All posts by SixWingedSeraph

Charles Wells. Professor Emeritus at Case Western Reserve University. Now living in Minneapolis, Minnesota, USA. CWRU provides support for this blog with software and library privileges.
category theory, exposition, language, language of math, math, representations, understanding math

Naming mathematical objects

1 Comment

Commonword names confuse

Many technical words and phrases in math are ordinary English words ("commonwords") that are assigned a different and precisely defined mathematical meaning.  

  • Group  This sounds to the "layman" as if it ought to mean the same things as "set".  You get no clue from the name that it involves a binary operation with certain properties.  
  • Formula  In some texts on logic, a formula is a precisely defined expression that becomes a true-or-false sentence (in the semantics) when all its variables are instantiated.  So $(\forall x)(x>0)$ is a formula.  The word "formula" in ordinary English makes you think of things like "$\textrm{H}_2\textrm{O}$", which has no semantics that makes it true or false — it is a symbolic expression for a name.
  • Simple group This has a technical meaning: a group with no nontrivial normal subgroup.  The Monster Group is "simple".  Yes, the technical meaning is motivated by the usual concept of "simple", but to say the Monster Group is simple causes cognitive dissonance.

Beginning students come with the (generally subconscious) expectation that they will pick up clues about the meanings of words from connotations they are already familiar with, plus things the teacher says using those words.  They think in terms of refining an understanding they already have.  This is more or less what happens in most non-math classes.  They need to be taught what definition means to a mathematician.

Names that don't confuse but may intimidate

Other technical names in math don't cause the problems that commonwords cause.

Named after somebody The phrase "Hausdorff space" leads a math student to understand that it has a technical meaning.  They may not even know it is named after a person, but it screams "geek word" and "you don't know what it means".  That is a signal that you can find out what it means.  You don't assume you know its meaning. 

New made-up words  Words such as "affine", "gerbe"  and "logarithm" are made up of words from other languages and don't have an ordinary English meaning.  Acronyms such as "QED", "RSA" and "FOIL" don't occur often.  I don't know of any math objects other than "RSA algorithm" that have an acronymic name.  (No doubt I will think of one the minute I click the Publish button.)  Whole-cloth words such as "googol" are also rare.  All these sorts of words would be good to name new things since they do not fool the readers into thinking they know what the words mean.

Both types of words avoid fooling the student into thinking they know what the words mean, but some students are intimidated by the use of words they haven't seen before.  They seem to come to class ready to be snowed.  A minority of my students over my 35 years of teaching were like that, but that attitude was a real problem for them.

Audience

You can write for several different audiences.

Math fans (non-mathematicians who are interested in math and read books about it occasionally) In my posts Explaining higher math to beginners and in Renaming technical concepts, I wrote about several books aimed at explaining some fairly deep math to interested people who are not mathematicians.  They renamed some things. For example, Mark Ronan in Symmetry and the Monster used the phrase "atom" for "simple group" presumably to get around the cognitive dissonance.  There are other examples in my posts.  

Math newbies  (math majors and other students who want to understand some aspect of mathematics).  These are the people abstractmath.org is aimed at. For such an audience you generally don't want to rename mathematical objects. In fact, you need to give them a glossary to explain the words and phrases used by people in the subject area.   

Postsecondary math students These people, especially the math majors, have many tasks:

  • Gain an intuitive understanding of the subject matter.
  • Understand in practice the logical role of definitions.
  • Learn how to come up with proofs.
  • Understand the ins and outs of mathematical English, particularly the presence of ordinary English words with technical definitions.
  • Understand and master the appropriate parts of the symbolic language of math — not just what the symbols mean but how to tell a statement from a symbolic name.

It is appropriate for books for math fans and math newbies to try to give an understanding of concepts without necessary proving theorems.  That is the aim of much of my work, which has more an emphasis on newbies than on fans. But math majors need as well the traditional emphasis on theorem and proof and clear correct explanations.

Lately, books such as Visual Group Theory have addressed beginning math majors, trying for much more effective ways to help the students develop good intuition, as well as getting into proofs and rigor. Visual Group Theory uses standard terminology.  You can contrast it with Symmetry and the Monster and The Mystery of the Prime Numbers (read the excellent reviews on Amazon) which are clearly aimed at math fans and use nonstandard terminology.  

Terminology for algebraic structures

I have been thinking about the section of Abstracting Algebra on binary operations.  Notice this terminology:

boptable

The "standard names" are those in Wikipedia.  They give little clue to the meaning, but at least most of them, except "magma" and "group", sound technical, cluing the reader in to the fact that they'd better learn the definition.

I came up with the names in the right column in an attempt to make some sense out of them.  The design is somewhat like the names of some chemical compounds.  This would be appropriate for a text aimed at math fans, but for them you probably wouldn't want to get into such an exhaustive list.

I wrote various pieces meant to be part of Abstracting Algebra using the terminology on the right, but thought better of it. I realized that I have been vacillating between thinking of AbAl as for math fans and thinking of it as for newbies. I guess I am plunking for newbies.

I will call groups groups, but for the other structures I will use the phrases in the middle column.  Since the book is for newbies I will include a table like the one above.  I also expect to use tree notation as I did in Visual Algebra II, and other graphical devices and interactive diagrams.

Magmas

In the sixties magmas were called groupoids or monoids, both of which now mean something else.  I was really irritated when the word "magma" started showing up all over Wikipedia. It was the name given by Bourbaki, but it is a bad name because it means something else that is irrelevant.  A magma is just any binary operation. Why not just call it that?  

Well, I will tell you why, based on my experience in Ancient Times (the sixties and seventies) in math. (I started as an assistant professor at Western Reserve University in 1965). In those days people made a distinction between a binary operation and a "set with a binary operation on it".  Nowadays, the concept of function carries with it an implied domain and codomain.  So a binary operation is a function $m:S\times S\to S$.  Thinking of a binary operation this way was just beginning to appear in the common mathematical culture in the late 60's, and at least one person remarked to me: "I really like this new idea of thinking of 'plus' and 'times' as functions."  I was startled and thought (but did not say), "Well of course it is a function".  But then, in the late sixties I was being indoctrinated/perverted into category theory by the likes of John Isbell and Peter Hilton, both of whom were briefly at Case Western Reserve University.  (Also Paul Dedecker, who gave me a glimpse of Grothendieck's ideas).

Now, the idea that a binary operation is a function comes with the fact that it has a domain and a codomain, and specifically that the domain is the Cartesian square of the codomain.  People who didn't think that a binary operation was a function had to introduce the idea of the universe (universal algebraists) or the underlying set (category theorists): you had to specify it separately and introduce terminology such as $(S,\times)$ to denote the structure.   Wikipedia still does it mostly this way, and I am not about to start a revolution to get it to change its ways.

Groups

In the olden days, people thought of groups in this way:

  • A group is a set $G$ with a binary operation denoted by juxtaposition that is closed on $G$, meaning that if $a$ and $b$ are any elements of $G$, then $ab$ is in $G$.
  • The operation is associative, meaning that if $a,\ b,\ c\in G$, then $(ab)c=a(bc)$.
  • The operation has a unity element, meaning an element $e$ for which for any element $a\in G$, $ae=ea=a$.
  • For each element $a\in G$, there is an element $b$ for which $ab=ba=e$.

This is a better way to describe a group:

  • A group consist of a nullary operation e, a unary operation inv,  and a binary operation denoted by juxtaposition, all with the same codomain $G$. (A nullary operation is a map from a singleton set to a set and a unary operation is a map from a set to itself.)
  • The value of e is denoted by $e$ and the value of inv$(a)$ is denoted by $a^{-1}$.
  • These operations are subject to the following equations, true for all $a,\ b,\ c\in G$:

     

    • $ae=ea=a$.
    • $aa^{-1}=a^{-1}a=e$.
    • $(ab)c=a(bc)$.

This definition makes it clear that a group is a structure consisting of a set and three operations whose axioms are all equations.  It was formulated by people in universal algebra but you still see the older form in texts.

The old form is not wrong, it is merely inelegant.  With the old form, you have to prove the unity and inverses are unique before you can introduce notation, and more important, by making it clear that groups satisfy equational logic you get a lot of theorems for free: you construct products on the cartesian power of the underlying set, quotients by congruence relations, and other things. (Of course, in AbAl those theorem will be stated later than when groups are defined because the book is for newbies and you want lots of examples before theorems.)

References

  1. Three kinds of mathematical thinkers (G&G post)
  2. Technical meanings clash with everyday meanings (G&G post)
  3. Commonword names for technical concepts (G&G post)
  4. Renaming technical concepts (G&G post)
  5. Explaining higher math to beginners (G&G post)
  6. Visual Algebra II (G&G post)
  7. Monads for high school II: Lists (G&G post)
  8. The mystery of the prime numbers: a review (G&G post)
  9. Hersh, R. (1997a), "Math lingo vs. plain English: Double entendre". American Mathematical Monthly, volume 104, pages 48–51.
  10. Names (in abmath)
  11. Cognitive dissonance (in abmath)
Send to Kindle
abstracting algebra, category theory, exposition, language of math, math, representations, understanding math

Monads for high school II: Lists

5 Comments

The interactive examples in this post require installing Wolfram CDF player, which is free and works on most desktop computers using Firefox, Safari and Internet Explorer, but not Chrome. The source code is the Mathematica Notebook Monad.nb, which is available for free use under a Creative Commons Attribution-ShareAlike 2.5 License. The notebook can be read by CDF Player if you cannot make the embedded versions in this post work.

Introduction

This is the second part of a series of posts describing how I will lead up to introducing monads in my proposed e-book Abstracting Algebra (AbAl). It follows Monads for high school I. Comments in red are meta and mostly will not be included in the book.  

Lists 

A list is a specific kind of mathematical object. This is a reasonable specification for lists:

A list of length $n$ determines and is determined by what its first, second, $\ldots$, $n$th entries are. 

In this post, lists will always be finite in length.

For doing rigorous proofs you need a precise definition of a list, such as a function from $\{1,2,…,n\}$ to a set, or a recursive definition.  This book is not about proofs.

Terminology and representation

The most common way in the symbolic language of math to represent a finite list is to use a comma-delimited expression in parentheses.  For example, \[(4,4,2,8)\] is the list of length 4 whose first and second entries are both $4$, third entry $2$ and fourth entry $8$.

  • The order matters and repetitions are allowed. For example, $(4,4,2,8)$, $(4,2,8)$ and $(4,2,4,8)$ are all different lists.
  • Other words for lists are (finite) sequence, word, tuple and string.
  • Many mathematicians would call $(4,4,2,8)$ an $4$–tuple.
  • My Discrete math classnotes discusses the specification and the definition of lists called tuples there) at length on pages 50ff. This section of AbAl will incorporate some of the information there.
  • Some computer languages represent our list without the commas: $(4\,\,4\,\,2\,\,8)$.
  • Mathematica represents it this way: $\{4,4,2,8\}$.  This conflicts with the usual set notation, where the order does not matter and where repetitions are ignored  — the set $\{4,4,2,8\}$ has three elements.  But if you type Length[$\{4,4,2,8\}$] in Mathematica, you get the answer 4.
  • A list of characters (alphabetical, numerical, or other symbols) can be represented  by writing the characters down in order without spaces between them.  For example $(a,a,c,d)$ would be written "aacd".  This representation is referred to as a string or as a word in computing science.  The string "4428" is the base-10 representation of the integer $4,428$.  Of course, it is also the hexadecimal representation of the integer $17,448$. 
  • In the text, I will mostly use a cartouche representation: for example, $\boxed{1\ 2\ 3\ 4}$ is the list consisting of the first four positive integers in order.
  • The cartouche is more in-your-face than the other representations I've listed and as far as I know is not used to mean anything else.  I'm not sure I can give any better explanation for why I prefer it than that.  Math is supposed to be explicit and precisely defined and justified by clear reasoning, but after all deciding which representation to use is not math, it is art.

Lists with entries from a given set

If $S$ is any set, finite or infinite, $\textrm{Lists}(S)$ denotes the set of all lists of finite length whose entries come from $S$.  Thus the set $\textrm{Lists}(\{1,\  2,\  3\})$ contains:

  • $\boxed{2\ 2\ 3\ 2\ 2\ 1}$,
  • $\boxed{3\  3\  3\  3}$,
  • the list of length $42$ whose first entry is $3$ and every other entry is $1$,
  • the empty list $\boxed{\vphantom{n}}$,
  • the singleton lists $\boxed{1}$,  $\boxed{2}$ and  $\boxed{3}$, and
  • an infinite number of other lists, 
  • but the list $\boxed{4\  2\  3}$ is not an element of $\textrm{Lists}(\{1,\  2,\  3\})$.

$\textrm{Lists}$ is a function from sets to sets.  Its input is any set and its output is the set of all finitely-long lists whose entries are from the input set. We will also use the similar function $\textrm{Lists}^+$ which takes a set to the set of nonempty lists with entries from the set.

Associativity

(Review from Monads for high school I.)  If a binary operation is associative, then the operation is defined on any (finite) list of inputs in its underlying set.  For example, the sum of the list $\boxed{4\ 4\ 2\ 8}$  is 18.  It follows from associativity that you can add it up as $(4+4)+(2+8)$, $4+(4+(2+8))$, $4+((4+2)+8)$, $(4+(4+2))+8$ or as $((4+4)+2)+8$.  They all give the same answer. In other words, Plus is in fact an operation on lists of numbers.  It is customary to extend associative binary operations to lists of length $0$ and $1$ by setting the value at the empty list to be the identity element of the operation, and the value at a one element list to be its only entry.  Thus Plus($\boxed{4\ 4\ 2\ 8}$)$=18$, Plus($\boxed{\ \vphantom{0} }$)$=0$, Times($\boxed{\ \vphantom{0} }$)$=1$ and Plus($\boxed{3 }$)$=3$.

Operations defined on finite lists

 You can join two lists together in order to make one list.  

The order matters.  If you join $\boxed{5\ 7}$ to $\boxed{2\ 12\ 7 }$ in that order you get $\boxed{5\ 7\ 2\ 12\ 7}$.  

Join is in fact an associative binary operation on lists.  Example: 

This means we can define an operation on lists of lists that joins all the lists inside together to make one list. 

 Notice the blue rectangle disappears when you do the operation. What I have defined here is a function that has a list of lists as input and a list of numbers as output.

The operation of joining lists to get a single list has a property shown by the drawing below (which will be interactive when I work on it some more).  Start on the left with a list of lists of lists.  The border colors distinguish the innermost lists, bordered in black, from the second level lists, in blue, and the outside list, bordered in green.

  • There is only one outside list: It is a list of (blue) lists.  That is the kind of list you can apply join to, so when you do you get a single blue list with five lists inside it (on the bottom of the diagram). "Join outside first" means "apply join to the outside list first". 
  • The single blue list on the bottom is again the kind of list you can apply join to, and when you do you get the lower list on the right end of the diagram.
  • However, the green list also contains two lists each of which is a list of lists that you can apply join to.  Apply it to both of them and you get the list at the top of the diagram.  
  • Again, that list is the kind you can apply join to and when you do you get the upper list on the right.

JoinDiagram

The two lists on the right are the same.  That always happens, whatever lists you start with.  (Try it with others, and include some singleton and empty lists while you are at it.) 

You might not have thought of this property, and now that you see it, it may look like some sort of second-rate phenom to take note of.  Or not.  But in fact, it turns out that it means that our modest function  $\textrm{Lists}^+$, that takes a set to the nonempty set of lists of its elements is a monad.  (So is $\textrm{Lists}$.) In order to say this we must define some other concepts: functor and natural transformation, and we have to verify a number of other properties of the $\textrm{List}^+$ function:  It is not just a function, it is a functor on the category of sets, the join function is a natural transformation, and some other technicalities.

Once we do that, we can define what the algebras of the join monad are, and it turns out that they are exactly all the associative binary operations.  

In other words:

The binary operation of join on nonempty lists is the mother of all associative binary operations.

But that will have to wait for the next post.

References

 

Send to Kindle
language

Improve your language

3 Comments

(Note: Sentences in small print are incidental remarks.  I meant them to be in small print.  The other variations in size of print is due entirely to CKEditor and I didn't mean it to happen.)

"Improve your language" probably makes you think of commands from certain uppity friends like:

  • Don't say, "I have to work just like everybody has to work" — say "just as".
  • Don't say, "Who are you talking to?", say "To whom are you talking?"

These are statements made by people who believe in "correct English" (conforming to a standard imposed by some educated white people).

This blog is not about that sort of thing  It is about bugs in the English language.  I have written about that before (Bugs in English and in math, Comma rule found dysfunctional). 

1. Flammable and Inflammable

Both these words mean the same thing.  This is a bug that can do real damage.  In fact companies that make flammable products have policies requiring the use of "flammable" and "fireproof" to avoid what could be serious damage.  Webster's New World Dictionary warns against using "inflammable", but under "flammable", which seems pointless.  (I was a contributing editor to the fourth edition but they didn't ask me about "inflammable".)  Wiktionary also warns against using "inflammable", but not the Oxford English Dictionary.

By the way, I recently learned that some government agency has instituted a standard that "Exit" signs should be in green lighting. Many older ones are red, which usually means "Stop".  As usual, the European Union agitated for this long before the USA did.  I wonder if red exit signs ever fooled anyone.

2. Unisex 3rd person singular pronoun needed

This bug does not cause explosions, except metaphorically, but it is a real problem.  Until the last few years, the only way to achieve neutrality was through clumsy rewording.  In my three books (two written with Michael Barr), we alternated using "he" and "she".  In academic prose, it is common to write things such as "If the reader factors the polynomial, he will discover…".  We would sometimes write "…she will discover…".  No one complained.  Lots of other recent academic writers do this trick, too.  

In these posts and in abstractmath I have Reached A Higher Level (or Lower, according to some people) and use "you" a lot, both in the usual meaning and in the colloquial use replacing "one" (meaning 8 in the OED).  Examples:

  • "If you factor the polynomial, you'll discover…" (Notice the "you'll" –contractions are happening a lot in academic writing these days, too, and in research papers, not just science popularizations.  See The revolution in technical exposition II.)
  • "When someone refers to imaginary numbers it makes you think they are fictitious."   

Brits to the rescue

However, many writers, especially in Britain, have been deliberately using "they" as a 3rd person singular pronoun.  This is the OED meaning 2, and it dates back a long way.  OED meaning 3 is also relevant.  This is discussed extensively in Wikipedia. I have given several other references below.  Note that they are mostly British.

My favorite OED quote is from Fielding: "Every Body fell a laughing, as how could they help it" . I sometimes say things like, "I'm a-running around doing errands" because I think of myself as a southerner (of the American variety), but that is purely posturing — I never heard anyone say a-anything when I was growing up in (the USA version of) Georgia

The next two entries were in a previous post.

3. Right

Q: "Should I turn left at the next corner?" A: "Right".  Probably most Americans who drive now know this bug.  The answer could mean "yes" or "turn right".  So we have to stop and think how to answer this question.  That makes it a bug.  When Jane and I drive together we have learned to answer that question "yes" or "correct".  

4. Too, two

Comment: " We will take Route 30".  Answer: "We will take Route 30 too".  (Say it out loud) This bug may be responsible for the survival of the word "also".  

Note that unlike the case of "right", this is a bug only of spoken English.

Repairing English

Examples 1 and 2 exhibit cases where English bugs cause genuine problems that need repair.  In both cases, deliberate efforts are being made in an organized bottom-up effort to solve the problems.   And both efforts seem to be working.  In the other examples, people come up with workarounds, but not in an organized fashion.  

Now, English does not have an Academy that thinks it runs the language. Spanish and French do.  But in fact when they try to do anything the least bit radical, they usually fail.

  • The Spanish Royal Academy has tried for years to enforce certain rules for the use of third person pronouns but they have apparently (correct me if I am wrong) failed to have any effect.  
  • There was a German spelling reform in the 1990's that the main German speaking countries agreed to and tried to enforce, but they failed miserably.
  • Three branches of the French goverment, along with the French Academy, had long furious discussions about how to translate "cloud computing"  into French.  Many people in the literary and government power structure do not want French people to use English words while speaking French.   But the stuffier types would not allow "informatique en nuage", so the "problem" was left unsolved.  Meanwhile the French go on calling it "cloud computing", as on this website.  

"Informatique en nuage" would be a calque on English, like "Adam's apple" is a calque in English on French "pomme d'Adam".  Meanwhile, English, which thankfully has no Academy, has made hundreds of calques on other languages, mostly to our benefit, not to mention borrowing an enormous number of words directly from French.  

People also change the way they talk without a good reason: consider "between you and I", which is an unneeded change, but it appears to be on its way to standard.  (I say that because, unlike usages such as "I don't want no cabbage", "between you and I" is very common among educated young people.)  Older people often can't stand any change in the way we talk, but as I said in another context, Old Fogies don't like "between you and I", but they die and then the younger people do what they like. 

I think it is safe to say that needed reform in a language often comes from the ingenuity of the people, sometimes in severe cases with leadership from nongovernmental groups of people, but most often simply by people changing how they talk.  

References

All purpose pronoun, New York Times blog post on singular they.

Bugs in English and in math, previous post

Category theory for computing science, by Michael Barr and Charles Wells

Handbook of mathematical discourse, by Charles Wells

If someone tells you singular 'they' is wrong, please do tell them to get stuffed (Telegraph blog post)

Singular they (Economist blog post)

Singular they (Wikipedia)

The French Get Lost in the Clouds Over a New Term in the Internet Age (Wall Street Journal) 

The revolution in technical exposition II, previous post

Toposes, triples and theories, by Michael Barr and Charles Wells

 

Send to Kindle
abstracting algebra, category theory, exposition, math, Mathematica, understanding math

Monads for high school I

4 Comments

 

Notes for viewing

The interactive examples in this post require installing Wolfram CDF player, which is free and works on most desktop computers using Firefox, Safari and Internet Explorer, but not Chrome. The source code is the Mathematica Notebook associative.nb, which is available for free use under a Creative Commons Attribution-ShareAlike 2.5 License. The notebook can be read by CDF Player if you cannot make the embedded versions in this post work.

Monads in Abstracting Algebra

I've been working on first drafts (topic posts) of several sections of my proposed book Abstracting algebra (AbAl), concentrating on the ideas leading up to monads.  This is going slowly because I want the book to be full of illustrations and interactive demos.  I am writing the demos in Mathematica simultaneously with writing the text, and designing demos is very s l o w work. It occurred to me that I should write an outline of the path leading up to monads, using some of the demos I have already produced. This is the first of probably two posts about the thread.

  • AbAl is intended to give people with a solid high school math background a mental picture of or way of thinking about the various levels of abstraction of high school algebra.
  • This outline is not a "Topic post" as described in the AbAl page. In particular, it is not aimed at high school students! It is a guided tour of my current thoughts about a particular thread through the book.
  • The AbAl page has a brief outline of the topics to be covered in the whole book.  Perhaps it should also have a list of threads like this post.

Associativity

AbAl will have sections introducing functions and binary operations using pictures and demos (not outlined in this thread).  The section on binary operations will introduce infix, prefix and postfix notation but will use trees (illustrated below) as the main display method.  Then it will introduce associativity, using demos such as this one: 

Using this computingscienceish tree notation makes it much easier to visualize what is happening (see Visible Algebra II), compared to, for example, \[(ab)(cd)=a(b(cd))=a((bc)d)=((ab)c)d=(a(bc))d\]  In this equation, the abstract structure is hidden.  You have to visualize doing the operation starting with the innermost parentheses and moving out.  With the trees you can see the computation going up the tree.

I will give examples of associative functions that are not commutative using $2\times2$ matrices and endofunctions on finite sets such as the one below, which gives all the functions from a two element set to itself. 


  • Note that each function is shown by a diagram, not by an arbitrary name such as "id" or "sw", which would add a burden to the memory for an example that occurs in one place in the book. (See structural notation in the Handbook.) 
  • The section on composition of functions will also look in some depth at permutations of a three-element set, anticipating a section on groups.

 By introducing a mechanism for transforming trees of associative binary operations, you can demonstrate (as in the demo below) that any associative binary operation is defined on any list of two or more elements of its domain.

For example, applying addition to three numbers $a$, $b$ and $c$ is uniquely defined. This sort of demo gives an understanding of why you get that unique definition but it is not a proof, which requires formal induction. AbAl is not concerned with showing the reader how to prove math statements.

​In this section I will also introduce the oneidentity concept: the value of an associative binary operation on a an element $a$ is $a$.  Thus applying addition or multiplication to $a$ gives $a$.  (The reason for this is that you want an associative binary operation to be a unique quotient of the free associative binary operation.  That will come up after we talk about some examples of monads.)  

The oneidentity property also implies that for an associative binary operation with identity element, applying the operation to the empty set gives the identity element.  Now we can say:

An associative binary operation with identity element is uniquely defined on any finite list of elements of its domain.

Thus, in prefix notation,$+(2,3)=5$, $+(2,3,5)=10$, $+(2)=2$ and $+()=0$.  Similarly $\times(2)=2$ and $\times()=1$.

This fact suggests that the natural definition of addition, multiplication, and other associative binary operations is as functions from lists of elements of the domain to elements of the domain.   This fits with our early intuition of addition from grade school, not to mention from Excel:  Addition is something you do to lists.  That feeling (for me) is not so strong for multiplication; for many common business applications you generally multiply two things like price and number sold. That's because multiplication is usually for things of different data types, but you usually add things of the same data type (not apples and oranges?).   

That raises the question: Does every function taking lists to elements come from an associative binary operation?  I will give an example that says no.  But the next thing is to introduce joining lists (concatenation), where we discover that joining lists is an associative binary operation.  So it is really an operation on lists of lists.  This will turn out to give us a systematic way to define all associative binary operations by one mechanism, because it is an example of a monad.  That is for the second installment of this outline.

Send to Kindle
exposition, math, proof, representations, understanding math

Modules for mathematical objects

Leave a comment

Notes on viewing.

A recent article in Scientific American mentions discusses the idea that concepts are represented in the brain by clumps of neurons.  Other neuroscientists have proposed that each concept is distributed among millions of neurons, or that each concept corresponds to one neuron.  

I have written many posts about the idea that:  

  • Each mathematical concept is embodied in some kind of module in the brain.
  • This idea is a useful metaphor for understanding how we think about mathematical objects.
  • You don't have to know the details of the method of storage for this metaphor to be useful.  
  • The metaphor clears up a number of paradoxes and conundrums that have agitated philosophers of math.

The SA article inspired me to write about just how such a module may work in some specific cases.  

Integers

Mathematicians normally thinks of a particular integer, say $42$, as some kind of abstract object, and the decimal representation "42" as a representation of the integer, along with XLII and 2A$_{16}$.  You can visualize the physical process like this: 

  • The mathematician has a module Int (clump of neurons or whatever) that represents integers, and a module FT that represents the particular integer $42$. 
  • There is some kind of asymmetric three-way connection from FT to Int and a module EO (for "element of" or "IS_A"). 
  • That the connection is "asymmetric" means that the three modules play different roles in the connection, meaning something like "$42$ IS_A Integer"
  • The connection is a physical connection, not a sentence, and when  FT is alerted ("fired"?), Int and EO are both alerted as well. 
  • That means that if someone asks the mathematician, "Is $42$ an integer?", they answer immediately without having to think about it — it is a random access concept like (for many people) knowing that September has 30 days.
  • The module for $42$ has many other connections to other modules in the brain, and these connections vary among mathematicians.

The preceding description gives no details about how the modules and interconnections are physically processed.  Neuroscientists probably would have lots of ideas about this (with no doubt considerable variation) and would criticize what I wrote as misrepresenting the physical details in some ways.  But the physical details are their job, not mine.  What I claim is that this way of thinking makes it plausible to view abstract objects and their properties and relationships as physical objects in the brain.  You don't have to know the details any more than you have to know the details of how a rainbow works to see it (but you know that a rainbow is a physical phenomenon).

This way of thinking provides a metaphor for thinking about math objects, a metaphor that is plausibly related to what happens in the real world.

Students

A student may have a rather different representation of $42$ in the brain.  For one thing, their module for $42$ may not distinguish the symbol "42" from the number $42$, which is an abstract object.   As a result they ask questions such as, "Is $42$ composite in hexadecimal?"  This phenomenon reveals a complicated situation. 

  • People think they are talking about the same thing when in fact their internal modules for that thing may be very differently connected to other concepts in their brain.
  • Mathematicians generally share many more similarities in their modules for $42$ than people in general do.  When they differ, the differences may be of the sort that one of them is a number theorist, so knows more about $42$ (for example, that it is a Catalan number) than another mathematician does.  Or has read The Hitchhiker's Guide to the Galaxy.
  • Mathematicians also share a stance that there are right and wrong beliefs about mathematical objects, and that there is a received method for distinguishing correct from erroneous statements about a particular kind of object. (I am not saying the method always gives an answer!).
  • Of course, this stance constitutes a module in the brain. 
  • Some philosophers of education believe that this stance is erroneous, that the truth or falsity of statements are merely a matter of social acceptance.
  • In fact, the statements in purple are true of nearly all mathematicians.  
  • The fact that the truth or falsity of statements is merely a matter of social acceptance is also true, but the word "merely" is misleading.
  • The fact is that overwhelming evidence provided by experience shows that the "received method" (proof) for determining the truth of math statements works well and can be depended on. Teachers need to convince their students of this by examples rather that imposing the received method as an authority figure.

Real numbers

A mathematician thinks of a real number as having a decimal representation.

  • The representation is an infinitely long list of decimal digits, together with a location for the decimal point. (Ignoring conventions about infinite strings of zeroes.)
  • There is a metaphor that you can go along the list from left to right and when you do you get a better approximation of the "value" of the real number. (The "value" is typically thought of in terms of the metaphor of a point on the real line.)
  • Mathematicians nevertheless think of the entries in the decimal expansion of a real number as already in existence, even though you may not be able to say what they all are.
  • There is no contradiction between the points of view expressed in the last two bullets.
  • Students frequently do not believe that the decimal entries are "already there".  As a result they may argue fiercely that $.999\ldots$ cannot possibly be the same number as $1$.  (The Wikipedia article on this topic has to be one of the most thoroughly reworked math articles in the encyclopedia.)

All these facts correspond to modules in mathematicians' and students' brains.  There are modules for real number, metaphor, infinite list, decimal digit, decimal expansion, and so on.  This does not mean that the module has a separate link to each one of the digits in the decimal expansion.  The idea that there is an entry at every one of the infinite number of locations is itself a module, and no one has ever discovered a contradiction resulting from holding that belief.

References

  • Brain cells for Grandmother, by Rodrigo Quian Quiroga, Itzhak Fried and Christof Koch.  Scientific American, February 2013, pages 31ff.

Gyre&Gimble posts on modules

Notes on Viewing  

This post uses MathJax. If you see mathematical expressions with dollar signs around them, or badly formatted formulas, try refreshing the screen. Sometimes you have to do it two or three times.

 

 

 
Send to Kindle
abstractmath.org, exposition, math, Mathematica, Uncategorized, understanding math

Abstracting algebra

Leave a comment

This post has been turned into a page on WordPress, accessible in the upper right corner of the screen.  The page will be referred to by all topic posts for Abstracting Algebra.

 

Send to Kindle
election systems, exposition, math, Mathematica

Apportionment 1

Leave a comment

The interactive examples in this post require installing Wolfram CDF player, which is free and works on most desktop computers using Firefox, Safari and Internet Explorer, but not Chrome.

Notes on viewing.

Election systems

This post begins a new thread in Gyre&Gimble: Election systems.  In 1968 I created a game called Parlement and ran a few games by mail before being absorbed by family and career.  In it you ran a political party in a parliament in the style of many European countries: The parliament forms a government, votes on bills & budgets, then an election is held where each party runs on its record.

My interest in election systems has continued sporadically since them.  Since the nineties I have been programming in Mathematica and made stabs at implementing various systems for achieving proportionality. Now I expect to devote several posts to Mathematica demos of election and apportionment systems.  

Proportional representation

An elected body is chosen by a list method of proportional representation this way: 

  • The election is by districts, each electing several members.  In most cases the number of seats each district has in the body is fixed ahead of the election. 
  • Each voter votes for a party list.
  • The proportion of the integer number of representatives that party will have out of the total number of seats alloted to the district is chosen to be near to the proportion of votes the party list receives out of the total votes.
  • The method for choosing the number of seats for the party can be any one of many methods proposed in the past 200 or so years.  Only a few methods are actually used in practice.
  • Once the number of members for the party is determined, that many persons on the list are chosen according to some method.  There are quite a few different methods used for this.  I will not write about this aspect at all.

This post looks at the method of equal proportions. This method may also be used to apportion the legislature of countries such as the USA that has states or provinces so that each state has a suitably proportionate number of seats.  For most of the history of the USA, that method has been the method of equal proportions, but in the early days other methods were used.

My impression is that the equal proportion method is the most common method used in legislatures elected by the list system, and is also the most common method used for apportioning legislative seats among states or provinces.  There is much information about these things scattered over many articles in Wikipedia, and a close study may prove me wrong about this. 

Note: The summary above is oversimplified and leaves out many details.  The references list more details than most people would ever want to know.

The equal proportions method

Several sites listed in the references describe the equal proportions method in detail.  The method for calculation used in the demo (there are others) works this way:  

  • Create a list $V$ of weights indexed by the party number.  
  • For proportional representation for parties, each party starts with $0$ seats and  $V_p$ is initially the number of votes party $p$ has for the district.
  • For state representation, each state starts with $1$ seat and the the initial $V_s$ is the number of votes state $s$ receives divided by $\sqrt{2}$.
  • Suppose $S$ seats are to be assigned . 
  • Assign the first one to the party $m$ for which $V_m$ is the maximum of the list $V$.  (In the case of states, this is the first seat after the initial one.)
  • Change the list by setting $V_m:=\frac{V_m}{\sqrt{2}}$ ($V_m:=\frac{V_m}{\sqrt{6}}$ in the case of states).
  • At the $k$th step, if $n$ is the party for which $V_n$ is the max of the list $V$ in its current state, assign the $k+1$st seat to party $n$ and set $V_n:= \frac{V_n}{\sqrt{u(u+1)}}$ (the geometric mean), where $u$ is the number of seats party $n$ had before the new one was assigned. 
  • Stop which $k=S$. 

Interactive demos of the method of equal proportions

To manipulate these demos, click on "Enable Dynamics".  At present the demo has a bug that makes the table pink (Mathematica's way of giving an error message). Move any slider and the pink will disappear forever.  The demos are also available on my website: PartySimple.cdf and PartyComparison.cdf.  If you download them onto a machine with CDF player installed and run them the pink table does not happen.  I can't imagine what could cause an error like that only when run embedded in html. These notebooks are available for free use under a Creative Commons Attribution-ShareAlike 2.5 License..

Both demos have the same controls.

  • Five sliders labeled n1 through n5 control the number of votes they get in the election.  
  • The bottom slider controls the number of seats that district gets to elect.
  • By moving a slider you control the information it represents.
  • By clicking on the plus sign to the right of the slider, you toggle a list of controls below it.  The party vote sliders begin with the controls invisible and the seats slider begins with them visible.

How the EP method works

The demo below assumes that five parties, numbered 1 through 5, are running to get seats in the elected body.  You may change the votes (Column Votes) received by any party, and also the total number of representatives to be chosen (Seats).

Data

  • Total votes is the total number of votes received by all five parties.
  • Seats is the total number of representatives assigned to the elected body. It can be changed using the Seats slider.
  • Quota is Total votes divided by Seats.  Some systems use slight variations on this quota.
  • The Name of the party is given just as a number.  In a suitably fancy demo you might give each party a real name.
  • Votes is the number of votes the party receives in the election, controlled by the relevant slider.
  • SeatsAsgd  is the number of seats the equal proportions method assigns to that party.
  • Weight: This number is $\frac{\text{Votes}}{\sqrt{\text{SeatsAsgd}\times(\text{SeatsAsgd}+1)}}$

Playing with the demo

  • Start with any settings for the sliders, and press the $+$ button underneath the seat slider. This allots one more seat to the representatives.  
  • You will see that the party with the largest weight gets the new seat.  That is how the method works: The algorithm starts with no seats and adds one at a time until the correct number of seats is reached.
  • The weight is a function of the number of seats allotted to the party. 
  • Try changing the Votes for a single party, letting the total number of seats remain the same.  Which parts of the data get changed by doing this?  Do you understand why?
  • Try changing the votes for all the parties, so that one has most of the votes and the others have only a few votes apiece.  Start with Seats at zero and step the seats up one at a time.  Notice what happens in column SeatsAsgd and what happens to Weight.

 

 

 

How close to an accurate apportionment does it get?

  • Quota is Total votes divided by Seats.  Note how it changes when you move any slider.
  • The Name of the party is given just as a number.  In a suitably fancy demo you might give each party a real name.
  • Votes is the number of votes the party receives in the election, controlled by the relevant slider.
  • SeatsAsgd is the number of seats the equal proportions method assigns to that party, given the total number of Seats allotted.
  • SeatsIdeal is the party's Votes divided by Quota.  Note that this is generally close to SeatsAsgd, which is usually but not always either the floor or the ceiling of SeatsIdeal. Try to find a setting where that is not true (hint: Give one party most of the votes.)
  • VotesPerSeat is Votes divided by SeatsAsgd.  Compare it to SeatsIdeal.  They are usually close, but can  be quite far off if only a few Seats are assigned.
  • Deviation is VotesPerSeat divided by Quota.  This is a relative measure of how far away from exactness the representations of the parties are.

Playing with the demo

  • Move Seats down to 2 or 3.  Notice that the deviations are quite bad, even $40\%$ off sometimes. Move Seats  up and see that deviations get much better.  Can you understand why that happens?
  • Note that usually Seats is either the integer just below SeatsIdeal (the floor) or the integer just above it (the ceiling).  This is reasonable and is called "keeping quota".
  • Make one or two parties large and the others small, then move the Seats slider around.  You find examples where Seats busts through the ceiling, "breaking quota".  Sometimes Seats is several units bigger than the ceiling. 
  • Note that if you step Seats up one at a time, the only thing that ever happens is that one party's seats goes up one unit.  Some other common systems cause some other party's seats to go down occasionally when the total seats is incremented.  That obviously never happens with EP.  

 

About demos

These demos were designed for people to learn about a concept by experimenting with them.  Such demo should be fairly simple with only choices and displays relevant to what you are trying to show.  

You can also build elaborate CDF's. Elaborate Riemann Sums Demo.nb contains a command PlotRiemann which allows for many options showing different kinds of Riemann sums with different options, and you could design a single demo of apportionment with many buttons, sliders and other gadgets that allow for all sorts of possibilities (but I have not designed such a monster).  

I do expect to eventually design a command such as PlotRiemann that does for voting systems something like what PlotRiemann does for Riemann Sums, but the way to do that is to create one feature or option at a time.  I will be doing that and they will result in other election systems demos that I will post here from time to time.  (Promises, promises).

References 

 

Notes on Viewing  

This post uses MathJax. If you see mathematical expressions with dollar signs around them, or badly formatted formulas, try refreshing the screen. Sometimes you have to do it two or three times.

To manipulate the demo in this post, you must have Wolfram CDF Player installed on your computer. It is available free from the Wolfram website. The code for the demo is in the Mathematica notebook Equal Proportions.nb.

Send to Kindle
language, language of math, math, representations, understanding math

Shared mental objects

3 Comments

Notes on viewing

Shared mental objects

I propose the phrase "shared mental object" to name the sort of thing that includes mathematical objects, abstract objects, fictional objects and other concepts with the following properties: ​

  • They are not physical objects
  • We think of them as objects 
  • We share them with other people

It is the name "shared mental object" that is a new idea; the concept has been around in philosophy and math ed for awhile and has been called various things, especially "abstract object", which is the name I have used in abstractmath.

I will go into detail concerning some examples in order to make the concept clear.  If you examine this concept deeply you discover many fine points, nested ideas and circles of examples that go back on themselves.  I will not get very far into these fine points here, but I have written about some of them posts and in abmath (see references).  I am working on a post about some of the fine points and will publish it if I can control its tendency to expand into infinite proliferation and recursion.

Examples

 

Messages

There is a story about the early days of telegraphy:  A man comes into the newly-opened telegraph station and asks to send a telegram to his son who is working in another city. He writes out the message and gives it to the operator with his payment.  The operator puts the message on a spike and clicks the key in front of him for a while, then says, "I have sent your message.  Thanks for shopping at Postal Telegraph".  The man looks astonished and points at the message and says, "But it is still here!"

A message is a shared mental object.  

  • It may be represented by a physical object, such as a piece of paper with writing on it, and people commonly refer to the paper as the message.  
  • It may be a verbal message from you, perhaps delivered by another person to a third person by speech.  
  • The delivery process may introduce errors (so can sending a telegraph).  So the thoughts in the three brains (the sender, the deliverer and the recipient) can differ from each other, but they can still talk about "the message" as if it were one object.

Other examples that are similar in nature to messages are schedules and the month of September (see Math Objects in abmath, where they are called abstract objects.).  In English-speaking communities, September is a cultural default: you are expected to know what it is. You can know that September is a month and that right this minute it is not September (unless it is September). You may think that September has 31 days and most people would say you are wrong, but they would agree that you and they are talking about the same month.

The general concept of the month of September and facts concerning it have been in shared existence in English-speaking cultural groups for (maybe) a thousand years.  In contrast, a message is usually shared by only two or three people and it has a short life; a few years from now, it may be that none of the people involved with the message remember what it said or even that it existed.

Symbols

A symbol, such as the letter "a" or the integral sign "$\int$", is a shared mental object.  Like the month of September, but unlike messages, letters are shared by large cultural entities, every language community that uses the Latin alphabet (and more) in the case of "a", and math and tech people in the case of "$\int$". 

The letter "a" is represented physically on paper, a blackboard or a screen, among other things.  If you are literate in English and recognize an occurrence as representing the letter, you probably do this using a process in the brain that is automatic and that operates outside your awareness. 

Literate readers of English also generally agree that a string of letters either does or does not represent the word "default" but there are borderline cases (as in those little boxes where you have to prove you are not a robot) where they may disagree or admit that they don't know.  Even so, the letter "a" and the word "default" are shared in the minds of many people and there is general (but not absolutely universal) agreement on when you are seeing representations of them.

Fictional objects

Fictional objects such as Sherlock Holmes and unicorns are shared mental objects.  I wrote briefly about them in Mathematical objects and will not go into them here.  

Mathematical objects 

The integer $111$, the integral $\int_0^1 x^2\,dx$ and the set of all real numbers are all mathematical objects.   They are all shared mental objects.  In most of the world, people with a little education will know that $111$ is a number and what it means to have $111$ beans in a jar (for example).  They know that it is one more that $110$ and a lot more than $42$.  

Mathematicians, scientists and STEM students will know something about what  $\int_0^1 x^2\,dx$ means and they will probably know how to calculate it.  Most  of them may be able to do it in their head.  I have taught calculus so many times that I know it "by heart", which means that it is associated in my brain with the number $1/3$ in such a way that when I see the integral the number automatically and without effort pops us (in the same way that I know September has 30 days).

Beginning calculus students may have a confused and incorrect understanding of the set of all real numbers in several ways, but practicing mathematicians (and many others) know that it is an uncountably infinite dense set and they think of it as an object.  A student very likely does not think of it as an object, but as a sprawling unimaginable space that you cannot possibly regard as a thing. Students may picture a real number as having another real number sitting right beside it — the next biggest one. Most practicing mathematicians think of the set of real numbers as a completed infinity — every real number is already there —  and they know that between any two of them there is another one.

As a consequence, when students and professors talk about real numbers the student finds that some times the professor says things that sound completely wrong and the professor hears the student say things that are bizarre and confused.  They firmly believe they are talking about the same thing, the real numbers, but the student is seen by the professor as wrong and the professor is seen by the students as talking meaningless nonsense.  Even so, they believe they are talking about the same thing.

Nomenclature

I tried various other names before I came to "shared mental objects".

  • I called them abstract objects in abstractmath.  The word "abstract" does not convey their actual character — they are mental and they are shared.
  • They are non-physical objects, a phrase widely used in philosophy, but naming something by a negation is always a bad idea.  
  • Co-mental objects is ugly and comental looks like a misspelling.
  • Intermental objects sounds like it has something to do with burial.  Maybe InterMental?
  • The word entity may avoid some confusion caused by the word "object", which suggests physical object.  But "object" is widely used in philosophy and in math ed in the way it is used here.
  • Meme?  Well, in some sense a shared mental object is a meme.  Memes have a connotation of forcing themselves into your brain that I don't want, but I want to consider the relationship further.

The major advantage of "shared mental object" is that it describes the important properties of the concept: It is a mental object and it is shared by people.  It has no philosophical implications concerning platonism, either. Mathematical objects do have special properties of verifiability that general shared mental objects do not, but my terminology does not suggest any existence of absolute truth or of an Ideal existing in another world.  I don't believe in such things, but some people do and I want to point out that "shared mental object" does not rule such things out — it merely gives a direct evidence-based description of a phenomenon that actually exists in the real world.

References  

Abstract objects in the Stanford Encyclopedia of Philosophy

Abstract object in Wikipedia

Mathematical objects in abstractmath

Mathematical objects in Wikipedia

What is Mathematics, Really?  R. Hersh, Oxford University Press, 1997. 

Previous posts

Representations of mathematical objects 

Representations III: Rigor and Rigor Mortis

Representations II: Dry Bones

Notes on Viewing  

This post uses MathJax. If you see mathematical expressions with dollar signs around them, or badly formatted formulas, try refreshing the screen. Sometimes you have to do it two or three times.

Send to Kindle
exposition, math, representations, understanding math

Representations of mathematical objects

4 Comments

This is a long post. Notes on viewing.

About this post

A mathematical object, or a type of math object, is represented in practice in a great variety of ways, including some that mathematicians rarely think of as "representations".  

In this post you will find examples and comments about many different types of representations as well as references to the literature. I am not aware that anyone has considered all these different ideas of representation in one place before. Reading through this post should raise your consciousness about what is going on when you do math.  

This is also an experiment in exposition.  The examples are discussed in a style similar to the way a Mathematica command is discussed in the Documentation Center, using mostly nonhierarchical bulleted lists. I find it easy to discover what I want to know when it is written in that way.  (What is hard is discovering the name of a command that will do what I want.)

Types of representations

Using language

  • Language can be used to define a type of object.
  • A definition is intended to be precise enough to determine all the properties that objects of that type all have.  (Pay attention to the two uses of the word "all" in that sentence; they are both significant, in very different ways.)
  • Language can be used to describe an object, exhibiting properties without determining all properties.
  • It can also provide metaphors, making use of one of the basic tools of our brain to understand the world. 
  • The language used is most commonly mathematical English, a special dialect of English.
  • The symbolic language of mathematics (distinct from mathematical English) is used widely in calculations. Phrases from the symbolic language are often embedded in a statement in math English. The symbolic language includes among others algebraic notation and logical notation. 
  • The language may also be a formal language, a language that is mathematically defined and is thus itself a mathematical object. Logic texts generally present the first order predicate calculus as a formal language. 
  • Neither mathematical English nor the symbolic language is a formal language. Both allow irregularities and ambiguities.

Mathematical objects

The representation itself may be a mathematical object, such as:

  • A linear representation of a group. Not only are the groups mathematical objects, so is the representation.
  • An embedding of a manifold into Euclidean space. A definition given in a formal language of the first order predicate calculus of the property of commutativity of binary operations. (Thus a property can be represented as a math object.)

Visual representations

A math object can be represented visually using a physical object such as a picture, graph (in several senses), or diagram.  

  • The visual processing of our brain is our major source of knowledge of the world and takes about a fifth of the brain's processing power.  We can learn many things using our vision that would take much longer to learn using verbal descriptions.  (Proofs are a different matter.)
  • When you look at a graph (for example) your brain creates a mental representation of the graph (see below).

Mental representations

If you are a mathematician, a math object such as "$42$", "the real numbers" or "continuity" has a mental representation in your brain.  

  • In the math ed literature, such a representation is called "mental image", "concept image", "procept", or "schema".   (The word "image" in these names is not thought of as necessarily visual.) 
  • The procept or schema describe all the things that come to mind when you think about a particular math object: The definition, important theorems, visual images, important examples, and various metaphors that help you understand it. 
  • The visual images occuring in a mental schema for an object may themselves be mental representations of physical objects. The examples and theorems may be mental representations of ideas you learned from language or pictures, and so on.  The relationships between different kinds of representations get quite convoluted.

Metaphors

Conceptual metaphors are a particular kind of mental representation of an object which involve mentally associating some aspects of the objects with some aspects of something else — a physical object, an image, an action or another abstract object.

  • A conceptual metaphor may give you new insight into the object.
  • It may also mislead you because you think of properties of the other object that the math object doesn't have.
  • A graph of a function is a conceptual metaphor.
  • When you say that a point on a graph "rises as it goes from left to right" your metaphor is an action. 
  • When you say that the cosets of a normal subgroup of a group "get along" with the group multiplication, your metaphor identifies a property they have with an aspect of human behavior.

Properties of representations

A representation of a math object may or may not

  • determine it completely
  • exhibit some of its properties
  • suggest easy proofs of some theorems
  • provide a useful way of thinking about it
  • mislead you about the object's properties
  • mislead you about what is significant about the object

Examples of representations

This list shows many of the possibilities of representation.  In each case I discuss the example in terms of the two bulleted lists above. Some of the examples are reused from my previous publications.

Functions

Example (F1) "Let $f(x)$ be the function defined by $f(x)=x^3-x$."

  • This is an expression in mathematical English that a fluent reader of mathematical English will recognize gives a definition of a specific function.
  • (F1) is therefore a representation of that function.  
  • The word "representation" is not usually used in this way in math.  My intention is that it should be recognized as the same kind of object as many other representations.
  • The expression contains the formula $x^3-x$.  This is an encapsulated computation in the symbolic language of math. It allows someone who knows basic algebra and calculus to perform calculations that find the roots, extrema and inflection points of the function $f$.  
  • The word "let" suggests to the fluent reader of mathematical English that (F1) is a definition which is probably going to hold for the next chunk of text, but probably not for the whole article or book.
  • Statements in mathematical English are generally subject to conventions.  In a calculus text (F1) would automatically mean that the function had the real numbers as domain and codomain.
  • The last two remarks show that a beginner has to learn to read mathematical English. 
  • Another convention is discussed in the following diatribe.

Diatribe 

You would expect $f(x)$ by itself to mean the value of $f$ at $x$, but in (F1) the $x$ has the property of a bound variable.  In mathematical English, "let" binds variables. However, after the definition, in the text the "$x$" in the expression "$f(x)$" will be free, but the $f$ will be bound to the specific meaning.  It is reasonable to say that the term "$f(x)$" represents the expression "$x^3-x$" and that $f$ is the (temporary) name of the function. Nevertheless, it is very common to say "the function $f(x)$" to mean $f$.  

A fluent reader of mathematical English knows all this, but probably no one has ever said it explicitly to them.  Mathematical English and the symbolic language should be taught explicitly, including its peculiarities such as "the function $f(x)$".  (You may want to deprecate this usage when you teach it, but students deserve to understand its meaning.)

The positive integers

You have a mental representation of the positive integers $1,2,3,\ldots$.  In this discussion I will assume that "you" know a certain amount of math.  Non-mathematicians may have very different mental representations of the integers.

  • You have a concept of "an integer" in some operational way as an abstract object.
  • "Abstract object" needs a post of its own. Meanwhile see Mathematical Objects (abstractmath) and the Wikipedia articles on Mathematical objects and Abstract objects.
  • You have a connection in your brain between the concept of integer and the concept of listing things in order, numbering them by $1,2,3,\ldots$.
  • You have a connection in your brain between the concept of an integer and the concept of counting a finite number of objects.  But then you need zero!
  • You understand how to represent an integer using the decimal representation, and perhaps representations to other bases as well. 
  • Your mental image has the integer "$42"$ connected to but not the same as the decimal representation "42". This is not true of many students.
  • The decimal rep has a picture of the string "42" associated to it, and of course the picture of the string may come up when you think of the integer $42$ as well (it does for me — it is a an icon for the number $42$.)
  • You have a concept of the set of integers. 
  • Students need to be told that by convention "the set of integers" means the set of all integers.  This particularly applies to students whose native language does not have articles, but American students have trouble with this, too.
  • Your concept of  "the set of integers" may have the icon "$\mathbb{N}$" associated with it.  If you are a mathematician, the icon and the concept of the set of integers are associated with each other but not identified with each other.
  • For me, at least, the concept "set of integers" is mentally connected to each integer by the "element of" relation. (See third bullet below.)
  • You have a mental representation of the fact that the set of integers is infinite.  
  • This does not mean that your brain contains an infinite number of objects, but that you have a representation of infinity as a concept, it is brain-connected to the concept of the set of integers, and also perhaps to a proof of the fact that $\mathbb{N}$ is infinite.
  • In particular, the idea that the set of integers is mentally connected to each integer does not mean that the whole infinite number of integers is attached in your brain to the concept of the set of integers.  Rather, the idea is a predicate in your brain.  When it is connected to "$42$", it says "yes".  To "$\pi$" it says "No".
  • Philosophers worry about the concept of completed infinity.  It exists as a concept in your brain that interacts as a meme with concepts in other mathematicians' brains. In that way, and in that way only (as far as I am concerned) it is a physical object, in particular an object that exists in scattered physical form in a social network.

Graph of a function

This is a graph of the function $y=x^3-x$:

Graph of a cubic function

  • The graph is a physical object, either on a screen or on paper
  • It is processed by your visual system, the most powerful sensory management system in your brain
  • It also represents the graph in the mathematical sense (set of ordered pairs) of the function $y=x^3-x$
  • Both the mathematical graph and the physical graph are represented by modules in your brain, which associates the two of them with each other by a conceptual metaphor. 
  • The graph shows some properties of the function: inflection point, going off to infinity in a specific way, and so on.
  • These properties are made apparent (if you are knowledgeable) by means of the powerful pattern recognition system in your brain. You see them much more quickly than you can discover them by calculation.
  • These properties are not proved by the graph. Nevertheless, the graph communicates information: for example, it suggests that you can prove that there is an inflection point near $(0,0)$.
  • The graph does not determine or define the function: It is inaccurate and it does not (cannot) show all of the graph.
  • More subtle details about this graph are discussed in my post Representations 2.

Continuity

Example (C1) The $\epsilon-\delta$ definition of the continuity of a function $f:\mathbb{R}\to\mathbb{R}$ may be given in the symbolic language of math:

A function $f$ is continuous at a number $c$ if \[\forall\epsilon(\epsilon\gt0\implies(\forall x(\exists\delta(|x-c|\lt\delta\implies|f(x)-f(c)|\lt\epsilon)))\]

  • To understand (C1), you must be familiar with the notation of first order logic.  For most students, getting the notation right is quite a bit of work.  
  • You must also understand  the concepts, rules and semantics of first order logic.  
  • Even if you are familiar with all that, continuity is still a difficult concept to understand.
  • This statement does show that the concept is logically complicated. I don't see how it gives any other intuition about the concept. 

Example (C2) The definition of continuity can also be represented in mathematical English like this:

A function $f$ is continuous at a number $c$ if for any $\epsilon\gt0$ and for any $x$ there is a $\delta$ such that if $|x-c|\lt\delta$, then $|f(x)-f(c)|\lt\epsilon$. 

  • This definition doesn't give any more intuition that (C1) does.
  • It is easier to read that (C1) for most math students, but it still requires intimate familiarity with the quirks of math English.
  • The fact that "continuous" is in boldface signals that this is a definition.  This is a convention.
  • The phrase "For any $\epsilon\gt0$" contains an unmarked parenthetic insertion that makes it grammatically incoherent.  It could be translated as: "For any $\epsilon$ that is greater than $0$".  Most math majors eventually understand such things subconsciously.  This usage is very common.
  • Unless it is explicitly pointed out, most students won't notice that  if you change the phrase "for any $x$ there is a $\delta$"  to "there is a $\delta$ for any $x$" the result means something quite different.  Cauchy never caught onto this.
  • In both (C1) and (C2), the "if" in the phrase "A function $f$ is continuous at a number $c$ if…" means "if and only if" because it is in a definition.  Students rarely see this pointed out explicitly.  

Example (C3) The definition of continuity can be given in a formally defined first order logical theory. 

  • The theory would have to contain function symbols and axioms expressing the algebra of real numbers as an ordered field. 
  • I don't know that such a definition has ever been given, but there are various semi-automated and automated theorem-proving systems (which I know little about) that might be able to state such a definition.  I would appreciate information about this.
  • Such a definition would make the property of continuity a mathematical object.
  • An automated theorem-proving system might be able to prove that $x^3-x$ is continuous, but I wonder if the resulting proof would aid your intuition much.

Example (C4) A function from one topological space to another is continuous if the inverse of every open set in the codomain is an open set in the domain.

  • This definition is stated in mathematical English.
  • All definitions start with primitive data. 
  • In definitions (C1) – (C3), the primitive data are real numbers and the statement uses properties of an ordered field.
  • In (C4), the data are real numbers and the arithmetic operations of a topological field, along with the open sets of the field. The ordering is not mentioned.
  • This shows that a definition need not mention some important aspects of the structure. 
  • One marvelous example of this is that  a partition of a set and an equivalence relation on a set are based on essentially disjoint sets of data, but they define exactly the same type of structure.

Example (C4) "The graph of a continuous function can be drawn without picking up the chalk".

  • This is a metaphor that associates an action with the graph.
  • It is incorrect: The graphs of some continuous functions cannot be drawn.  For example, the function $x\mapsto x^2\sin(1/x)$ is continuous on the interval $[-1,1]$ but cannot be drawn at $x=0$. 
  • Generally speaking, if the function can be drawn then it can be drawn without picking up the chalk, so the metaphor provides a useful insight, and it provides an entry into consciousness-raising examples like the one in the preceding bullet.

References

  1. 1.000… and .999… (post)
  3. Conceptual blending (post)
  4. Conceptual blending (Wikipedia)
  5. Conceptual metaphors (Wikipedia)
  6. Convention (abstractmath)
  7. Definitions (abstractmath)
  8. Embodied cognition (Wikipedia)
  9. Handbook of mathematical discourse (see articles on conceptual blend, mental representation, representation, metaphor, parenthetic assertion)
  10. Images and Metaphors (abstractmath).
  11. The interplay of text, symbols and graphics in math education, Lin Hammill
  12. Math and the modules of the mind (post)
  13. Mathematical discourse: Language, symbolism and visual images, K. L. O’Halloran.
  14. Mathematical objects (abmath)
  15. Mathematical objects (Wikipedia)
  16. Mathematical objects are “out there?” (post)
  17. Metaphors in computing science ​(post)
  18. Procept (Wikipedia)
  19. Representations 2 (post)     
  20. Representations and models (abstractmath)
  21. Representations II: dry bones (post)
  22. Representation theorems (Wikipedia) Concrete representations of abstractly defined objects.
  23. Representation theory (Wikipedia) Linear representations of algebraic structures.
  24. Semiotics, symbols and mathematical visualization, Norma Presmeg, 2006.
  25. The transition to formal thinking in mathematics, David Tall, 2010
  26. Theory in mathematical logic (Wikipedia)
  27. What is the object of the encapsulation of a process? Tall et al., 2000.
  28. Where mathematics comes from, by George Lakoff and Rafael Núñez, Basic Books, 2000. 
  29. Where mathematics comes from (Wikipedia) This is a review of the preceding book.  It is a permanent link to the version of 04:23, 25 October 2012.  The review is opinionated, partly wrong, not well written and does not fit the requirements of a Wikipedia entry.  I recommend it anyway; it is well worth reading.  It contains links to three other reviews.

Notes on Viewing  

This post uses MathJax. If you see mathematical expressions with dollar signs around them, or badly formatted formulas, try refreshing the screen. Sometimes you have to do it two or three times.

Send to Kindle