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Probability and Cumulative Dice Sums

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The Kelly Criterion and a Sure Thing

The Kelly Criterion is an alternative to standard utility theory, which seeks to maximize expected utility. Instead, the Kelly Criterion seeks to maximize expected growth. That is, if we start out with an initial bankroll \(B_0\), we seek to maximize \(\mathrm{E}[g(t)]\), where \(B_t = B_0\cdot e^{g(t)}\).
As a simple example, consider the following choice. We can have a sure $3000, or we can take the gamble of a \(\frac{4}{5}\) chance of $4000 and a \(\frac{1}{5}\) chance of $0. What does Kelly say?
Assume we have a current bankroll of \(B_0\). After the first choice we have \(B_1 = B_0+3000\), which we can write as \[\mathrm{E}[g(1)] = \log\left(\frac{B_0+3000}{B_0}\right);\]for the second choice we have \[\mathrm{E}[g(1)] = \frac{4}{5} \log\left(\frac{B_0+4000}{B_0}\right).\]And so we want to compare \(\log\left(\frac{B_0+3000}{B_0}\right)\) and \(\frac{4}{5} \log\left(\frac{B_0+4000}{B_0}\right)\).
Exponentiating, we're looking for the positive root of \[{\left({B_0+3000}\right)}^5 - {B_0}\cdot {\left({B_0+4000}\right)}^4=0.\]Wolfram Alpha now tells us that we should go with the sure thing if \(B_0 < $4942.92\), and take the gamble otherwise.

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