Does this discrete distribution have a name? Does this discrete distribution have a name? For $i \in 1...N$
$f(i) = \frac{1}{N} \sum_{j = i}^N \frac{1}{j}$
I came across this distribution from the following: I have a list of $N$ items ranked by some utility function. I want to randomly select one of the items, biasing toward the start of the list. So, I first choose an index $j$ between 1 and $N$ uniformly. I then select an item between indices 1 and $j$. I believe this process results in the above distribution.
 A: This appears to be related to the Whitworth distribution. (I don't believe it is the Whitworth distribution, since if I remember right, that's the distribution of a set of ordered values, but it seems to be connected to it, and relies on the same summation-scheme.)
There's some discussion of the Whitworth (and numerous references) in 
Anthony Lawrance and Robert Marks, (2008)
"Firm size distributions in an industry with constrained resources,"
Applied Economics, vol. 40, issue 12, pages 1595-1607
(There looks to be a working paper version here)
Also see
Nancy L Geller, (1979)
A test of significance for the Whitworth distribution,
Journal of the American Society for Information Science, Vol.30(4), pp.229-231 
A: You have a discretized version of the negative log distribution, that is, the distribution whose support is $[0, 1]$ and whose pdf is $f(t) = - \log t$.
To see this, I'm going to redefine your random variable to take values in the set $\{ 0, 1/N, 2/N, \ldots, 1 \}$ instead of $\{0, 1, 2, \ldots, N \}$ and call the resulting distribution $T$.  Then, my claim is that
$$ Pr\left( T = \frac{t}{N} \right) \rightarrow - \frac{1}{N} \log \left( \frac{t}{N} \right) $$
as $N, t \rightarrow \infty$ while $\frac{t}{N}$ is held (approximately) constant.  
First, a little simulation experiment demonstrating this convergence.  Here's a small implementation of a sampler from your distribution:
t_sample <- function(N, size) {
  bounds <- sample(1:N, size=size, replace=TRUE)
  samples <- sapply(bounds, function(t) {sample(1:t, size=1)})
  samples / N
}

Here's a histogram of a large sample taken from your distribution:
ss <- t_sample(100, 200000)
hist(ss, freq=FALSE, breaks=50)


and here's the logarithmic pdf overlaid:
linsp <- 1:100 / 100
lines(linsp, -log(linsp))


To see why this convergence occurs, start with your expression
$$ Pr \left( T = \frac{t}{N} \right) = \frac{1}{N} \sum_{j=t}^N \frac{1}{j} $$
and multiply and divide by $N$
$$ Pr \left( T = \frac{t}{N} \right) = \frac{1}{N} \sum_{j=t}^N \frac{N}{j} \frac{1}{N} $$
The summation is now a Riemann sum for the function $g(x) = \frac{1}{x}$, integrated from $\frac{t}{N}$ to $1$.  That is, for large $N$,
$$ Pr \left( T = \frac{t}{N} \right) \approx \frac{1}{N} \int_{\frac{t}{N}}^1 \frac{1}{x} dx = - \frac{1}{N} \log \left( \frac{t}{N} \right)$$ 
which is the expression I wanted to arrive at.
