Suppose $u(\cdot)$ and $v(\cdot)$ are two differentiable, strictly increasing, and strictly concave real functions. Specifically, $v(\cdot)$ is "more concave" than $u(\cdot)$ in the sense that there exists an increasing and strictly concave function $\phi(\cdot)$ such that $v(x)=\phi(u(x))$ at all $x$. It is also equivalent to \begin{equation} \frac{v''(x)}{v'(x)}<\frac{u''(x)}{u'(x)} \textrm{ for any }x\,. \end{equation}
Let $p_i\in(0,1), \sum_{i\in I}p_i=1$ be probabilities and $|I|>2$. Let $x_i$ and $y_i$ be strictly positive for all $i\in I$. Assume \begin{equation} \sum_{i\in I}p_ix_i<\sum_{i\in I}p_iy_i, \end{equation} and \begin{equation} \sum_{i\in I}p_iu(x_i)=\sum_{i\in I}p_iu(y_i). \end{equation}
Conjecture: \begin{equation} \sum_{i\in I}p_iv(x_i)>\sum_{i\in I}p_iv(y_i). \end{equation} I believe this is right (after trying many numerical examples) and I think a clever use of Jensen's inequality (or its variants) will do this. But I'm stuck on doing it formally. Any hints/thoughts on providing a formal proof?
Remark: this is related to my other post: Proving an inequality of the expectation of concave functions?
Update: after some more attempts, I believe some techniques in convex analysis would be helpful. Geometrically, the middle equation represents a hyperplane in the $R^{|I|}$ space, and the desired result (very roughly) says that a concave transformation of that hyperplane should be separated from a convex transformation of it.
To be clear, I wasn't saying the conjecture should be generally true. Any thoughts on finding any sufficient conditions to make it work would be very helpful.