The subjectivist may do what he likes. The frequentist must still sit patiently and wait for an infinite number of trials of this process to complete before telling us the probability.
Your homework (in the comments) is to give more good examples, or if you’re clever, examples which seem good but which aren’t.