Gibbs inequality

For probability distributions $p$ and $q$ on the same finite set,with equality exactly when $p=q$ on the support of $p$.

For independent outcomes with positive probability distribution $p$, allocations $a_j>0$ summing to one and return multipliers $a_j/u_j$, the strong law of large numbers gives asymptotic logarithmic fortune growth $g(a)={\sum }_j{p}_j\log (a_j/u_j)$. Gibbs inequality gives $g(a)=g(p)-D(p\Vert a)$, so the unique optimum is $a=p$. Writing $U={\sum }_j{u}_j$ gives $g(p)=D(p\Vert u/U)-\log U$. Thus $U<1$ guarantees positive optimal growth, whereas $U>1$ allows the house to force negative growth by choosing $u_j=U{p}_j$, but does not guarantee decline for every choice of $u$.