He concluded ""We (the indivisible divinity that works in us) have dreamed the world. We have dreamed it resistant, mysterious, visible, ubiquitous in space and firm in time, but we have allowed slight, and eternal, bits of the irrational to form part of its architecture so as to know that it is false."

I think I might have something interesting to say about that and I tried to write it here. If you must waste your time reading this blog, read that one not this one. But going from the sublime to the ridiculous I have been a twit (but honestly not a troll) on Twitter. I said I thought we don't need the concept of a derivative (in the simple case of a scalar function of a scalar the limit as delta x goes to zero of ther ratio delta Y over delta x - I insult you with the definition just to be able to write that my tweet got very very ratiod).

In avatars of the Tortoise II I argued that we can consider space time to be a finite set of points with each point in space the same little distance from its nearest neighbors and each unit of time the same discrete jump littleT from the most recent past. If I am right we don't need derivatives to analyse functions of space, there are just slopes, or time, or position as a function of time (velocity and acceleration and such). In such a model, there are only slopes as any series which goes to zero, gets to zero after a finite number of steps and the formula for a derivative must include 0/0.

I will make more arguments against derivatives. First I will say that we learn nothing useful if we know the first, second, ... nth ... derivative of a function at X. Second I will argue that we can do what we do with derivatives using slopes. Third I will argue that current actual applied math consists almost entirely of numerical simulations on computers which are finite state autometa and which do not, in fact, handle continuums when doing the simulating. They take tiny little steps (just as I propose).

I am going to make things simple (because I don't type so good and plane ascii formulas are a pain). I will consider scalar functions of scalars (so the derivative will be AB ap calculus). I will also consider only derivatives at zero.

f'(0) = limit as x goes to zero of (f(x)-f(0))/x

that is, for any positive epsilon there is a positive delta so small that if |x|

To go back to Avatars of the tortoise I, another equally valid definition of a derivative at zero is, consider the infinite series X_t = (-0.5)^t.

f'(0) = the limit as t goes to infinity of (f(x_t)-f(0))/x_t that is, for any positive epsilon there is a positive N so big that, if t>N then |f'(0) - (f(x_t)-f(0))/x_t|< epsilon.

so we have again the limit as t goes to infinity and the large enough N with know way of knowing if the n which interests us (say 10^1000) is large enough. Knowing the limit tells us nothing about the billionth element. The exact same number is the billionth element of a large infinity of series some of which converge to A for any real number A, so any number is as valid an asymptotic approximation as any other, so none is valid.

Now very often the second to last step of the derivation of a derivative includes an explicit formula for f'(0) - (f(x)-f(0))/x and then the last step consists of proving it goes to zero by finding a small enough delta as a function of epsilon. That formula right near the end is useful. Te derivative is not. Knowing that there is a delta is not useful if we have no idea how small it must be.

In general for any delta no matter how small for any epsilon no matter how small, there is a function f such that |f'(0) - (f(delta)-f(0))/delta|>1/epsilon (I will give an example soon). for any function there is a delta does not imply that there is a delta which works for any function. The second would be useful. The first is not always useful.

One might consider the first, second, ... nth derivatives and an nth order Taylor series approximation which I will call TaylorN(x)

for any N no matter how big, for any delta no matter how small for any epsilon no matter how small, there is a function f such that |TaylorN(delta) - f(delta)|>1/epsilon

for example consider the function f such that

f(0) = 0, if x is not zero f(x) = (2e/epsilon)e^(-(delta^2/x^2))

f(delta) = 2/epsilon > 1/epsilon.

f'(0) is the limit as x goes to zero of

-(2e/epsilon)(2delta^2/x^3)e^(-(delta^2/x^2)) = 0.

the nth derivative the limit as x goes to zero of an n+2 order polynomial times e^(-(delta^2/x^2)) and so equals zero.

The Nth order taylor series approximation of f(x) equals zero for every x. for x = delta it is off by 2/epsilon > 1/epsilon.

There is no distance from zero so small and no error so big that there is no example in which the Nth order Taylor series approximation is definitely not off by a larger error at that distance.

Knowing all the derivatives at zero, we know nothing about f at any particular x other than zero. Again for any function, for any epsilon, there is a delta, but there isn't a delta for any function. Knowing all the derivatives tells us nothing about how small that delta must be, so nothing we can use.

So if things are so bad, why does ordinary caluclus work so well ? It works for a (large) subset of problems. People have learned about them and how to recognise them either numerically or with actual experiments or empirical observtions. But that succesful effort involved numerical calculations (that is arithmetic not calculus) or experiments or observations. It is definitely Not a mathematical result that the math we use works. Indeed there are counterexamples (of which I presented just one).

part 2 of 3 (not infinite even if it seems that way but 3. If the world is observationally equivalent to a word with a finite set of times and places, then everything in physics is a slope. More generally, we can do what we do with derivatives and such stuff with discrete steps and slopes. We know this because that is what we do when faced with hard problems without closed form solutions. We hand them over to computers which consider a finite set of numbers with a smallest step. and that quickly gets me to part 3 of 3 (finally). One person on Twitter says we need to use derivatives etc to figure out how to write the numerical programs we actually use in applications. This is an odd claim. I can read (some) source code (OK barely source code literate as I am old but some). I can write (some) higher higher language source code. I can force myself to think in some (simple higher higher language) source code (although in practice I use derivatives and such like). Unpleasant but not impossible.

Someone else says we use derivatives to know if the simulation converges or, say, if a dynamical system has a steady state which is a sink or stuff like that. We do, but tehre is no theorem that this is a valid approach and there are counterexamples (basically based on the super simple one I presented). All that about dynamics is about *local* dynamics and is valid if you start out close enough and there is no general way to know how close is close enough. In practice people have found cases where linear and Taylor series (and numerical) approximations work and other cases where they don't (consider chaotic dynamical systems with positive Lyaponoff exponents and no I will not define any of those terms).

Always the invalid pretend pure math is tested with numerical simulations or experiments or observations. People learn when it works and tell other people about the (many) cases where it works and those other people forget the history and pour contempt on me on Twitter.