Would synthetic “world simulations” be useful for training long-horizon decision-making AI?

I’m exploring an idea and would love feedback from people who work with ML / agents / RL.

Instead of generating synthetic datasets, the idea is to generate synthetic worlds: – populations – economic dynamics – constraints – shocks – time evolution

The goal wouldn’t be prediction, but providing controllable environments where AI agents can be trained or stress-tested on long-horizon decisions (policy, planning, resource allocation, etc.).

Think more like “SimCity-style environments for AI training” rather than tabular synthetic data.

Questions I’m genuinely unsure about: – Would this be useful compared to real-world logs + replay? – What kinds of agents or models would benefit most? – What would make this not useful in practice?

Not selling anything — just sanity-checking whether this makes sense outside my head.

PS: I did you AI to help me write/frame this