The expected square of the determinant of a random row stochastic matrix In this
question Anthony Quas asks about the expected absolute value of
the determinant of an $n\times n$ row stochastic matrix $A$, where
the rows are independently selected from the uniform distribution on
the unit $(n-1)$-dimensional simplex $x_1+\cdots+x_n=1$, $x_i\geq
0$. I can show by a messy computation that the integral over all such
matrices of $(\det A)^2$ is $1/(n+1)!^{n-1}$. Is there a
noncomputational reason for such a simple value?
 A: Hidden in the comment by Victor Kleptsyn is a really nice argument. Since nobody upvoted that comment yet (I just did) it's probably worth expanding it.
From a probabilistic approach it's more natural to show the equivalent statement that the expectation of $(\det A)^2$ equals $\frac{(n+1)!}{(n(n+1))^n}$. The trick is to realize the uniform measure on the simplex as the distribution of
$$ (X_1,\dots,X_n) := \frac{(Y_1,\dots,Y_n)}{Y_1 + \cdots +Y_n} $$
where $(Y_i)$ are i.i.d. exponential random variables (say, of parameter 1). All you need to know is that $\mathbf{E} [Y_i] =1$ and $\mathbf{E} [Y_i^2] =2$. Moreover in this representation $(X_i)$ are independent from $S:=Y_1+\cdots+Y_n$. It follows that we have the following identity in distribution
$$ B = D A $$
where $B$ is a matrix with i.i.d. exponential entries and $D$ a diagonal matrix whose entries are independent copies of $S$, with $A$ and $D$ moreover independent. Therefore
$$ \mathbf{E} [\det B^2] = \mathbf{E} [\det D^2] \cdot \mathbf{E} [\det A^2] .$$
It is very easy to check that $\mathbf{E} [\det D^2] = (n(n+1))^n$, and $\mathbf{E} [\det B^2]$ can be expanded as
$$ \sum_{\sigma,\tau \in \mathfrak{S}_n} \varepsilon(\sigma) \varepsilon(\tau) 
2^{Fix(\sigma \tau^{-1})} = n! \sum_{\pi \in \mathfrak{S}_n} \varepsilon(\pi)
2^{Fix(\pi)} = n! \det \begin{pmatrix} 2 & 1 & \cdots & 1 \\ 1 & 2 & \ddots & \vdots \\ \vdots & \ddots & \ddots & \vdots \\ 1 & \cdots & \cdots & 2 \end{pmatrix} = (n+1)!$$
(Here $Fix$ denotes the number of fixed points.)
A: Not a full answer.
The determinant of an $n\times n$ matrix $A$ is the volume of the parallelotope spanned by its rows. That volume is equal to $n!$ times the volume of the $n$-simplex whose vertices are at the origin and at the vectors which are the rows of $A$.
When those vectors are on the standard $(n-1)$-simplex, we can compute the volume of the $n$-simplex by the formula for the volume of a pyramid: the volume of the base times the height, divided by $n$.
In this case, the base is the $(n-1)$-simplex formed by the rows of $A$, and the height is $\frac{1}{\sqrt{n}}$.
Thus the determinant of $A$ is $\frac{n! V}{n\sqrt{n}}$, where $V$ is the volume of the $(n-1)$-simplex spanned by the rows of $A$.
The square of the determinant is $(\det A)^2 = \frac{n!^2 V^2}{n^3}$.
Therefore the average value over all such $A$ of the square of the determinant is $\frac{n!^2}{n^3}E(V^2)$.
"All" that remains is to compute the expected value $E(V^2)$ of the squared volume of a subsimplex of a standard simplex. Note that the analogous question for the expected volume $E(V)$ is apparently quite difficult, see http://mathworld.wolfram.com/SimplexSimplexPicking.html
