Insurance is usually defined as "the contribution of the many to the misfortune of the few". This idea of pooling risks together using the law of large numbers legitimates the use of the expected value as actuarial "fair" premium. In the context of heterogeneous risks, nevertheless, it is possible to use price segmentation based on observable characteristics. But in the context of "Big Data", intensive segmentation can be observed, with a much wider range of offered premium, on a given portfolio. In this talk, we will briefly get back on economical, actuarial and philosophical approaches of insurance pricing, trying to link a fair unique premium on a given population and a highly segmented one. We will then get back on recent experiments (so-called "actuarial pricing game") organized since 2015, where (real) actuaries were playing in a competitive (artificial) market that mimics the real insurance market. We will get back on conclusions obtained on two editions, and possibly a third one, that will be launched during Fall 2020.