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Celebrate independence day by thinking for yourself.
Although most would not consider omniscience a reasonable requirement for a software system, it’s nevertheless a common complaint that computer systems are not omniscient. It’s not often worded quite that way, but we’re all familiar with the argument that a system (say, a database) must be able to represent a fact that it doesn’t contain, or rather, to represent missing values somehow.
That “somehow” normally takes the form of a special symbol, ω (omega), or “null”. This special symbol is assigned to variables that have not yet been initialized, that have no meaningful value in that context, or whose value is unknown, etc.
Now, let’s take a step back to reality. In reality (the world that’s actually out there), things either are or are not. Reality doesn’t care if we know about it or not, and there are no facts which aren’t facts about something in particular. And there are certainly no existents out there which are “not yet initialized”.
To be clear, the issue we are talking about pertains to variables in whatever language we’re using. A variable properly has some value, but may have any value (of the appropriate type). The notion of null is a variable which has no value at all. It’s my opinion that this amounts to a philosophical error known as reification of the zero.
A software system models some abstraction from reality. To model the facts of reality (or a virtual reality), a very simple logical system suffices, since things either are or are not (law of the excluded middle). A simple enough system represents only facts about reality (or, rather, abstractions from such facts), and those things which aren’t facts about reality are simply omitted. This is not to say that it represents all facts of reality (that would be absurd), but only the ones about which we have something to say in the context of our system.
For a simple but contrived example, let’s make a kind of language that represents facts about people’s birthdays. We have a type of expression saying “The birthday of Person p is on Date d.” Call it a logical theorem that there exists some person p whose birthday is on some date d. To prove this theorem, one merely needs to supply some (but any) person who has some (but any) birthday. According to the Curry-Howard isomorphism, we could think of it as a function, a constructor of values of a specific type. Call the type and the constructor function BD, and let it take two arguments, p and d, which represent a person and a date, respectively, yielding values of the form BD(p,d).
Now comes the question of what to do when a fact is not yet known, not applicable, or can’t be found out (because of some error, for example). There are some options for representation here, of which, I will consider two. For example, in a system that tracks people’s birthdays, we could handle unknown birthdays in two ways:
These are different approaches to the problem in a profound way. In the former, we have stayed within the framework of representing “that which is”, i.e. truths only, and used the semantics of the language to represent facts about the unknown (the fact that it is unknown). In the latter, we have imported the notion of the unknown, from the meta-level, into our algebra. As far as our algebra is concerned, ω is a kind of something that is nothing in particular and has no specific type. It has become a way of formalizing vagueness in our logic. We have in fact changed the logic of our language in such a way that the Curry-Howard isomorphism no longer holds, and the law of excluded middle is no longer applicable. Supplying null for either of the variables of BD(p,d) creates a value of the form BD(p,d) that is not a proof of the logical formula that BD(p,d) represents. Observe that “The birthday of person John is on date ω” does not constitute a proof of the hypothesis that there exists some person p whose birthday is on some date d.
In short, the difference between these two approaches is that the former treats the fact that something is unknown as just another fact, using grammar that already exists, while the latter is an attempt to formalize “unknown” as a new grammatical construct.
But once we invite null into the system, we have to come up with the semantics for how to handle it. We have to ask the question: “What exactly do we mean by null?” For every operator, we must decide what it means for one of its operands to be null. What does ω = ω yield? In some languages (most imperative programming languages), ω is considered equal to itself. In others, it’s considered an error to even ask the question, resulting in a runtime error or a non-terminating program. In yet others (SQL), such an operation always yields ω itself. This last kind is a kind of three-valued logic, which is fraught with difficulties. For example, in SQL, A=A is not true for all values of A. In other words, we have arrived at a logic in which the law of identity is not a tautology!
The implication of this last point is that a Boolean expression in such a system is to be considered valid even though it is neither true nor false. This means we can’t consider such a system to deal with propositions, since all valid propositions are either true or false (a proposition that is neither true nor false is arbitrary, i.e. invalid). From the standpoint of propositional logic, then, a database is corrupt to the extent that it contains nulls.
In fact, once we allow it, we may find that we need more than one kind of null, with differing semantics depending on what we mean by it in that context. One kind may mean “unknown”, another might mean “inapplicable”, and of course we might need a third to say that “we do not know if this is applicable or not”. When taken to its logical conclusion, this leads to an explosion of kinds of null. As a concrete example, consider a database that tracks people’s death dates. For the predicate Person p has a death Date of d, one might need at least three kinds of null: one to say that the person is not yet dead (inapplicable), another to say that the person is dead, but we don’t know when they died (unknown), and a third to say that we don’t know whether or not they have died (unknown whether or not a value is applicable). And this is just one really simple example.
But in practice, the meaning of null is often decided ad hoc by the user of the system or language, and may even not be decided at all, in which case the result of a program employing nulls has been consigned to chance to some degree.
It should be abundantly clear that introducing null into a language is not much of a solution to anything. We don’t do that with natural language, and certainly not in formal logic, so why do it with programming languages? I believe it’s partially because people are used to thinking of a programming language as a machine rather than a logic. They are still thinking in terms of initializing pointers or registers and transitioning between memory states. But the power of languages is to abstract these things away, so that we can concentrate on the algebra while the machine does the arithmetic. Yet, I think we owe the acceptance of null largely to the fact that people are used to sloppy thinking. They’re used to treating nothing as if it were a thing, and using concepts of which they only know the approximate meaning.
Fortunately, there are real-world solutions for obviating the notion of null. Functional programming, for example, does away with it by employing monads. Haskell has the Maybe monad which is an abstraction from case analysis such that a value of type Maybe A can take one of two possible forms: Nothing or Just a where a is a value of type A. This might seem like it’s the same thing as null, but the fact that only certain operations are defined on the Maybe type class (i.e. it’s a monoid) keeps everything well defined. This concept is applicable to any language that supports functional style, and can even be implemented in Ruby. In some other languages, this monad is called Option.
In the realm of databases, work is being done on more abstract database languages than most of us are used to. Chris Date and Hugh Darwen have been instrumental in putting database systems on the sound theoretical footing of predicate logic, although products based on their research are largely in the early stages of development.