Consider the following optimization $$x^*(s) = \max_{x\in X} \big(\,f(x)-sx\,\big)$$ where $f$ is assumed to be a strictly concave function and $X$ is an interval constraint, e.g $X = [0,b]$. We do not know the exact function $f$.
Assume that we can provide a parameter $s$ and obtain the corresponding (unique) optimizer $x^*(s)$.
My goal is to estimate $f''(x)$ from a collection of parameters $(s_1,s_2,\ldots)$ and associated optimizers $(x^*(s_1),x^*(s_2),\ldots)$.
How would one go about doing this? I was thinking of applying the envelope theorem or using finite differences in some way but I'm not quite sure how to proceed.