TITLE: Game theory Computing pure Nash equilibrium probability
QUESTION [6 upvotes]: We have a $2$-player game and each player has $n$ strategies. The payoffs for each player are in range $\left[0,1\right]$ and are selected at random. 
Show that the probability that this random game has a pure deterministic Nash equilibrium approaches $1-1/\mathrm e$ as $n$ goes to infinity.
Can anyone find a solution to this problem?

REPLY [2 votes]: I believe you wanted the fact that the probability of pure Nash Equilibrium existing is at least $1-\frac{1}{e}$. The fact that it is exactly $1-\frac{1}{e}$ is highly non-trivial (I believe) and that is why it finds its place in the above mentioned research paper. Showing the inequality is not difficult.
First let us take the payoff matrix and break it into two matrices- one for the row player (call this matrix $R$) and one for the column player (call it $C$). Now I will make sure that we understand Nash Equilibrium properly in this setup. So Nash Equilibrium is $(i,j)$ means that row player wouldn't change his strategy of playing $i$ which means that $(i,j)$ is maximum in its column in $R$. Similarly column player wouldn't change his choice of $j$ meaning that $(i,j)$ is maximum in its row in $C$.
Now I will do some unnecessary operations which will help us visualize the problem in a simpler way. I will rearrange the action space of row player-which allows me to swap rows of $R$ and $C$ simultaneously without changing the game. I will do this in such a way that the first $k_1$ rows of $C$ contain their respective maximum elements in their first columns. Similarly the next $k_2$ rows of $C$ contain their respective maximum elements in their second columns. Like this I go on and there are $k_i$ rows with maximas in their $i$-th columns. (Note that $\forall i, 0\le k_i\le n$)

Now we will have a pure Nash Equilibrium if and only if the first column, $C_1$, of $R$ has its maximum element in the first $k_1$ elements or if the second column $C_2$ has its maximum element among the elements $k_1+1,k_1+2,...,k_1+k_2$ and so on (i.e. $C_i$ needs to have its maximum element in the corresponding $i$ elements :-namely, $(k_1+k_2+...+k_i)+1,(k_1+k_2+...+k_i)+2,...,$ $(k_1+k_2+...+k_i+k_{i+1}$)
Whethere there is a pure Nash Equilibrium corresponding to the first $k_1$ rows of $C$ is independent of whether there is a pure Nash Equilibrium corresponding to the next $k_2$ rows of $C$ (or equivalently corresponding to first row of $R$) since the first event depends only on first $k_1$ rows of $C$ and first column of $R$ while the next event depends only on the next $k_2$ rows of $C$ and the second column of $R$ which are independent to each other. Similarly, it is easy to see that the event of a pure Nash Equilibria corresponding to the relevant $k_i$ rows of $C$ is independent of the event of a pure Nash Equilibrium in the relevant $k_j$ rows of $C$. $(i\ne j)$
Now we do the calculations :
Probability of no pure Nash Equilibrium corresponding to the first column of $R$ $=1-\frac{k_1}{n}$
Probability of no pure Nash Equilibrium corresponding to the second column of $R$ $=1-\frac{k_2}{n}$
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Probability of no pure Nash Equilibrium corresponding to the second column of $R$ $=1-\frac{k_n}{n}$
And these are all independent so probability of no pure Nash Equilibrium in game $$=\prod_{i=1}^{n}(1-\frac{k_i}{n})\le\prod_{i=1}^{n}(1-\frac{1}{n})$$
This is true since the terms $1-\frac{k_i}{n}$ sum up to $n-1$ and are all positive. So their product is maximum when they are all equal. Also we have the following :
$$\lim_{n\to\infty}\prod_{i=1}^{n}(1-\frac{1}{n})=\frac{1}{e}$$
So probability of no pure Nash Equilibrium is $\le \frac{1}{e}$ as $n\to \infty$
And hence probability of a pure Nash Equilibrium existing is $\ge 1-\frac{1}{e}$