# Maximum entropy of dice

How can be proven that the entropy of a die with equal probability for all its faces is at its maximum?

It's clear that the entropy will be smaller if there are more chances for a particular face, but how can be this proven?

• You can see my answer here. I found this snippet from this thesis can exactly answer your question: > In information theory, entropy or uncertainty is generally identified > with quantity of information. To understand why this correspondence > makes sense, consider how the informational state changes when an > actual event occurs, if you already knew the underlying probability > distribution. In the case of the heavily biased coin, actual flips > tel – Lerner Zhang Dec 16 '18 at 6:27

It is a direct consequence of the concavity of the function $$-x \log(x)$$ for arguments between $$0$$ and $$1$$.

The entropy of a die with $$n$$ sides and probabilities $$p_1, p_2, \ldots, p_n$$ is defined to be the sum of the $$-p_i \log(p_i)$$, which is a continuous function of the $$p_i$$ for all possible probability assignments (including possibly setting some of them to zero). Taking second derivatives gives a diagonal hessian with values $$-1/p_i$$, showing the function is everywhere concave. This immediately implies it has a unique critical point where none of the $$p_i$$ is zero and that it corresponds to a global maximum. But the entropy is a symmetric function of the $$p_i$$, whence it must have a critical point where all the $$p_i$$ are equal, QED.

### Edit

There is a proof which is at once elementary and pretty. It uses two simple, well-known ideas:

1. A function is optimized simultaneously with any monotonic re-expression of its values. In particular, the entropy $$H = -\sum p_i \log(p_i)$$ is maximized when $$e^{-H} = \prod (p_i)^{p_i}$$ is minimized.

2. The (weighted) Geometric Mean-Harmonic Mean Inequality. Let $$x_i$$ be arbitrary positive numbers and $$p_i$$ be positive "weights" summing to $$1$$. The weighted geometric mean (GM) of the $$x_i$$ is $$\prod x_i^{p_i}$$. Similarly, the weighted harmonic mean (HM) of the $$x_i$$ is the reciprocal of $$\sum p_i(1/x_i)$$. The GM-HM Inequality asserts that $$GM \ge HM$$ and that equality holds if and only if all the $$x_i$$ are equal to each other. There are many elementary proofs of this. (A good account of the weighted version of the GM-HM Inequality is difficult to find on the Web, although it is well covered in various texts. See the top of page 5 in Bjorn Poonen's notes on inequalities, for instance.)

Looking at #1, we recognize $$e^{-H}$$ as the GM of the $$x_i$$ with weights $$p_i$$ where $$x_i=p_i$$. From the GM-HM Inequality, this value is never less than the HM, which is the reciprocal of $$\sum_i p_i \left(1/x_i\right)$$ = $$\sum_i p_i/p_i$$ = $$\sum_i 1 = n$$. Also, the GM and HM are equal to each other (and therefore equal to $$1/n$$) if and only if all the $$p_i$$ are equal. It is immediate that $$H$$ is maximized when the $$p_i$$ are equal and will have the maximum value $$\log(n)$$.

This argument covers all but the cases where some of the $$p_i$$ may be zero. But in such cases, where there are $$n' \lt n$$ nonzero $$p_i$$, the foregoing shows that $$H$$ cannot exceed $$n'$$, whence it is not possible to maximize the entropy by setting any of the probabilities to zero.