Uncle's epic journey
One year ago, my uncle set off from our village on an epic journey, in which every day he travels to the nearest unvisited village and stays there for the night. The villages in our country are randomly located (independently and uniformly) with an average of $1$ village per square mile. Our village is in the middle of a very large country, such that my uncle could not possibly reach the border within one year.
Here is an example of what the first two weeks of his journey could look like.
I haven't heard from my uncle since he left, and I don't have a map. How far away do you reckon he is now? That is,
What is the expectation of his distance (as the crow flies) from our village?
My attempt
I have only been able to get a crude approximation, $\frac14\sqrt{365\pi}\approx8.47$ miles, by making the following two assumptions.
- Assume his journey is effectively just like a $2D$ random walk with constant step size $\lambda$, where $\lambda$ is defined as the expected step size of his actual journey.
- Assume $\lambda=$ expected nearest neighbor distance in a $2D$ Poisson process with intensity $1$.
Under the second assumption, $\lambda=\frac12$. Proof: If $X$ is the distance to a village's nearest neighbor, then the density function is $f(x)=2\pi xe^{-\pi x^2}$, so $\mathbb{E}(X)=\int_0^\infty xf(x)\mathrm dx=\frac12$.
Then under the first assumption, my uncle's expected distance from our village is $\frac{\lambda}{2}\sqrt{365\pi}\approx8.47$.
I suspect the first assumption causes an underestimate: my uncle never revisits a village, so his path involves less "back-tracking" than a $2D$ random walk. The second assumption definitely causes an underestimate, because usually my uncle's step size is greater than the nearest neighbor distance. So I suspect the correct answer is considerably larger than $8.47$.
Context
Recently I have been investigating inherent properties of the $2D$ Poisson process, for example here and here.
Best Answer
It doesn't seem easy to me to even prove that the asymptotic behavior is $E(N) \sim \alpha \sqrt{N}$ for some constant $\alpha$. But we can certainly simulate the problem (and a couple of its variants) to try to find out. Suppose we look at three scenarios:
Now, we can simulate each scenario a large number of times for a few different values of $N$, and estimate $E(N)/\sqrt{N}$ for each. Here's what I find (taking $10^5$ or $10^6$ samples for each $N$ for the first two methods, and $10^3$ samples for the third and most computationally intensive method):
\begin{array}{|c|c|c|} \hline N & {\text {Method A}} & {\text {Method B}} & {\text {Method C}} \\ \hline 50 & 0.4440 \pm 0.0002 & 0.6845 \pm 0.0003 & 1.38 \pm 0.02 \\ 100 & 0.4436 \pm 0.0007 & 0.688 \pm 0.001 & 1.45 \pm 0.02 \\ 200 & 0.4432 \pm 0.0007 & 0.688 \pm 0.001 & 1.56 \pm 0.02 \\ 400 & 0.4432 \pm 0.0007 & 0.687 \pm 0.001 & 1.66 \pm 0.02 \\ 1000 & 0.4428 \pm 0.0007 & 0.687 \pm 0.001 & 1.78 \pm 0.03 \\ \hline \end{array}
The first two scenarios converge more or less immediately to a consistent final value for $E(N)/\sqrt{N}$, giving us confidence that we have the correct form for the large-$N$ behavior.
The third scenario, however, appears to have a coefficient that is still increasing with $N$. This suggests that the large-$N$ behavior for OP's problem may in fact be something more like $E(N) \sim \alpha \sqrt{N \log N}$. But I don't have even a heuristic justification for any particular functional form... many (including $\alpha\sqrt{N}$, if $\alpha$ converges slowly enough) are consistent with the data.
Update:
Expanding on the discussion in the comments. The root mean square of the distance traveled after $N$ steps can be expressed in terms of the autocorrelation expectations $\xi_k^{(n)} \equiv E[r_n \cdot r_{n+k}]$, where $r_n$ is the $n$-th step taken. Let $R_N = \sum_{i=1}^{N} r_i$ be the total displacement after $N$ steps; then $$ E[R_N\cdot R_N]=\sum_{i,j=1}^{N}E[r_i \cdot r_j]=\sum_{i=1}^{N}\xi_0^{(i)} + 2\sum_{k=1}^{N-1}\sum_{i=1}^{N-k}\xi_k^{(i)} \\ = N\xi_0 + 2\sum_{k=1}^{N-1}(N-k)\xi_k \\ = N\xi_0 + 2N\sum_{k=1}^{N-1}\xi_k - 2\sum_{k=1}^{N-1}k\xi_k, $$ where we made the assumption that the $\xi_k$ are time-invariant... i.e., $\xi_k^{(i)}$ does not depend on $i$. If $\xi_k$ goes to zero fast enough as $k\rightarrow\infty$ (for instance, if $\sum_{k=1}^{\infty}k\xi_k$ and $\sum_{k=1}^{\infty}\xi_k$ converge), then clearly $E[R_N \cdot R_N]$ is $\Theta(N)$ and the root mean square of the distance traveled is $\Theta(\sqrt{N})$. On the other hand, if, say, $\xi_k \sim \beta / k$, then the middle term dominates, and the RMS of the distance traveled is instead $\sim \sqrt{2\beta N \log N}$.
My estimates for $\xi_k$ based on $10^3$ simulated journeys of length $1000$ are the following:
\begin{array}{|c|c|} \hline k & \xi_k \\ \hline 1 & 0.1989 \pm 0.0005 \\ 2 & 0.1414 \pm 0.0005 \\ 4 & 0.0849 \pm 0.0005 \\ 8 & 0.0425 \pm 0.0005 \\ 16 & 0.0159 \pm 0.0005 \\ 32 & 0.0069 \pm 0.0006 \\ 64 & 0.0048 \pm 0.0006 \\ 128 & 0.0018 \pm 0.0006 \\ \hline \end{array}
These are certainly consistent with $\xi_k \sim \beta / k $, where $\beta \in (0.2, 0.3)$. In which case we would expect the distance traveled to grow as $\alpha \sqrt{N \log N}$, where $\alpha \in (0.63, 0.77)$. And, indeed, this reproduces the observed behavior very well... assuming $E(N)=\alpha\sqrt{N\log N}$, the direct estimates of $\alpha$ (from the first part of this answer) are $\in (0.675, 0.698)$.