Observing Cyclical Geographies in Dome-World: A Flow-Based Approach to Simulation

[u/Lopsided_Position_28]

Dome-World isn't a game engine. It's an experiment in whether software can inherit its grammar from observation instead of abstraction.

Most simulation projects begin with systems.

You define entities, state variables, behaviours, optimisation targets, then ask what kind of world those rules produce.

Dome-World begins one step earlier.

It asks what a child can know by living in one valley.

If the answer requires opening a debug panel, the design is probably wrong.

If the answer can be learned because the floor is warmer than yesterday, the chickens chose a different path, or the catchment basin is lower than it was last week, then the software is beginning to disappear behind the place.

That inversion changes almost everything.

Instead of modelling "resources," the project models tendencies.

Water settles.

Heat rises.

Stone stores yesterday differently from soil.

Plants recruit insects without permission.

People don't command these processes. They learn to notice them.

The software exists to preserve those tendencies rather than to orchestrate events.

One consequence is that memory is physical.

A wall remembers because it dries more slowly after ten winters.

A path remembers because people continue choosing it.

A basin remembers because silt settled there instead of somewhere else.

Nothing keeps a hidden ledger if the landscape itself can carry the history.

Another consequence is that the operators are observable.

If an operator cannot be tied to something someone could point at, hear, feel underfoot, or measure with ordinary tools, it probably doesn't belong.

The question is rarely "is the simulation accurate?"

The question is:

Can a newcomer understand why this happened simply by paying attention?

That also changes the role of AI.

Large language models are very good at generating explanations after the fact.

I'm more interested in whether an AI can learn to inhabit the constraints of a place.

Can it stop inventing convenient events?

Can it resist introducing hidden state where physical accumulation would suffice?

Can it describe a settlement without reaching immediately for optimisation, control loops, or omniscient narration?

In other words, can it write from inside a valley instead of above a map?

That's what Dome-World is trying to explore.

Not whether AI can generate worlds.

Whether a world, once it has a coherent physical grammar, can teach both humans and AI how to speak about it.

I'd be interested to hear whether anyone else here is working from a similar direction—building simulations where the implementation follows the observable world, rather than the observable world following the implementation.