There are a lot of good points being made in this thread. I have had a theory that it is in part related to the funding available for lab equipment and computers. During the latter half of the 20th century, in Russia you were very lucky to get access to a “real” computer.
I spent some time in 1992 in Protvino, RU, a science city of (at the time) some 20,000 scientists and engineers. The city hadn’t been listed on any official map from the USSR, even though it had existed since 1958; it was devoted to a large synchrotron. I was really struck by the contrast between the super-abundance of material resources to get the job done in the US and EU, vs. the creativity and thought by the Russians. At the risk of oversimplifying, I noticed that because of the traditional scarcity of equipment, Russian students and scientists had to think rather than experiment, whether with computers or accelerators; it was often all that was available to them.
For instance, there was much more of an effort in Protvino to repurpose equipment than to have new equipment machined as I had seen at FermiLab or CERN. The rank and file Russian engineers that I saw in the ’90s were using a home-grown knockoff of the Intel 8088 series. The managers got imported IBM XTs. At the same time in the US working on the SSC, on my desk I had a SuperSPARC minicomputer, a Mac, and an HP 80486 Windows machine, as well as access to a twin Hypercube.
As a result, “computer experiments” like Monte Carlo simulation were not used very often in Protvino except by those for whom it was essential (and often not even then – such experiments were usually pushed down so far in the queue that they never got executed). Rather, there was much more emphasis on closed-form or approximate analytical solutions. Coding up a simulation and having a computer torture it until it confessed the results you wanted certainly takes talent, but it is arguably a different kind of talent than thinking deeply about the problem itself. Thinking about the simulation often leads to thinking that improves the computing methods and hardware used. Thinking about the problem itself gives insight into the nature of the problem itself and its connections with other areas of study.
Madison’s most recent Math Task Force
Remedial Math at the UW-Madison.