For most people, a mathematical notation is like a language: you learn it and stick with it. For people doing mathematical research, however, this is not enough: they must design new notations for new problems. The design of good notation is both hard and worthwhile since a bad initial notation can retard a line of research greatly.
Before we had mathematical notation, equations were all written out in language. Since words have multiple meanings and variable precedences, long equations written out in language can be extraordinarily difficult and sometimes fundamentally ambiguous. A good representative example of this is the legalese in the tax code. Since we want greater precision and clarity, we adopt mathematical notation.
One fundamental thing to understand about mathematical notation, is that humans as logic verifiers, are barely capable. This is the fundamental reason why one notation can be much better than another. This observation is easier to miss than you might expect because, for a problem that you are working on, you have already expended the effort to reach an understanding.
I don’t know of any systematic method for designing notation, but there are a set of heuristics learned over time which may be more widely helpful.
- Notation should be minimized. If there are two ways to express things, then choose the (objectively, by symbol count) simpler one. If notation is only used once, it should be removable (this often arises in presentations).
- Notation divergence should be minimized. If the people working on some problem have a standard notation, then sticking with it is easier. For example, in machine learning x is almost always a set of features from which predictions are made.
- A reasonable mechanism for notation design is to first name and define the quantities you are working with (for example, reward r and time t), and then make derived quantities by combination (for example rt is reward at time t).
- Variables should be alliterated. Time is t, reward is r, cost is c, hypothesis is h.
- Name collisions (or near collisions) should be avoided. E and p are terrible variable names in some contexts.
- Sub-sub-scripts should be avoided. It is often possible to change a sub-sub-script into a sub-script by redefinition.
- Superscripts are dangerous because of overloading with exponentiation.
- Inessential dependences should be suppressed in the definition. (For example, in reinforcement learning the MDP M you are working with is often suppressable because it never changes.)
- A dependence must be either uniformly suppressed or uniformly explicit.
- Short theorem statements are very nice. There seem to be two styles of theorem statements: long including all definitions and short with definitions made before the statement. As computer scientists, we have to prefer “short” because long is nonmodular. As humans, it’s easier to read.
- It is very easy to forget the quantification of a variable (“for all” or “there exists”) when you are working on a theorem, and it is essential for readers that you specify it explicitly.
- Avoid strange alphabets. It is hard for people to think with unfamiliar symbols. english lowercase > english upper case > greek lower case > greek upper case > hebrew > other strange things.
- The definitions section of a paper often should not contain all the definitions in a paper. Instead, it should cover the universally used definitions. Others can be introduced just before they are used.
These heuristics often come into conflict, which can be hard to resolve. When trying to resolve the conflict, it’s important to understand that it’s easy to fail to imagine what a notation would be like. Trying out different notations and comparing is reasonable.
Are there other useful heuristics for notation design? (Or disagreements with the above heuristics?)