Survey Practice devoted the entire current issue to a discussion of training in survey methodology. This is a very useful review of what is currently done and suggestions for the future.
As they observe, survey methodology is a broad discipline that draws upon a diverse set of fields of research. I expect that increasing this diversity would be positive. That is, there are a number of fields of study that would find applications for their methods in the field of survey research.
A couple of key examples include operations research and computer science. Operations research could help us think more rigorously about designing data collection to optimize specified quantities. That doesn't mean we have to pursue one goal. But it would help, or maybe force us to quantify the vague trade offs we usually deal in. The paper by Greenberg and Stokes is an early example. The paper by Calinescu and colleagues is a recent one.
Computer science is another such field. Researchers studying reinforcement learning seek to optimize complex, multi-stage decision problems. These methods have been used to optimize adaptive treatment regimes. I think they may be a natural fit for some survey design problems. For example, the design of mixed mode surveys. Hopefully, we can direct ourselves toward such a future.