# How exactly is the St Petersburg Paradox giving bounded payoff in average-of-N-trials?

I understand why the expected value of the St Petersburg Paradox is algebraically infinite, but intuition tells me that in practice any given round of the game will not go on multiplying the pot for an infinite number of steps, so I am attempting a more nuanced analysis.

I wrote a computer program to measure the actual payoff in practice.

In pseudocode, the linked algorithm is basically

for each trial
pot = 2
if (coin toss is tails)
end this trial with no winnings
pot <-- pot x 2
winnings <-- pot
end trial loop
print winnings / number of trials


Here are the raw results

TRIALS      AVERAGE PAYOFF (one column per run of the program)
10          2.6     6.8     3.8     2.4     0.6     3.2
100         14.02   6.44    9.8     5.5     5.54    6.3
1000        7.494   9.516   9.254   4.162   4.676   4.36
10000       6.864   7.362   217.462 11.3302 5.722   13.2424
100000      19.248  9.78776 14.4392 15.9848 14.1321 14.9646
1000000     26.0934 10.4752 13.1372 15.6501 10.6382 9.93312
10000000    12.3089 12.9838 11.7851 17.3922 12.9416 27.9271
100000000   23.467  14.6506 15.1155 16.2025 12.4644 15.5596
1000000000  18.4466 13.9933 16.7371 14.888  15.5726


Let's assume the random number generator is behaving as documented and there is no bias in the program. Note that the numbers involved are well within the limits of numerical stability for a FPU.

Apart from one outlier, where apparently sufficiently many trials in the 10,000 case were lengthy enough to move the average, the average payoff seems to be less than 30. This result is remarkably consistent.

Now, the analytical response to this would be "a long trial is exponentially unlikely to happen but produces an exponentially increasing payoff if you wait long enough until you see it". However, unless you play the game an infinite number of times, you will not see an infinite payoff in practice.

My next observation is that as the number of trials increases the average payoff over all trials seems to stabilise at around 15. (If you stick to taking an average of, say, 10 trials, then you soon start to see larger average payoffs as you re-run the program, dropping off exponentially as you would expect).

What I'm getting at is that, from an economic and decision-theoretic point of view, it appears to be demonstrably irrational to put down more than about $15,000,000,000 to play the game 1,000,000,000 times. Intuitively we could say that, in the long run, one expects that most games are short and this constrains the average payoff in practice; the algebraic limit doesn't matter because we never actually get there. How can we quantify this notion? How can we derive this apparently stable practical-limit-of-the-average-over-many-trials, which seems to be about 15? • Have you looked at the Wikipedia article on this? It has quite a bit of discussion about various approaches. Jun 20 '16 at 21:34 ## 1 Answer Your results are not statistically significant. If you limit the number of trials, the expected winnings is finite. For$n$trials we have $$E(W_n) = \sum_{k=1}^n2^{-k}2^k = n.$$ So you should see expected winnings of$10$for$10$trials,$100$for$100$trials, etc. However, $$E(W_n^2) = \sum_{k=1}^n2^{-k}2^{2k} = \sum_{k=1}^n2^{k} = 2^n - 2$$ and the variance is enormous $$\sigma_n^2 = E(W_n^2) - [E(W_n)]^2= 2^n-2 - n^2.$$ With only$N = 1000$Monte Carlo paths, the sampling error is for, say$n = 100$trials, $$E \approx \frac{\sigma_n}{\sqrt{N}}= \frac{\sqrt{2^{100}- 2- (100)^2}}{\sqrt{1000}} \approx 3 \times10^{13}.$$ You are also going to encounter overflow issues with winnings that can grow to$2^n$when$n$is very large. • But one trial is tossing a coin until it turns to heads, right? Isn't the point of the paradox that the expected value of a single trial is infinite (50% chance of £2 + 25% chance of £4 + ...)? Jun 22 '16 at 8:00 • @spraff: That is correct - hence impossible to confirm with simulation. I apologize if I used "trial" to mean single coin toss. It seems you use it to represent the infinite binary string. My point was that even if you truncate the game (i.e., game ends if either a heads appears or we reach n coin flips) the expected payoff is difficult to estimate with confidence as n gets large -- unless the number of "trials" becomes inordinately large. The infinite expected payoff results because all possible outcomes no matter how rare (such as 100 tails followed by 1 head) make the same contribution. – RRL Jun 22 '16 at 15:55 • In your case, there is nothing special about the number 15. The expected stopping time here is$\sum_{k=1}^\infty k 2^{-k} = 2.\$ On average your simulated trial terminates after only 2 flips! Even with your largest number of simulation paths you never reach sufficiently long plays to get an estimate of the expected payoff much beyond 15.
– RRL
Jun 22 '16 at 16:01
• I would suggest repeating the simulation where the game ends if n flips are reached. See how many simulations you need to confidently estimate the theoretical expected payoff of n. This should give you a better sense of what is happening here.
– RRL
Jun 22 '16 at 16:02
• In practice, studies show that rational players would not pay more than the expected payoff of 2-3 flips to participate. This was resolved long ago by introducing the concept of utility -- specifically log utility for this problem
– RRL
Jun 22 '16 at 16:07