# Why is $!n\pmod{n+k}$ a multiple of $k+1$ so often?

Motivation:

A permutation of a set with no fixed points is called a derangement. The number of derangements of $$n$$ elements is notated as $$!n$$ or "$$n$$ subfactorial".

The relation $$!n=(n-1)(!(n-1)+!(n-2))$$ implies that $$!n\equiv0\pmod{n-1}$$. Likewise, $$!n=n(!(n-1))+(-1)^n$$ implies that $$!n\equiv(-1)^n\pmod{n}$$. I wanted to see if there was any similar relation involving other factors $$n+k$$. I wasn't able to find one; instead, I stumbled upon something weirder.

A weird pattern:

When looking at the table of $$!n\pmod{n+1}$$ (where by $$\text{mod}$$ we mean the least non-negative residue), there's no apparent pattern. However, if you look close enough, you'll notice that a disproportionate amount of elements are multiples of $$2$$. After calculating the first $$5000$$ values, this number seems to converge to around $$76\%$$, greater than the $$50\%$$ you'd expect if this sequence was random.

Likewise, a disproportionate amount of elements in the table of $$!n\pmod{n+2}$$ are multiples of $$3$$: $$56\%$$, contrasted to the expected $$33\%$$.

This trend continues: there's no single $$k\leq1000$$ for which there are less than the expected $$\frac1{k+1}$$ from the first $$\require{cancel}\cancel{5000}$$ $$50000$$ values of $$!n\pmod{n+k}$$ that are multiples of $$k+1$$. Looking at the data, I'd even argue that the trend gets more pronounced the farther you go. Here's a Pastebin with my data [edit: and with the predicted data from Haran's answer].

My question:

Why does this pattern hold? Why is $$!n\pmod{n+k}$$ a multiple of $$k+1$$ so much more often than it would be by chance? And where do these percentages come from?

• +1 very interesting question Dec 27, 2019 at 3:57

Here is a table of the first few derangement values: $$\begin{array}{c}\\ n & !n \\ \hline 1 & 0 \\ 2 & 1\\ 3 & 2 \\ 4 & 9 \\ 5 & 44 \\ 6 & 265 \\ 7 & 1854 \\ 8 & 14833 \\ 9 & 133496 \\ 10 & 1334961 \\ \end{array}$$

First, we can observe that all odd inputs of $$n$$ are giving even outputs while all even inputs of $$n$$ are giving odd outputs. This can be justified using induction. It clearly works for the first few cases from the above table. Now, by induction hypothesis, $$!n+!(n-1) \equiv 1 \pmod{2}$$ which shows that $$!(n+1) \equiv n \pmod{2}$$ which shows that odd inputs give even outputs while even inputs give odd outputs.

For all odd values of $$n$$, we can see that $$!n$$ and $$n+1$$ are even which shows that the residue $$!n \pmod{n+1}$$ is also even. Thus, we have already acquired $$50\%$$ of the multiples of $$2$$. On the other hand, for even values of $$n$$, it seems like there is a $$50\%$$ chance of the least residue being odd or even, as expected. This gives us an expected ratio of $$50\%+\frac{1}{2}(50\%)=75\%$$ of even least residues which agrees with your data.

For modulo $$3$$, you can observe that $$!n \pmod{3}$$ is $$0,1,2,0,2,1 \pmod{3}$$ based on $$n \pmod{6}$$. As our formula is based on recursion, it is easy to see that similar to the above case, we can evaluate $$!n \pmod{3}$$ using $$n \pmod{6}$$. Again, like the above case, whenever $$n \equiv 1,4 \pmod{6}$$ i.e. whenever $$n \equiv 1 \pmod{3}$$, we have $$!n \equiv 0 \pmod{3}$$. Thus, the least residue of $$!n \pmod{n+2}$$ will be divisible by $$3$$. This is $$1$$ out of every $$3$$ cases. That gives an expected ratio of $$\frac{1}{3}(100\%)+\frac{2}{3}(33.33\%)=55.55\%$$, agreeing with your data.

We would like to show that in general, $$!n \equiv 0 \pmod{m}$$ for $$n\equiv 1 \pmod{m}$$. This holds for base case $$n=1$$ as $$!1=0$$. We can use the summation formula for derangements to prove this easily- $$!(qm+1)=(qm+1)!\sum_{i=0}^{qm+1} \frac{(-1)^i}{i!}$$ We only need to consider the last two terms as the rest of the terms are divisible by $$qm$$. So- $$!(qm+1) \equiv (qm+1)(-1)^{qm}+(-1)^{qm+1} \equiv (-1)^{qm}+(-1)^{qm+1} \equiv 0 \pmod{m}$$

We have proved our hypothesis that if $$n \equiv 1 \pmod{m}$$ then $$!n \equiv 0 \pmod{m}$$. When we take $$!n \pmod{n+k}$$ and check its value $$\pmod{k+1}$$, if $$\gcd(k+1,n+k)=1$$, we can always expect equal chance for all values $$\pmod{k+1}$$. So, if $$k+1$$ is prime, we would expect the expected ratio to be- $$\frac{1}{k+1}(100\%)+\frac{k}{k+1}\bigg(\frac{1}{k+1} \cdot 100\%\bigg) = \frac{2k+1}{(k+1)^2}(100\%)=\bigg(1-\frac{k^2}{(k+1)^2}\bigg)(100\%)>\frac{100\%}{k+1}$$

which agrees with values such as $$k=1,2,4,6$$ where $$k+1$$ is prime.

When $$k+1=\prod_{i=1}^tp_i^{a_i}$$ is composite, we cannot always say that $$\gcd(k+1,n+k)=1$$. Thus, we must check for all possible $$\gcd$$ values. If we have $$\gcd(k+1,n+k)=\prod_{i=1}^tp_i^{b_i}$$, we have $$n \equiv 1 \pmod{\prod_{i=1}^tp_i^{b_i}}$$ and $$n+k \equiv 0 \pmod{\prod_{i=1}^tp_i^{b_i}}$$. This $$\gcd$$ is seen for $$\phi(\prod_{i=1}^tp_i^{a_i-b_i})$$ cases. Moreover, we must also have divisibility by $$\prod_{i=1}^tp_i^{a_i}$$ given divisibility by $$\prod_{i=1}^tp_i^{b_i}$$, which happens with a chance of $$1$$ in $$\prod_{i=1}^tp_i^{a_i-b_i}$$. Thus our expected value is: $$E=\frac{1}{\prod_{i=1}^tp_i^{a_i}}\sum \frac{\phi(\prod_{i=1}^tp_i^{a_i-b_i})}{\prod_{i=1}^tp_i^{a_i-b_i}}=\frac{1}{k+1} \cdot \sum_{d \mid (k+1)} \frac{\phi(d)}{d}=\frac{1}{k+1} \cdot \prod_{i=1}^t \bigg(1+\frac{p_i-1}{p_i} \cdot a_i\bigg)$$

Thus, we conclude that for $$k+1=\prod_{i=1}^tp_i^{a_i}$$, the expected value (in fraction) is: $$E=\frac{1}{k+1} \cdot \prod_{i=1}^t \bigg(1+\frac{p_i-1}{p_i} \cdot a_i\bigg)$$

This seems to agree with all values of $$k$$ settling the problem.

• I think this interprets the phenomenon pretty well. Great job! Dec 27, 2019 at 7:17
• @URL I have completed my answer. The formula below gives you the expected value for any $k$. I have checked for a few values whether it agrees with your data. The answer looks promising. You could check for all the $k$ values with a program if you want (because I have no idea how to check). Dec 27, 2019 at 7:38
• @URL Could you join me in chat? chat.stackexchange.com/rooms/102606/room-for-haran-and-url Dec 27, 2019 at 7:51

Possible explanation for $$k=1$$ case:

It is easy to prove that $$!n$$ is even iff $$n$$ is odd. For such $$n$$, the expression $$!n \pmod{n+1}$$ means you're dividing an even number by another even number - the remainder must therefore be even. (I assume you count a remainder of $$0$$ as even / a multiple of $$2$$.)

Therefore, every odd $$n$$ satisfies your condition. I have no idea if even $$n$$ values satisfy your condition, but if those behave randomly, you'd get approximately half the even $$n$$ values, plus all the odd $$n$$ values, to satisfy your condition. This would make up a proportion of $$3/4$$, very close to your observed $$76\%$$.