John Mount continues a series on risk optimization:
I want to discuss how fragile optimization solutions to real world problems can be. And how to solve that.
Small changes in modeling strategy, assumptions, data, estimates, constraints, or objective can lead to unstable and degenerate solutions. To warm up let’s discuss one of the most famous optimization examples: Stigler’s minimal subsistence diet problem.
There are some neat stories in the post as you walk through problems of linear programming.
Also, Nina Zumel has a post on overestimation bias:
Revenue optimization projects can be particularly valuable and exciting. They involve:
- Estimating demand as a function of offered features, price, and match to market.
- Picking a set of offerings and prices optimizing the above inferred demand.
The great opportunity of these projects is that one can derive value from improving the inference of the demand estimate function, improving the optimization, and even improving the synergy between these two steps.
However, there is a common situation that can lose client trust and sink revenue optimization projects.
Read on for that article.