A post on Decision Science about a problem of Uri Wilensky‘s has been making the rounds:

Imagine a room full of 100 people with 100 dollars each. With every tick of the clock, every person with money gives a dollar to one randomly chosen other person. After some time progresses, how will the money be distributed?

People often expect the distribution to be close to uniform. But this isn’t right; the simulations in the post show clearly that inequality of wealth rapidly appears and then persists (though each individual person bobs up and down from rich to poor.) What’s going on? Why would this utterly fair and random process generate winners and losers?

Here’s one way to think about it. The possible states of the system are the sets of nonnegative integers (m_1, .. m_100) summing to 10,000; if you like, the lattice points inside a simplex. (From now on, let’s write N for 100 because who cares if it’s 100?)

The process is a random walk on a graph G, whose vertices are these states and where two vertices are connected if you can get from one to the other by taking a dollar from one person and giving it to another. We are asking: when you run the random walk for a long time, where are you on this graph? Well, we know what the stationary distribution for random walk on an undirected graph is; it gives each vertex a probability proportional to its degree. On a regular graph, you get uniform distribution.

Our state graph G isn’t regular, but it almost is; most nodes have degree N, where by “most” I mean “about 1-1/e”; since the number of states is

and, of these, the ones with degree N are exactly those in which nobody’s out of money; if each person has a dollar, the number of ways to distribute the remaining N^2 – N dollars is

and so the proportion of states where someone’s out of money is about

.

So, apart from those states where somebody’s broke, in the long run *every possible state is equally likely;* we are just as likely to see $9,901 in one person’s hands and everybody else with $1 as we are to see exact equidistribution again.

What is a random lattice point in this simplex like? Good question! An argument just like the one above shows that the probability nobody goes below $c is on order e^-c, at least when c is small relative to N; in other words, it’s highly likely that somebody’s very nearly out of money.

If X is the maximal amount of money held by any player, what’s the distribution of X? I didn’t immediately see how to figure this out. You might consider the continuous version, where you pick a point at random from the *real* simplex

.

Equivalently; break a stick at N-1 randomly chosen points; what is the length of the longest piece? This is a well-studied problem; the mean size of the longest piece is about N log N. So I guess I think maybe that’s the expected value of the net worth of the richest player?

But it’s not obvious to me whether you can safely approximate the finite problem by its continuous limit (which corresponds to the case where we keep the number of players at N but reduce the step size so that each player can give each other a cent, or a picocent, or whatever.)

What happens if you give each of the N players just one dollar? Now the uniformity really breaks down, because it’s incredibly unlikely that nobody’s broke. The probability distribution on the set of (m_1, .. m_N) summing to N assigns each vector a probability proportional to the size of its support (i.e. the number of m_i that are nonzero.) That must be a well-known distribution, right? What does the corresponding distribution on partitions of N look like?

**Update**: Kenny Easwaran points out that this is basically the same computation physicists do when they compute the Boltzmann distribution, which was new to me.